System

Abstract

An object of the system according to the embodiment is to accurately grasp the individuality and interest of a child and provide a learning plan and experience based on the individuality and interest.SOLUTION: A system according to an embodiment includes an interaction input unit, a work input unit, an analysis unit, a learning plan providing unit, an experience providing unit, and a parent support unit. The dialog input unit inputs a dialog with the child. The work input unit inputs a work created by a child. An analysis part analyzes the individuality and interest of the child on the basis of information obtained from the interaction input part and the work input part. The study plan providing unit provides a customized study plan based on the analysis result obtained by the analysis unit. The experience providing unit provides a customized experience based on the analysis result. The parent support unit provides information for understanding the talent and growth of the child to the parent based on the analysis result.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] Conventional technology has the drawback of making it difficult to accurately grasp a child's personality and interests and provide learning plans and experiences based on them.

[0005] The system according to the embodiment aims to accurately grasp a child's personality and interests and provide learning plans and experiences based on that. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue input unit, a work input unit, an analysis unit, a learning plan providing unit, an experience providing unit, and a parent support unit. The dialogue input unit inputs dialogues with the child. The work input unit inputs works created by the child. The analysis unit analyzes the child's personality and interests based on information obtained from the dialogue input unit and the work input unit. The learning plan providing unit provides a customized learning plan based on the analysis results obtained by the analysis unit. The experience providing unit provides a customized experience based on the analysis results. The parent support unit provides information to parents to understand their child's talents and growth based on the analysis results. [Effects of the Invention]

[0007] The system according to the embodiment can accurately grasp a child's personality and interests and provide learning plans and experiences based on them. [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 AI ​​platform of an embodiment of the present invention is a system for discovering and nurturing children's potential. The system uses interactions with children and their creations as input, and the generative AI performs a detailed analysis of their personality and interests to provide customized learning plans and experiences. This allows the AI ​​platform to help parents gain a deeper understanding of their children's talents and development.

[0029] An AI platform according to an embodiment includes a dialogue input unit, a work input unit, an analysis unit, a learning plan providing unit, an experience providing unit, and a parent support unit. The dialogue input unit inputs dialogue with a child. For example, if a child answers "I like math" to the question "What is your favorite subject?", the content of the dialogue is input. The dialogue input unit can also input everyday conversations and question-and-answer sessions with a child. For example, if a child asks "What did you do today?", the response is input. The dialogue input unit can also input storytelling. For example, if a child tells a story they made up, the content of that story is input. The work input unit inputs works created by a child. For example, a picture drawn by a child is input. The work input unit can also input crafts or essays created by a child. For example, a craft made by a child or an essay written by a child is input. The work input unit can also input digital works created by children. For example, it can input digital drawings or videos created by children. The analysis unit analyzes the child's personality and interests based on information obtained from the dialogue input unit and work input unit. For example, if a child answers, "I like math," the generation AI can analyze the child's interests based on that information. The generation AI can also analyze the content and style of the child's drawings to identify the child's creativity and interests. The generation AI can also analyze the child's dialogue content and work data to clarify the child's characteristics and interests. The lesson plan provision unit provides a customized lesson plan based on the analysis results obtained by the analysis unit. For example, the generation AI can suggest math workbooks and related activities for a child interested in math. The generation AI can also generate a lesson plan tailored to the child's personality and interests and provide it to the parent. The generation AI can also adjust the lesson plan according to the child's learning progress. The experience provision unit provides a customized experience based on the analysis results. For example, the generation AI can suggest science experiment kits and virtual museum tours for a child interested in science. Generative AI can also suggest experiences tailored to the child's characteristics and provide them to parents.Furthermore, the generation AI can suggest new experiences based on the child's interests and concerns. The parent support unit provides parents with information to understand their child's talents and growth based on the analysis results. For example, the generation AI provides parents with records of their child's learning progress and growth, clarifying the child's strengths and areas for improvement. The generation AI can also provide information for parents to support their child's growth. Furthermore, the generation AI can provide visualized information to help parents understand their child's growth more easily. In this way, the AI ​​platform according to the embodiment can help parents gain a deeper understanding of their child's talents and growth. For example, parents can discover their child's interests and talents and learn specific ways to develop them. Parents can also obtain appropriate information to support their child's growth. Furthermore, parents can grasp their child's learning progress and growth in real time and provide appropriate support.

[0030] The dialogue input unit analyzes the word choice and speech patterns used during a dialogue, allowing the generation AI to identify a child's personality traits. For example, the dialogue input unit analyzes the word choice and speech patterns used by a child to identify personality traits. For example, a child who frequently uses positive words such as "fun" and "happy" may be determined to have an optimistic personality. The dialogue input unit can also analyze the word choice and speech patterns used during a dialogue to identify a child's personality traits. For example, a child who provides detailed answers to questions may be determined to be curious. The dialogue input unit can also analyze a dialogue with a child to identify personality traits from the word choice and speech patterns used. For example, a child who asks many questions during a dialogue may be determined to have a strong sense of inquiry. This allows the generation AI to identify a child's personality traits in detail.

[0031] The dialogue input unit translates the dialogue content into multiple languages, allowing the generation AI to compare and analyze the interests of children from different cultures. The dialogue input unit, for example, translates the dialogue content with a child into multiple languages ​​and compares and analyzes the interests of children from different cultures. For example, a dialogue in Japanese can be translated into English or French to compare the interests of children from each country. The dialogue input unit can also translate the dialogue content into multiple languages ​​and compare and analyze the interests of children from different cultures. For example, the differences in the interests of children from Asia and Europe can be analyzed. The dialogue input unit can also translate the dialogue content with a child into multiple languages ​​and compare and analyze the interests of children from different cultures. For example, the similarities and differences between the interests of American and Chinese children can be identified. This allows for a comparative analysis of the interests of children from different cultures.

[0032] The dialogue input unit visualizes the content of the dialogue, allowing the generation AI to enable parents to intuitively understand changes in their child's interests and emotions. The dialogue input unit, for example, visualizes the content of the dialogue with their child, allowing parents to intuitively understand changes in their child's interests and emotions. For example, the dialogue input unit may display the content of the dialogue in a graph or chart. The dialogue input unit may also visualize the content of the dialogue, allowing parents to intuitively understand changes in their child's interests and emotions. For example, changes in emotions may be indicated by color. The dialogue input unit may also visualize the content of the dialogue with their child, allowing parents to intuitively understand changes in their child's interests and emotions. For example, the dialogue content may be represented by illustrations or icons. This allows parents to intuitively understand changes in their child's interests and emotions.

[0033] The work input unit uses image recognition technology to analyze children's artwork, allowing the generation AI to identify creative tendencies from the use of color and composition. For example, the work input unit analyzes a child's drawing using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who frequently uses bright colors may be determined to be highly creative. The work input unit can also analyze children's artwork using image recognition technology to identify creative tendencies from the use of color and composition. For example, a work with a unique composition may be determined to indicate that the child has an original idea. The work input unit can also analyze children's artwork using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who draws complex patterns may be determined to be a person who pays attention to detail. This allows for detailed identification of children's creative tendencies.

[0034] The work input unit analyzes the themes and motifs of the works created, allowing the generation AI to track changes in a child's interests. The work input unit, for example, analyzes the themes and motifs of works created by a child to track changes in a child's interests. For example, a child who often draws animals may be determined to be interested in animals. The work input unit can also analyze the themes and motifs of works created by a child to track changes in a child's interests. For example, a child who draws different themes for each season may be determined to be sensitive to seasonal changes. The work input unit can also analyze the themes and motifs of works created by a child to track changes in a child's interests. For example, a child who repeatedly draws a particular character may be determined to have a strong interest in that character. This allows changes in a child's interests to be tracked in detail.

