system

Abstract

The system according to this embodiment aims to understand children's learning data, personality, and thinking tendencies, and to provide parents with appropriate learning and childcare consultations. [Solution] The system according to this embodiment comprises a reception unit, a learning unit, a storage unit, an analysis unit, and a consultation unit. When a child arrives at the after-school program, the reception unit receives a tablet that has been distributed to each child. The learning unit uses the tablet received by the reception unit to take courses on content that interests the child once a day. The storage unit stores the child's learning data and diary data based on the content taken by the learning unit. The analysis unit analyzes the data stored by the storage unit to understand the child's personality and thinking tendencies. The consultation unit provides learning and childcare consultations to parents based on the data understood by the analysis unit.

Description

Technical Field

[0004] ,

[0006] , , ,

[0005] , , , ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot; 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it has not been sufficiently carried out to efficiently grasp the learning data, personality, and thinking tendencies of children and provide appropriate learning consultations and parenting consultations to parents.

[0005] The system according to the embodiment aims to grasp the learning data, personality, and thinking tendencies of children and provide appropriate learning consultations and parenting consultations to parents.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a learning unit, a storage unit, an analysis unit, and a consultation unit. When a child arrives at the school, the reception unit receives a tablet that has been distributed to each child. The learning unit uses the tablet received by the reception unit to take courses on content of interest once a day. The storage unit stores the child's learning data and diary data based on the content taken by the learning unit. The analysis unit analyzes the data stored by the storage unit to understand the child's personality and thinking tendencies. The consultation unit provides learning and parenting consultations to parents based on the data understood by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can grasp children's learning data, personality, and thinking tendencies, and provide parents with appropriate learning and childcare consultations. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of 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), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The after-school care system according to an embodiment of the present invention is a system that utilizes generative AI to accumulate data on children's learning, personality, and thinking tendencies, and allows parents to consult about childcare and learning. In this after-school care system, when a child arrives at the after-school care center, they receive a tablet that is distributed to them. Next, once a day, the child accesses content such as videos, games, ebooks, and quizzes that are of interest to them on the tablet. They can also optionally keep a diary. Through these activities, the child's learning data and diary data are accumulated. This data is analyzed by the generative AI to understand the child's personality and thinking tendencies. Based on this data, parents can consult the generative AI about learning and childcare. For example, if a parent asks, "I want to know how to support my child's learning," the generative AI will suggest learning support methods that are appropriate for each child based on the accumulated data. Also, if a parent asks, "I want advice on my child's personality and thinking tendencies," the generative AI will analyze the personality and thinking tendencies and provide appropriate advice. This mechanism allows parents to easily consult about their child's learning and childcare and obtain highly accurate answers. Furthermore, by semi-compulsorily engaging in learning and games at after-school programs, sufficient data can be accumulated, allowing for an accurate understanding of children's personalities and thinking patterns. This enables parents to receive advice on their children's future aptitudes and how to interact with them going forward. In addition, providing after-school care services ensures that children do not slack off, allowing for the accumulation of the correct amount of data. Moreover, after-school care services are in short supply and the number of children can be guaranteed, resulting in high demand for the service. This also allows for expected advertising effects for businesses. In summary, after-school care systems accumulate data on children's learning, personalities, and thinking patterns, enabling parents to seek advice on childcare and learning.

[0029] The after-school childcare system according to this embodiment comprises a reception unit, a learning unit, a storage unit, an analysis unit, and a consultation unit. The reception unit receives a tablet distributed to each child upon arrival at the after-school childcare center. The reception unit may include a system that automatically distributes tablets to children upon their arrival. The reception unit may also confirm the child's arrival and hand over the tablet. Furthermore, the reception unit may record the child's arrival time and manage attendance. The learning unit uses the tablet received by the reception unit to access content of interest once a day. The learning unit may include a system that provides content such as videos, games, ebooks, and quizzes. The learning unit may also allow children to select and access content that interests them. Furthermore, the learning unit may also record the child's learning history and manage their learning progress. The storage unit stores the child's learning data and diary data based on the content accessed by the learning unit. The storage unit may include a system that automatically saves data on the content accessed by the child. Furthermore, the storage unit may also save diary data written by the child. Furthermore, the storage unit can organize the stored data and save it in a searchable format. The analysis unit analyzes the data stored by the storage unit to understand the children's personalities and thinking tendencies. The analysis unit can, for example, be equipped with a system that analyzes the stored data using generative AI. The analysis unit can also analyze the children's learning data and diary data to understand their personalities and thinking tendencies. Furthermore, the analysis unit can also create reports to provide the analysis results to parents. The consultation unit provides parents with learning and childcare consultations based on the data obtained by the analysis unit. The consultation unit can, for example, be equipped with a system that responds to consultations from parents using generative AI. Furthermore, the consultation unit can suggest learning support methods tailored to each child based on the stored data. Furthermore, the consultation unit can analyze personalities and thinking tendencies and provide appropriate advice. As a result, the after-school care system according to this embodiment stores children's learning, personalities, and thinking tendencies as data, and allows parents to consult about childcare and learning.

[0030] The reception area receives a tablet distributed to each child upon arrival at the after-school program. The reception area can be equipped with a system that automatically distributes tablets upon arrival. Specifically, the reception area is equipped with an automated recognition system using RFID tags and facial recognition technology, which automatically confirms a child's arrival as they pass through the entrance of the after-school program and distributes a tablet. Alternatively, the reception area can confirm a child's arrival and hand out tablets by hand. For example, reception staff can confirm the child's name and hand out tablets by hand, allowing for individualized attention for each child. Furthermore, the reception area can record the child's arrival time and manage attendance. This allows for accurate tracking of children's attendance and real-time notification to parents. For example, parents can confirm their child's arrival at the after-school program through a dedicated app. The reception area can also save children's arrival times and attendance status in a database for later reference. This improves the efficiency of attendance management and ensures the safety of the children.

[0031] Students in the learning department use tablets received from the reception desk to access content of their interest once a day. The learning department can be equipped with a system that provides content such as videos, games, ebooks, and quizzes. Specifically, the tablets are pre-installed with a variety of educational content, which students can freely choose according to their interests. For example, there is a wealth of content available to stimulate students' motivation to learn, such as science experiment videos, history quizzes, and math games. Furthermore, students can select and access content that interests them. This allows students to learn at their own pace and experience the joy of learning. In addition, the learning department can record students' learning history and manage their learning progress. For example, the tablets have a function to automatically record learning history, allowing for detailed tracking of which content students have studied and for how long. This enables accurate monitoring of students' learning progress and adjustments to learning content as needed.

[0032] The data storage unit stores children's learning data and diary data based on the content they have accessed through the learning unit. For example, the data storage unit can be equipped with a system that automatically saves data on the content children have accessed. Specifically, the history of content accessed on a tablet and learning outcomes are automatically uploaded to a cloud server and stored. The data storage unit can also store diary data written by children. For example, a diary app may be installed on the tablet, allowing children to write about daily events and their thoughts. This allows for the recording of children's growth and emotional changes. Furthermore, the data storage unit can organize the stored data and save it in a searchable format. For example, learning data and diary data can be tagged and categorized, allowing for quick retrieval of necessary information. This enables the data storage unit to efficiently manage records of children's learning progress and growth and to quickly provide necessary information.

[0033] The analysis unit analyzes the data accumulated by the storage unit to understand the child's personality and thinking patterns. The analysis unit can be equipped with a system that analyzes the accumulated data using, for example, generative AI. Specifically, the generative AI analyzes the child's learning data and diary data to understand the child's learning patterns and interests. For example, the generative AI can analyze which content the child spends the most time on and what learning style the child prefers, and propose an optimal learning plan for each individual child. The analysis unit can also analyze the child's learning data and diary data to understand their personality and thinking patterns. For example, it can detect changes in the child's emotions and signs of stress from diary data, allowing for early intervention. Furthermore, the analysis unit can create reports to provide parents with the analysis results. For example, the report created by the generative AI will contain detailed information on the child's learning progress, personality patterns, and future learning strategies, which parents can use as a reference to help with their child's learning and parenting. In this way, the analysis unit can comprehensively support the child's learning and growth and provide useful information to parents.

