system

Abstract

The system according to this embodiment aims to record a child's daily life, identify their interests, and ensure their safety. [Solution] The system according to the embodiment comprises a recording unit, an analysis unit, a provision unit, a notification unit, and a dialogue unit. The recording unit records the child's daily life. The analysis unit analyzes the data recorded by the recording unit and identifies the child's interests. The provision unit provides learning materials and activities based on the interests identified by the analysis unit. The notification unit detects abnormal movements or sounds and notifies the guardian. The dialogue unit interacts with the child.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the recording of children's daily lives, the identification of interests, and even the ensuring of safety are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to record children's daily lives, identify interests, and ensure safety.

Means for Solving the Problems

[0007] The system according to this embodiment can record a child's daily life, identify their interests, and ensure their safety. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls 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 child growth recording system according to an embodiment of the present invention is a system designed to record children's growth amidst busy daily lives, identify their interests, provide appropriate education, and ensure their safety. The child growth recording system incorporates an AI device with cameras, sensors, voice recognition, etc., to automatically record new discoveries and growth in the child's daily life. This record is digitized and made available to parents via an app or website. For example, it automatically records moments such as the child's first steps or first words, allowing parents to review them later. Next, the child growth recording system uses AI to observe the child's daily conversations and playtime, analyzing their interests and concerns. Based on this, it provides recommended learning materials, activities, and optimal learning methods. For example, if a child is interested in dinosaurs, it suggests picture books, videos, and activities related to dinosaurs. Furthermore, the child growth recording system incorporates an AI-powered monitoring system to ensure the child's safety by notifying parents if there are unusual movements or sounds, or if the child returns home later than the designated time. In addition, even when the child is alone, the AI ​​can talk to them or play with them as needed to alleviate boredom. For example, when a child is alone, the AI ​​might say, "Let's draw together," and suggest an activity for the child to draw. This system allows parents to record their children's growth, discover their interests, provide appropriate education, and ensure their safety, even amidst busy daily routines.

[0029] The child growth recording system according to this embodiment comprises a recording unit, an analysis unit, a provision unit, a notification unit, and a dialogue unit. The recording unit records the child's daily life. The recording unit records the child's daily life in detail using, for example, a camera, sensors, and voice recognition. The recording unit records the child's movements using, for example, a fixed camera. The recording unit can also record the child's biometric data using wearable sensors. Furthermore, the recording unit can also record the child's conversations using voice recognition technology. For example, the recording unit records the child's playtime using a fixed camera. Wearable sensors record the child's heart rate and activity level. Voice recognition technology records the child's speech as text data. The analysis unit analyzes the data recorded by the recording unit to identify the child's interests. The analysis unit analyzes the data using, for example, machine learning algorithms. The analysis unit analyzes behavioral patterns to identify the child's interests. The analysis unit can also analyze speech content to identify the child's interests. Furthermore, the analysis unit can analyze the selected play and learning materials to identify the child's interests. For example, the analysis unit analyzes behavioral patterns to identify that the child is interested in dinosaurs. It analyzes the child's speech to identify that the child is interested in space. It analyzes the games and learning materials the child chooses to identify that the child is interested in music. The provision unit provides learning materials and activities based on the interests identified by the analysis unit. The provision unit provides, for example, online learning materials. The provision unit can also provide, for example, field activities. The provision unit can also provide games. For example, the provision unit provides online learning materials about dinosaurs. As a field activity, it suggests a visit to a dinosaur museum. As a game, it provides a dinosaur quiz. The notification unit detects unusual movements or sounds and notifies parents. The notification unit uses, for example, motion sensors to detect unusual movements. The notification unit uses, for example, speech recognition technology to detect unusual sounds. The notification unit can also notify if the child returns home later than a set time. For example, the notification unit uses motion sensors to detect unusual movements. It uses speech recognition technology to detect unusual sounds. It notifies parents if the child returns home later than a set time.The dialogue unit interacts with the child and suggests activities to alleviate boredom. The dialogue unit can, for example, engage in voice dialogue. The dialogue unit can also, for example, engage in text dialogue. Furthermore, the dialogue unit can suggest topics for dialogue. For example, the dialogue unit might use voice dialogue to say, "Let's draw a picture together," or use text dialogue to ask, "What do you want to do today?" It might suggest drawing as a topic for dialogue. In this way, the child growth record system according to this embodiment can record the child's daily life, identify their interests, provide appropriate learning materials and activities, detect abnormalities and notify parents, and engage in dialogue with the child.