[0035] The work input unit digitizes children's artwork, which the generation AI can then share with other children and parents in an online gallery. The work input unit, for example, digitizes artwork created by children and shares it with other children and parents in an online gallery. For example, it scans drawings and crafts and saves them as digital data. The work input unit can also share children's digitized artwork in an online gallery and receive feedback from other children and parents. For example, it can add a comment function to exchange opinions. The work input unit can also digitize children's artwork and share it with other children and parents in an online gallery. For example, it can classify and display artwork by theme. This allows children's artwork to be digitized and shared.

[0036] The work input unit can broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work. The work input unit can broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work created by the child. For example, the work input unit can suggest sculpture or pottery to a child who likes drawing. The work input unit can also broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work. For example, the work input unit can suggest writing poetry or short stories to a child who is good at writing. The work input unit can also broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work created by the child. For example, the work input unit can suggest playing an instrument or composing music to a child who is interested in music. In this way, creative activities can be suggested to broaden a child's interests.

[0037] The analysis unit integrates and analyzes the child's conversation content and artwork data, allowing the generation AI to identify correlations between personality and interests. For example, the analysis unit integrates and analyzes the child's conversation content and artwork data to identify correlations between personality and interests. For example, if a child who said "I like animals" in the conversation draws many pictures of animals, it is determined that the child has a strong interest in animals. The analysis unit can also integrate and analyze the conversation content and artwork data to identify correlations between personality and interests. For example, if a child who said "I like adventure" in the conversation creates many adventure-themed artworks, it is determined that the child has a strong interest in them. The analysis unit can also integrate and analyze the child's conversation content and artwork data to identify correlations between personality and interests. For example, if a child who said "I like music" in the conversation creates many music-related artworks, it is determined that the child has a strong interest in them. This allows for detailed identification of correlations between a child's personality and interests.

[0038] The analysis unit classifies a child's areas of interest in detail, allowing the generative AI to identify deep interests in specific areas. For example, the analysis unit classifies a child's areas of interest in detail and identifies deep interests in specific areas. For example, if a child who is interested in science is particularly interested in space, that area will be explored in depth. The analysis unit can also classify a child's areas of interest in detail and identify deep interests in specific areas. For example, if a child who is interested in sports is particularly interested in soccer, that area will be explored in depth. The analysis unit can also classify a child's areas of interest in detail and identify deep interests in specific areas. For example, if a child who is interested in art is particularly interested in painting, that area will be explored in depth. This allows a child's deep interests in specific areas to be explored in depth.

[0039] The analysis unit can compare the analysis results across different age groups and genders, allowing the generative AI to identify patterns of common interests and personality traits. For example, the analysis unit can compare the analysis results across different age groups and genders to identify patterns of common interests and personality traits. For example, it can identify interests that children of the same age group have in common. The analysis unit can also compare the analysis results across different age groups and genders to identify patterns of common interests and personality traits. For example, it can identify interests that are common between boys and girls. The analysis unit can also compare the analysis results across different age groups and genders to identify patterns of common interests and personality traits. For example, it can identify patterns of interests that change as people get older. This makes it possible to identify patterns of common interests and personality traits across different age groups and genders.

[0040] The analysis unit can visualize the analysis results so that the generative AI can help parents intuitively understand their child's personality and interests. The analysis unit can, for example, visualize the analysis results so that parents intuitively understand their child's personality and interests. For example, displaying them in graphs or charts. The analysis unit can also visualize the analysis results so that parents intuitively understand their child's personality and interests. For example, displaying them visually using colors or icons. The analysis unit can also visualize the analysis results so that parents intuitively understand their child's personality and interests. For example, providing an interactive dashboard. This allows parents to intuitively understand their child's personality and interests.

[0041] The learning plan providing unit continues to track the child's interests in the learning plan, and the generation AI can dynamically adjust the plan according to changes in interests. The learning plan providing unit, for example, continues to track the child's interests in the learning plan and dynamically adjusts the plan according to changes in interests. For example, if a child develops a new interest, learning materials in that field are added. The learning plan providing unit can also track the child's interests in the learning plan and dynamically adjust the plan according to changes in interests. For example, learning materials in a field in which the child has lost interest are reduced. The learning plan providing unit can also continue to track the child's interests in the learning plan and dynamically adjust the plan according to changes in interests. For example, if a child develops a deep interest in a particular field, learning materials in that field are increased. This makes it possible to dynamically adjust the learning plan according to changes in the child's interests.

[0042] The learning plan providing unit can provide learning materials that match the child's learning style in the learning plan. The learning plan providing unit, for example, provides learning materials that match the child's learning style in the learning plan. For example, for a visual child, learning materials that make extensive use of diagrams and illustrations are provided. The learning plan providing unit can also provide learning materials that match the child's learning style in the learning plan. For example, for an auditory child, learning materials that make extensive use of audio and music are provided. The learning plan providing unit can also provide learning materials that match the child's learning style in the learning plan. For example, for a hands-on child, learning materials that make extensive use of experiments and practical training are provided. In this way, learning materials that match the child's learning style can be provided.

[0043] The learning plan providing unit can share the learning plan with other children, and the generation AI can provide opportunities for collaborative learning. The learning plan providing unit, for example, shares the learning plan with other children and provides opportunities for collaborative learning. For example, it can suggest online group learning. The learning plan providing unit can also share the learning plan with other children and provide opportunities for collaborative learning. For example, it can suggest projects for children with the same interests. The learning plan providing unit can also share the learning plan with other children and provide opportunities for collaborative learning. For example, it can suggest presentations for sharing learning results. This can provide opportunities for collaborative learning between children.

[0044] The learning plan providing unit can provide a guide that is visualized by the generation AI so that parents can easily understand the learning plan. The learning plan providing unit, for example, provides a visualized guide so that parents can easily understand the learning plan. For example, the learning content may be displayed in diagrams or charts. The learning plan providing unit can also provide a visualized guide so that parents can easily understand the learning plan. For example, the learning progress may be indicated by color. The learning plan providing unit can also provide a visualized guide so that parents can easily understand the learning plan. For example, the learning results may be displayed in a graph. This allows parents to easily understand the learning plan.

[0045] The experience providing unit monitors the child's responses in real time during the experience provided, and the generation AI can dynamically adjust the experience content according to changes in interests. The experience providing unit, for example, monitors the child's responses in real time during the experience provided, and dynamically adjusts the experience content according to changes in interests. For example, it extends an experience in which the child has an interest. The experience providing unit can also monitor the child's responses in real time during the experience provided, and dynamically adjust the experience content according to changes in interests. For example, it shortens an experience in which the child has lost interest. The experience providing unit can also monitor the child's responses in real time during the experience provided, and dynamically adjust the experience content according to changes in interests. For example, it increases the number of activities that the child particularly enjoys. This makes it possible to dynamically adjust the experience content according to changes in the child's interests.

[0046] The experience providing unit can record the content of the experience in detail and generate a report for the generation AI to review later. The experience providing unit, for example, records the content of the experience in detail and generates a report for review later. For example, it records the reactions and emotions of the child during the experience and compiles them into a report. The experience providing unit can also record the content of the experience in detail and generate a report for review later. For example, it records the activities performed and what was learned during the experience and compiles them into a report. The experience providing unit can also record the content of the experience in detail and generate a report for review later. For example, it records photos and videos taken during the experience and compiles them into a report. In this way, it is possible to record the content of the experience in detail and generate a report for review later.

