System

Abstract

An object of a system according to an embodiment is to efficiently create and provide a work customized based on a child's interest and learning style.SOLUTION: A system includes a generation part, an analysis part, a provision part, and a market place part. The generation unit generates a workpiece on the basis of the child's interest and learning style by using the generation AI. The analysis unit analyzes the workpiece generated by the generation unit. The provision unit provides the work analyzed by the analysis unit to the protector or the educator. The marketplace unit transacts the work generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has faced the challenge of making it difficult to efficiently create and provide work that matches children's interests and learning styles.

[0005] The system according to the embodiment aims to efficiently create and provide customized work based on children's interests and learning styles. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, an analysis unit, a provision unit, and a marketplace unit. The generation unit uses a generation AI to generate work based on a child's interests and learning style. The analysis unit analyzes the work generated by the generation unit. The provision unit provides the work analyzed by the analysis unit to parents and educators. The marketplace unit trades the work generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently create and provide customized work based on a child's interests and learning style. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 customized work generation system according to an embodiment of the present invention uses generation AI to create customized work tailored to children's preferences and trades it through a marketplace. This allows the customized work generation system to maximize children's learning outcomes and reduce the burden on parents and educators.

[0029] A customized work generation system according to an embodiment includes a generation unit, an analysis unit, a provision unit, and a marketplace unit. The generation unit generates work based on a child's interests and learning style using a generation AI. For example, the generation AI receives information about a child's interests as input and generates optimal work based on the information. The generation AI can also generate work corresponding to a child's visual, auditory, or tactile learning style based on the child's learning style. The analysis unit analyzes the work generated by the generation unit. For example, the analysis unit evaluates the content of the generated work and identifies areas for improvement. The analysis unit can also measure the effectiveness of the generated work and reflect this in the next work generation. The provision unit provides the work analyzed by the analysis unit to parents or educators. For example, the provision unit provides the generated work to parents or educators in digital format. The provision unit can also print and provide the generated work. The marketplace unit trades the work generated by the generation unit. For example, the marketplace unit allows users to list work created by users and other users to purchase them. The marketplace unit can also evaluate the quality of the work based on user ratings and reviews and generate rankings. As a result, the customized work creation system according to the embodiment creates customized work based on a child's interests and learning style, provides it to parents and educators, and allows them to trade it on the marketplace. For example, parents and educators can use a variety of work created by other users. Users can also list their own work on the marketplace and earn revenue.

[0030] The generation unit can collect data on a child's daily life and customize tasks based on that data. The generation unit generates tasks that include physical activities based on, for example, the child's exercise data acquired from a smartwatch. For example, on days when the child is not very active, it creates tasks that incorporate physical games. The generation unit also generates tasks related to daily life based on the child's activity data acquired from IoT devices in the home. For example, it creates tasks related to housework and cooking based on activities at home. The generation unit also generates tasks that can be completed while out and about based on the child's location information acquired from a smartphone. For example, it creates tasks related to activities at parks and museums. This makes it possible to customize tasks based on data on a child's daily life.

[0031] The generation unit can track a child's learning history over the long term and generate the next work based on past learning outcomes and assignments. For example, the generation unit can analyze a child's past test results and generate work that allows them to focus on areas they are weak in. For example, if a child is weak in a particular math unit, it can provide problems specialized for that unit. The generation unit also generates the next work based on the child's learning progress obtained from a learning app. For example, it can provide basic problems if the child is not progressing well, and applied problems if the child is progressing well. The generation unit also generates the next work based on feedback provided by parents or educators. For example, it can provide work that strengthens that work based on feedback on a specific assignment. This makes it possible to generate the next work based on past learning outcomes and assignments.

[0032] The generation unit can analyze data on a child's friendships and social networks to generate work that can be completed collaboratively with friends. For example, the generation unit can analyze a child's social media friend list to generate group work that can be completed together with friends. For example, it can provide a puzzle to solve in cooperation with friends. The generation unit can also analyze chat history to generate work that can be completed collaboratively based on the content of conversations with friends. For example, it can provide a project based on a common interest. The generation unit can also analyze data from online games to generate game-style work that can be completed together with friends. For example, it can provide a mission to be completed collaboratively. This makes it possible to generate work that can be completed collaboratively with friends.

[0033] The generation unit can generate worksheets that gamify learning content based on the child's hobbies and special skills. For example, if a child's hobby is drawing, the generation unit generates worksheets that include art-related questions. For example, color theory can be learned through drawing assignments. If a child's special skill is music, the generation unit generates worksheets that include music-related questions. For example, rhythm and melody can be learned through playing an instrument. If a child's hobby is sports, the generation unit generates worksheets that include sports-related questions. For example, sports rules and tactics can be learned. In this way, worksheets that gamify learning content can be generated based on a child's hobbies and special skills.