[0030] The recording unit records the child's daily life. For example, it uses cameras, sensors, and voice recognition to meticulously record the child's daily activities. Specifically, when recording the child's movements using fixed cameras, the cameras are placed in corners or on the ceiling to cover a wide area. This allows for continuous recording of the child playing and learning. When recording the child's biometric data using wearable sensors, the sensors are worn on the child's body as wristwatch or band-type devices. This allows for real-time collection of data such as heart rate, activity level, and body temperature. Furthermore, when recording the child's conversations using voice recognition technology, microphones are placed in multiple locations in the room to capture the child's speech with high accuracy. The audio data is converted into text data for later analysis. For example, the recording unit records the child's playtime using fixed cameras, records the child's heart rate and activity level using wearable sensors, and records the child's speech as text data using voice recognition technology. This allows the recording unit to collect multifaceted data and gain a detailed understanding of the child's daily life. Additionally, the recording unit stores this data on a cloud server, making it accessible to parents and educators. The data is encrypted, ensuring privacy. Furthermore, the recording unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This enables the recording unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes data recorded by the recording department to identify children's interests. For example, the analysis department uses machine learning algorithms to analyze the data. Specifically, it builds behavioral prediction models based on past data to analyze behavioral patterns and identify children's interests. This model predicts what kinds of games and activities children prefer and identifies areas of interest. It also analyzes text data using natural language processing techniques to analyze spoken content and identify children's interests. For example, it extracts keywords that children frequently talk about and identifies interests related to those keywords. Furthermore, it analyzes selection history to analyze the games and learning materials children choose and identify their interests. For example, it analyzes what kinds of games and learning materials children choose and identifies areas of interest from their selection patterns. For example, the analysis department might analyze behavioral patterns to identify a child's interest in dinosaurs, analyze spoken content to identify a child's interest in space, and analyze chosen games and learning materials to identify a child's interest in music. This allows the analysis department to gain a multifaceted understanding of children's interests and provide foundational information for offering appropriate learning materials and activities. Furthermore, the analysis department can continuously update this data and respond to changes in children's interests. This allows the analysis department to always perform highly accurate analyses based on the latest information, supporting children's development.

[0032] The provision department provides learning materials and activities based on the interests identified by the analysis department. Specifically, when providing online materials, the provision department selects customized materials tailored to the child's interests and provides an interactive learning experience. For example, for a child interested in dinosaurs, online materials that teach about the history and types of dinosaurs would be provided. When providing field activities, the provision department would suggest appropriate field trips and workshops based on the child's interests. For example, a visit to a dinosaur museum would be suggested, providing an experience of actually viewing dinosaur fossils. Furthermore, when providing games, the provision department would select educational games based on the child's interests and provide an environment where learning is fun. For example, a dinosaur quiz game would be provided, allowing children to deepen their knowledge while having fun. In this way, the provision department can provide a variety of learning materials and activities that match the child's interests and enhance their motivation to learn. In addition, the provision department can monitor the effectiveness of these materials and activities and update the content as needed. For example, it can track the child's learning progress and suggest new materials or activities if their understanding in a particular area improves. The provision department can also collaborate with parents and educators to share the child's learning status, thereby strengthening support at home and school. This allows the service provider to comprehensively support children's growth and maximize learning effectiveness.

[0033] The notification unit detects unusual movements and sounds and notifies parents. Specifically, when motion sensors are used to detect unusual movements, sensors are installed in multiple locations in the room to continuously monitor the child's movements. If unusual movements are detected, the notification unit immediately sends an alert to the parents. When voice recognition technology is used to detect unusual sounds, microphones are installed in multiple locations in the room to capture unusual sounds with high accuracy. For example, if a child cries or something falls, the notification unit immediately notifies the parents. Furthermore, it has a function to notify if the child returns home later than a set time, and monitors the child's return time by tracking their location in real time. For example, if a child is late returning home from school, the notification unit sends an alert to the parents, urging them to check on the situation. In this way, the notification unit can ensure the child's safety and provide peace of mind to parents. In addition, the notification unit can customize the content of notifications, providing information tailored to the parents' needs. For example, it is possible to set notifications to be received only during specific times or situations, reducing unnecessary notifications. The notification unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the notification system to provide information to parents quickly and reliably, thus protecting the safety of children.