[0047] The experience providing unit can share the provided experience with other children, and the generating AI can provide opportunities for collaborative experiences. The experience providing unit, for example, shares the provided experience with other children and provides opportunities for collaborative experiences. For example, it can suggest an online group experience. The experience providing unit can also share the provided experience with other children and provide opportunities for collaborative experiences. For example, it can suggest a joint project between children with the same interests. The experience providing unit can also share the provided experience with other children and provide opportunities for collaborative experiences. For example, it can suggest a presentation to share the results of the experience. This can provide opportunities for collaborative experiences between children.

[0048] The experience providing unit can provide a guide that is visualized by the generation AI so that parents can easily understand the content of the experience. The experience providing unit, for example, provides a visualized guide so that parents can easily understand the content of the experience. For example, the experience content is displayed in a diagram or chart. The experience providing unit can also provide a visualized guide so that parents can easily understand the content of the experience. For example, the progress of the experience is indicated by color. The experience providing unit can also provide a visualized guide so that parents can easily understand the content of the experience. For example, the results of the experience are displayed in a graph. This allows parents to easily understand the content of the experience.

[0049] The parent support unit analyzes the information provided to parents in detail, and the generating AI can provide specific advice regarding their child's development. The parent support unit, for example, analyzes the information provided to parents in detail and provides specific advice regarding their child's development. For example, it may suggest the next task that a child should tackle based on their learning progress. The parent support unit can also analyze the information provided to parents in detail and provide specific advice regarding their child's development. For example, it may suggest appropriate learning resources based on the child's interests. The parent support unit can also analyze the information provided to parents in detail and provide specific advice regarding their child's development. For example, it may suggest a balanced learning plan based on the child's strengths and weaknesses. This allows the generation AI to provide specific advice regarding their child's development to parents.

[0050] The parent support unit can suggest resources (books, websites, experts) for parents to support their child's development. For example, the parent support unit can suggest resources for parents to support their child's development. For example, it can recommend books that match the child's interests. The parent support unit can also suggest resources for parents to support their child's development. For example, it can introduce websites that are useful for the child's learning. The parent support unit can also suggest resources for parents to support their child's development. For example, it can introduce experts to deal with specific problems the child has. This makes it possible to suggest resources for parents to support their child's development.

[0051] The parent support unit can form a community where parents share information with other parents and the generation AI jointly supports their children's growth. The parent support unit, for example, can provide an online forum to form a community where parents share information with other parents and jointly support their children's growth. For example, the parent support unit can hold regular online meetings to form a community where parents share information with other parents and jointly support their children's growth. For example, the parent support unit can provide a platform for sharing information about children's growth. This allows parents to share information with each other and jointly form a community where they support their children's growth.

[0052] The parent support unit can provide visualized reports by the generation AI so that parents can easily understand their child's growth. The parent support unit can, for example, provide visualized reports so that parents can easily understand their child's growth. For example, learning progress can be displayed in a graph. The parent support unit can also provide visualized reports so that parents can easily understand their child's growth. For example, changes in a child's interests can be shown in a chart. The parent support unit can also provide visualized reports so that parents can easily understand their child's growth. For example, a child's strengths and weaknesses can be shown in color. This makes it easy for parents to understand their child's growth.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The dialogue input unit analyzes the word choice and speaking patterns used during a dialogue, allowing the generation AI to identify a child's personality traits. For example, a child who frequently uses positive words such as "fun" and "happy" is judged to have an optimistic personality. The dialogue input unit can also analyze the word choice and speaking patterns during a dialogue to identify a child's personality traits. For example, a child who provides detailed answers to questions is judged to be curious. The dialogue input unit can also analyze a dialogue with a child to identify personality traits from the word choice and speaking patterns used. For example, a child who asks many questions during a dialogue is judged to have a strong sense of inquiry. This allows the generation AI to identify a child's personality traits in detail.

[0055] The dialogue input unit translates the dialogue content into multiple languages, allowing the generation AI to compare and analyze the interests of children in different cultures. For example, a dialogue in Japanese can be translated into English or French to compare the interests of children in each country. The dialogue input unit can also translate the dialogue content into multiple languages ​​to compare and analyze the interests of children in different cultures. For example, it can analyze the differences in the interests of children in Asia and Europe. The dialogue input unit can also translate the dialogue content with children into multiple languages ​​to compare and analyze the interests of children in different cultures. For example, it can identify the similarities and differences between the interests of American and Chinese children. This allows for a comparative analysis of the interests of children in different cultures.

[0056] The dialogue input unit can visualize the content of the dialogue, allowing the generation AI to enable parents to intuitively understand changes in their child's interests and emotions. For example, the content of the dialogue can be displayed in graphs or charts. The dialogue input unit can also visualize the content of the dialogue, allowing parents to intuitively understand changes in their child's interests and emotions. For example, changes in emotions can be indicated by color. The dialogue input unit can also visualize the content of the dialogue with their child, allowing parents to intuitively understand changes in their child's interests and emotions. For example, the content of the dialogue can be represented by illustrations or icons. This allows parents to intuitively understand changes in their child's interests and emotions.

[0057] The work input unit uses image recognition technology to analyze children's work, allowing the generation AI to identify creative tendencies from the use of color and composition. For example, a picture drawn by a child can be analyzed using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who uses a lot of bright colors can be determined to be highly creative. The work input unit can also analyze children's work using image recognition technology to identify creative tendencies from the use of color and composition. For example, a work with a unique composition can be determined to indicate that the child has an original idea. The work input unit can also analyze children's work using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who draws complex patterns can be determined to be a person who pays attention to detail. This makes it possible to identify children's creative tendencies in detail.

[0058] The work input unit analyzes the themes and motifs of the works created, allowing the generation AI to track changes in a child's interests over time. For example, the work input unit can analyze the themes and motifs of the works created by a child and track changes in a child's interests over time. For example, a child who often draws animals can be determined to be interested in animals. The work input unit can also analyze the themes and motifs of the works created by a child and track changes in a child's interests over time. For example, a child who draws different themes for each season can be determined to be sensitive to seasonal changes. The work input unit can also analyze the themes and motifs of the works created by a child and track changes in a child's interests over time. For example, a child who repeatedly draws a particular character can be determined to have a strong interest in that character. This allows for detailed tracking of changes in a child's interests over time.

[0059] The work input unit digitizes children's artwork, which the generative AI can then share with other children and parents in an online gallery. For example, artwork created by children can be digitized and shared with other children and parents in an online gallery. For example, drawings and crafts can be scanned and saved as digital data. The work input unit can also share children's digitized artwork in an online gallery and receive feedback from other children and parents. For example, a comment function can be added to exchange opinions. The work input unit can also digitize children's artwork and share it with other children and parents in an online gallery. For example, artworks can be categorized and displayed by theme. This allows children's artwork to be digitized and shared.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The dialogue input unit inputs dialogue with the child. For example, if a child answers "I like math" to the question "What is your favorite subject?", the content of that dialogue is input. The dialogue input unit can also input everyday conversations and question-and-answer sessions with the child. For example, if a child is asked "What did you do today?", the response is input. The dialogue input unit can also input storytelling. For example, if a child tells a story they made up, the content of that story is input. Step 2: The work input unit inputs a work created by the child. For example, a picture drawn by the child is input. The work input unit can also input a craft or essay created by the child. For example, a craft made by the child or an essay written by the child is input. The work input unit can also input a digital work created by the child. For example, a picture drawn digitally by the child or a video created digitally is input. Step 3: The analysis unit analyzes the child's personality and interests based on the information obtained from the dialogue input unit and the work input unit. For example, if a child answers, "I like math," the generation AI will analyze the child's interests based on that information. The generation AI can also analyze the content and style of the child's drawings to identify the child's creativity and interests. Furthermore, the generation AI can analyze the content of the child's dialogue and work data to clarify the child's characteristics and interests. Step 4: The learning plan provider provides a customized learning plan based on the analysis results obtained by the analyzer. For example, the generator AI may suggest math workbooks and related activities to a child who is interested in math. The generator AI may also generate a learning plan tailored to the child's personality and interests and provide it to the parent. The generator AI may also adjust the learning plan according to the child's learning progress. Step 5: The experience provider provides a customized experience based on the analysis results. For example, if a child is interested in science, the generator AI can suggest a science experiment kit or a virtual museum tour. The generator AI can also suggest experiences tailored to the child's characteristics and provide them to parents. The generator AI can also suggest new experiences based on the child's interests. Step 6: The parent support unit provides parents with information to understand their child's talents and growth based on the analysis results. For example, the generation AI provides parents with records of their child's learning progress and growth, clarifying their child's strengths and areas for improvement. The generation AI can also provide information for parents to support their child's growth. Furthermore, the generation AI can provide visualized information to help parents understand their child's growth more easily. In this way, the AI ​​platform according to the embodiment can help parents gain a deeper understanding of their child's talents and growth. For example, parents can discover their child's interests and talents and learn specific ways to develop them. Parents can also obtain appropriate information to support their child's growth. Furthermore, parents can grasp their child's learning progress and growth in real time and provide appropriate support.

