system

Abstract

The system of the embodiment aims to cater to children's individual learning needs and share information with parents and educators in real time. [Solution] The system according to the embodiment includes a collection unit, a generation unit, a dialogue unit, and a sharing unit. The collection unit collects children's behavioral data or learning history. The generation unit generates an individual educational plan based on the data collected by the collection unit. The dialogue unit advances learning by interacting with the children based on the educational plan generated by the generation unit. The sharing unit shares the learning progress and growth recorded by the dialogue unit with parents or educators in real time.

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] Previous technology has had issues in that it has not adequately addressed children's individual learning needs and shared information with parents and educators in real time.

[0005] The system of the embodiment aims to cater to children's individual learning needs and share information with parents and educators in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, a generation unit, a dialogue unit, and a sharing unit. The collection unit collects behavioral data or learning history of children. The generation unit generates an individual educational plan based on the data collected by the collection unit. The dialogue unit advances learning by interacting with the children based on the educational plan generated by the generation unit. The sharing unit shares the learning progress and growth recorded by the dialogue unit with parents or educators in real time. [Effects of the Invention]

[0007] The system according to the embodiment can respond to children's individual learning needs and share information with parents and educators in real time. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An educational character toy system according to an embodiment of the present invention supports children's learning and records their development. This system utilizes generative AI to provide education tailored to individual interests and needs, enabling effective educational support through information sharing with parents and educators. It also evolves as children grow and becomes a partner in lifelong learning. For example, the system collects children's behavioral data and learning history, and the generative AI generates an individual educational plan based on that data. Based on the generated educational plan, the system then interacts with the children to advance their learning. For example, the system supports learning by asking questions and providing quizzes. The system also records children's learning progress and development and shares this information with parents and educators in real time via the cloud. This allows parents and educators to understand children's learning status and provide appropriate support. Furthermore, the system evolves as children grow and becomes a partner in lifelong learning. All conversations and records are stored in the cloud, with data encryption and access restrictions in place to protect privacy. This ensures that precious memories and experiences are preserved forever. This allows the educational character toy system to support children's learning and record their development.

[0029] An educational character toy system according to an embodiment includes a collection unit, a generation unit, a dialogue unit, and a sharing unit. The collection unit collects children's behavioral data or learning history. The behavioral data includes, for example, but is not limited to, daily behavior and behavior during learning. The learning history includes, for example, but is not limited to, learning progress and past test results. The collection unit collects the behavioral data using, for example, a sensor or a camera. The collection unit can also digitally record the learning history and store it in the cloud. The generation unit generates an individual educational plan based on the data collected by the collection unit. The generation unit creates a curriculum tailored to the children's interests and needs, for example, using a generation AI. The generation AI generates the educational plan using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit customizes the individual educational plan based on, for example, the children's learning goals and progress. The dialogue unit advances learning by interacting with the children based on the educational plan generated by the generation unit. The dialogue unit understands the children's utterances using, for example, voice recognition technology and generates appropriate responses. The dialogue unit uses generative AI to ask questions and give quizzes to children to support their learning. The dialogue unit can also adjust the difficulty of the next question or quiz based on the children's answers, for example. The sharing unit shares the learning progress and growth recorded by the dialogue unit with parents and educators in real time. The sharing unit can share data, for example, via the cloud, allowing parents and educators to understand the children's learning situation. The sharing unit protects privacy by encrypting data and setting access restrictions. In this way, the educational character toy system according to the embodiment can support children's learning and record their growth.

[0030] The collection unit can analyze the children's past behavioral data and select a collection method. For example, the collection unit can identify, from the past behavioral data, the time periods when the children are most relaxed and collect data during those time periods. The collection unit can also collect data during activities when the children are most focused based on the past behavioral data. Furthermore, the collection unit can analyze the past behavioral data and customize the collection method based on specific patterns. This enables efficient data collection by selecting the optimal collection method based on the past behavioral data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past behavioral data into the generation AI and have the generation AI select the optimal collection method.

[0031] When collecting behavioral data or learning history, the collection unit can filter the data based on the children's current interests. For example, the collection unit prioritizes collecting data related to topics that the children are currently interested in. The collection unit can also collect detailed data on activities that the children are particularly interested in. Furthermore, if the children's interests change, the collection unit can adjust the data to be collected in response to those changes. This allows for more relevant data to be collected by filtering the data based on the children's interests. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data related to the children's interests into the generation AI and have the generation AI perform the filtering.

[0032] When collecting behavioral data or learning history, the collection unit can prioritize collecting highly relevant data based on the children's geographical location information. For example, if the children are in a specific location, the collection unit prioritizes collecting data related to that location. Also, if the children are traveling, the collection unit can collect data related to their destination. Furthermore, if the children are in a specific area, the collection unit can collect data related to the culture and history of that area. This allows for efficient collection of highly relevant data by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the children's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0033] The collection unit can analyze the children's social media activities and collect related data when collecting behavioral data and learning history. The collection unit can collect related data based on, for example, content shared by the children on social media. The collection unit can also analyze the children's social media activity patterns and determine the optimal collection timing. The collection unit can also collect data related to topics in which the children have shown interest on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the children's social media activity data into the generation AI and cause the generation AI to collect related data.