[0034] The generation unit can take into account the child's home environment and cultural background and customize the work based on that. For example, the generation unit generates work that includes an experiment using materials available at home, taking into account the child's home environment. For example, it provides a chemistry experiment using ingredients found at home. The generation unit also takes into account the child's cultural background and generates work that is related to the culture. For example, it provides work that teaches history and traditions based on a particular culture. The generation unit also takes into account bilingual education and generates work that can be studied in multiple languages. For example, it provides questions that can be studied in English and Japanese. This makes it possible to customize the work based on the child's home environment and cultural background.

[0035] The generation unit can classify children's learning styles in detail and generate worksheets specialized for each of the visual, auditory, and tactile styles. For example, the generation unit generates worksheets specialized for children's visual learning styles. For example, it provides problems that make extensive use of illustrations and diagrams. The generation unit also generates worksheets specialized for children's auditory learning styles. For example, it provides problems that incorporate audio and music. The generation unit also generates worksheets specialized for children's tactile learning styles. For example, it provides problems that include activities that require actual hands-on work. In this way, it is possible to generate worksheets specialized for children's learning styles.

[0036] The generation unit can monitor a child's learning progress in real time and adjust the difficulty of the work in accordance with the progress. For example, the generation unit monitors a child's learning progress in real time, and generates work with increased difficulty if progress is rapid. For example, the difficulty of the problems is gradually increased. Furthermore, the generation unit generates basic work if progress is slow. For example, it provides problems that review basic concepts. Furthermore, the generation unit adjusts the content of the work in accordance with progress. For example, it provides problems specialized in a particular field. In this way, the difficulty of the work can be adjusted in accordance with the child's learning progress.

[0037] The generation unit can generate work that the whole family can work on, taking into consideration the child's family structure and the learning styles of siblings. The generation unit, for example, generates work that the child can work on together with siblings, taking into consideration the child's family structure. For example, it provides quiz-style questions that the whole family can participate in. The generation unit also generates project-style work that can be worked on together, taking into consideration the learning styles of siblings. For example, it provides a project that the whole family can complete together in cooperation. The generation unit also generates work that includes activities that the whole family can enjoy. For example, it provides game-style questions that the whole family can participate in. In this way, it is possible to generate work that the whole family can work on.

[0038] The generation unit generates worksheets that are in line with the child's school curriculum and can be linked to school lessons. The generation unit generates worksheets that correspond to the lesson content based on, for example, the child's school curriculum. For example, it provides questions to review what was learned in class. The generation unit also generates worksheets for preparatory study, allowing the child to preview the learning content before class. For example, it provides questions related to the topic that will be covered in the next class. The generation unit also generates supplementary worksheets that can reinforce content that was not fully understood in class. For example, it provides questions that are specialized in parts that were difficult to understand in class. In this way, worksheets that are in line with the school curriculum can be generated and linked to lessons.

[0039] The marketplace unit can automatically evaluate the quality of works based on user ratings and reviews and generate rankings. The marketplace unit, for example, analyzes user ratings on the marketplace and automatically evaluates the quality of works based on the rating scores. For example, works with higher ratings are ranked higher. The marketplace unit also analyzes user reviews and evaluates the quality of works based on the review content. For example, works with many positive reviews are ranked higher. The marketplace unit also analyzes transaction history and ranks works with a high number of transactions higher. For example, works with a high number of transactions are ranked higher. This makes it possible to automatically evaluate the quality of works based on user ratings and reviews and generate rankings.

[0040] The marketplace unit can analyze transaction history, understand trends in popular works, and reflect this in the generation of new works. For example, the marketplace unit analyzes transaction history on the marketplace to identify the themes and formats of popular works. For example, if a specific theme is popular, it generates new works related to that theme. The marketplace unit also analyzes seasonal trends and generates works according to the season. For example, it provides works related to summer vacation and winter vacation. The marketplace unit also analyzes trends by age group and generates works that are popular with specific age groups. For example, it provides works for elementary school students and works for junior high school students. This allows the transaction history to be analyzed, trends in popular works to be understood, and the results to be reflected in the generation of new works.

[0041] The marketplace unit can add a communication function between users, allowing them to share improvements and ideas for the work. For example, the marketplace unit can add a forum function to the marketplace, allowing users to share improvements and ideas for the work. For example, the quality of the work can be improved through discussions in the forum. The marketplace unit can also add a comment function, allowing users to provide feedback on the work. For example, improvements and suggestions for the work can be posted as comments. The marketplace unit can also add a review function, allowing users to share their evaluations of the work. For example, evaluations and impressions of the work can be posted as reviews. This adds a communication function between users, allowing them to share improvements and ideas for the work.