[0062] (Example 2) The AI ​​platform of an embodiment of the present invention is a system for discovering and nurturing children's potential. The system uses interactions with children and their creations as input, and the generative AI performs a detailed analysis of their personality and interests to provide customized learning plans and experiences. This allows the AI ​​platform to help parents gain a deeper understanding of their children's talents and development.

[0063] An AI platform according to an embodiment includes a dialogue input unit, a work input unit, an analysis unit, a learning plan providing unit, an experience providing unit, and a parent support unit. The dialogue input unit inputs dialogue with a child. For example, if a child answers "I like math" to the question "What is your favorite subject?", the content of the dialogue is input. The dialogue input unit can also input everyday conversations and question-and-answer sessions with a child. For example, if a child asks "What did you do today?", the response is input. The dialogue input unit can also input storytelling. For example, if a child tells a story they made up, the content of that story is input. The work input unit inputs works created by a child. For example, a picture drawn by a child is input. The work input unit can also input crafts or essays created by a child. For example, a craft made by a child or an essay written by a child is input. The work input unit can also input digital works created by children. For example, it can input digital drawings or videos created by children. The analysis unit analyzes the child's personality and interests based on information obtained from the dialogue input unit and work input unit. For example, if a child answers, "I like math," the generation AI can analyze the child's interests based on that information. The generation AI can also analyze the content and style of the child's drawings to identify the child's creativity and interests. The generation AI can also analyze the child's dialogue content and work data to clarify the child's characteristics and interests. The lesson plan provision unit provides a customized lesson plan based on the analysis results obtained by the analysis unit. For example, the generation AI can suggest math workbooks and related activities for a child interested in math. The generation AI can also generate a lesson plan tailored to the child's personality and interests and provide it to the parent. The generation AI can also adjust the lesson plan according to the child's learning progress. The experience provision unit provides a customized experience based on the analysis results. For example, the generation AI can suggest science experiment kits and virtual museum tours for a child interested in science. Generative AI can also suggest experiences tailored to the child's characteristics and provide them to parents.Furthermore, the generation AI can suggest new experiences based on the child's interests and concerns. The parent support unit provides parents with information to understand their child's talents and growth based on the analysis results. For example, the generation AI provides parents with records of their child's learning progress and growth, clarifying the child's strengths and areas for improvement. The generation AI can also provide information for parents to support their child's growth. Furthermore, the generation AI can provide visualized information to help parents understand their child's growth more easily. In this way, the AI ​​platform according to the embodiment can help parents gain a deeper understanding of their child's talents and growth. For example, parents can discover their child's interests and talents and learn specific ways to develop them. Parents can also obtain appropriate information to support their child's growth. Furthermore, parents can grasp their child's learning progress and growth in real time and provide appropriate support.

[0064] The dialogue input unit converts a child's dialogue into text using voice recognition technology, and the generation AI can analyze the text to track changes in emotion. For example, the dialogue input unit converts what a child says into text in real time using voice recognition technology, and the generation AI analyzes the text. For example, if a child says, "Today was fun," the emotion is recorded as positive. The dialogue input unit can also record dialogue with a child and later convert it into text using voice recognition technology. For example, if a child is depressed at the beginning of a dialogue but becomes more energetic towards the end, this change is recorded. The dialogue input unit can also convert a child's dialogue into text using voice recognition technology, and the generation AI can analyze the text to track changes in emotion. For example, if a child says, "Something bad happened today," the emotion is recorded as negative. This allows for real-time tracking of changes in a child's emotion.

[0065] The dialogue input unit analyzes the word choice and speech patterns used during a dialogue, allowing the generation AI to identify a child's personality traits. For example, the dialogue input unit analyzes the word choice and speech patterns used by a child to identify personality traits. For example, a child who frequently uses positive words such as "fun" and "happy" may be determined to have an optimistic personality. The dialogue input unit can also analyze the word choice and speech patterns used during a dialogue to identify a child's personality traits. For example, a child who provides detailed answers to questions may be determined to be curious. The dialogue input unit can also analyze a dialogue with a child to identify personality traits from the word choice and speech patterns used. For example, a child who asks many questions during a dialogue may be determined to have a strong sense of inquiry. This allows the generation AI to identify a child's personality traits in detail.

[0066] The dialogue input unit uses the emotion estimation function to analyze the child's emotions during a dialogue in real time, and the generation AI can provide feedback according to changes in emotions. The dialogue input unit, for example, uses the emotion estimation function to analyze the child's emotions during a dialogue in real time and provide feedback according to changes in emotions. For example, if the child looks sad, the dialogue input unit can offer words of encouragement. The dialogue input unit can also analyze the child's emotions during a dialogue in real time and provide feedback according to changes in emotions. For example, if the child looks excited, the dialogue input unit can ask questions that will further excite the child. The dialogue input unit can also use the emotion estimation function to analyze the child's emotions during a dialogue in real time and provide feedback according to changes in emotions. For example, if the child looks anxious, the dialogue input unit can offer advice to ease the anxiety. This makes it possible to provide appropriate feedback according to changes in the child's emotions.

[0067] The dialogue input unit translates the dialogue content into multiple languages, allowing the generation AI to compare and analyze the interests of children from different cultures. The dialogue input unit, for example, translates the dialogue content with a child into multiple languages ​​and compares and analyzes the interests of children from different cultures. For example, a dialogue in Japanese can be translated into English or French to compare the interests of children from each country. The dialogue input unit can also translate the dialogue content into multiple languages ​​and compare and analyze the interests of children from different cultures. For example, the differences in the interests of children from Asia and Europe can be analyzed. The dialogue input unit can also translate the dialogue content with a child into multiple languages ​​and compare and analyze the interests of children from different cultures. For example, the similarities and differences between the interests of American and Chinese children can be identified. This allows for a comparative analysis of the interests of children from different cultures.

[0068] The dialogue input unit visualizes the content of the dialogue, allowing the generation AI to enable parents to intuitively understand changes in their child's interests and emotions. The dialogue input unit, for example, visualizes the content of the dialogue with their child, allowing parents to intuitively understand changes in their child's interests and emotions. For example, the dialogue input unit may display the content of the dialogue in a graph or chart. The dialogue input unit may also visualize the content of the dialogue, allowing parents to intuitively understand changes in their child's interests and emotions. For example, changes in emotions may be indicated by color. The dialogue input unit may also visualize the content of the dialogue with their child, allowing parents to intuitively understand changes in their child's interests and emotions. For example, the dialogue content may be represented by illustrations or icons. This allows parents to intuitively understand changes in their child's interests and emotions.