[0034] When generating an educational plan, the generation unit can adjust the level of detail of the plan based on the children's learning progress. For example, if the children's learning progress is fast, the generation unit can generate an educational plan including detailed content. Furthermore, if the children's learning progress is slow, the generation unit can generate an educational plan that focuses on basic content. Furthermore, the generation unit can generate an educational plan that gradually increases in difficulty according to the children's learning progress. In this way, an appropriate educational plan can be provided by adjusting the level of detail of the plan based on the learning progress. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the children's learning progress data into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0035] When generating an educational plan, the generation unit can apply different generation algorithms depending on the children's interests. For example, if the children are interested in science, the generation unit can apply a generation algorithm specialized for science. Furthermore, if the children are interested in art, the generation unit can apply a generation algorithm specialized for art. Furthermore, if the children are interested in sports, the generation unit can apply a generation algorithm specialized for sports. In this way, by applying a generation algorithm according to the children's interests, a more effective educational plan can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the children's interests into the generation AI and cause the generation AI to apply different generation algorithms.

[0036] When generating an educational plan, the generation unit can determine the priority of the plan based on the children's learning history. For example, the generation unit generates an educational plan that prioritizes the most effective content from the children's learning history. The generation unit can also analyze the children's learning history and generate an educational plan that prioritizes content that the children were weak at in the past. Furthermore, the generation unit can generate an educational plan that prioritizes content that needs to be reviewed based on the children's learning history. This enables effective learning to be supported by determining the priority of plans based on the learning history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the children's learning history data into the generation AI and have the generation AI determine the priority of the plans.

[0037] When generating an educational plan, the generation unit can adjust the content of the plan by referring to learning resources related to the children. For example, the generation unit can refer to learning materials that the children have used in the past and generate the educational plan based on the referenced learning materials. The generation unit can also refer to online resources that the children are interested in and generate the educational plan based on the referenced learning resources. Furthermore, the generation unit can refer to resources in learning communities in which the children participate and generate the educational plan based on the referenced learning resources. This makes it possible to provide a more appropriate educational plan by referring to related learning resources. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on learning resources related to the children into the generation AI and have the generation AI adjust the content of the plan.

[0038] During the dialogue, the dialogue unit can adjust the difficulty of questions and quizzes based on the children's learning progress. For example, if the children's learning progress is rapid, the dialogue unit can pose more difficult questions and quizzes. Furthermore, if the children's learning progress is slow, the dialogue unit can pose more basic questions and quizzes. Furthermore, the dialogue unit can pose questions and quizzes of gradually increasing difficulty according to the children's learning progress. This allows for appropriate learning support by adjusting the difficulty of questions and quizzes based on the children's learning progress. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input children's learning progress data into the generation AI and cause the generation AI to adjust the difficulty of questions and quizzes.

[0039] The dialogue unit can apply different dialogue scenarios during dialogue depending on the children's interests and concerns. For example, if the children are interested in science, the dialogue unit can apply a dialogue scenario related to science. Furthermore, if the children are interested in art, the dialogue unit can apply a dialogue scenario related to art. Furthermore, if the children are interested in sports, the dialogue unit can apply a dialogue scenario related to sports. This enables more effective dialogue by applying a dialogue scenario according to the children's interests and concerns. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input data on the children's interests and concerns into the generation AI and cause the generation AI to apply different dialogue scenarios.

[0040] During the dialogue, the dialogue unit can determine dialogue priorities based on the children's learning history. For example, the dialogue unit can prioritize the most effective content from the children's learning history to include in the dialogue. The dialogue unit can also analyze the children's learning history and prioritize content that the children were weak at in the past to include in the dialogue. Furthermore, the dialogue unit can also prioritize content that needs to be reviewed based on the children's learning history to include in the dialogue. This enables effective learning support by determining dialogue priorities based on learning history. Some or all of the above-described processing in the dialogue unit can be performed using, for example, AI, or can be performed without using AI. For example, the dialogue unit can input the children's learning history data into the generation AI and have the generation AI determine the dialogue priorities.

[0041] During the dialogue, the dialogue unit can adjust the content of the dialogue by referring to the children's relevant learning resources. For example, the dialogue unit can refer to learning materials the children have used in the past and adjust the content of the dialogue based on that. The dialogue unit can also refer to online resources that the children are interested in and adjust the content of the dialogue based on that. Furthermore, the dialogue unit can refer to resources in learning communities in which the children participate and adjust the content of the dialogue based on that. In this way, by referring to the relevant learning resources, more appropriate dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the children's relevant learning resources to the generation AI and cause the generation AI to adjust the content of the dialogue.

[0042] The sharing unit can adjust the content of information sharing based on feedback from parents and educators. For example, the sharing unit shares detailed information about children's learning progress based on feedback from parents and educators. The sharing unit can also analyze feedback from parents and educators and prioritize sharing of necessary information. Furthermore, the sharing unit can adjust the frequency and timing of information sharing based on feedback from parents and educators. This makes it possible to optimize the content of sharing and share information effectively by referring to the feedback. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input feedback data from parents and educators into a generation AI and have the generation AI adjust the content of sharing.