[0034] The consultation department provides parents with learning and parenting consultations based on data collected by the analysis department. The consultation department can, for example, utilize a system that responds to parental inquiries using generative AI. Specifically, the generative AI analyzes parental questions and consultations to provide optimal advice and information. For example, in response to questions about a child's learning progress, the generative AI can provide specific advice based on accumulated data. Furthermore, the consultation department can suggest learning support methods tailored to each child based on accumulated data. For example, the generative AI can analyze a child's learning data and propose the most suitable learning methods and materials for each individual child. In addition, the consultation department can analyze personality and thinking tendencies to provide appropriate advice. For example, it can understand a child's personality and thinking tendencies and provide parenting advice and support to parents. This allows the consultation department to help parents resolve their concerns about their child's learning and parenting, and to provide a better parenting environment. Moreover, through communication with parents, the consultation department can support the child's growth and deepen the parent-child bond. For example, by establishing regular consultation sessions, sharing the situation of parents and children, and providing appropriate advice, the parent-child relationship can be strengthened. This allows the counseling department to provide comprehensive support for children's learning and development, and to offer useful information and advice to parents.

[0035] The learning unit can access at least one of the following types of content: videos, games, ebooks, and quizzes. For example, the learning unit can provide videos that children might find interesting, such as educational videos or animated videos. It can also provide games that children can enjoy, such as educational games or entertainment games. Furthermore, it can provide ebooks that children can read, such as educational ebooks or narrative ebooks. Finally, it can provide quizzes that children can try, such as educational quizzes or entertainment quizzes. This allows children to access a diverse range of content that interests them. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can provide content using an AI model that analyzes children's interests and recommends the most suitable content.

[0036] The storage unit can store children's learning data and diary data. For example, the storage unit can automatically save data on content that children have taken. This includes, for example, the viewing history of videos and the play history of games that children have taken. The storage unit can also save diary data that children have written. This includes, for example, diary data in which children have written about daily events and their thoughts. Furthermore, the storage unit can organize the stored data and save it in a searchable format. For example, it can organize the stored data by category so that the necessary data can be quickly searched. This allows for the efficient storage of children's learning data and diary data. Some or all of the above processing in the storage unit may be performed using, for example, AI, or not using AI. For example, the storage unit can store data using an AI model that automatically classifies and saves children's learning data and diary data.

[0037] The analysis unit can analyze accumulated data to understand children's personalities and thinking patterns. For example, the analysis unit can use generative AI to analyze the accumulated data. For example, it can analyze children's learning data and diary data to understand their personalities and thinking patterns. The analysis unit can also analyze children's behavioral patterns to identify their personalities and thinking patterns. For example, it can analyze what kind of content children are interested in and what kind of actions they take. Furthermore, the analysis unit can create reports to provide to parents based on the analysis results. For example, it can create a report summarizing the child's personality and thinking patterns and provide it to the parents. This allows for an accurate understanding of the child's personality and thinking patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the data using an AI model that takes accumulated data as input and outputs personality and thinking patterns.

[0038] The consultation department can provide parents with learning and childcare advice. For example, the consultation department can use generative AI to respond to parents' inquiries. If a parent asks for advice on how to support their child's learning, the generative AI will use accumulated data to suggest learning support methods tailored to each child. If a parent asks for advice on their child's personality and thinking patterns, the generative AI will analyze the personality and thinking patterns and provide appropriate advice. Furthermore, the consultation department can also suggest learning support methods tailored to each child based on accumulated data. For example, the generative AI will analyze a child's learning data and propose the optimal learning support method. This allows parents to seek advice on childcare and learning. Some or all of the above-described processes in the consultation department may be performed using AI, or not. For example, the consultation department can respond to inquiries using an AI model that takes parental inquiries as input and outputs appropriate advice.

[0039] The consultation department can provide learning support methods tailored to each child based on accumulated data. For example, the consultation department can use generative AI to analyze the accumulated data and provide learning support methods tailored to each child. For example, the generative AI proposes the optimal learning support method based on the child's learning data. The consultation department can also analyze the child's personality and thinking tendencies to provide appropriate learning support methods. For example, the generative AI proposes individualized learning support methods based on the child's personality and thinking tendencies. Furthermore, the consultation department can explain specific learning support methods to parents. For example, the generative AI provides parents with specific advice on how to support their children. This allows for the provision of learning support methods tailored to each child. Some or all of the above-described processes in the consultation department may be performed using AI, or not. For example, the consultation department can use an AI model that takes accumulated data as input and outputs learning support methods to provide learning support methods.

[0040] The counseling department can analyze personality and thinking tendencies and provide appropriate advice. For example, the counseling department can use generative AI to analyze accumulated data and understand personality and thinking tendencies. For example, the generative AI can provide appropriate advice based on the child's personality and thinking tendencies. The counseling department can also provide specific advice to parents. For example, the generative AI can give parents specific advice on how to support their children. Furthermore, the counseling department can provide advice on future aptitudes and how to interact with children based on their personality and thinking tendencies. For example, the generative AI analyzes the child's personality and thinking tendencies and provides advice on future aptitudes and how to interact with them. This allows for the provision of appropriate advice based on personality and thinking tendencies. Some or all of the above-described processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can provide advice using an AI model that takes accumulated data as input and outputs advice.

[0041] The reception department can analyze a child's past arrival history and select the optimal reception method. For example, if a child has been late in the past, the reception department can send a reminder to arrive earlier. Also, if a child has arrived early in the past, the reception department can adjust the reception time to reduce waiting time. Furthermore, the reception department can analyze the child's arrival patterns and suggest the optimal reception time. This allows the reception department to provide the optimal reception method based on the child's past arrival history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the child's arrival history data into a generating AI and have the generating AI select the optimal reception method.

[0042] The reception desk can prioritize obtaining highly relevant information by considering the child's geographical location during registration. For example, if the child is far from home, the reception desk can guide them to a nearby after-school facility. Furthermore, if the child is in a specific area, the reception desk can provide information relevant to that area. In addition, the reception desk can suggest the optimal route based on the child's location. This allows for the provision of highly relevant information based on the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the child's geographical location into a generating AI and have the generating AI perform the task of obtaining highly relevant information.

[0043] The reception desk can analyze a child's social media activity and obtain relevant information at the time of registration. For example, the reception desk can prioritize providing content that the child has shown interest in on social media. The reception desk can also analyze the child's social media activity and suggest appropriate activities. Furthermore, the reception desk can suggest group activities considering the child's social media friendships. This allows the reception desk to provide relevant information based on the child's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the child's social media activity data into a generating AI and have the generating AI perform the task of obtaining relevant information.

[0044] The learning unit can adjust the level of detail of content based on the child's interests during the lesson. For example, the learning unit can provide detailed information on topics that the child has shown interest in. It can also prioritize displaying content related to areas of interest to the child. Furthermore, the learning unit can adjust the difficulty level of the content according to the child's interests. This allows for the provision of content with a level of detail that matches the child's interests. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input data on the child's interests into a generating AI and have the generating AI determine the level of detail of the content.

[0045] The learning unit can apply different learning algorithms depending on the content category during learning. For example, in the case of learning content, the learning unit can adjust the pace according to the level of understanding. In the case of game content, the learning unit can adjust the difficulty level according to the child's skill level. Furthermore, in the case of diary content, the learning unit can adjust the questions according to the child's emotions. This allows the learning unit to provide learning algorithms tailored to the content category. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input content category data into a generating AI and have the generating AI execute the learning algorithm.