[0042] The marketplace unit can translate works created by users in different countries or regions, enabling them to be traded internationally. For example, the marketplace unit adds an automatic translation function to the marketplace, translating and providing works created in different languages. For example, translating a work created in English into Japanese. The marketplace unit also provides a multilingual interface, enabling users of different languages ​​to trade works. For example, it supports multiple languages ​​such as English, Japanese, and Spanish. The marketplace unit also adds a translation review function, enabling users to evaluate the content of the translation. For example, it evaluates the quality of the translation and suggests areas for improvement. This allows works created by users in different countries or regions to be translated and traded internationally.

[0043] The Marketplace section can feature work tailored to a particular theme or event. For example, the Marketplace section can create seasonal features on the Marketplace and provide work related to seasonal events. For example, the Marketplace section can feature work related to Christmas or Halloween. The Marketplace section can also feature work tailored to a particular event. For example, the Marketplace section can provide work related to the Olympics or the World Cup. The Marketplace section can also feature work based on an educational theme. For example, the Marketplace section can provide work related to environmental issues or science and technology. This allows the Marketplace section to feature work tailored to a particular theme or event.

[0044] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0045] The generation unit can monitor the child's learning environment and generate work that suits the environment. For example, the generation unit can detect the brightness of the child's learning environment with a sensor and provide work that suits the brightness. In a bright environment, it generates visual work, and in a dark environment, it generates auditory work. The generation unit can also monitor the temperature of the child's learning environment and provide work that suits the temperature. For example, if the temperature is high, it generates work that is relaxing, and if the temperature is low, it generates work that improves concentration. The generation unit can also monitor the noise level of the child's learning environment and provide work that suits the noise level. For example, if the noise level is high, it generates work that can be done in a quiet environment, and if the noise level is low, it generates work that requires concentration. In this way, it is possible to generate work that suits the child's learning environment.

[0046] The generation unit can monitor the child's health condition and generate activities according to the health condition. For example, the generation unit monitors the child's body temperature and provides relaxing activities when the body temperature is high. For example, reading or puzzles are provided when the body temperature is high. The generation unit also monitors the child's sleep data and provides activities including light activities when the child is sleep deprived. For example, activities incorporating light exercise are provided when the child is sleep deprived. The generation unit also monitors the child's dietary data and provides activities according to the dietary content. For example, health-related activities are provided when the child's nutritional balance is unbalanced. In this way, activities according to the child's health condition can be generated.

[0047] The generation unit can track a child's learning history over the long term and generate the next work based on past learning outcomes and assignments. For example, the generation unit can analyze a child's past test results and generate work that allows them to focus on areas they are weak in. For example, if a child is weak in a particular math unit, it can provide problems specialized for that unit. The generation unit also generates the next work based on the child's learning progress obtained from a learning app. For example, it can provide basic problems if the child is not progressing well, and applied problems if the child is progressing well. The generation unit also generates the next work based on feedback provided by parents or educators. For example, it can provide work that strengthens that work based on feedback on a specific assignment. This makes it possible to generate the next work based on past learning outcomes and assignments.

[0048] The generation unit can analyze data on a child's friendships and social networks to generate work that can be completed collaboratively with friends. For example, the generation unit can analyze a child's social media friend list to generate group work that can be completed together with friends. For example, it can provide a puzzle to solve in cooperation with friends. The generation unit can also analyze chat history to generate work that can be completed collaboratively based on the content of conversations with friends. For example, it can provide a project based on a common interest. The generation unit can also analyze data from online games to generate game-style work that can be completed together with friends. For example, it can provide a mission to be completed collaboratively. This makes it possible to generate work that can be completed collaboratively with friends.

[0049] The generation unit can generate worksheets that gamify learning content based on the child's hobbies and special skills. For example, if a child's hobby is drawing, the generation unit generates worksheets that include art-related questions. For example, color theory can be learned through drawing assignments. If a child's special skill is music, the generation unit generates worksheets that include music-related questions. For example, rhythm and melody can be learned through playing an instrument. If a child's hobby is sports, the generation unit generates worksheets that include sports-related questions. For example, sports rules and tactics can be learned. In this way, worksheets that gamify learning content can be generated based on a child's hobbies and special skills.