[0043] When sharing information, the sharing unit can adjust the level of detail of the shared content based on the children's learning progress. For example, if the children's learning progress is fast, the sharing unit can provide shared content including detailed information. Furthermore, if the children's learning progress is slow, the sharing unit can provide shared content centered on basic information. Furthermore, the sharing unit can provide shared content with gradually increasing levels of detail according to the children's learning progress. This enables appropriate information sharing by adjusting the level of detail of the shared content based on the learning progress. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the children's learning progress data into the generation AI and cause the generation AI to adjust the level of detail of the shared content.

[0044] When sharing information, the sharing unit can select a sharing method based on the geographic location information of the parent or educator. For example, if the parent or educator is nearby, the sharing unit can share information through direct dialogue. Alternatively, if the parent or educator is far away, the sharing unit can prioritize online information sharing. Furthermore, if the parent or educator is in a specific location, the sharing unit can share information related to that location. This allows the optimal information sharing method to be selected by taking geographic location information into consideration. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the geographic location information of the parent or educator into the generation AI and have the generation AI select the optimal sharing method.

[0045] When sharing information, the sharing unit can adjust the content to be shared by analyzing the social media activity of the parent or educator. For example, the sharing unit can share relevant information based on the content shared by the parent or educator on social media. The sharing unit can also analyze the social media activity patterns of the parent or educator and determine the optimal timing for sharing. Furthermore, the sharing unit can share information related to topics in which the parent or educator has shown interest on social media. In this way, relevant information can be shared efficiently by analyzing social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input social media activity data of the parent or educator into a generation AI and have the generation AI adjust the content to be shared.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The educational character toy system may further include a reward unit. The reward unit provides rewards according to children's learning progress and behavior. For example, the reward unit may award digital badges or points when children achieve specific learning goals. The reward unit may also provide virtual currency or in-game items when children behave well. Furthermore, the reward unit may provide special rewards when specific conditions set by parents or educators are met. This can increase children's motivation to learn and promote positive behavior.

[0048] The generation unit can analyze the learning styles of children and generate an optimal educational plan. For example, if the children are visual learners, the generation unit can generate an educational plan that includes a lot of visual content. If the children are auditory learners, the generation unit can also generate an educational plan that includes a lot of audio and music. Furthermore, if the children are experiential learners, the generation unit can also generate an educational plan that includes a lot of hands-on activities. This makes it possible to provide an optimal educational plan according to the learning styles of children.

[0049] The sharing unit can adjust how it shares information based on feedback from parents and educators. For example, if a parent or educator wants detailed information, the sharing unit can provide a detailed report. Alternatively, if a parent or educator wants concise information, the sharing unit can provide a summary that summarizes the key points. Furthermore, if a parent or educator wants to receive information at a specific time, the sharing unit can share information at that time. This makes it possible to share information according to the needs of parents and educators.

[0050] The collection unit can prioritize collection of highly relevant data based on the children's geographical location information. For example, if the children are in a specific location, the collection unit prioritizes collection of data related to that location. Also, if the children are traveling, the collection unit can collect data related to their destination. Furthermore, if the children are in a specific region, the collection unit can collect data related to the culture and history of that region. In this way, highly relevant data can be collected efficiently by taking geographical location information into consideration.

[0051] During the dialogue, the dialogue unit can adjust the difficulty of questions and quizzes based on the children's learning progress. For example, if the children's learning progress is fast, the dialogue unit can ask them difficult questions and quizzes. Alternatively, if the children's learning progress is slow, the dialogue unit can ask them basic questions and quizzes. Furthermore, the dialogue unit can ask questions and quizzes of gradually increasing difficulty according to the children's learning progress. This allows for appropriate learning support by adjusting the difficulty of questions and quizzes based on the children's learning progress.

[0052] During a dialogue, the dialogue unit can adjust the content of the dialogue by referring to the children's related learning resources. For example, the dialogue unit can refer to learning materials that the children have used in the past and adjust the content of the dialogue based on that. The dialogue unit can also refer to online resources that the children are interested in and adjust the content of the dialogue based on that. Furthermore, the dialogue unit can refer to resources in learning communities in which the children participate and adjust the content of the dialogue based on that. This makes it possible to provide more appropriate dialogue content by referring to related learning resources.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The collection unit collects children's behavioral data or learning history. Behavioral data includes daily behavior and behavior during learning, and learning history includes learning progress and past test results. The collection unit collects behavioral data using sensors and cameras, and can digitally record learning history and store it in the cloud. Step 2: The generator generates an individualized educational plan based on the data collected by the collector. The generator uses generative AI to create a curriculum tailored to the children's interests and needs, and customizes the individual educational plan based on their learning goals and progress. Step 3: The dialogue unit engages with the children to advance their learning based on the educational plan generated by the generation unit. The dialogue unit uses speech recognition technology to understand what the children are saying and generate appropriate responses. Furthermore, the generation AI is used to ask questions and provide quizzes to support learning, adjusting the difficulty of the next question or quiz based on the children's answers. Step 4: The sharing unit shares the learning progress and growth recorded by the dialogue unit with parents and educators in real time. The sharing unit shares data via the cloud, allowing parents and educators to understand their children's learning status. Furthermore, data is encrypted and access is restricted to protect privacy.