[0046] The learning unit can determine the priority of learning based on the timing of content provision at the time of learning. For example, the learning unit can suggest prioritizing learning new content when it becomes available. It can also suggest prioritizing review of content that the student has previously learned. Furthermore, the learning unit can suggest the optimal learning order according to the student's learning progress. This allows for the provision of learning priorities based on the timing of content provision. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input content provision timing data into a generating AI and have the generating AI perform the learning priority calculation.

[0047] The learning unit can adjust the order of learning based on the relevance of the content during learning. For example, the learning unit can prioritize displaying content related to topics that the child has shown interest in. Furthermore, the learning unit can provide relevant content in a sequence according to the child's learning progress. In addition, the learning unit can display highly relevant content consecutively according to the child's interests. This allows for a learning order based on the relevance of the content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input content relevance data into a generating AI and have the generating AI execute the learning order.

[0048] The data storage unit can analyze a child's past learning data during storage to select the optimal storage method. For example, the storage unit can select the optimal data storage method based on what the child has learned in the past. The storage unit can also adjust the data storage method according to the child's learning progress. Furthermore, the storage unit can analyze the child's learning patterns and propose the optimal data storage method. This allows the storage unit to provide the optimal storage method based on the child's past learning data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the child's past learning data into a generating AI and have the generating AI select the optimal storage method.

[0049] The data storage unit can filter data based on the child's current learning status during storage. For example, the storage unit can prioritize storing data related to what the child is currently learning. Furthermore, the storage unit can filter out unnecessary data according to the child's learning progress. In addition, the storage unit can analyze the child's learning status and store optimal data. This allows for data filtering tailored to the child's current learning status. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the child's current learning status data into a generating AI and have the generating AI perform data filtering.

[0050] The data storage unit can prioritize storing highly relevant data by considering the child's geographical location information during storage. For example, if a child is in a specific area, the data storage unit can prioritize storing data related to that area. The data storage unit can also store optimal data based on the child's location information. Furthermore, the data storage unit can analyze the child's geographical location information and prioritize storing highly relevant data. This allows the storage unit to provide highly relevant data based on the child's geographical location information. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input the child's geographical location information into a generating AI and have the generating AI perform the storage of highly relevant data.

[0051] The data storage unit can analyze a child's social media activity and store relevant data during the storage process. For example, the data storage unit can prioritize storing data related to content that a child has shown interest in on social media. The data storage unit can also analyze a child's social media activity and store appropriate data. Furthermore, the data storage unit can store relevant data while considering the child's social media friendships. This allows the storage unit to provide relevant data based on the child's social media activity. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input the child's social media activity data into a generating AI and have the generating AI store the relevant data.

[0052] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit can optimize the current learning progress based on a child's past learning data. Furthermore, the analysis unit can predict the child's current behavior by referring to their past behavior patterns. In addition, the analysis unit can analyze the child's current emotions based on their past emotional data. This allows for the provision of an optimal analysis based on past analysis data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI to optimize the current analysis.

[0053] The analysis unit can apply different analysis methods to each data category during analysis. For example, in the case of training data, the analysis unit can apply an analysis method appropriate to the level of understanding. In the case of diary data, the analysis unit can apply an emotion analysis method. Furthermore, in the case of game data, the analysis unit can apply a behavioral pattern analysis method. This allows the system to provide the optimal analysis method according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the analysis method.

[0054] The analysis unit can adjust the order of analysis based on the timing of data provision during analysis. For example, the analysis unit can prioritize analysis when new data is provided. The analysis unit can also adjust the order of analysis according to the student's learning progress. Furthermore, the analysis unit can adjust the order of analysis based on the student's behavioral patterns. This allows for an analysis order based on the timing of data provision. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data provision into a generating AI and have the generating AI execute the analysis order.

[0055] The analysis unit can perform analysis by referring to relevant market data during the analysis process. For example, the analysis unit can analyze children's learning data by comparing it with market learning trends. It can also analyze children's behavioral data by comparing it with market behavioral trends. Furthermore, it can analyze children's emotional data by comparing it with market emotional trends. This allows for analysis based on relevant market data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0056] The consultation department can select the most suitable consultation method by referring to the parent's past consultation history during the consultation. For example, the consultation department can select the most suitable consultation method based on the content of past consultations the parent has had. The consultation department can also analyze the parent's past consultation history and provide appropriate advice. Furthermore, the consultation department can propose the most suitable consultation method by referring to the parent's consultation patterns. This allows the consultation department to provide the most suitable consultation method based on the parent's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the parent's past consultation history data into a generating AI and have the generating AI select the most suitable consultation method.

[0057] The consultation department can customize the content of consultations based on the child's current learning situation. For example, the consultation department can suggest appropriate learning support methods according to the child's learning progress. The consultation department can also analyze the child's learning situation and provide optimal parenting advice. Furthermore, the consultation department can customize specific consultation content based on the child's learning data. This allows the consultation department to provide content that is tailored to the child's current learning situation. Some or all of the above processes in the consultation department may be performed using AI, for example, or not. For example, the consultation department can input the child's current learning situation data into a generating AI and have the generating AI perform the customization of the consultation content.

[0058] The consultation department can select the most suitable consultation method by considering the parent's geographical location information during the consultation. For example, if the parent is far from home, the consultation department can suggest an online consultation. Furthermore, if the parent is in a specific region, the consultation department can provide information relevant to that region. In addition, the consultation department can suggest the most suitable consultation method based on the parent's location information. This allows for the provision of the most suitable consultation method based on the parent's geographical location information. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the parent's geographical location information into a generating AI and have the generating AI select the most suitable consultation method.

[0059] The consultation department can analyze the parents' social media activity during consultations and propose consultation methods. For example, the consultation department can provide consultation content related to topics that the parents have shown interest in on social media. The consultation department can also analyze the parents' social media activity and propose appropriate consultation methods. Furthermore, the consultation department can consider the parents' social media friendships and propose the optimal consultation method. This allows for the provision of optimal consultation methods based on the parents' social media activity. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the parents' social media activity data into a generating AI and have the generating AI execute the consultation methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception area can measure children's body temperature and check their health status upon arrival. For example, the reception area can measure a child's temperature using a non-contact thermometer and notify parents if a fever is detected. Furthermore, the reception area can take appropriate action based on the child's health condition. For instance, it can encourage unwell children to rest and guide healthy children into their normal activities. In addition, the reception area can accumulate children's health data and conduct regular health checks. This allows for constant monitoring of children's health status and appropriate responses.

[0062] The learning department can customize the content delivery method according to the child's learning style. For example, children who prefer visual learning can be provided with content that makes extensive use of videos and illustrations. Children who prefer auditory learning can be provided with audio explanations and podcast-style content. Furthermore, children who prefer practical learning can be provided with interactive quizzes and simulation games. This allows for the provision of optimal content tailored to each child's learning style.

[0063] The data storage unit can enhance data privacy protection when storing children's learning data. For example, the storage unit can encrypt and store data to prevent unauthorized access. Furthermore, the storage unit can set data access permissions, ensuring that only authorized personnel can access the data. In addition, the storage unit can set data retention periods and automatically delete data after a certain period. This ensures the privacy of children's learning data and allows for secure storage.

[0064] The analysis unit can evaluate the effectiveness of learning when analyzing children's learning data. For example, the analysis unit can quantitatively evaluate children's learning progress and quantify the effectiveness of learning. Furthermore, the analysis unit can compare children's learning data and compare their learning effectiveness with that of other children. In addition, the analysis unit can visualize the effectiveness of learning and report it clearly to parents and teachers. This allows for an accurate understanding of children's learning effectiveness and enables appropriate learning support.