[0050] The generation unit can take into account the child's home environment and cultural background and customize the work based on that. For example, the generation unit generates work that includes an experiment using materials available at home, taking into account the child's home environment. For example, it provides a chemistry experiment using ingredients found at home. The generation unit also takes into account the child's cultural background and generates work that is related to the culture. For example, it provides work that teaches history and traditions based on a particular culture. The generation unit also takes into account bilingual education and generates work that can be studied in multiple languages. For example, it provides questions that can be studied in English and Japanese. This makes it possible to customize the work based on the child's home environment and cultural background.

[0051] The generation unit can classify children's learning styles in detail and generate worksheets specialized for each of the visual, auditory, and tactile styles. For example, the generation unit generates worksheets specialized for children's visual learning styles. For example, it provides problems that make extensive use of illustrations and diagrams. The generation unit also generates worksheets specialized for children's auditory learning styles. For example, it provides problems that incorporate audio and music. The generation unit also generates worksheets specialized for children's tactile learning styles. For example, it provides problems that include activities that require actual hands-on work. In this way, it is possible to generate worksheets specialized for children's learning styles.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The generator uses AI to generate worksheets based on the child's interests and learning style. For example, the AI ​​receives information about the child's interests as input and generates optimal worksheets based on that information. The AI ​​can also generate worksheets that are suited to visual, auditory, and tactile learning styles based on the child's learning style. Step 2: The analysis unit analyzes the work generated by the generation unit. For example, the analysis unit evaluates the content of the generated work and identifies areas for improvement. The analysis unit can also measure the effectiveness of the generated work and reflect this in the next work generation. Step 3: The providing unit provides the work analyzed by the analyzing unit to the parent or educator. For example, the providing unit provides the generated work to the parent or educator in a digital format. The providing unit can also print and provide the generated work. Step 4: The marketplace unit trades the works generated by the generation unit. For example, the marketplace unit allows users to list works they have created and other users to purchase them. The marketplace unit can also evaluate the quality of the works based on user ratings and reviews and generate rankings.

[0054] (Example 2) The customized work generation system according to an embodiment of the present invention uses generation AI to create customized work tailored to children's preferences and trades it through a marketplace. This allows the customized work generation system to maximize children's learning outcomes and reduce the burden on parents and educators.

[0055] A customized work generation system according to an embodiment includes a generation unit, an analysis unit, a provision unit, and a marketplace unit. The generation unit generates work based on a child's interests and learning style using a generation AI. For example, the generation AI receives information about a child's interests as input and generates optimal work based on the information. The generation AI can also generate work corresponding to a child's visual, auditory, or tactile learning style based on the child's learning style. The analysis unit analyzes the work generated by the generation unit. For example, the analysis unit evaluates the content of the generated work and identifies areas for improvement. The analysis unit can also measure the effectiveness of the generated work and reflect this in the next work generation. The provision unit provides the work analyzed by the analysis unit to parents or educators. For example, the provision unit provides the generated work to parents or educators in digital format. The provision unit can also print and provide the generated work. The marketplace unit trades the work generated by the generation unit. For example, the marketplace unit allows users to list work created by users and other users to purchase them. The marketplace unit can also evaluate the quality of the work based on user ratings and reviews and generate rankings. As a result, the customized work creation system according to the embodiment creates customized work based on a child's interests and learning style, provides it to parents and educators, and allows them to trade it on the marketplace. For example, parents and educators can use a variety of work created by other users. Users can also list their own work on the marketplace and earn revenue.

[0056] The generation unit can collect data on a child's daily life and customize tasks based on that data. The generation unit generates tasks that include physical activities based on, for example, the child's exercise data acquired from a smartwatch. For example, on days when the child is not very active, it creates tasks that incorporate physical games. The generation unit also generates tasks related to daily life based on the child's activity data acquired from IoT devices in the home. For example, it creates tasks related to housework and cooking based on activities at home. The generation unit also generates tasks that can be completed while out and about based on the child's location information acquired from a smartphone. For example, it creates tasks related to activities at parks and museums. This makes it possible to customize tasks based on data on a child's daily life.

[0057] The generation unit can analyze the child's emotional state in real time and generate work that corresponds to the child's emotions at any given time. For example, the generation unit analyzes the child's facial expressions using a camera, and generates work that includes fun activities when the child smiles a lot. For example, game-style problems are provided when the child smiles a lot. The generation unit also analyzes the child's vocal tone, and generates work that includes energetic activities when the child is excited. For example, an action game is provided when the vocal tone is high. The generation unit also analyzes the child's heart rate, and generates work that includes quiet activities when the child is relaxed. For example, reading or puzzles are provided when the heart rate is low. This makes it possible to generate work that corresponds to the child's emotional state in real time.