[0055] (Example 2) An educational character toy system according to an embodiment of the present invention supports children's learning and records their development. This system utilizes generative AI to provide education tailored to individual interests and needs, enabling effective educational support through information sharing with parents and educators. It also evolves as children grow and becomes a partner in lifelong learning. For example, the system collects children's behavioral data and learning history, and the generative AI generates an individual educational plan based on that data. Based on the generated educational plan, the system then interacts with the children to advance their learning. For example, the system supports learning by asking questions and providing quizzes. The system also records children's learning progress and development and shares this information with parents and educators in real time via the cloud. This allows parents and educators to understand children's learning status and provide appropriate support. Furthermore, the system evolves as children grow and becomes a partner in lifelong learning. All conversations and records are stored in the cloud, with data encryption and access restrictions in place to protect privacy. This ensures that precious memories and experiences are preserved forever. This allows the educational character toy system to support children's learning and record their development.

[0056] An educational character toy system according to an embodiment includes a collection unit, a generation unit, a dialogue unit, and a sharing unit. The collection unit collects children's behavioral data or learning history. The behavioral data includes, for example, but is not limited to, daily behavior and behavior during learning. The learning history includes, for example, but is not limited to, learning progress and past test results. The collection unit collects the behavioral data using, for example, a sensor or a camera. The collection unit can also digitally record the learning history and store it in the cloud. The generation unit generates an individual educational plan based on the data collected by the collection unit. The generation unit creates a curriculum tailored to the children's interests and needs, for example, using a generation AI. The generation AI generates the educational plan using a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit customizes the individual educational plan based on, for example, the children's learning goals and progress. The dialogue unit advances learning by interacting with the children based on the educational plan generated by the generation unit. The dialogue unit understands the children's utterances using, for example, voice recognition technology and generates appropriate responses. The dialogue unit uses generative AI to ask questions and give quizzes to children to support their learning. The dialogue unit can also adjust the difficulty of the next question or quiz based on the children's answers, for example. The sharing unit shares the learning progress and growth recorded by the dialogue unit with parents and educators in real time. The sharing unit can share data, for example, via the cloud, allowing parents and educators to understand the children's learning situation. The sharing unit protects privacy by encrypting data and setting access restrictions. In this way, the educational character toy system according to the embodiment can support children's learning and record their growth.

[0057] The collection unit can estimate the children's emotions and adjust the timing of collecting behavioral data and learning histories based on the estimated emotions. For example, if the children are excited, the collection unit can delay the collection timing to collect data when they are calm. Also, if the children are tired, the collection unit can advance the collection timing to collect data before fatigue accumulates. Furthermore, if the children are concentrating, the collection unit can collect detailed behavioral data at that time. This allows for more appropriate data collection by adjusting the collection timing according to the children's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image data of the children taken with a camera into the generation AI and cause the generation AI to estimate the children's emotions.

[0058] The collection unit can analyze the children's past behavioral data and select a collection method. For example, the collection unit can identify, from the past behavioral data, the time periods when the children are most relaxed and collect data during those time periods. The collection unit can also collect data during activities when the children are most focused based on the past behavioral data. Furthermore, the collection unit can analyze the past behavioral data and customize the collection method based on specific patterns. This enables efficient data collection by selecting the optimal collection method based on the past behavioral data. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the past behavioral data into the generation AI and have the generation AI select the optimal collection method.

[0059] When collecting behavioral data or learning history, the collection unit can filter the data based on the children's current interests. For example, the collection unit prioritizes collecting data related to topics that the children are currently interested in. The collection unit can also collect detailed data on activities that the children are particularly interested in. Furthermore, if the children's interests change, the collection unit can adjust the data to be collected in response to those changes. This allows for more relevant data to be collected by filtering the data based on the children's interests. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input data related to the children's interests into the generation AI and have the generation AI perform the filtering.

[0060] The collection unit can estimate the children's emotions and determine the priority of data to be collected based on the estimated emotions. For example, if the children are feeling stressed, the collection unit can prioritize collecting data related to stress reduction. Furthermore, if the children are having fun, the collection unit can prioritize collecting data related to that activity. Furthermore, if the children are tired, the collection unit can prioritize collecting data related to rest. Thus, by prioritizing data according to the children's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input image data of the children taken with a camera into the generation AI and cause the generation AI to estimate the children's emotions.