[0065] The consultation department can provide expert advice tailored to the parents' concerns. For example, it can provide advice from education specialists for learning-related consultations and from childcare specialists for childcare-related consultations. Furthermore, the consultation department can propose specific solutions based on expert advice. In addition, the consultation department can strengthen its collaboration with experts and provide opportunities for parents to consult directly with them. This allows parents to receive expert advice and take appropriate action.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reception desk receives the tablets that have been distributed to each child upon their arrival at the after-school program. The reception desk can, for example, have a system in place to automatically distribute tablets when children arrive at the after-school program. Alternatively, the reception desk can confirm the children's arrival and hand them the tablets manually. Furthermore, the reception desk can record the children's arrival times and manage attendance. Step 2: Students access content of their interest once a day using a tablet received from the reception desk. The learning center can be equipped with a system that provides content such as videos, games, ebooks, and quizzes. The learning center can also select and access content that interests the student. Furthermore, the learning center can record the student's learning history and manage their learning progress. Step 3: The storage unit stores the children's learning data and diary data based on the content they have received through the learning unit. The storage unit can, for example, have a system that automatically saves data on the content the children have received. The storage unit can also save diary data written by the children. Furthermore, the storage unit can organize the stored data and save it in a searchable format. Step 4: The analysis unit analyzes the data accumulated by the storage unit to understand the children's personalities and thinking patterns. The analysis unit can, for example, be equipped with a system that analyzes the accumulated data using generative AI. The analysis unit can also analyze the children's learning data and diary data to understand their personalities and thinking patterns. Furthermore, the analysis unit can also create reports to provide the analysis results to the parents. Step 5: The consultation department provides learning and parenting consultations to parents based on the data collected by the analysis department. The consultation department can, for example, use a system that responds to parental inquiries using generative AI. Furthermore, the consultation department can suggest learning support methods tailored to each child based on the accumulated data. In addition, the consultation department can analyze personality and thinking tendencies to provide appropriate advice.

[0068] (Example of form 2) The after-school care system according to an embodiment of the present invention is a system that utilizes generative AI to accumulate data on children's learning, personality, and thinking tendencies, and allows parents to consult about childcare and learning. In this after-school care system, when a child arrives at the after-school care center, they receive a tablet that is distributed to them. Next, once a day, the child accesses content such as videos, games, ebooks, and quizzes that are of interest to them on the tablet. They can also optionally keep a diary. Through these activities, the child's learning data and diary data are accumulated. This data is analyzed by the generative AI to understand the child's personality and thinking tendencies. Based on this data, parents can consult the generative AI about learning and childcare. For example, if a parent asks, "I want to know how to support my child's learning," the generative AI will suggest learning support methods that are appropriate for each child based on the accumulated data. Also, if a parent asks, "I want advice on my child's personality and thinking tendencies," the generative AI will analyze the personality and thinking tendencies and provide appropriate advice. This mechanism allows parents to easily consult about their child's learning and childcare and obtain highly accurate answers. Furthermore, by semi-compulsorily engaging in learning and games at after-school programs, sufficient data can be accumulated, allowing for an accurate understanding of children's personalities and thinking patterns. This enables parents to receive advice on their children's future aptitudes and how to interact with them going forward. In addition, providing after-school care services ensures that children do not slack off, allowing for the accumulation of the correct amount of data. Moreover, after-school care services are in short supply and the number of children can be guaranteed, resulting in high demand for the service. This also allows for expected advertising effects for businesses. In summary, after-school care systems accumulate data on children's learning, personalities, and thinking patterns, enabling parents to seek advice on childcare and learning.

[0069] The after-school childcare system according to this embodiment comprises a reception unit, a learning unit, a storage unit, an analysis unit, and a consultation unit. The reception unit receives a tablet distributed to each child upon arrival at the after-school childcare center. The reception unit may include a system that automatically distributes tablets to children upon their arrival. The reception unit may also confirm the child's arrival and hand over the tablet. Furthermore, the reception unit may record the child's arrival time and manage attendance. The learning unit uses the tablet received by the reception unit to access content of interest once a day. The learning unit may include a system that provides content such as videos, games, ebooks, and quizzes. The learning unit may also allow children to select and access content that interests them. Furthermore, the learning unit may also record the child's learning history and manage their learning progress. The storage unit stores the child's learning data and diary data based on the content accessed by the learning unit. The storage unit may include a system that automatically saves data on the content accessed by the child. Furthermore, the storage unit may also save diary data written by the child. Furthermore, the storage unit can organize the stored data and save it in a searchable format. The analysis unit analyzes the data stored by the storage unit to understand the children's personalities and thinking tendencies. The analysis unit can, for example, be equipped with a system that analyzes the stored data using generative AI. The analysis unit can also analyze the children's learning data and diary data to understand their personalities and thinking tendencies. Furthermore, the analysis unit can also create reports to provide the analysis results to parents. The consultation unit provides parents with learning and childcare consultations based on the data obtained by the analysis unit. The consultation unit can, for example, be equipped with a system that responds to consultations from parents using generative AI. Furthermore, the consultation unit can suggest learning support methods tailored to each child based on the stored data. Furthermore, the consultation unit can analyze personalities and thinking tendencies and provide appropriate advice. As a result, the after-school care system according to this embodiment stores children's learning, personalities, and thinking tendencies as data, and allows parents to consult about childcare and learning.

[0070] The reception area receives a tablet distributed to each child upon arrival at the after-school program. The reception area can be equipped with a system that automatically distributes tablets upon arrival. Specifically, the reception area is equipped with an automated recognition system using RFID tags and facial recognition technology, which automatically confirms a child's arrival as they pass through the entrance of the after-school program and distributes a tablet. Alternatively, the reception area can confirm a child's arrival and hand out tablets by hand. For example, reception staff can confirm the child's name and hand out tablets by hand, allowing for individualized attention for each child. Furthermore, the reception area can record the child's arrival time and manage attendance. This allows for accurate tracking of children's attendance and real-time notification to parents. For example, parents can confirm their child's arrival at the after-school program through a dedicated app. The reception area can also save children's arrival times and attendance status in a database for later reference. This improves the efficiency of attendance management and ensures the safety of the children.

[0071] Students in the learning department use tablets received from the reception desk to access content of their interest once a day. The learning department can be equipped with a system that provides content such as videos, games, ebooks, and quizzes. Specifically, the tablets are pre-installed with a variety of educational content, which students can freely choose according to their interests. For example, there is a wealth of content available to stimulate students' motivation to learn, such as science experiment videos, history quizzes, and math games. Furthermore, students can select and access content that interests them. This allows students to learn at their own pace and experience the joy of learning. In addition, the learning department can record students' learning history and manage their learning progress. For example, the tablets have a function to automatically record learning history, allowing for detailed tracking of which content students have studied and for how long. This enables accurate monitoring of students' learning progress and adjustments to learning content as needed.

[0072] The data storage unit stores children's learning data and diary data based on the content they have accessed through the learning unit. For example, the data storage unit can be equipped with a system that automatically saves data on the content children have accessed. Specifically, the history of content accessed on a tablet and learning outcomes are automatically uploaded to a cloud server and stored. The data storage unit can also store diary data written by children. For example, a diary app may be installed on the tablet, allowing children to write about daily events and their thoughts. This allows for the recording of children's growth and emotional changes. Furthermore, the data storage unit can organize the stored data and save it in a searchable format. For example, learning data and diary data can be tagged and categorized, allowing for quick retrieval of necessary information. This enables the data storage unit to efficiently manage records of children's learning progress and growth and to quickly provide necessary information.

[0073] The analysis unit analyzes the data accumulated by the storage unit to understand the child's personality and thinking patterns. The analysis unit can be equipped with a system that analyzes the accumulated data using, for example, generative AI. Specifically, the generative AI analyzes the child's learning data and diary data to understand the child's learning patterns and interests. For example, the generative AI can analyze which content the child spends the most time on and what learning style the child prefers, and propose an optimal learning plan for each individual child. The analysis unit can also analyze the child's learning data and diary data to understand their personality and thinking patterns. For example, it can detect changes in the child's emotions and signs of stress from diary data, allowing for early intervention. Furthermore, the analysis unit can create reports to provide parents with the analysis results. For example, the report created by the generative AI will contain detailed information on the child's learning progress, personality patterns, and future learning strategies, which parents can use as a reference to help with their child's learning and parenting. In this way, the analysis unit can comprehensively support the child's learning and growth and provide useful information to parents.