[0034] The dialogue unit interacts with children and suggests activities to alleviate boredom. Specifically, in voice interactions, the dialogue unit uses natural language processing technology to understand the child's statements and generate appropriate responses. For example, the dialogue unit suggests an activity by saying, "Let's draw a picture together." In text interactions, the dialogue unit interacts with children in the form of a chatbot and suggests activities that interest them. For example, it might ask, "What do you want to do today?" and suggest an appropriate activity based on the child's answer. Furthermore, the dialogue unit can also suggest topics for conversation, providing topics that match the child's interests. For example, if a child is interested in dinosaurs, it might suggest, "Let's research dinosaurs together." In this way, the dialogue unit can engage in conversations that match the child's interests and alleviate boredom. In addition, the dialogue unit can monitor the child's responses and continuously improve the content of the conversation. For example, if a child shows no interest in a particular activity, it will suggest an alternative activity. The dialogue unit can also collaborate with parents and educators, sharing the child's conversation content to strengthen support at home and school. In this way, the dialogue unit can comprehensively support the child's growth and maximize learning effectiveness.

[0035] The recording unit can record a child's daily life using cameras, sensors, and voice recognition. For example, the recording unit can record a child's movements using a camera. The recording unit can also record a child's biometric data using sensors. Furthermore, the recording unit can record a child's conversations using voice recognition technology. For example, the recording unit can record a child's playtime using a camera. Sensors can record a child's heart rate and activity level. Voice recognition technology records what the child says as text data. In this way, by using cameras, sensors, and voice recognition, a child's daily life can be recorded in detail. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input video data acquired by a camera into a generating AI and have the generating AI perform the conversion from video data to text data.

[0036] The analysis unit can analyze the data recorded by the recording unit to identify the child's interests. The analysis unit can analyze the data using, for example, machine learning algorithms. The analysis unit can analyze behavioral patterns to identify the child's interests. The analysis unit can also analyze the content of speech to identify the child's interests. Furthermore, the analysis unit can analyze the games and learning materials the child chooses to identify the child's interests. For example, the analysis unit can analyze behavioral patterns to identify that the child is interested in dinosaurs. It can analyze the content of speech to identify that the child is interested in space. It can analyze the games and learning materials the child chooses to identify that the child is interested in music. In this way, the child's interests can be identified by analyzing the recorded data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data recorded by the recording unit into a generating AI and have the generating AI perform the identification of interests.

[0037] The provisioning unit can provide learning materials and activities based on the interests identified by the analysis unit. For example, the provisioning unit can provide online learning materials. The provisioning unit can also provide field activities. Furthermore, the provisioning unit can provide games. For example, the provisioning unit can provide online learning materials about dinosaurs. As a field activity, it can suggest a visit to a dinosaur museum. As a game, it can provide a quiz about dinosaurs. This allows for the provision of appropriate learning materials and activities based on identified interests. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the interests identified by the analysis unit into a generating AI and have the generating AI perform the provision of learning materials and activities.

[0038] The notification unit can detect abnormal movements or sounds and notify parents. For example, the notification unit uses a motion sensor to detect abnormal movements. For example, the notification unit uses speech recognition technology to detect abnormal sounds. The notification unit can also notify if a child returns home later than a set time. For example, the notification unit uses a motion sensor to detect abnormal movements. It uses speech recognition technology to detect abnormal sounds. It notifies parents if a child returns home later than a set time. This ensures the safety of children by detecting abnormalities and notifying parents. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on abnormal movements or sounds into a generating AI and have the generating AI perform abnormality detection and notification.

[0039] The dialogue unit can interact with children and suggest activities to alleviate boredom. The dialogue unit can, for example, engage in voice dialogue. The dialogue unit can also, for example, engage in text dialogue. Furthermore, the dialogue unit can suggest topics for dialogue. For example, the dialogue unit might use voice dialogue to say, "Let's draw together." Using text dialogue, it might ask, "What do you want to do today?" It could then suggest drawing as a topic for dialogue. This allows the dialogue unit to engage with children and suggest activities to alleviate boredom, thereby keeping the children interested. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the child's dialogue data into a generating AI and have the generating AI suggest dialogue content and topics.