[0058] The generation unit can track a child's learning history over the long term and generate the next work based on past learning outcomes and assignments. For example, the generation unit can analyze a child's past test results and generate work that allows them to focus on areas they are weak in. For example, if a child is weak in a particular math unit, it can provide problems specialized for that unit. The generation unit also generates the next work based on the child's learning progress obtained from a learning app. For example, it can provide basic problems if the child is not progressing well, and applied problems if the child is progressing well. The generation unit also generates the next work based on feedback provided by parents or educators. For example, it can provide work that strengthens that work based on feedback on a specific assignment. This makes it possible to generate the next work based on past learning outcomes and assignments.

[0059] The generation unit can analyze data on a child's friendships and social networks to generate work that can be completed collaboratively with friends. For example, the generation unit can analyze a child's social media friend list to generate group work that can be completed together with friends. For example, it can provide a puzzle to solve in cooperation with friends. The generation unit can also analyze chat history to generate work that can be completed collaboratively based on the content of conversations with friends. For example, it can provide a project based on a common interest. The generation unit can also analyze data from online games to generate game-style work that can be completed together with friends. For example, it can provide a mission to be completed collaboratively. This makes it possible to generate work that can be completed collaboratively with friends.

[0060] The generation unit can generate worksheets that gamify learning content based on the child's hobbies and special skills. For example, if a child's hobby is drawing, the generation unit generates worksheets that include art-related questions. For example, color theory can be learned through drawing assignments. If a child's special skill is music, the generation unit generates worksheets that include music-related questions. For example, rhythm and melody can be learned through playing an instrument. If a child's hobby is sports, the generation unit generates worksheets that include sports-related questions. For example, sports rules and tactics can be learned. In this way, worksheets that gamify learning content can be generated based on a child's hobbies and special skills.

[0061] The generation unit can use the emotion estimation function to identify moments when the child is having the most fun and generate work related to those moments. For example, the generation unit analyzes the child's facial expressions using a camera to identify moments when the child smiles most, and generates work related to those moments. For example, the generation unit provides problems based on the theme the child was working on when the child smiled most. The generation unit can also analyze the child's vocal tone to identify moments when the child is excited, and generate work related to those moments. For example, the generation unit provides problems based on the activity the child was working on when the vocal tone was high. The generation unit can also analyze the child's heart rate to identify moments when the child is relaxed, and generate work related to those moments. For example, the generation unit provides problems based on reading or puzzles the child was working on when the heart rate was low. This makes it possible to generate work related to moments when the child is having the most fun.

[0062] The generation unit can take into account the child's home environment and cultural background and customize the work based on that. For example, the generation unit generates work that includes an experiment using materials available at home, taking into account the child's home environment. For example, it provides a chemistry experiment using ingredients found at home. The generation unit also takes into account the child's cultural background and generates work that is related to the culture. For example, it provides work that teaches history and traditions based on a particular culture. The generation unit also takes into account bilingual education and generates work that can be studied in multiple languages. For example, it provides questions that can be studied in English and Japanese. This makes it possible to customize the work based on the child's home environment and cultural background.

[0063] The generation unit can classify children's learning styles in detail and generate worksheets specialized for each of the visual, auditory, and tactile styles. For example, the generation unit generates worksheets specialized for children's visual learning styles. For example, it provides problems that make extensive use of illustrations and diagrams. The generation unit also generates worksheets specialized for children's auditory learning styles. For example, it provides problems that incorporate audio and music. The generation unit also generates worksheets specialized for children's tactile learning styles. For example, it provides problems that include activities that require actual hands-on work. In this way, it is possible to generate worksheets specialized for children's learning styles.

[0064] The generation unit can monitor a child's learning progress in real time and adjust the difficulty of the work in accordance with the progress. For example, the generation unit monitors a child's learning progress in real time, and generates work with increased difficulty if progress is rapid. For example, the difficulty of the problems is gradually increased. Furthermore, the generation unit generates basic work if progress is slow. For example, it provides problems that review basic concepts. Furthermore, the generation unit adjusts the content of the work in accordance with progress. For example, it provides problems specialized in a particular field. In this way, the difficulty of the work can be adjusted in accordance with the child's learning progress.

[0065] The generation unit can generate work that the whole family can work on, taking into consideration the child's family structure and the learning styles of siblings. The generation unit, for example, generates work that the child can work on together with siblings, taking into consideration the child's family structure. For example, it provides quiz-style questions that the whole family can participate in. The generation unit also generates project-style work that can be worked on together, taking into consideration the learning styles of siblings. For example, it provides a project that the whole family can complete together in cooperation. The generation unit also generates work that includes activities that the whole family can enjoy. For example, it provides game-style questions that the whole family can participate in. In this way, it is possible to generate work that the whole family can work on.