[0074] The consultation department provides parents with learning and parenting consultations based on data collected by the analysis department. The consultation department can, for example, utilize a system that responds to parental inquiries using generative AI. Specifically, the generative AI analyzes parental questions and consultations to provide optimal advice and information. For example, in response to questions about a child's learning progress, the generative AI can provide specific advice based on accumulated data. Furthermore, the consultation department can suggest learning support methods tailored to each child based on accumulated data. For example, the generative AI can analyze a child's learning data and propose the most suitable learning methods and materials for each individual child. In addition, the consultation department can analyze personality and thinking tendencies to provide appropriate advice. For example, it can understand a child's personality and thinking tendencies and provide parenting advice and support to parents. This allows the consultation department to help parents resolve their concerns about their child's learning and parenting, and to provide a better parenting environment. Moreover, through communication with parents, the consultation department can support the child's growth and deepen the parent-child bond. For example, by establishing regular consultation sessions, sharing the situation of parents and children, and providing appropriate advice, the parent-child relationship can be strengthened. This allows the counseling department to provide comprehensive support for children's learning and development, and to offer useful information and advice to parents.

[0075] The learning unit can access at least one of the following types of content: videos, games, ebooks, and quizzes. For example, the learning unit can provide videos that children might find interesting, such as educational videos or animated videos. It can also provide games that children can enjoy, such as educational games or entertainment games. Furthermore, it can provide ebooks that children can read, such as educational ebooks or narrative ebooks. Finally, it can provide quizzes that children can try, such as educational quizzes or entertainment quizzes. This allows children to access a diverse range of content that interests them. Some or all of the above processing in the learning unit may be performed using AI, for example, or not. For example, the learning unit can provide content using an AI model that analyzes children's interests and recommends the most suitable content.

[0076] The storage unit can store children's learning data and diary data. For example, the storage unit can automatically save data on content that children have taken. This includes, for example, the viewing history of videos and the play history of games that children have taken. The storage unit can also save diary data that children have written. This includes, for example, diary data in which children have written about daily events and their thoughts. Furthermore, the storage unit can organize the stored data and save it in a searchable format. For example, it can organize the stored data by category so that the necessary data can be quickly searched. This allows for the efficient storage of children's learning data and diary data. Some or all of the above processing in the storage unit may be performed using, for example, AI, or not using AI. For example, the storage unit can store data using an AI model that automatically classifies and saves children's learning data and diary data.

[0077] The analysis unit can analyze accumulated data to understand children's personalities and thinking patterns. For example, the analysis unit can use generative AI to analyze the accumulated data. For example, it can analyze children's learning data and diary data to understand their personalities and thinking patterns. The analysis unit can also analyze children's behavioral patterns to identify their personalities and thinking patterns. For example, it can analyze what kind of content children are interested in and what kind of actions they take. Furthermore, the analysis unit can create reports to provide to parents based on the analysis results. For example, it can create a report summarizing the child's personality and thinking patterns and provide it to the parents. This allows for an accurate understanding of the child's personality and thinking patterns. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the data using an AI model that takes accumulated data as input and outputs personality and thinking patterns.

[0078] The consultation department can provide parents with learning and childcare advice. For example, the consultation department can use generative AI to respond to parents' inquiries. If a parent asks for advice on how to support their child's learning, the generative AI will use accumulated data to suggest learning support methods tailored to each child. If a parent asks for advice on their child's personality and thinking patterns, the generative AI will analyze the personality and thinking patterns and provide appropriate advice. Furthermore, the consultation department can also suggest learning support methods tailored to each child based on accumulated data. For example, the generative AI will analyze a child's learning data and propose the optimal learning support method. This allows parents to seek advice on childcare and learning. Some or all of the above-described processes in the consultation department may be performed using AI, or not. For example, the consultation department can respond to inquiries using an AI model that takes parental inquiries as input and outputs appropriate advice.

[0079] The consultation department can provide learning support methods tailored to each child based on accumulated data. For example, the consultation department can use generative AI to analyze the accumulated data and provide learning support methods tailored to each child. For example, the generative AI proposes the optimal learning support method based on the child's learning data. The consultation department can also analyze the child's personality and thinking tendencies to provide appropriate learning support methods. For example, the generative AI proposes individualized learning support methods based on the child's personality and thinking tendencies. Furthermore, the consultation department can explain specific learning support methods to parents. For example, the generative AI provides parents with specific advice on how to support their children. This allows for the provision of learning support methods tailored to each child. Some or all of the above-described processes in the consultation department may be performed using AI, or not. For example, the consultation department can use an AI model that takes accumulated data as input and outputs learning support methods to provide learning support methods.

[0080] The counseling department can analyze personality and thinking tendencies and provide appropriate advice. For example, the counseling department can use generative AI to analyze accumulated data and understand personality and thinking tendencies. For example, the generative AI can provide appropriate advice based on the child's personality and thinking tendencies. The counseling department can also provide specific advice to parents. For example, the generative AI can give parents specific advice on how to support their children. Furthermore, the counseling department can provide advice on future aptitudes and how to interact with children based on their personality and thinking tendencies. For example, the generative AI analyzes the child's personality and thinking tendencies and provides advice on future aptitudes and how to interact with them. This allows for the provision of appropriate advice based on personality and thinking tendencies. Some or all of the above-described processes in the counseling department may be performed using AI, for example, or without AI. For example, the counseling department can provide advice using an AI model that takes accumulated data as input and outputs advice.

[0081] The reception desk can estimate a child's emotions and adjust the tablet distribution method based on the estimated emotions. For example, if a child is feeling anxious, the reception desk can distribute the tablet in a gentle voice to provide reassurance. If a child is excited, the reception desk can distribute the tablet in a calm environment to enhance concentration. Furthermore, if a child is tired, the reception desk can make it possible for them to receive the tablet with simple operations. This provides a tablet distribution method that is tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0082] The reception department can analyze a child's past arrival history and select the optimal reception method. For example, if a child has been late in the past, the reception department can send a reminder to arrive earlier. Also, if a child has arrived early in the past, the reception department can adjust the reception time to reduce waiting time. Furthermore, the reception department can analyze the child's arrival patterns and suggest the optimal reception time. This allows the reception department to provide the optimal reception method based on the child's past arrival history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the child's arrival history data into a generating AI and have the generating AI select the optimal reception method.

[0083] The reception desk can filter children based on their current physical condition and mood at the time of registration. For example, if a child is unwell, the reception desk can instruct them to prioritize rest. If a child is well, the reception desk can suggest activities that the child can actively participate in. Furthermore, the reception desk can select appropriate content according to the child's mood. This allows for filtering tailored to the child's physical condition and mood. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, or not using AI. For example, the reception desk can input the child's physical condition and mood data into a generative AI and have the generative AI perform the filtering.

[0084] The reception desk can estimate a child's emotions and determine the priority of the reception process based on the estimated emotions. For example, if a child is feeling anxious, the reception desk can prioritize their reception to provide a sense of security. If a child is excited, the reception desk can provide a calm environment for the reception process to enhance their concentration. Furthermore, if a child is tired, the reception desk can make the reception process easy to complete. This allows for the provision of reception priorities tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the child's emotion data into a generative AI and have the generative AI determine the reception priority.

[0085] The reception desk can prioritize obtaining highly relevant information by considering the child's geographical location during registration. For example, if the child is far from home, the reception desk can guide them to a nearby after-school facility. Furthermore, if the child is in a specific area, the reception desk can provide information relevant to that area. In addition, the reception desk can suggest the optimal route based on the child's location. This allows for the provision of highly relevant information based on the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the child's geographical location into a generating AI and have the generating AI perform the task of obtaining highly relevant information.