[0061] When collecting behavioral data or learning history, the collection unit can prioritize collecting highly relevant data based on the children's geographical location information. For example, if the children are in a specific location, the collection unit prioritizes collecting data related to that location. Also, if the children are traveling, the collection unit can collect data related to their destination. Furthermore, if the children are in a specific area, the collection unit can collect data related to the culture and history of that area. This allows for efficient collection of highly relevant data by taking geographical location information into consideration. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the children's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0062] The collection unit can analyze the children's social media activities and collect related data when collecting behavioral data and learning history. The collection unit can collect related data based on, for example, content shared by the children on social media. The collection unit can also analyze the children's social media activity patterns and determine the optimal collection timing. The collection unit can also collect data related to topics in which the children have shown interest on social media. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the children's social media activity data into the generation AI and cause the generation AI to collect related data.

[0063] The generation unit can estimate the children's emotions and adjust the content of the educational plan based on the estimated emotions. For example, if the children are excited, the generation unit can generate an educational plan with content that will help them relax. Furthermore, if the children are tired, the generation unit can generate an educational plan that can be completed in a short time. Furthermore, if the children are concentrating, the generation unit can generate an educational plan with content that is more difficult. This enables more effective education by adjusting the content of the educational plan according to the children's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the children's emotion data into the generation AI and have the generation AI adjust the content of the educational plan.

[0064] When generating an educational plan, the generation unit can adjust the level of detail of the plan based on the children's learning progress. For example, if the children's learning progress is fast, the generation unit can generate an educational plan including detailed content. Furthermore, if the children's learning progress is slow, the generation unit can generate an educational plan that focuses on basic content. Furthermore, the generation unit can generate an educational plan that gradually increases in difficulty according to the children's learning progress. In this way, an appropriate educational plan can be provided by adjusting the level of detail of the plan based on the learning progress. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the children's learning progress data into the generation AI and cause the generation AI to adjust the level of detail of the plan.

[0065] When generating an educational plan, the generation unit can apply different generation algorithms depending on the children's interests. For example, if the children are interested in science, the generation unit can apply a generation algorithm specialized for science. Furthermore, if the children are interested in art, the generation unit can apply a generation algorithm specialized for art. Furthermore, if the children are interested in sports, the generation unit can apply a generation algorithm specialized for sports. In this way, by applying a generation algorithm according to the children's interests, a more effective educational plan can be provided. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input data on the children's interests into the generation AI and cause the generation AI to apply different generation algorithms.

[0066] The generation unit can estimate the children's emotions and adjust the length of the educational plan based on the estimated emotions. For example, if the children are tired, the generation unit can generate an educational plan that can be completed in a short time. Furthermore, if the children are relaxed, the generation unit can generate a long educational plan. Furthermore, if the children are concentrating, the generation unit can generate an educational plan of an appropriate length. This allows for appropriate study time by adjusting the length of the educational plan according to the children's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, an AI. For example, the generation unit can input the children's emotion data into the generation AI and have the generation AI adjust the length of the educational plan.

[0067] When generating an educational plan, the generation unit can determine the priority of the plan based on the children's learning history. For example, the generation unit generates an educational plan that prioritizes the most effective content from the children's learning history. The generation unit can also analyze the children's learning history and generate an educational plan that prioritizes content that the children were weak at in the past. Furthermore, the generation unit can generate an educational plan that prioritizes content that needs to be reviewed based on the children's learning history. This enables effective learning to be supported by determining the priority of plans based on the learning history. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the children's learning history data into the generation AI and have the generation AI determine the priority of the plans.

[0068] When generating an educational plan, the generation unit can adjust the content of the plan by referring to learning resources related to the children. For example, the generation unit can refer to learning materials that the children have used in the past and generate the educational plan based on the referenced learning materials. The generation unit can also refer to online resources that the children are interested in and generate the educational plan based on the referenced learning resources. Furthermore, the generation unit can refer to resources in learning communities in which the children participate and generate the educational plan based on the referenced learning resources. This makes it possible to provide a more appropriate educational plan by referring to related learning resources. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on learning resources related to the children into the generation AI and have the generation AI adjust the content of the plan.

[0069] The dialogue unit can estimate the children's emotions and adjust the content and tone of the dialogue based on the estimated emotions. For example, if the children are nervous, the dialogue unit can use a calm tone. Furthermore, if the children are having fun, the dialogue unit can use a bright tone. Furthermore, if the children are tired, the dialogue unit can use a gentle tone. This allows for more effective dialogue by adjusting the content and tone of the dialogue according to the children's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the dialogue unit can be performed using, for example, AI, or without AI. For example, the dialogue unit can input the children's emotion data into the generation AI and have the generation AI adjust the content and tone of the dialogue.

[0070] During the dialogue, the dialogue unit can adjust the difficulty of questions and quizzes based on the children's learning progress. For example, if the children's learning progress is rapid, the dialogue unit can pose more difficult questions and quizzes. Furthermore, if the children's learning progress is slow, the dialogue unit can pose more basic questions and quizzes. Furthermore, the dialogue unit can pose questions and quizzes of gradually increasing difficulty according to the children's learning progress. This allows for appropriate learning support by adjusting the difficulty of questions and quizzes based on the children's learning progress. Some or all of the above-described processing in the dialogue unit may be performed using, or without, AI, for example. For example, the dialogue unit can input children's learning progress data into the generation AI and cause the generation AI to adjust the difficulty of questions and quizzes.