[0066] The generation unit generates worksheets that are in line with the child's school curriculum and can be linked to school lessons. The generation unit generates worksheets that correspond to the lesson content based on, for example, the child's school curriculum. For example, it provides questions to review what was learned in class. The generation unit also generates worksheets for preparatory study, allowing the child to preview the learning content before class. For example, it provides questions related to the topic that will be covered in the next class. The generation unit also generates supplementary worksheets that can reinforce content that was not fully understood in class. For example, it provides questions that are specialized in parts that were difficult to understand in class. In this way, worksheets that are in line with the school curriculum can be generated and linked to lessons.

[0067] The generation unit can use the emotion estimation function to identify the time period when a child can concentrate best and generate work that is optimal for that time period. The generation unit, for example, analyzes data about the child's daily life to identify the time period when the child can concentrate best and generates work that can be done during that time period. For example, if the child is most likely to concentrate in the morning, work that can be done in the morning is provided. The generation unit also identifies the time period when the child is most likely to concentrate based on the emotion score and generates work that is optimal for that time period. For example, problems that can be done during the time period when the emotion score is high are provided. The generation unit also combines the data about the child's daily life with the emotion score to identify the time period when the child can concentrate best and generate work that is optimal for that time period. For example, if the child is most likely to concentrate best during a particular time period, work that can be done during that time period is provided. This makes it possible to generate work that is optimal for the time period when the child can concentrate best.

[0068] The marketplace unit can automatically evaluate the quality of works based on user ratings and reviews and generate rankings. The marketplace unit, for example, analyzes user ratings on the marketplace and automatically evaluates the quality of works based on the rating scores. For example, works with higher ratings are ranked higher. The marketplace unit also analyzes user reviews and evaluates the quality of works based on the review content. For example, works with many positive reviews are ranked higher. The marketplace unit also analyzes transaction history and ranks works with a high number of transactions higher. For example, works with a high number of transactions are ranked higher. This makes it possible to automatically evaluate the quality of works based on user ratings and reviews and generate rankings.

[0069] The marketplace unit can analyze transaction history, understand trends in popular works, and reflect this in the generation of new works. For example, the marketplace unit analyzes transaction history on the marketplace to identify the themes and formats of popular works. For example, if a specific theme is popular, it generates new works related to that theme. The marketplace unit also analyzes seasonal trends and generates works according to the season. For example, it provides works related to summer vacation and winter vacation. The marketplace unit also analyzes trends by age group and generates works that are popular with specific age groups. For example, it provides works for elementary school students and works for junior high school students. This allows the transaction history to be analyzed, trends in popular works to be understood, and the results to be reflected in the generation of new works.

[0070] The marketplace unit can add a communication function between users, allowing them to share improvements and ideas for the work. For example, the marketplace unit can add a forum function to the marketplace, allowing users to share improvements and ideas for the work. For example, the quality of the work can be improved through discussions in the forum. The marketplace unit can also add a comment function, allowing users to provide feedback on the work. For example, improvements and suggestions for the work can be posted as comments. The marketplace unit can also add a review function, allowing users to share their evaluations of the work. For example, evaluations and impressions of the work can be posted as reviews. This adds a communication function between users, allowing them to share improvements and ideas for the work.

[0071] The marketplace unit can translate works created by users in different countries or regions, enabling them to be traded internationally. For example, the marketplace unit adds an automatic translation function to the marketplace, translating and providing works created in different languages. For example, translating a work created in English into Japanese. The marketplace unit also provides a multilingual interface, enabling users of different languages ​​to trade works. For example, it supports multiple languages ​​such as English, Japanese, and Spanish. The marketplace unit also adds a translation review function, enabling users to evaluate the content of the translation. For example, it evaluates the quality of the translation and suggests areas for improvement. This allows works created by users in different countries or regions to be translated and traded internationally.

[0072] The Marketplace section can feature work tailored to a particular theme or event. For example, the Marketplace section can create seasonal features on the Marketplace and provide work related to seasonal events. For example, the Marketplace section can feature work related to Christmas or Halloween. The Marketplace section can also feature work tailored to a particular event. For example, the Marketplace section can provide work related to the Olympics or the World Cup. The Marketplace section can also feature work based on an educational theme. For example, the Marketplace section can provide work related to environmental issues or science and technology. This allows the Marketplace section to feature work tailored to a particular theme or event.