[0086] The reception desk can analyze a child's social media activity and obtain relevant information at the time of registration. For example, the reception desk can prioritize providing content that the child has shown interest in on social media. The reception desk can also analyze the child's social media activity and suggest appropriate activities. Furthermore, the reception desk can suggest group activities considering the child's social media friendships. This allows the reception desk to provide relevant information based on the child's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the child's social media activity data into a generating AI and have the generating AI perform the task of obtaining relevant information.

[0087] The learning unit can estimate a child's emotions and adjust how content is displayed based on the estimated emotions. For example, if a child is relaxed, the unit can display content that proceeds at a relaxed pace. If a child is excited, the unit can display content with visually stimulating effects. Furthermore, if a child is tired, the unit can display simple and highly visible content. This provides a way to display content that is appropriate to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input the child's emotion data into a generative AI and have the generative AI execute the content display method.

[0088] The learning unit can adjust the level of detail of content based on the child's interests during the lesson. For example, the learning unit can provide detailed information on topics that the child has shown interest in. It can also prioritize displaying content related to areas of interest to the child. Furthermore, the learning unit can adjust the difficulty level of the content according to the child's interests. This allows for the provision of content with a level of detail that matches the child's interests. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input data on the child's interests into a generating AI and have the generating AI determine the level of detail of the content.

[0089] The learning unit can apply different learning algorithms depending on the content category during learning. For example, in the case of learning content, the learning unit can adjust the pace according to the level of understanding. In the case of game content, the learning unit can adjust the difficulty level according to the child's skill level. Furthermore, in the case of diary content, the learning unit can adjust the questions according to the child's emotions. This allows the learning unit to provide learning algorithms tailored to the content category. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input content category data into a generating AI and have the generating AI execute the learning algorithm.

[0090] The learning unit can estimate a child's emotions and adjust the length of the content based on the estimated emotions. For example, if a child is in a hurry, the unit can provide short, concise content. If a child is relaxed, the unit can provide longer content with detailed explanations. Furthermore, if a child is excited, the unit can provide content with visually stimulating effects. This allows for content length tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input child emotion data into a generative AI and have the generative AI determine the content length.

[0091] The learning unit can determine the priority of learning based on the timing of content provision at the time of learning. For example, the learning unit can suggest prioritizing learning new content when it becomes available. It can also suggest prioritizing review of content that the student has previously learned. Furthermore, the learning unit can suggest the optimal learning order according to the student's learning progress. This allows for the provision of learning priorities based on the timing of content provision. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input content provision timing data into a generating AI and have the generating AI perform the learning priority calculation.

[0092] The learning unit can adjust the order of learning based on the relevance of the content during learning. For example, the learning unit can prioritize displaying content related to topics that the child has shown interest in. Furthermore, the learning unit can provide relevant content in a sequence according to the child's learning progress. In addition, the learning unit can display highly relevant content consecutively according to the child's interests. This allows for a learning order based on the relevance of the content. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input content relevance data into a generating AI and have the generating AI execute the learning order.

[0093] The data storage unit can estimate the child's emotions and adjust the data storage method based on the estimated emotions. For example, if the child is relaxed, the storage unit can store detailed data. If the child is excited, the storage unit can prioritize storing visually stimulating data. Furthermore, if the child is tired, the storage unit can store simple data. This provides a data storage method that corresponds to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the child's emotion data into the generative AI and have the generative AI execute the data storage method.

[0094] The data storage unit can analyze a child's past learning data during storage to select the optimal storage method. For example, the storage unit can select the optimal data storage method based on what the child has learned in the past. The storage unit can also adjust the data storage method according to the child's learning progress. Furthermore, the storage unit can analyze the child's learning patterns and propose the optimal data storage method. This allows the storage unit to provide the optimal storage method based on the child's past learning data. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the child's past learning data into a generating AI and have the generating AI select the optimal storage method.

[0095] The data storage unit can filter data based on the child's current learning status during storage. For example, the storage unit can prioritize storing data related to what the child is currently learning. Furthermore, the storage unit can filter out unnecessary data according to the child's learning progress. In addition, the storage unit can analyze the child's learning status and store optimal data. This allows for data filtering tailored to the child's current learning status. Some or all of the above processing in the storage unit may be performed using AI, for example, or without AI. For example, the storage unit can input the child's current learning status data into a generating AI and have the generating AI perform data filtering.

[0096] The data storage unit can estimate a child's emotions and prioritize data based on the estimated emotions. For example, if a child is relaxed, the storage unit can prioritize storing detailed data. If a child is excited, the storage unit can prioritize storing visually stimulating data. Furthermore, if a child is tired, the storage unit can prioritize storing simple data. This provides data prioritization according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the storage unit may be performed using AI, for example, or not using AI. For example, the storage unit can input the child's emotion data into the generative AI and have the generative AI prioritize the data.

[0097] The data storage unit can prioritize storing highly relevant data by considering the child's geographical location information during storage. For example, if a child is in a specific area, the data storage unit can prioritize storing data related to that area. The data storage unit can also store optimal data based on the child's location information. Furthermore, the data storage unit can analyze the child's geographical location information and prioritize storing highly relevant data. This allows the storage unit to provide highly relevant data based on the child's geographical location information. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input the child's geographical location information into a generating AI and have the generating AI perform the storage of highly relevant data.

[0098] The data storage unit can analyze a child's social media activity and store relevant data during the storage process. For example, the data storage unit can prioritize storing data related to content that a child has shown interest in on social media. The data storage unit can also analyze a child's social media activity and store appropriate data. Furthermore, the data storage unit can store relevant data while considering the child's social media friendships. This allows the storage unit to provide relevant data based on the child's social media activity. Some or all of the above processing in the data storage unit may be performed using AI, for example, or without AI. For example, the data storage unit can input the child's social media activity data into a generating AI and have the generating AI store the relevant data.

[0099] The analysis unit can estimate a child's emotions and adjust the data analysis method based on the estimated emotions. For example, if a child is relaxed, the analysis unit can perform a detailed data analysis. If a child is excited, the analysis unit can perform a visually stimulating data analysis. Furthermore, if a child is tired, the analysis unit can perform a simple data analysis. This allows for the provision of a data analysis method tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's emotion data into the generative AI and have the generative AI perform the data analysis method.

[0100] The analysis unit can optimize the current analysis by referring to past analysis data during the analysis process. For example, the analysis unit can optimize the current learning progress based on a child's past learning data. Furthermore, the analysis unit can predict the child's current behavior by referring to their past behavior patterns. In addition, the analysis unit can analyze the child's current emotions based on their past emotional data. This allows for the provision of an optimal analysis based on past analysis data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past analysis data into a generating AI to optimize the current analysis.

[0101] The analysis unit can apply different analysis methods to each data category during analysis. For example, in the case of training data, the analysis unit can apply an analysis method appropriate to the level of understanding. In the case of diary data, the analysis unit can apply an emotion analysis method. Furthermore, in the case of game data, the analysis unit can apply a behavioral pattern analysis method. This allows the system to provide the optimal analysis method according to the data category. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI execute the analysis method.

[0102] The analysis unit can estimate a child's emotions and determine the priority of analysis based on the estimated emotions. For example, if a child is relaxed, the analysis unit can prioritize detailed data analysis. If a child is excited, the analysis unit can prioritize visually stimulating data analysis. Furthermore, if a child is tired, the analysis unit can prioritize simple data analysis. This provides an analysis priority tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the child's emotion data into a generative AI and have the generative AI execute the analysis priorities.