[0071] The dialogue unit can apply different dialogue scenarios during dialogue depending on the children's interests and concerns. For example, if the children are interested in science, the dialogue unit can apply a dialogue scenario related to science. Furthermore, if the children are interested in art, the dialogue unit can apply a dialogue scenario related to art. Furthermore, if the children are interested in sports, the dialogue unit can apply a dialogue scenario related to sports. This enables more effective dialogue by applying a dialogue scenario according to the children's interests and concerns. Some or all of the above-mentioned processing in the dialogue unit may be performed using AI, for example, or may be performed without using AI. For example, the dialogue unit can input data on the children's interests and concerns into the generation AI and cause the generation AI to apply different dialogue scenarios.

[0072] The dialogue unit can estimate the children's emotions and adjust the length of the dialogue based on the estimated emotions. For example, if the children are tired, the dialogue unit can conduct a dialogue that is completed in a short time. Furthermore, if the children are relaxed, the dialogue unit can conduct a dialogue of a longer length. Furthermore, if the children are concentrating, the dialogue unit can conduct a dialogue of an appropriate length. This allows for an appropriate dialogue time to be provided by adjusting the length of the dialogue according to the children's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the dialogue unit can input the children's emotion data into the generation AI and cause the generation AI to adjust the length of the dialogue.

[0073] During the dialogue, the dialogue unit can determine dialogue priorities based on the children's learning history. For example, the dialogue unit can prioritize the most effective content from the children's learning history to include in the dialogue. The dialogue unit can also analyze the children's learning history and prioritize content that the children were weak at in the past to include in the dialogue. Furthermore, the dialogue unit can also prioritize content that needs to be reviewed based on the children's learning history to include in the dialogue. This enables effective learning support by determining dialogue priorities based on learning history. Some or all of the above-described processing in the dialogue unit can be performed using, for example, AI, or can be performed without using AI. For example, the dialogue unit can input the children's learning history data into the generation AI and have the generation AI determine the dialogue priorities.

[0074] During the dialogue, the dialogue unit can adjust the content of the dialogue by referring to the children's relevant learning resources. For example, the dialogue unit can refer to learning materials the children have used in the past and adjust the content of the dialogue based on that. The dialogue unit can also refer to online resources that the children are interested in and adjust the content of the dialogue based on that. Furthermore, the dialogue unit can refer to resources in learning communities in which the children participate and adjust the content of the dialogue based on that. In this way, by referring to the relevant learning resources, more appropriate dialogue content can be provided. Some or all of the above-mentioned processing in the dialogue unit may be performed using, for example, AI, or may be performed without using AI. For example, the dialogue unit can input data on the children's relevant learning resources to the generation AI and cause the generation AI to adjust the content of the dialogue.

[0075] The sharing unit can estimate the children's emotions and adjust the information sharing method based on the estimated emotions. For example, if the children are nervous, the sharing unit can provide a simple, highly visible information sharing method. Furthermore, if the children are relaxed, the sharing unit can provide an information sharing method that includes detailed information. Furthermore, if the children are in a hurry, the sharing unit can provide an information sharing method that focuses on the main points. This enables more effective information sharing by adjusting the information sharing method according to the children's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the sharing unit can be performed using, for example, AI, or without AI. For example, the sharing unit can input the children's emotion data into the generation AI and have the generation AI adjust the information sharing method.

[0076] The sharing unit can adjust the content of information sharing based on feedback from parents and educators. For example, the sharing unit shares detailed information about children's learning progress based on feedback from parents and educators. The sharing unit can also analyze feedback from parents and educators and prioritize sharing of necessary information. Furthermore, the sharing unit can adjust the frequency and timing of information sharing based on feedback from parents and educators. This makes it possible to optimize the content of sharing and share information effectively by referring to the feedback. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input feedback data from parents and educators into a generation AI and have the generation AI adjust the content of sharing.

[0077] When sharing information, the sharing unit can adjust the level of detail of the shared content based on the children's learning progress. For example, if the children's learning progress is fast, the sharing unit can provide shared content including detailed information. Furthermore, if the children's learning progress is slow, the sharing unit can provide shared content centered on basic information. Furthermore, the sharing unit can provide shared content with gradually increasing levels of detail according to the children's learning progress. This enables appropriate information sharing by adjusting the level of detail of the shared content based on the learning progress. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the children's learning progress data into the generation AI and cause the generation AI to adjust the level of detail of the shared content.

[0078] The sharing unit can estimate the children's emotions and determine the priority of information sharing based on the estimated emotions. For example, if the children are feeling stressed, the sharing unit can prioritize sharing information related to stress reduction. Furthermore, if the children are having fun, the sharing unit can prioritize sharing information related to that activity. Furthermore, if the children are tired, the sharing unit can prioritize sharing information related to rest. In this way, by determining the priority of information sharing according to the children's emotions, important information can be shared preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the sharing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the sharing unit can input the children's emotion data into the generation AI and have the generation AI determine the priority of information sharing.