[0073] The marketplace unit can use the emotion estimation function to analyze users' emotional responses on the marketplace and feature works that are likely to resonate emotionally. For example, the marketplace unit analyzes users' emotional responses on the marketplace and features works that evoke a lot of positive emotions. For example, works with high user emotion scores are displayed at the top. The marketplace unit also uses the emotion estimation function to analyze users' emotional responses in real time and feature works on themes that are likely to resonate. For example, works related to themes with high emotion scores are provided. The marketplace unit also uses the emotion estimation function to identify areas for improvement in the works based on users' emotional responses and reflect these in the next feature. For example, the characteristics of works that evoke a lot of positive emotions are analyzed and used in the next feature. In this way, the emotion estimation function can be used to feature works that are likely to resonate emotionally.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The generation unit can monitor the child's learning environment and generate work that suits the environment. For example, the generation unit can detect the brightness of the child's learning environment with a sensor and provide work that suits the brightness. In a bright environment, it generates visual work, and in a dark environment, it generates auditory work. The generation unit can also monitor the temperature of the child's learning environment and provide work that suits the temperature. For example, if the temperature is high, it generates work that is relaxing, and if the temperature is low, it generates work that improves concentration. The generation unit can also monitor the noise level of the child's learning environment and provide work that suits the noise level. For example, if the noise level is high, it generates work that can be done in a quiet environment, and if the noise level is low, it generates work that requires concentration. In this way, it is possible to generate work that suits the child's learning environment.

[0076] The generation unit can monitor the child's health condition and generate activities according to the health condition. For example, the generation unit monitors the child's body temperature and provides relaxing activities when the body temperature is high. For example, reading or puzzles are provided when the body temperature is high. The generation unit also monitors the child's sleep data and provides activities including light activities when the child is sleep deprived. For example, activities incorporating light exercise are provided when the child is sleep deprived. The generation unit also monitors the child's dietary data and provides activities according to the dietary content. For example, health-related activities are provided when the child's nutritional balance is unbalanced. In this way, activities according to the child's health condition can be generated.

[0077] The generation unit can analyze the child's emotional state in real time and generate work that corresponds to the child's emotions at any given time. For example, the generation unit analyzes the child's facial expressions using a camera, and generates work that includes fun activities when the child smiles a lot. For example, game-style problems are provided when the child smiles a lot. The generation unit also analyzes the child's vocal tone, and generates work that includes energetic activities when the child is excited. For example, an action game is provided when the vocal tone is high. The generation unit also analyzes the child's heart rate, and generates work that includes quiet activities when the child is relaxed. For example, reading or puzzles are provided when the heart rate is low. This makes it possible to generate work that corresponds to the child's emotional state in real time.

[0078] The generation unit can track a child's learning history over the long term and generate the next work based on past learning outcomes and assignments. For example, the generation unit can analyze a child's past test results and generate work that allows them to focus on areas they are weak in. For example, if a child is weak in a particular math unit, it can provide problems specialized for that unit. The generation unit also generates the next work based on the child's learning progress obtained from a learning app. For example, it can provide basic problems if the child is not progressing well, and applied problems if the child is progressing well. The generation unit also generates the next work based on feedback provided by parents or educators. For example, it can provide work that strengthens that work based on feedback on a specific assignment. This makes it possible to generate the next work based on past learning outcomes and assignments.

[0079] The generation unit can analyze data on a child's friendships and social networks to generate work that can be completed collaboratively with friends. For example, the generation unit can analyze a child's social media friend list to generate group work that can be completed together with friends. For example, it can provide a puzzle to solve in cooperation with friends. The generation unit can also analyze chat history to generate work that can be completed collaboratively based on the content of conversations with friends. For example, it can provide a project based on a common interest. The generation unit can also analyze data from online games to generate game-style work that can be completed together with friends. For example, it can provide a mission to be completed collaboratively. This makes it possible to generate work that can be completed collaboratively with friends.

[0080] The generation unit can generate worksheets that gamify learning content based on the child's hobbies and special skills. For example, if a child's hobby is drawing, the generation unit generates worksheets that include art-related questions. For example, color theory can be learned through drawing assignments. If a child's special skill is music, the generation unit generates worksheets that include music-related questions. For example, rhythm and melody can be learned through playing an instrument. If a child's hobby is sports, the generation unit generates worksheets that include sports-related questions. For example, sports rules and tactics can be learned. In this way, worksheets that gamify learning content can be generated based on a child's hobbies and special skills.

[0081] The generation unit can use the emotion estimation function to identify moments when the child is having the most fun and generate work related to those moments. For example, the generation unit analyzes the child's facial expressions using a camera to identify moments when the child smiles most, and generates work related to those moments. For example, the generation unit provides problems based on the theme the child was working on when the child smiled most. The generation unit can also analyze the child's vocal tone to identify moments when the child is excited, and generate work related to those moments. For example, the generation unit provides problems based on the activity the child was working on when the vocal tone was high. The generation unit can also analyze the child's heart rate to identify moments when the child is relaxed, and generate work related to those moments. For example, the generation unit provides problems based on reading or puzzles the child was working on when the heart rate was low. This makes it possible to generate work related to moments when the child is having the most fun.