[0103] The analysis unit can adjust the order of analysis based on the timing of data provision during analysis. For example, the analysis unit can prioritize analysis when new data is provided. The analysis unit can also adjust the order of analysis according to the student's learning progress. Furthermore, the analysis unit can adjust the order of analysis based on the student's behavioral patterns. This allows for an analysis order based on the timing of data provision. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the timing of data provision into a generating AI and have the generating AI execute the analysis order.

[0104] The analysis unit can perform analysis by referring to relevant market data during the analysis process. For example, the analysis unit can analyze children's learning data by comparing it with market learning trends. It can also analyze children's behavioral data by comparing it with market behavioral trends. Furthermore, it can analyze children's emotional data by comparing it with market emotional trends. This allows for analysis based on relevant market data. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input relevant market data into a generating AI and have the generating AI perform the analysis.

[0105] The counseling unit can estimate a child's emotions and adjust the counseling method based on the estimated emotions. For example, if the child is relaxed, the counseling unit can provide detailed counseling content. If the child is excited, the counseling unit can provide visually stimulating counseling content. Furthermore, if the child is tired, the counseling unit can provide simple counseling content. This allows for the provision of counseling methods tailored to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the counseling unit may be performed using AI, for example, or not using AI. For example, the counseling unit can input the child's emotion data into a generative AI and have the generative AI execute the counseling method.

[0106] The consultation department can select the most suitable consultation method by referring to the parent's past consultation history during the consultation. For example, the consultation department can select the most suitable consultation method based on the content of past consultations the parent has had. The consultation department can also analyze the parent's past consultation history and provide appropriate advice. Furthermore, the consultation department can propose the most suitable consultation method by referring to the parent's consultation patterns. This allows the consultation department to provide the most suitable consultation method based on the parent's past consultation history. Some or all of the above processes in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the parent's past consultation history data into a generating AI and have the generating AI select the most suitable consultation method.

[0107] The consultation department can customize the content of consultations based on the child's current learning situation. For example, the consultation department can suggest appropriate learning support methods according to the child's learning progress. The consultation department can also analyze the child's learning situation and provide optimal parenting advice. Furthermore, the consultation department can customize specific consultation content based on the child's learning data. This allows the consultation department to provide content that is tailored to the child's current learning situation. Some or all of the above processes in the consultation department may be performed using AI, for example, or not. For example, the consultation department can input the child's current learning situation data into a generating AI and have the generating AI perform the customization of the consultation content.

[0108] The counseling unit can estimate a child's emotions and determine the priority of consultations based on the estimated emotions. For example, if a child is relaxed, the counseling unit can prioritize providing detailed consultation content. If a child is excited, the counseling unit can prioritize providing visually stimulating consultation content. Furthermore, if a child is tired, the counseling unit can prioritize providing simple consultation content. This allows for prioritizing consultations according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the counseling unit may be performed using AI, for example, or without AI. For example, the counseling unit can input the child's emotion data into a generative AI and have the generative AI prioritize consultations.

[0109] The consultation department can select the most suitable consultation method by considering the parent's geographical location information during the consultation. For example, if the parent is far from home, the consultation department can suggest an online consultation. Furthermore, if the parent is in a specific region, the consultation department can provide information relevant to that region. In addition, the consultation department can suggest the most suitable consultation method based on the parent's location information. This allows for the provision of the most suitable consultation method based on the parent's geographical location information. Some or all of the above processing in the consultation department may be performed using AI, for example, or without AI. For example, the consultation department can input the parent's geographical location information into a generating AI and have the generating AI select the most suitable consultation method.

[0110] The consultation department can analyze the parents' social media activity during consultations and propose consultation methods. For example, the consultation department can provide consultation content related to topics that the parents have shown interest in on social media. The consultation department can also analyze the parents' social media activity and propose appropriate consultation methods. Furthermore, the consultation department can consider the parents' social media friendships and propose the optimal consultation method. This allows for the provision of optimal consultation methods based on the parents' social media activity. Some or all of the above processing in the consultation department may be performed using AI, for example, or not using AI. For example, the consultation department can input the parents' social media activity data into a generating AI and have the generating AI execute the consultation methods.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception area can measure children's body temperature and check their health status upon arrival. For example, the reception area can measure a child's temperature using a non-contact thermometer and notify parents if a fever is detected. Furthermore, the reception area can take appropriate action based on the child's health condition. For instance, it can encourage unwell children to rest and guide healthy children into their normal activities. In addition, the reception area can accumulate children's health data and conduct regular health checks. This allows for constant monitoring of children's health status and appropriate responses.

[0113] The learning department can customize the content delivery method according to the child's learning style. For example, children who prefer visual learning can be provided with content that makes extensive use of videos and illustrations. Children who prefer auditory learning can be provided with audio explanations and podcast-style content. Furthermore, children who prefer practical learning can be provided with interactive quizzes and simulation games. This allows for the provision of optimal content tailored to each child's learning style.

[0114] The data storage unit can enhance data privacy protection when storing children's learning data. For example, the storage unit can encrypt and store data to prevent unauthorized access. Furthermore, the storage unit can set data access permissions, ensuring that only authorized personnel can access the data. In addition, the storage unit can set data retention periods and automatically delete data after a certain period. This ensures the privacy of children's learning data and allows for secure storage.

[0115] The analysis unit can evaluate the effectiveness of learning when analyzing children's learning data. For example, the analysis unit can quantitatively evaluate children's learning progress and quantify the effectiveness of learning. Furthermore, the analysis unit can compare children's learning data and compare their learning effectiveness with that of other children. In addition, the analysis unit can visualize the effectiveness of learning and report it clearly to parents and teachers. This allows for an accurate understanding of children's learning effectiveness and enables appropriate learning support.

[0116] The consultation department can provide expert advice tailored to the parents' concerns. For example, it can provide advice from education specialists for learning-related consultations and from childcare specialists for childcare-related consultations. Furthermore, the consultation department can propose specific solutions based on expert advice. In addition, the consultation department can strengthen its collaboration with experts and provide opportunities for parents to consult directly with them. This allows parents to receive expert advice and take appropriate action.

[0117] The reception desk can estimate a child's emotions and adjust its response based on that estimation. For example, if a child is nervous, the reception desk can greet them with gentle words to help them relax. If a child is excited, the reception desk can provide a calm environment to help them concentrate. Furthermore, if a child is tired, the reception desk can complete the check-in process with simple procedures, allowing them to rest sooner. This enables flexible responses tailored to each child's emotions.

[0118] The learning component can estimate a child's emotions and adjust the difficulty level of the learning content based on those estimates. For example, if a child is relaxed, it can provide more challenging content to encourage learning. If a child is excited, it can provide visually stimulating content to increase their motivation to learn. Furthermore, if a child is tired, it can provide easier content to allow them to continue learning without difficulty. This allows for the provision of optimal learning content tailored to each child's emotions.

[0119] The data storage unit can estimate a child's emotions and adjust the data organization method based on the estimated emotions. For example, if a child is relaxed, detailed data can be organized to meticulously record learning progress. If a child is excited, visually easy-to-understand data can be organized to visualize learning outcomes. Furthermore, if a child is tired, simple data can be organized to reduce the learning burden. This allows for data organization methods tailored to the child's emotions.

[0120] The analysis unit can estimate the child's emotions and adjust the reporting method of the analysis results based on the estimated emotions. For example, if the child is relaxed, detailed analysis results can be reported, and the learning outcomes can be explained in detail. If the child is excited, visually stimulating analysis results can be reported to increase their motivation to learn. Furthermore, if the child is tired, simpler analysis results can be reported to reduce the burden of learning. In this way, it is possible to provide a method of reporting analysis results that is tailored to the child's emotions.