[0040] The recording unit can automatically select different recording modes depending on the type of child's activity during recording. For example, if the child is playing, the recording unit will prioritize recording scenes with a lot of movement. If the child is studying, the recording unit will prioritize recording quiet scenes. The recording unit can also record details of the child eating. For example, if the child is playing, the recording unit will prioritize recording scenes with a lot of movement. If the child is studying, it will prioritize recording quiet scenes. If the child is eating, it will record details of the child eating. This allows for detailed recording by selecting the appropriate recording mode according to the type of child's activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the child's activity data into a generating AI and have the generating AI select the recording mode.

[0041] The recording unit can analyze the recorded data in real time and automatically tag important moments as highlights. For example, the recording unit can tag the moment a child speaks their first word as a highlight. For example, the recording unit can tag the moment a child starts a new game as a highlight. The recording unit can also tag the moment a child achieves a specific goal as a highlight. For example, the recording unit can tag the moment a child speaks their first word as a highlight. For example, the recording unit can tag the moment a child starts a new game as a highlight. For example, the recording unit can tag the moment a child achieves a specific goal as a highlight. By tagging important moments as highlights, they can be easily reviewed later. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the recorded data into a generating AI and have the generating AI perform the tagging of important moments.

[0042] The recording unit can prioritize recording activities at specific locations, taking into account the child's location information. For example, if the child is playing in a park, the recording unit will prioritize recording that activity. For example, if the child is studying at school, the recording unit will prioritize recording that activity. The recording unit can also prioritize recording activities when the child is at home. For example, if the child is playing in a park, the recording unit will prioritize recording that activity. If the child is studying at school, the recording unit will prioritize recording that activity. If the child is at home, the recording unit will prioritize recording that activity. This allows for detailed recording of important activities by prioritizing the recording of activities at specific locations. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the child's location information into a generating AI and have the generating AI record activities at specific locations.

[0043] The recording unit can automatically upload recorded data to the cloud, allowing parents to access it from anywhere. For example, the recording unit can upload recorded data to the cloud in real time, allowing parents to access it from their smartphones. Alternatively, the recording unit can periodically back up recorded data to the cloud, allowing parents to access it from their computers. Furthermore, the recording unit can encrypt recorded data and store it in the cloud, ensuring secure access for parents. For example, the recording unit can upload recorded data to the cloud in real time, allowing parents to access it from their smartphones. It can periodically back up recorded data to the cloud, allowing parents to access it from their computers. It can also encrypt recorded data and store it in the cloud, ensuring secure access for parents. This allows parents to access recorded data from anywhere by uploading it to the cloud. Some or all of the above-described processes in the recording unit may be performed using AI, or not. For example, the recording unit can input recorded data into a generating AI and have the generating AI perform the upload to the cloud.

[0044] The analysis unit can identify new changes in interests by comparing current data with past data during the analysis. For example, the analysis unit can identify themes that children have recently become interested in by comparing them with past data. For example, the analysis unit can identify themes that children have lost interest in by comparing them with past data. The analysis unit can also analyze changes in children's interests over time by comparing them with past data. For example, the analysis unit can identify themes that children have recently become interested in by comparing them with past data. For example, the analysis unit can identify themes that children have lost interest in by comparing them with past data. For example, the analysis unit can input past data into a generating AI and have the generating AI identify changes in new interests.

[0045] The analysis department can visualize the analysis results so that parents can understand them intuitively. For example, the analysis department can visualize the analysis results in graphs and charts so that parents can understand them intuitively. For example, the analysis department can visualize the analysis results in infographics so that parents can easily understand them. The analysis department can also visualize the analysis results in a dashboard format so that parents can check them in real time. For example, the analysis department can visualize the analysis results in graphs and charts so that parents can understand them intuitively. For example, the analysis department can visualize the analysis results in infographics so that parents can easily understand them. For example, the analysis department can visualize the analysis results in a dashboard format so that parents can check them in real time. This allows parents to understand the analysis results intuitively by visualizing them. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the analysis results into a generating AI and have the generating AI perform the visualization.