[0079] When sharing information, the sharing unit can select a sharing method based on the geographic location information of the parent or educator. For example, if the parent or educator is nearby, the sharing unit can share information through direct dialogue. Alternatively, if the parent or educator is far away, the sharing unit can prioritize online information sharing. Furthermore, if the parent or educator is in a specific location, the sharing unit can share information related to that location. This allows the optimal information sharing method to be selected by taking geographic location information into consideration. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input the geographic location information of the parent or educator into the generation AI and have the generation AI select the optimal sharing method.

[0080] When sharing information, the sharing unit can adjust the content to be shared by analyzing the social media activity of the parent or educator. For example, the sharing unit can share relevant information based on the content shared by the parent or educator on social media. The sharing unit can also analyze the social media activity patterns of the parent or educator and determine the optimal timing for sharing. Furthermore, the sharing unit can share information related to topics in which the parent or educator has shown interest on social media. In this way, relevant information can be shared efficiently by analyzing social media activity. Some or all of the above-mentioned processing in the sharing unit may be performed using, for example, AI, or may be performed without using AI. For example, the sharing unit can input social media activity data of the parent or educator into a generation AI and have the generation AI adjust the content to be shared. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, generation unit, dialogue unit, and sharing unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects behavioral data of children using the camera 42 and sensors of the smart device 14, and records their learning history using the specific processing unit 290 of the data processing device 12. The generation unit generates an individual educational plan using a generation AI using the specific processing unit 290 of the data processing device 12. The dialogue unit advances learning by interacting with the children using, for example, the control unit 46A of the smart device 14. The sharing unit shares learning progress and growth with parents and educators via the cloud using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, generation unit, dialogue unit, and sharing unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects children's behavioral data using the camera 42 and sensors of the smart glasses 214, and records their learning history using the specific processing unit 290 of the data processing device 12. The generation unit generates an individual educational plan using a generation AI using the specific processing unit 290 of the data processing device 12. The dialogue unit advances learning by interacting with the children using, for example, the control unit 46A of the smart glasses 214. The sharing unit shares learning progress and growth with parents and educators via the cloud using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, generation unit, dialogue unit, and sharing unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects behavioral data of children using the camera 42 and sensors of the headset-type terminal 314, and records their learning history using the specific processing unit 290 of the data processing device 12. The generation unit generates an individual educational plan using a generation AI using the specific processing unit 290 of the data processing device 12, for example. The dialogue unit advances learning by interacting with the children using, for example, the control unit 46A of the headset-type terminal 314. The sharing unit shares learning progress and growth with parents and educators via the cloud using, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, generation unit, dialogue unit, and sharing unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects behavioral data of children using the camera 42 and sensors of the robot 414, and records their learning history using the specific processing unit 290 of the data processing device 12. The generation unit generates an individual educational plan using a generation AI using the specific processing unit 290 of the data processing device 12, for example. The dialogue unit advances learning by interacting with the children using, for example, the control unit 46A of the robot 414. The sharing unit shares learning progress and growth with parents and educators via the cloud using, for example, the specific processing unit 290 of the data processing device 12.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The educational character toy system may further include a reward unit. The reward unit provides rewards according to children's learning progress and behavior. For example, the reward unit may award digital badges or points when children achieve specific learning goals. The reward unit may also provide virtual currency or in-game items when children behave well. Furthermore, the reward unit may provide special rewards when specific conditions set by parents or educators are met. This can increase children's motivation to learn and promote positive behavior.

[0083] The collection unit can estimate the emotions of the children and adjust the timing of collecting behavioral data and learning histories based on the estimated emotions. For example, if the children are excited, the collection unit can delay the collection timing to collect data when they are calm. Also, if the children are tired, the collection unit can advance the collection timing to collect data before fatigue accumulates. Furthermore, if the children are concentrating, the collection unit can collect detailed behavioral data at that time. This allows for more appropriate data collection by adjusting the collection timing according to the children's emotions.

[0084] The generation unit can analyze the learning styles of children and generate an optimal educational plan. For example, if the children are visual learners, the generation unit can generate an educational plan that includes a lot of visual content. If the children are auditory learners, the generation unit can also generate an educational plan that includes a lot of audio and music. Furthermore, if the children are experiential learners, the generation unit can also generate an educational plan that includes a lot of hands-on activities. This makes it possible to provide an optimal educational plan according to the learning styles of children.

[0085] The dialogue unit can estimate the emotions of the children and adjust the content and tone of the dialogue based on the estimated emotions. For example, if the children are nervous, the dialogue unit can use a calm tone. If the children are having fun, the dialogue unit can use a bright tone. If the children are tired, the dialogue unit can use a gentle tone. This allows for more effective dialogue by adjusting the content and tone of the dialogue according to the children's emotions.

[0086] The sharing unit can adjust how it shares information based on feedback from parents and educators. For example, if a parent or educator wants detailed information, the sharing unit can provide a detailed report. Alternatively, if a parent or educator wants concise information, the sharing unit can provide a summary that summarizes the key points. Furthermore, if a parent or educator wants to receive information at a specific time, the sharing unit can share information at that time. This makes it possible to share information according to the needs of parents and educators.