[0121] The counseling department can estimate a child's emotions and adjust the content of the consultation based on those estimates. For example, if a child is relaxed, detailed consultation content can be provided, along with specific advice. If a child is excited, visually stimulating consultation content can be provided to increase their motivation to learn. Furthermore, if a child is tired, simple consultation content can be provided to allow them to continue learning without difficulty. In this way, the counseling department can provide optimal consultation content tailored to the child's emotions.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk receives the tablets that have been distributed to each child upon their arrival at the after-school program. The reception desk can, for example, have a system in place to automatically distribute tablets when children arrive at the after-school program. Alternatively, the reception desk can confirm the children's arrival and hand them the tablets manually. Furthermore, the reception desk can record the children's arrival times and manage attendance. Step 2: Students access content of their interest once a day using a tablet received from the reception desk. The learning center can be equipped with a system that provides content such as videos, games, ebooks, and quizzes. The learning center can also select and access content that interests the student. Furthermore, the learning center can record the student's learning history and manage their learning progress. Step 3: The storage unit stores the children's learning data and diary data based on the content they have received through the learning unit. The storage unit can, for example, have a system that automatically saves data on the content the children have received. The storage unit can also save diary data written by the children. Furthermore, the storage unit can organize the stored data and save it in a searchable format. Step 4: The analysis unit analyzes the data accumulated by the storage unit to understand the children's personalities and thinking patterns. The analysis unit can, for example, be equipped with a system that analyzes the accumulated data using generative AI. The analysis unit can also analyze the children's learning data and diary data to understand their personalities and thinking patterns. Furthermore, the analysis unit can also create reports to provide the analysis results to the parents. Step 5: The consultation department provides learning and parenting consultations to parents based on the data collected by the analysis department. The consultation department can, for example, use a system that responds to parental inquiries using generative AI. Furthermore, the consultation department can suggest learning support methods tailored to each child based on the accumulated data. In addition, the consultation department can analyze personality and thinking tendencies to provide appropriate advice.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] For example, each of the multiple elements, including the reception unit, learning unit, storage unit, analysis unit, and consultation unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, which automatically distributes tablets to children when they arrive at the school. The learning unit is implemented by the control unit 46A of the smart device 14, which allows children to select and take courses on content that interests them. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, which stores children's learning data and diary data based on the content they have taken courses on. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the stored data to understand the children's personalities and thinking tendencies. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice in response to consultations from parents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 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.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] For example, each of the multiple elements, including the reception unit, learning unit, storage unit, analysis unit, and consultation unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, which automatically distributes tablets to children when they arrive at the school. The learning unit is implemented by the control unit 46A of the smart glasses 214, which allows children to select and take courses on content that interests them. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, which stores children's learning data and diary data based on the content they have taken courses on. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the stored data to understand the children's personalities and thinking tendencies. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice in response to consultations from parents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] For example, each of the multiple elements, including the reception unit, learning unit, storage unit, analysis unit, and consultation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, which automatically distributes tablets to children when they arrive at the school. The learning unit is implemented by the control unit 46A of the headset terminal 314, which allows children to select and take courses on content that interests them. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, which stores children's learning data and diary data based on the content they have taken courses on. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the stored data to understand the children's personalities and thinking tendencies. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice in response to consultations from parents. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] For example, each of the multiple elements, including the reception unit, learning unit, storage unit, analysis unit, and consultation unit, is implemented by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, which automatically distributes tablets to children when they arrive at the school. The learning unit is implemented by the control unit 46A of the robot 414, which allows children to select and take courses on content that interests them. The storage unit is implemented by the specific processing unit 290 of the data processing unit 12, which stores children's learning data and diary data based on the content they have taken courses on. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, which analyzes the stored data to understand the children's personalities and thinking tendencies. The consultation unit is implemented by the specific processing unit 290 of the data processing unit 12, which provides appropriate advice in response to consultations from parents. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) When the children arrive at the after-school program, they receive their tablets at the reception desk, The tablet received by the reception desk allows students to take courses on content of their interest once a day, and the course section allows students to take courses on content of their interest. A storage unit that stores children's learning data and diary data based on the content received by the aforementioned learning unit, An analysis unit analyzes the data accumulated by the aforementioned storage unit to understand the child's personality and thinking tendencies, The system includes a consultation unit that provides learning and childcare consultations to parents based on the data obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned training section is, Take a course in at least one of the following: videos, games, ebooks, or quizzes. The system described in Appendix 1, characterized by the features described herein. (Note 3) The storage unit is Accumulate children's learning data and diary data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, By analyzing the accumulated data, we can understand the children's personalities and thinking patterns. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned consultation department, We provide parents with academic and childcare consultations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned consultation department, Based on accumulated data, we will present learning support methods tailored to each child. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned consultation department, We analyze personality and thinking patterns and provide appropriate advice. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the children's emotions and adjusts the tablet distribution method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the child's past arrival history and select the appropriate check-in method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is At registration, filtering is performed based on the child's current physical condition and mood. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is The system estimates the children's emotions and determines the priority of registration based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is During registration, the system prioritizes obtaining highly relevant information by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is During registration, we analyze the child's social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned training section is, The system estimates children's emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned training section is, During the lesson, the level of detail in the content will be adjusted based on the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned training section is, When you enroll, a different enrollment algorithm will be applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned training section is, The system estimates children's emotions and adjusts the length of content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned training section is, When enrolling, you will determine the priority of your course based on when the content is available. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned training section is, When you take the course, the order in which you take the courses will be adjusted based on the relevance of the content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The storage unit is We estimate the children's emotions and adjust the data storage method based on the estimated emotions of the children. The system described in Appendix 1, characterized by the features described herein. (Note 21) The storage unit is During data storage, the system analyzes the children's past learning data to select the optimal storage method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The storage unit is During data storage, the data is filtered based on the children's current learning status. The system described in Appendix 1, characterized by the features described herein. (Note 23) The storage unit is The system estimates the children's emotions and prioritizes data based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The storage unit is During data storage, the system prioritizes storing highly relevant data, taking into account the children's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The storage unit is During data accumulation, the system analyzes children's social media activities and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned analysis unit, We estimate the children's emotions and adjust the data analysis method based on the estimated emotions of the children. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned analysis unit, During analysis, past analysis data is referenced to make the current analysis more appropriate. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit, During analysis, different analytical methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit, The system estimates the children's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on when the data was provided. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit, During the analysis, the analysis is performed by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned consultation department, We estimate the child's emotions and adjust the counseling method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned consultation department, During the consultation, the most suitable consultation method will be selected by referring to the parents' past consultation history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned consultation department, During the consultation, the content of the consultation will be customized based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned consultation department, We estimate the child's emotions and determine the priority of consultations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned consultation department, When conducting a consultation, the most suitable consultation method will be selected, taking into account the parents' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned consultation department, During consultations, we analyze the parents' social media activity and suggest appropriate methods for seeking advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. When the children arrive at the after-school program, they receive their tablets at the reception desk, The tablet received by the reception desk allows students to take courses on content of their interest once a day, and the course section allows students to take courses on content of their interest. A storage unit that stores children's learning data and diary data based on the content received by the aforementioned learning unit, An analysis unit analyzes the data accumulated by the aforementioned storage unit to understand the child's personality and thinking tendencies, The system includes a consultation unit that provides learning and childcare consultations to parents based on the data obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned training section is, Take a course in at least one of the following: videos, games, ebooks, or quizzes. The system according to feature 1.

3. The storage unit is Accumulate children's learning data and diary data. The system according to feature 1.

4. The aforementioned analysis unit, By analyzing the accumulated data, we can understand the children's personalities and thinking patterns. The system according to feature 1.

5. The aforementioned consultation department, We provide parents with academic and childcare consultations. The system according to feature 1.

6. The aforementioned consultation department, Based on accumulated data, we will present learning support methods tailored to each child. The system according to feature 1.

7. The aforementioned consultation department, We analyze personality and thinking patterns and provide appropriate advice. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the children's emotions and adjusts the tablet distribution method based on the estimated emotions. The system according to feature 1.

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