[0082] The generation unit can take into account the child's home environment and cultural background and customize the work based on that. For example, the generation unit generates work that includes an experiment using materials available at home, taking into account the child's home environment. For example, it provides a chemistry experiment using ingredients found at home. The generation unit also takes into account the child's cultural background and generates work that is related to the culture. For example, it provides work that teaches history and traditions based on a particular culture. The generation unit also takes into account bilingual education and generates work that can be studied in multiple languages. For example, it provides questions that can be studied in English and Japanese. This makes it possible to customize the work based on the child's home environment and cultural background.

[0083] The generation unit can classify children's learning styles in detail and generate worksheets specialized for each of the visual, auditory, and tactile styles. For example, the generation unit generates worksheets specialized for children's visual learning styles. For example, it provides problems that make extensive use of illustrations and diagrams. The generation unit also generates worksheets specialized for children's auditory learning styles. For example, it provides problems that incorporate audio and music. The generation unit also generates worksheets specialized for children's tactile learning styles. For example, it provides problems that include activities that require actual hands-on work. In this way, it is possible to generate worksheets specialized for children's learning styles.

[0084] The generation unit can use the emotion estimation function to identify the time period when a child can concentrate best and generate work that is optimal for that time period. The generation unit, for example, analyzes data about the child's daily life to identify the time period when the child can concentrate best and generates work that can be done during that time period. For example, if the child is most likely to concentrate in the morning, work that can be done in the morning is provided. The generation unit also identifies the time period when the child is most likely to concentrate based on the emotion score and generates work that is optimal for that time period. For example, problems that can be done during the time period when the emotion score is high are provided. The generation unit also combines the data about the child's daily life with the emotion score to identify the time period when the child can concentrate best and generate work that is optimal for that time period. For example, if the child is most likely to concentrate best during a particular time period, work that can be done during that time period is provided. This makes it possible to generate work that is optimal for the time period when the child can concentrate best.

[0085] The processing flow of the second embodiment will be briefly explained below.

[0086] Step 1: The generator uses AI to generate worksheets based on the child's interests and learning style. For example, the AI ​​receives information about the child's interests as input and generates optimal worksheets based on that information. The AI ​​can also generate worksheets that are suited to visual, auditory, and tactile learning styles based on the child's learning style. Step 2: The analysis unit analyzes the work generated by the generation unit. For example, the analysis unit evaluates the content of the generated work and identifies areas for improvement. The analysis unit can also measure the effectiveness of the generated work and reflect this in the next work generation. Step 3: The providing unit provides the work analyzed by the analyzing unit to the parent or educator. For example, the providing unit provides the generated work to the parent or educator in a digital format. The providing unit can also print and provide the generated work. Step 4: The marketplace unit trades the works generated by the generation unit. For example, the marketplace unit allows users to list works they have created and other users to purchase them. The marketplace unit can also evaluate the quality of the works based on user ratings and reviews and generate rankings.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0091] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0113] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0115] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0116] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0117] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes 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.

[0119] 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.

[0120] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0131] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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."

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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]

[0154] 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 generator uses AI to generate work based on children's interests and learning styles. an analysis unit that analyzes the work generated by the generation unit; a providing unit that provides the work analyzed by the analyzing unit to parents or educators; a marketplace unit that trades the work generated by the generation unit; A system characterized by:

2. The generation unit Analyze the child's emotional state in real time and generate the work that corresponds to their emotions at that time.

2. The system of claim 1.

3. The generation unit Analyze data on children's friendships and social networks to generate the tasks they can work on collaboratively with their friends.

2. The system of claim 1.

4. The generation unit Consider the child's home environment and cultural background and customize the work accordingly.

2. The system of claim 1.

5. The marketplace unit Automatically evaluate the quality of the work based on user ratings and reviews, and generate a ranking.

2. The system of claim 1.

6. The generation unit Using emotion estimation, the moment when the child is having the most fun is identified and the work related to that moment is generated.

2. The system of claim 1.

7. The generation unit Using emotion estimation, the system identifies the time periods when children can concentrate best and generates the most suitable activities for those times.

2. The system of claim 1.

8. The marketplace unit Using emotion estimation function, we analyze users' emotional reactions in the marketplace and feature works that are likely to resonate emotionally.

2. The system of claim 1.

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