[0046] The analysis unit can identify interests by considering children's friendships and social interactions during analysis. For example, the analysis unit can analyze children's friendships to identify common interests. For example, the analysis unit can analyze children's social interactions to identify new interests. The analysis unit can also analyze children's friendships and social interactions over time to identify changes in interests. For example, the analysis unit can analyze children's friendships to identify common interests. For example, it can analyze children's social interactions to identify new interests. For example, it can analyze children's friendships to identify common interests. For example, it can analyze children's social interactions to identify new interests. For example, it can analyze children's friendships and social interactions over time to identify changes in interests. This makes it possible to identify interests more accurately by considering friendships and social interactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on children's friendships and social interactions into a generating AI and have the generating AI perform the identification of interests.

[0047] The analysis department can provide parents with analysis results in the form of regular reports. For example, the analysis department can provide parents with analysis results as a weekly report. For example, the analysis department can provide parents with analysis results as a monthly report. The analysis department can also provide parents with customized reports based on the analysis results. For example, the analysis department can provide parents with analysis results as a weekly report. For example, the analysis department can provide parents with analysis results as a monthly report. For example, the analysis department can provide parents with customized reports based on the analysis results. This allows parents to continuously monitor their child's development by providing reports regularly. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input analysis results into a generating AI and have the generating AI create and provide the reports.

[0048] The provider can select different types of learning materials at the time of delivery, depending on the child's learning style. For example, if the child has a visual learning style, the provider will provide visual materials. For example, if the child has an auditory learning style, the provider will provide audio materials. The provider can also provide practical activities if the child has an experiential learning style. For example, if the provider has a visual learning style, the provider will provide visual materials. If the child has an auditory learning style, the provider will provide audio materials. If the child has an experiential learning style, the provider will provide practical activities. By selecting materials that match the learning style, the effectiveness of the child's learning can be enhanced. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the child's learning style data into a generating AI and have the generating AI select the learning materials.

[0049] The service provider can evaluate the effectiveness of the materials and activities they provide and reflect the results in future offerings. For example, the service provider can evaluate the effectiveness of the materials and activities they have provided and improve the content of future offerings. For example, the service provider can collect feedback on the materials and activities they have provided and reflect the results in future offerings. The service provider can also quantitatively evaluate the effectiveness of the materials and activities they have provided and optimize the content of future offerings. For example, the service provider can evaluate the effectiveness of the materials and activities they have provided and improve the content of future offerings. They can collect feedback on the materials and activities they have provided and reflect the results in future offerings. They can quantitatively evaluate the effectiveness of the materials and activities they have provided and optimize the content of future offerings. This allows them to improve the content of future offerings by evaluating the effectiveness of the materials and activities they have provided. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the effectiveness data of the materials and activities they have provided into a generating AI and have the generating AI perform the evaluation and improvement of the content of future offerings.

[0050] The service provider can select the most suitable learning materials at the time of delivery, taking into account the child's past learning history. For example, the service provider can analyze the child's past learning history and select the most suitable materials. For example, the service provider can select materials that match the child's interests, taking into account the child's past learning history. The service provider can also select materials that match the child's learning progress, based on the child's past learning history. For example, the service provider can analyze the child's past learning history and select the most suitable materials. For example, the service provider can select materials that match the child's interests, taking into account the child's past learning history. For example, the service provider can select materials that match the child's learning progress, based on the child's past learning history. In this way, by taking into account the child's past learning history, the service provider can provide the child with the most suitable learning materials. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's past learning history data into a generating AI and have the generating AI select the most suitable learning materials.

[0051] The service provider can customize the materials and activities it offers to meet the individual needs of each child. For example, the service provider can provide customized materials based on the child's interests. For example, the service provider can provide customized activities according to the child's learning style. The service provider can also provide customized materials and activities according to the child's learning progress. For example, the service provider can provide customized materials based on the child's interests. For example, the service provider can provide customized activities according to the child's learning style. For example, the service provider can provide customized materials and activities according to the child's learning progress. In this way, by customizing the materials and activities, the service provider can meet the individual needs of each child. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the child's individual needs into a generating AI and have the generating AI perform the customization of materials and activities.