[0069] The dialogue input unit uses the emotion estimation function to analyze the child's emotions during a dialogue, and the generation AI can automatically generate questions to elicit positive emotions. The dialogue input unit, for example, uses the emotion estimation function to analyze the child's emotions during a dialogue and automatically generate questions to elicit positive emotions. For example, it suggests topics that the child is likely to be interested in. The dialogue input unit can also analyze the child's emotions during a dialogue and automatically generate questions to elicit positive emotions. For example, it can increase opportunities for the child to talk about things they like. The dialogue input unit can also use the emotion estimation function to analyze the child's emotions during a dialogue and automatically generate questions to elicit positive emotions. For example, it can suggest activities that the child finds enjoyable. This makes it possible to automatically generate questions to elicit positive emotions in children.

[0070] The work input unit uses image recognition technology to analyze children's artwork, allowing the generation AI to identify creative tendencies from the use of color and composition. For example, the work input unit analyzes a child's drawing using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who frequently uses bright colors may be determined to be highly creative. The work input unit can also analyze children's artwork using image recognition technology to identify creative tendencies from the use of color and composition. For example, a work with a unique composition may be determined to indicate that the child has an original idea. The work input unit can also analyze children's artwork using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who draws complex patterns may be determined to be a person who pays attention to detail. This allows for detailed identification of children's creative tendencies.

[0071] The work input unit analyzes the themes and motifs of the works created, allowing the generation AI to track changes in a child's interests. The work input unit, for example, analyzes the themes and motifs of works created by a child to track changes in a child's interests. For example, a child who often draws animals may be determined to be interested in animals. The work input unit can also analyze the themes and motifs of works created by a child to track changes in a child's interests. For example, a child who draws different themes for each season may be determined to be sensitive to seasonal changes. The work input unit can also analyze the themes and motifs of works created by a child to track changes in a child's interests. For example, a child who repeatedly draws a particular character may be determined to have a strong interest in that character. This allows changes in a child's interests to be tracked in detail.

[0072] The work input unit can use the emotion estimation function to analyze the child's emotions when creating a work, and the generation AI can provide feedback according to changes in emotions. The work input unit, for example, uses the emotion estimation function to analyze the child's emotions when creating a work and provide feedback according to changes in emotions. For example, if the child is enjoying creating a work, the work input unit can provide advice to further enhance that enjoyment. The work input unit can also analyze the child's emotions when creating a work and provide feedback according to changes in emotions. For example, if the child is concentrating while creating a work, the work input unit can provide advice to help the child maintain that concentration. The work input unit can also use the emotion estimation function to analyze the child's emotions when creating a work and provide feedback according to changes in emotions. For example, if the child is feeling anxious while creating a work, the work input unit can provide advice to ease that anxiety. This makes it possible to provide appropriate feedback according to changes in the child's emotions.

[0073] The work input unit digitizes children's artwork, which the generation AI can then share with other children and parents in an online gallery. The work input unit, for example, digitizes artwork created by children and shares it with other children and parents in an online gallery. For example, it scans drawings and crafts and saves them as digital data. The work input unit can also share children's digitized artwork in an online gallery and receive feedback from other children and parents. For example, it can add a comment function to exchange opinions. The work input unit can also digitize children's artwork and share it with other children and parents in an online gallery. For example, it can classify and display artwork by theme. This allows children's artwork to be digitized and shared.

[0074] The work input unit can broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work. The work input unit can broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work created by the child. For example, the work input unit can suggest sculpture or pottery to a child who likes drawing. The work input unit can also broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work. For example, the work input unit can suggest writing poetry or short stories to a child who is good at writing. The work input unit can also broaden a child's interests by suggesting creative activities in different genres based on the analysis results of the work created by the child. For example, the work input unit can suggest playing an instrument or composing music to a child who is interested in music. In this way, creative activities can be suggested to broaden a child's interests.

[0075] The work input unit can use the emotion estimation function to analyze the child's emotions when creating the work and suggest a creative theme that will elicit positive emotions from the generation AI. The work input unit, for example, uses the emotion estimation function to analyze the child's emotions when creating the work and suggest a creative theme that will elicit positive emotions. For example, it can continuously suggest themes that the child enjoys. The work input unit can also analyze the child's emotions when creating the work and suggest a creative theme that will elicit positive emotions. For example, it can delve deeper into themes that the child is interested in. The work input unit can also use the emotion estimation function to analyze the child's emotions when creating the work and suggest a creative theme that will elicit positive emotions. For example, it can suggest a theme that will relax the child. This makes it possible to suggest a creative theme that will elicit positive emotions from the child.

[0076] The analysis unit integrates and analyzes the child's conversation content and artwork data, allowing the generation AI to identify correlations between personality and interests. For example, the analysis unit integrates and analyzes the child's conversation content and artwork data to identify correlations between personality and interests. For example, if a child who said "I like animals" in the conversation draws many pictures of animals, it is determined that the child has a strong interest in animals. The analysis unit can also integrate and analyze the conversation content and artwork data to identify correlations between personality and interests. For example, if a child who said "I like adventure" in the conversation creates many adventure-themed artworks, it is determined that the child has a strong interest in them. The analysis unit can also integrate and analyze the child's conversation content and artwork data to identify correlations between personality and interests. For example, if a child who said "I like music" in the conversation creates many music-related artworks, it is determined that the child has a strong interest in them. This allows for detailed identification of correlations between a child's personality and interests.

[0077] The analysis unit classifies a child's areas of interest in detail, allowing the generative AI to identify deep interests in specific areas. For example, the analysis unit classifies a child's areas of interest in detail and identifies deep interests in specific areas. For example, if a child who is interested in science is particularly interested in space, that area will be explored in depth. The analysis unit can also classify a child's areas of interest in detail and identify deep interests in specific areas. For example, if a child who is interested in sports is particularly interested in soccer, that area will be explored in depth. The analysis unit can also classify a child's areas of interest in detail and identify deep interests in specific areas. For example, if a child who is interested in art is particularly interested in painting, that area will be explored in depth. This allows a child's deep interests in specific areas to be explored in depth.

[0078] The analysis unit uses the emotion estimation function to enable the generation AI to identify a child's emotional strengths and weaknesses based on the analysis results of the personality and interests. The analysis unit, for example, uses the emotion estimation function to identify a child's emotional strengths and weaknesses based on the analysis results of the personality and interests. For example, areas in which positive emotions are prevalent in conversations or artwork may be determined to be strengths. The analysis unit can also use the emotion estimation function to identify a child's emotional strengths and weaknesses based on the analysis results of the personality and interests. For example, areas in which negative emotions are prevalent may be determined to be weaknesses. The analysis unit can also use the emotion estimation function to identify a child's emotional strengths and weaknesses based on the analysis results of the personality and interests. For example, if a child feels enjoyment in a particular activity, the activity may be determined to be a strength. This allows the child's emotional strengths and weaknesses to be identified in detail.

[0079] The analysis unit can compare the analysis results across different age groups and genders, allowing the generative AI to identify patterns of common interests and personality traits. For example, the analysis unit can compare the analysis results across different age groups and genders to identify patterns of common interests and personality traits. For example, it can identify interests that children of the same age group have in common. The analysis unit can also compare the analysis results across different age groups and genders to identify patterns of common interests and personality traits. For example, it can identify interests that are common between boys and girls. The analysis unit can also compare the analysis results across different age groups and genders to identify patterns of common interests and personality traits. For example, it can identify patterns of interests that change as people get older. This makes it possible to identify patterns of common interests and personality traits across different age groups and genders.