[0087] The collection unit can prioritize collection of highly relevant data based on the children's geographical location information. For example, if the children are in a specific location, the collection unit prioritizes collection of data related to that location. Also, if the children are traveling, the collection unit can collect data related to their destination. Furthermore, if the children are in a specific region, the collection unit can collect data related to the culture and history of that region. In this way, highly relevant data can be collected efficiently by taking geographical location information into consideration.

[0088] The generation unit can estimate the emotions of the children and adjust the content of the educational plan based on the estimated emotions. For example, if the children are excited, the generation unit can generate an educational plan with content that will help them relax. If the children are tired, the generation unit can also generate an educational plan that can be completed in a short time. Furthermore, if the children are concentrating, the generation unit can also generate an educational plan with content that is more difficult. This allows for more effective education by adjusting the content of the educational plan according to the children's emotions.

[0089] During the dialogue, the dialogue unit can adjust the difficulty of questions and quizzes based on the children's learning progress. For example, if the children's learning progress is fast, the dialogue unit can ask them difficult questions and quizzes. Alternatively, if the children's learning progress is slow, the dialogue unit can ask them basic questions and quizzes. Furthermore, the dialogue unit can ask questions and quizzes of gradually increasing difficulty according to the children's learning progress. This allows for appropriate learning support by adjusting the difficulty of questions and quizzes based on the children's learning progress.

[0090] The sharing unit can estimate the children's emotions and adjust the information sharing method based on the estimated emotions. For example, if the children are nervous, the sharing unit can provide a simple, highly visible information sharing method. If the children are relaxed, the sharing unit can also provide an information sharing method that includes detailed information. Furthermore, if the children are in a hurry, the sharing unit can also provide an information sharing method that focuses on the main points. This allows for more effective information sharing by adjusting the information sharing method according to the children's emotions.

[0091] During a dialogue, the dialogue unit can adjust the content of the dialogue by referring to the children's related learning resources. For example, the dialogue unit can refer to learning materials that the children have used in the past and adjust the content of the dialogue based on that. The dialogue unit can also refer to online resources that the children are interested in and adjust the content of the dialogue based on that. Furthermore, the dialogue unit can refer to resources in learning communities in which the children participate and adjust the content of the dialogue based on that. This makes it possible to provide more appropriate dialogue content by referring to related learning resources.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The collection unit collects children's behavioral data or learning history. Behavioral data includes daily behavior and behavior during learning, and learning history includes learning progress and past test results. The collection unit collects behavioral data using sensors and cameras, and can digitally record learning history and store it in the cloud. Step 2: The generator generates an individualized educational plan based on the data collected by the collector. The generator uses generative AI to create a curriculum tailored to the children's interests and needs, and customizes the individual educational plan based on their learning goals and progress. Step 3: The dialogue unit engages with the children to advance their learning based on the educational plan generated by the generation unit. The dialogue unit uses speech recognition technology to understand what the children are saying and generate appropriate responses. Furthermore, the generation AI is used to ask questions and provide quizzes to support learning, adjusting the difficulty of the next question or quiz based on the children's answers. Step 4: The sharing unit shares the learning progress and growth recorded by the dialogue unit with parents and educators in real time. The sharing unit shares data via the cloud, allowing parents and educators to understand their children's learning status. Furthermore, data is encrypted and access is restricted to protect privacy.

[0094] 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.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0109] 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.

[0110] 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.

[0111] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0122] 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.

[0123] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0124] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0125] 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.

[0126] 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.

[0127] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0141] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 collection unit that collects behavioral data or learning history of children; a generation unit that generates an individual education plan based on the data collected by the collection unit; a dialogue unit that advances learning by dialogue with children based on the educational plan generated by the generation unit; a sharing unit that shares the learning progress and growth recorded by the dialogue unit with parents or educators in real time; Equipped with A system characterized by:

2. The collecting unit Estimate children's emotions and adjust the timing of collecting behavioral data and learning history based on the estimated emotions.

2. The system of claim 1.

3. The collecting unit Analyze children's past behavioral data and select collection methods 2. The system of claim 1.

4. The collecting unit When collecting behavioral data or learning history, filter it based on children's current interests and concerns.

2. The system of claim 1.

5. The collecting unit Estimate children's emotions and prioritize data collection based on those emotions 2. The system of claim 1.

6. The collecting unit When collecting behavioral data or learning history, prioritize the collection of relevant data based on children's geographic location.

2. The system of claim 1.

7. The collecting unit Analyze children's social media activity and collect relevant data when collecting behavioral data or learning history.

2. The system of claim 1.

8. The generation unit Estimate children's emotions and adjust educational plans based on those emotions 2. The system of claim 1.

9. The generation unit When generating an educational plan, adjust the level of detail based on the child's learning progress.

2. The system of claim 1.

10. The generation unit When generating educational plans, different generation algorithms are applied depending on the children's interests.

2. The system of claim 1.

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