[0052] The notification unit can select different notification methods depending on the type of anomaly when it issues a notification. For example, if abnormal movement is detected, the notification unit will issue an emergency notification. For example, if an abnormal sound is detected, the notification unit will issue an audio notification. The notification unit can also issue a text notification if the person returns home later than a set time. For example, if abnormal movement is detected, the notification unit will issue an emergency notification. If an abnormal sound is detected, it will issue an audio notification. If the person returns home later than a set time, it will issue a text notification. This allows for more effective notifications by selecting the appropriate notification method according to the type of anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly type data into a generating AI and have the generating AI select the notification method.

[0053] The notification unit can save notification history, allowing parents to review past notifications. For example, the notification unit can save notification history to the cloud, making it accessible to parents at any time. The notification unit can also periodically back up notification history to ensure data security. Furthermore, the notification unit can visualize notification history, allowing parents to intuitively review it. For example, the notification unit can save notification history to the cloud, making it accessible to parents at any time. It can periodically back up notification history to ensure data security. It can visualize notification history to allow parents to intuitively review it. This allows parents to review past notifications by saving notification history. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input notification history data into a generating AI and have the generating AI perform saving and visualization.

[0054] The notification unit can select a notification method considering the parent's current situation when sending a notification. For example, if the parent is at work, the notification unit will select a quiet notification method. For example, if the parent is driving, the notification unit will select an audio notification. The notification unit can also select a more detailed notification method if the parent is at home. For example, if the parent is at work, the notification unit will select a quiet notification method. If the parent is driving, it will select an audio notification. If the parent is at home, it will select a more detailed notification method. This allows for more appropriate notifications by selecting a notification method according to the parent's situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the parent's current situation into a generating AI and have the generating AI select the notification method.

[0055] The notification unit can make notification content multilingual and accommodate parents who speak different languages. For example, the notification unit can automatically translate notification content based on the parent's language settings. For example, the notification unit can provide notification content in multiple languages ​​and allow parents to choose. The notification unit can also make notification content concise and easy to understand across language barriers. For example, the notification unit can automatically translate notification content based on the parent's language settings. For example, it can provide notification content in multiple languages ​​and allow parents to choose. For example, it can make notification content concise and easy to understand across language barriers. This allows the system to accommodate parents who speak different languages ​​by providing multilingual notification content. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input notification content into a generation AI and have the generation AI perform multilingual translation.

[0056] The dialogue unit can use different dialogue scripts depending on the child's age and developmental stage during a conversation. For example, the dialogue unit will use simple words and short sentences when interacting with toddlers. For example, the dialogue unit will use slightly more complex words and sentences when interacting with elementary school children. Furthermore, the dialogue unit can also use more advanced words and sentences when interacting with middle school students and older. For example, the dialogue unit will use simple words and short sentences when interacting with toddlers. For elementary school children, it will use slightly more complex words and sentences. For middle school students and older, it will use more advanced words and sentences. This allows for conversations appropriate to the child by using dialogue scripts that match their age and developmental stage. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not. For example, the dialogue unit can input the child's age and developmental stage data into a generating AI and have the generating AI select a dialogue script.

[0057] The dialogue unit can save the dialogue history and reflect it in the next dialogue. For example, the dialogue unit can save the dialogue history to the cloud and reflect it in the next dialogue. For example, the dialogue unit can analyze the dialogue history and optimize the content of the next dialogue. The dialogue unit can also visualize the dialogue history so that parents can review it. For example, the dialogue unit can save the dialogue history to the cloud and reflect it in the next dialogue. It can analyze the dialogue history and optimize the content of the next dialogue. It can visualize the dialogue history so that parents can review it. This allows the dialogue history to be saved and reflected in the next dialogue. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input dialogue history data into a generating AI and have the generating AI perform saving and reflecting it in the next dialogue.

[0058] The dialogue unit can suggest relevant activities based on the child's interests during the conversation. For example, if the child is interested in dinosaurs, the dialogue unit will suggest activities related to dinosaurs. If the child is interested in space, the dialogue unit will suggest activities related to space. The dialogue unit can also suggest activities related to music if the child is interested in music. For example, if the dialogue unit is interested in dinosaurs, it will suggest activities related to dinosaurs. If the child is interested in space, it will suggest activities related to space. If the child is interested in music, it will suggest activities related to music. This allows the dialogue unit to keep the child interested by suggesting activities based on their interests. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's interest data into a generating AI and have the generating AI suggest activities.