[0080] The analysis unit can visualize the analysis results so that the generative AI can help parents intuitively understand their child's personality and interests. The analysis unit can, for example, visualize the analysis results so that parents intuitively understand their child's personality and interests. For example, displaying them in graphs or charts. The analysis unit can also visualize the analysis results so that parents intuitively understand their child's personality and interests. For example, displaying them visually using colors or icons. The analysis unit can also visualize the analysis results so that parents intuitively understand their child's personality and interests. For example, providing an interactive dashboard. This allows parents to intuitively understand their child's personality and interests.

[0081] The analysis unit can use the emotion estimation function to suggest activities that will elicit positive emotions for the generation AI based on the analysis results. The analysis unit, for example, uses the emotion estimation function to suggest activities that will elicit positive emotions based on the analysis results. For example, it may continue to suggest activities that the child enjoys. The analysis unit can also use the emotion estimation function to suggest activities that will elicit positive emotions based on the analysis results. For example, it may suggest new activities in areas that the child is interested in. The analysis unit can also use the emotion estimation function to suggest activities that will elicit positive emotions based on the analysis results. For example, it may suggest activities that will help the child relax. In this way, it is possible to suggest activities that will elicit positive emotions for the child.

[0082] The learning plan providing unit continues to track the child's interests in the learning plan, and the generation AI can dynamically adjust the plan according to changes in interests. The learning plan providing unit, for example, continues to track the child's interests in the learning plan and dynamically adjusts the plan according to changes in interests. For example, if a child develops a new interest, learning materials in that field are added. The learning plan providing unit can also track the child's interests in the learning plan and dynamically adjust the plan according to changes in interests. For example, learning materials in a field in which the child has lost interest are reduced. The learning plan providing unit can also continue to track the child's interests in the learning plan and dynamically adjust the plan according to changes in interests. For example, if a child develops a deep interest in a particular field, learning materials in that field are increased. This makes it possible to dynamically adjust the learning plan according to changes in the child's interests.

[0083] The learning plan providing unit can provide learning materials that match the child's learning style in the learning plan. The learning plan providing unit, for example, provides learning materials that match the child's learning style in the learning plan. For example, for a visual child, learning materials that make extensive use of diagrams and illustrations are provided. The learning plan providing unit can also provide learning materials that match the child's learning style in the learning plan. For example, for an auditory child, learning materials that make extensive use of audio and music are provided. The learning plan providing unit can also provide learning materials that match the child's learning style in the learning plan. For example, for a hands-on child, learning materials that make extensive use of experiments and practical training are provided. In this way, learning materials that match the child's learning style can be provided.

[0084] The learning plan providing unit can use the emotion estimation function to monitor the child's emotions while the learning plan is in progress and make adjustments so that the generation AI can elicit positive emotions. The learning plan providing unit can, for example, use the emotion estimation function to monitor the child's emotions while the learning plan is in progress and make adjustments so that positive emotions are elicited. For example, by increasing the amount of learning materials that the child enjoys. The learning plan providing unit can also monitor the child's emotions while the learning plan is in progress and make adjustments so that positive emotions are elicited. For example, by reducing the amount of learning materials in which the child has lost interest. The learning plan providing unit can also use the emotion estimation function to monitor the child's emotions while the learning plan is in progress and make adjustments so that positive emotions are elicited. For example, by adding learning materials that help the child relax. This makes it possible to make adjustments so that positive emotions are elicited in the child.

[0085] The learning plan providing unit can share the learning plan with other children, and the generation AI can provide opportunities for collaborative learning. The learning plan providing unit, for example, shares the learning plan with other children and provides opportunities for collaborative learning. For example, it can suggest online group learning. The learning plan providing unit can also share the learning plan with other children and provide opportunities for collaborative learning. For example, it can suggest projects for children with the same interests. The learning plan providing unit can also share the learning plan with other children and provide opportunities for collaborative learning. For example, it can suggest presentations for sharing learning results. This can provide opportunities for collaborative learning between children.

[0086] The learning plan providing unit can provide a guide that is visualized by the generation AI so that parents can easily understand the learning plan. The learning plan providing unit, for example, provides a visualized guide so that parents can easily understand the learning plan. For example, the learning content may be displayed in diagrams or charts. The learning plan providing unit can also provide a visualized guide so that parents can easily understand the learning plan. For example, the learning progress may be indicated by color. The learning plan providing unit can also provide a visualized guide so that parents can easily understand the learning plan. For example, the learning results may be displayed in a graph. This allows parents to easily understand the learning plan.

[0087] The learning plan providing unit can use the emotion estimation function to monitor the child's emotions while the learning plan is in progress, and the generation AI can suggest activities that will elicit positive emotions. The learning plan providing unit can, for example, use the emotion estimation function to monitor the child's emotions while the learning plan is in progress, and suggest activities that will elicit positive emotions. For example, by increasing the number of activities that the child enjoys. The learning plan providing unit can also monitor the child's emotions while the learning plan is in progress, and suggest activities that will elicit positive emotions. For example, by adding activities that the child is interested in. The learning plan providing unit can also use the emotion estimation function to monitor the child's emotions while the learning plan is in progress, and suggest activities that will elicit positive emotions. For example, by suggesting activities that will help the child relax. This makes it possible to suggest activities that will elicit positive emotions in the child.

[0088] The experience providing unit monitors the child's responses in real time during the experience provided, and the generation AI can dynamically adjust the experience content according to changes in interests. The experience providing unit, for example, monitors the child's responses in real time during the experience provided, and dynamically adjusts the experience content according to changes in interests. For example, it extends an experience in which the child has an interest. The experience providing unit can also monitor the child's responses in real time during the experience provided, and dynamically adjust the experience content according to changes in interests. For example, it shortens an experience in which the child has lost interest. The experience providing unit can also monitor the child's responses in real time during the experience provided, and dynamically adjust the experience content according to changes in interests. For example, it increases the number of activities that the child particularly enjoys. This makes it possible to dynamically adjust the experience content according to changes in the child's interests.

[0089] The experience providing unit can record the content of the experience in detail and generate a report for the generation AI to review later. The experience providing unit, for example, records the content of the experience in detail and generates a report for review later. For example, it records the reactions and emotions of the child during the experience and compiles them into a report. The experience providing unit can also record the content of the experience in detail and generate a report for review later. For example, it records the activities performed and what was learned during the experience and compiles them into a report. The experience providing unit can also record the content of the experience in detail and generate a report for review later. For example, it records photos and videos taken during the experience and compiles them into a report. In this way, it is possible to record the content of the experience in detail and generate a report for review later.

[0090] The experience providing unit can use the emotion estimation function to monitor the child's emotions during the experience and make adjustments so that the generation AI can elicit positive emotions. The experience providing unit can, for example, use the emotion estimation function to monitor the child's emotions during the experience and make adjustments so that positive emotions are elicited. For example, by increasing the number of activities that the child enjoys. The experience providing unit can also monitor the child's emotions during the experience and make adjustments so that positive emotions are elicited. For example, by adding activities that the child is interested in. The experience providing unit can also use the emotion estimation function to monitor the child's emotions during the experience and make adjustments so that positive emotions are elicited. For example, by suggesting activities that will help the child relax. This makes it possible to make adjustments so that positive emotions are elicited from the child.

[0091] The experience providing unit can share the provided experience with other children, and the generating AI can provide opportunities for collaborative experiences. The experience providing unit, for example, shares the provided experience with other children and provides opportunities for collaborative experiences. For example, it can suggest an online group experience. The experience providing unit can also share the provided experience with other children and provide opportunities for collaborative experiences. For example, it can suggest a joint project between children with the same interests. The experience providing unit can also share the provided experience with other children and provide opportunities for collaborative experiences. For example, it can suggest a presentation to share the results of the experience. This can provide opportunities for collaborative experiences between children.

[0092] The experience providing unit can provide a guide that is visualized by the generation AI so that parents can easily understand the content of the experience. The experience providing unit, for example, provides a visualized guide so that parents can easily understand the content of the experience. For example, the experience content is displayed in a diagram or chart. The experience providing unit can also provide a visualized guide so that parents can easily understand the content of the experience. For example, the progress of the experience is indicated by color. The experience providing unit can also provide a visualized guide so that parents can easily understand the content of the experience. For example, the results of the experience are displayed in a graph. This allows parents to easily understand the content of the experience.

[0093] The experience provision unit can use the emotion estimation function to monitor the child's emotions during the experience, and the generation AI can suggest activities that will elicit positive emotions. The experience provision unit, for example, uses the emotion estimation function to monitor the child's emotions during the experience, and suggest activities that will elicit positive emotions. For example, by increasing the number of activities that the child enjoys. The experience provision unit can also monitor the child's emotions during the experience, and suggest activities that will elicit positive emotions. For example, by adding activities that the child is interested in. The experience provision unit can also use the emotion estimation function to monitor the child's emotions during the experience, and suggest activities that will elicit positive emotions. For example, by suggesting activities that will help the child relax. In this way, activities that will elicit positive emotions in the child can be suggested.

[0094] The parent support unit analyzes the information provided to parents in detail, and the generating AI can provide specific advice regarding their child's development. The parent support unit, for example, analyzes the information provided to parents in detail and provides specific advice regarding their child's development. For example, it may suggest the next task that a child should tackle based on their learning progress. The parent support unit can also analyze the information provided to parents in detail and provide specific advice regarding their child's development. For example, it may suggest appropriate learning resources based on the child's interests. The parent support unit can also analyze the information provided to parents in detail and provide specific advice regarding their child's development. For example, it may suggest a balanced learning plan based on the child's strengths and weaknesses. This allows the generation AI to provide specific advice regarding their child's development to parents.

[0095] The parent support unit can suggest resources (books, websites, experts) for parents to support their child's development. For example, the parent support unit can suggest resources for parents to support their child's development. For example, it can recommend books that match the child's interests. The parent support unit can also suggest resources for parents to support their child's development. For example, it can introduce websites that are useful for the child's learning. The parent support unit can also suggest resources for parents to support their child's development. For example, it can introduce experts to deal with specific problems the child has. This makes it possible to suggest resources for parents to support their child's development.

[0096] The parent support unit can use the emotion estimation function to monitor the emotions parents feel when understanding their child's growth, and the generation AI can provide information to elicit positive emotions. For example, the parent support unit can use the emotion estimation function to monitor the emotions parents feel when understanding their child's growth, and provide information to elicit positive emotions. For example, the parent support unit can highlight the child's successes. The parent support unit can also monitor the emotions parents feel when understanding their child's growth, and provide information to elicit positive emotions. For example, the parent support unit can specifically show the child's progress. The parent support unit can also use the emotion estimation function to monitor the emotions parents feel when understanding their child's growth, and provide information to elicit positive emotions. For example, the parent support unit can provide a message acknowledging the child's efforts. This can provide information to elicit positive emotions parents feel when understanding their child's growth.

[0097] The parent support unit can form a community where parents share information with other parents and the generation AI jointly supports their children's growth. The parent support unit, for example, can provide an online forum to form a community where parents share information with other parents and jointly support their children's growth. For example, the parent support unit can hold regular online meetings to form a community where parents share information with other parents and jointly support their children's growth. For example, the parent support unit can provide a platform for sharing information about children's growth. This allows parents to share information with each other and jointly form a community where they support their children's growth.

[0098] The parent support unit can provide visualized reports by the generation AI so that parents can easily understand their child's growth. The parent support unit can, for example, provide visualized reports so that parents can easily understand their child's growth. For example, learning progress can be displayed in a graph. The parent support unit can also provide visualized reports so that parents can easily understand their child's growth. For example, changes in a child's interests can be shown in a chart. The parent support unit can also provide visualized reports so that parents can easily understand their child's growth. For example, a child's strengths and weaknesses can be shown in color. This makes it easy for parents to understand their child's growth.

[0099] The parent support unit can use the emotion estimation function to monitor the emotions parents feel when understanding their child's growth, and the generation AI can provide advice to elicit positive emotions. For example, the parent support unit can use the emotion estimation function to monitor the emotions parents feel when understanding their child's growth, and provide advice to elicit positive emotions. For example, by emphasizing the child's successful experiences. The parent support unit can also monitor the emotions parents feel when understanding their child's growth, and provide advice to elicit positive emotions. For example, by specifically showing the child's progress. The parent support unit can also use the emotion estimation function to monitor the emotions parents feel when understanding their child's growth, and provide advice to elicit positive emotions. For example, by providing a message acknowledging the child's efforts. This makes it possible to provide advice to elicit positive emotions when parents understand their child's growth.

[0100] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0101] The dialogue input unit converts a child's dialogue into text using voice recognition technology, and the generation AI can analyze the text to track changes in emotions. For example, if a child says, "Today was fun," the emotion is recorded as positive. The dialogue input unit can also record dialogue with a child and later convert it into text using voice recognition technology. For example, if a child is depressed at the beginning of the dialogue but becomes more energetic towards the end, this change is recorded. The dialogue input unit can also convert a child's dialogue into text using voice recognition technology, and the generation AI can analyze the text to track changes in emotions. For example, if a child says, "Something bad happened today," the emotion is recorded as negative. This makes it possible to track changes in a child's emotions in real time.

[0102] The dialogue input unit analyzes the word choice and speaking patterns used during a dialogue, allowing the generation AI to identify a child's personality traits. For example, a child who frequently uses positive words such as "fun" and "happy" is judged to have an optimistic personality. The dialogue input unit can also analyze the word choice and speaking patterns during a dialogue to identify a child's personality traits. For example, a child who provides detailed answers to questions is judged to be curious. The dialogue input unit can also analyze a dialogue with a child to identify personality traits from the word choice and speaking patterns used. For example, a child who asks many questions during a dialogue is judged to have a strong sense of inquiry. This allows the generation AI to identify a child's personality traits in detail.

[0103] The dialogue input unit uses the emotion estimation function to analyze the child's emotions during a conversation in real time, and the generation AI can provide feedback according to changes in emotions. For example, if the child looks sad, the generation AI can offer words of encouragement. The dialogue input unit can also analyze the child's emotions during a conversation in real time and provide feedback according to changes in emotions. For example, if the child looks excited, the generation AI can ask questions that will further excite the child. The dialogue input unit can also use the emotion estimation function to analyze the child's emotions during a conversation in real time and provide feedback according to changes in emotions. For example, if the child looks anxious, the generation AI can offer advice to ease the anxiety. This makes it possible to provide appropriate feedback according to changes in the child's emotions.

[0104] The dialogue input unit translates the dialogue content into multiple languages, allowing the generation AI to compare and analyze the interests of children in different cultures. For example, a dialogue in Japanese can be translated into English or French to compare the interests of children in each country. The dialogue input unit can also translate the dialogue content into multiple languages ​​to compare and analyze the interests of children in different cultures. For example, it can analyze the differences in the interests of children in Asia and Europe. The dialogue input unit can also translate the dialogue content with children into multiple languages ​​to compare and analyze the interests of children in different cultures. For example, it can identify the similarities and differences between the interests of American and Chinese children. This allows for a comparative analysis of the interests of children in different cultures.

[0105] The dialogue input unit can visualize the content of the dialogue, allowing the generation AI to enable parents to intuitively understand changes in their child's interests and emotions. For example, the content of the dialogue can be displayed in graphs or charts. The dialogue input unit can also visualize the content of the dialogue, allowing parents to intuitively understand changes in their child's interests and emotions. For example, changes in emotions can be indicated by color. The dialogue input unit can also visualize the content of the dialogue with their child, allowing parents to intuitively understand changes in their child's interests and emotions. For example, the content of the dialogue can be represented by illustrations or icons. This allows parents to intuitively understand changes in their child's interests and emotions.

[0106] The dialogue input unit uses the emotion estimation function to analyze a child's emotions during a conversation, and the generation AI can automatically generate questions to elicit positive emotions. For example, it can suggest topics that the child is likely to be interested in. The dialogue input unit can also analyze a child's emotions during a conversation and automatically generate questions to elicit positive emotions. For example, it can increase opportunities for the child to talk about things they like. The dialogue input unit can also use the emotion estimation function to analyze a child's emotions during a conversation and automatically generate questions to elicit positive emotions. For example, it can suggest activities that the child finds enjoyable. This makes it possible to automatically generate questions to elicit positive emotions in children.

[0107] The work input unit uses image recognition technology to analyze children's work, allowing the generation AI to identify creative tendencies from the use of color and composition. For example, a picture drawn by a child can be analyzed using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who uses a lot of bright colors can be determined to be highly creative. The work input unit can also analyze children's work using image recognition technology to identify creative tendencies from the use of color and composition. For example, a work with a unique composition can be determined to indicate that the child has an original idea. The work input unit can also analyze children's work using image recognition technology to identify creative tendencies from the use of color and composition. For example, a child who draws complex patterns can be determined to be a person who pays attention to detail. This makes it possible to identify children's creative tendencies in detail.

[0108] The work input unit analyzes the themes and motifs of the works created, allowing the generation AI to track changes in a child's interests over time. For example, the work input unit can analyze the themes and motifs of the works created by a child and track changes in a child's interests over time. For example, a child who often draws animals can be determined to be interested in animals. The work input unit can also analyze the themes and motifs of the works created by a child and track changes in a child's interests over time. For example, a child who draws different themes for each season can be determined to be sensitive to seasonal changes. The work input unit can also analyze the themes and motifs of the works created by a child and track changes in a child's interests over time. For example, a child who repeatedly draws a particular character can be determined to have a strong interest in that character. This allows for detailed tracking of changes in a child's interests over time.

[0109] The work input unit uses the emotion estimation function to analyze the child's emotions while creating the work, and the generation AI can provide feedback according to changes in emotions. For example, if the child is enjoying creating the work, the AI ​​can give advice to further enhance that enjoyment. The work input unit can also analyze the child's emotions while creating the work and provide feedback according to changes in emotions. For example, if the child is concentrating while creating the work, the AI ​​can give advice to help the child maintain that concentration. The work input unit can also use the emotion estimation function to analyze the child's emotions while creating the work and provide feedback according to changes in emotions. For example, if the child is feeling anxious while creating the work, the AI ​​can give advice to ease that anxiety. This makes it possible to provide appropriate feedback according to changes in the child's emotions.

[0110] The work input unit digitizes children's artwork, which the generative AI can then share with other children and parents in an online gallery. For example, artwork created by children can be digitized and shared with other children and parents in an online gallery. For example, drawings and crafts can be scanned and saved as digital data. The work input unit can also share children's digitized artwork in an online gallery and receive feedback from other children and parents. For example, a comment function can be added to exchange opinions. The work input unit can also digitize children's artwork and share it with other children and parents in an online gallery. For example, artworks can be categorized and displayed by theme. This allows children's artwork to be digitized and shared.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The dialogue input unit inputs dialogue with the child. For example, if a child answers "I like math" to the question "What is your favorite subject?", the content of that dialogue is input. The dialogue input unit can also input everyday conversations and question-and-answer sessions with the child. For example, if a child is asked "What did you do today?", the response is input. The dialogue input unit can also input storytelling. For example, if a child tells a story they made up, the content of that story is input. Step 2: The work input unit inputs a work created by the child. For example, a picture drawn by the child is input. The work input unit can also input a craft or essay created by the child. For example, a craft made by the child or an essay written by the child is input. The work input unit can also input a digital work created by the child. For example, a picture drawn digitally by the child or a video created digitally is input. Step 3: The analysis unit analyzes the child's personality and interests based on the information obtained from the dialogue input unit and the work input unit. For example, if a child answers, "I like math," the generation AI will analyze the child's interests based on that information. The generation AI can also analyze the content and style of the child's drawings to identify the child's creativity and interests. Furthermore, the generation AI can analyze the content of the child's dialogue and work data to clarify the child's characteristics and interests. Step 4: The learning plan provider provides a customized learning plan based on the analysis results obtained by the analyzer. For example, the generator AI may suggest math workbooks and related activities to a child who is interested in math. The generator AI may also generate a learning plan tailored to the child's personality and interests and provide it to the parent. The generator AI may also adjust the learning plan according to the child's learning progress. Step 5: The experience provider provides a customized experience based on the analysis results. For example, if a child is interested in science, the generator AI can suggest a science experiment kit or a virtual museum tour. The generator AI can also suggest experiences tailored to the child's characteristics and provide them to parents. The generator AI can also suggest new experiences based on the child's interests. Step 6: The parent support unit provides parents with information to understand their child's talents and growth based on the analysis results. For example, the generation AI provides parents with records of their child's learning progress and growth, clarifying their child's strengths and areas for improvement. The generation AI can also provide information for parents to support their child's growth. Furthermore, the generation AI can provide visualized information to help parents understand their child's growth more easily. In this way, the AI ​​platform according to the embodiment can help parents gain a deeper understanding of their child's talents and growth. For example, parents can discover their child's interests and talents and learn specific ways to develop them. Parents can also obtain appropriate information to support their child's growth. Furthermore, parents can grasp their child's learning progress and growth in real time and provide appropriate support.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 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.

[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 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.

[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 (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).

[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] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0132] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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]

[0180] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a dialogue input unit for inputting dialogue with a child; A work input section for inputting works created by children; an analysis unit that analyzes the personality and interests of the child based on information obtained from the dialogue input unit and the work input unit; a learning plan providing unit that provides a customized learning plan based on the analysis results obtained by the analysis unit; an experience providing unit that provides a customized experience based on the analysis results; a parent support unit that provides parents with information to understand their child's talents and growth based on the analysis results. A system characterized by:

2. The dialogue input unit includes: The child's conversation is converted into text using voice recognition technology, and the generative AI analyzes the text to track changes in emotions.

2. The system of claim 1.

3. The dialogue input unit includes: Generative AI identifies a child's personality traits by analyzing word choice and speaking patterns used in the conversation.

2. The system of claim 1.

4. The dialogue input unit includes: The AI ​​analyzes the child's emotions in real time during the conversation and provides feedback according to changes in emotions.

2. The system of claim 1.

5. The dialogue input unit includes: The dialogue will be translated into multiple languages, and the AI ​​will compare and analyze the interests of children from different cultures.

2. The system of claim 1.

6. The dialogue input unit includes: By visualizing the content of conversations, generative AI helps parents intuitively understand changes in their child's interests and emotions.

2. The system of claim 1.

7. The dialogue input unit includes: Analyzes the child's emotions during the conversation, and the generative AI automatically generates questions to elicit positive emotions.

2. The system of claim 1.

8. The work input unit Children's artwork is analyzed using image recognition technology, and generative AI identifies creative trends based on color usage and composition.

2. The system of claim 1.

Images