[0059] The dialogue unit can customize the dialogue content to meet the individual needs of children. For example, the dialogue unit can conduct customized dialogue based on the child's interests. For example, the dialogue unit can conduct customized dialogue according to the child's learning style. Furthermore, the dialogue unit can conduct customized dialogue according to the child's developmental stage. For example, the dialogue unit can conduct customized dialogue based on the child's interests. For example, the dialogue unit can conduct customized dialogue according to the child's learning style. For example, the dialogue unit can conduct customized dialogue according to the child's developmental stage. In this way, by customizing the dialogue content, the individual needs of children can be met. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the child's individual needs into a generating AI and have the generating AI perform the customization of the dialogue content.

[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 child growth record system can also include a health management section. This section monitors the child's health and notifies parents if any abnormalities are detected. For example, it can regularly measure the child's body temperature and heart rate and issue an alert if abnormal values ​​are detected. It can also record the child's diet and exercise and provide advice to support healthy lifestyle habits. Furthermore, it can monitor the child's sleep patterns and suggest ways to ensure adequate sleep. This allows for comprehensive management of the child's health, giving parents peace of mind as they watch their child grow.

[0062] The child growth record system can also include a schedule management unit. This unit manages the child's learning and play schedules, supporting efficient time allocation. For example, it can register the child's school timetable and extracurricular activity schedules and send reminders at appropriate times. It can also suggest activities to help children make the most of their free time. Furthermore, it can link with the parent's schedule to coordinate the entire family's plans. This supports the child's time management and helps maintain an efficient balance between learning and play.

[0063] The child growth record system can also include a communication section. This section supports communication between children and parents, strengthening family bonds. For example, the communication section allows parents to report on their child's activities and what they learned during the day. It also allows parents to send messages to their children, who can then respond. Furthermore, the communication section can create shared diaries and albums for the whole family to record memories. This promotes family communication and allows families to share in the child's growth.

[0064] The child growth record system can also include a learning progress management unit. This unit monitors the child's learning progress and provides appropriate feedback. For example, it records the results of assignments and tests the child is working on, visualizing their progress. It can also analyze the child's strengths and weaknesses and propose individually customized learning plans. Furthermore, it can report the child's learning status to parents and advise on how to support them at home. This effectively supports the child's learning and maximizes learning outcomes.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The recording unit records the child's daily life. The recording unit uses cameras, sensors, and voice recognition to record the child's daily life in detail. For example, it uses a fixed camera to record the child's movements and wearable sensors to record the child's biometric data. Furthermore, it uses voice recognition technology to record the child's conversations. Step 2: The analysis unit analyzes the data recorded by the recording unit to identify the child's interests. The analysis unit uses machine learning algorithms to analyze the data, identifying the child's interests by analyzing behavioral patterns, statements, and the types of games and learning materials they choose. Step 3: The provision department provides learning materials and activities based on the interests identified by the analysis department. The provision department provides online materials, field activities, games, etc., such as online materials about dinosaurs, museum visits, and quizzes. Step 4: The notification unit detects abnormal movements or sounds and notifies the parent or guardian. The notification unit uses motion sensors and voice recognition technology to detect abnormalities and also notifies if the child returns home later than the designated time. Step 5: The dialogue team engages with the child and suggests activities to alleviate boredom. The dialogue team uses voice and text dialogue to suggest topics for conversation. For example, they might say, "Let's draw a picture together," or ask, "What do you want to do today?"

[0067] (Example of form 2) The child growth recording system according to an embodiment of the present invention is a system designed to record children's growth amidst busy daily lives, identify their interests, provide appropriate education, and ensure their safety. The child growth recording system incorporates an AI device with cameras, sensors, voice recognition, etc., to automatically record new discoveries and growth in the child's daily life. This record is digitized and made available to parents via an app or website. For example, it automatically records moments such as the child's first steps or first words, allowing parents to review them later. Next, the child growth recording system uses AI to observe the child's daily conversations and playtime, analyzing their interests and concerns. Based on this, it provides recommended learning materials, activities, and optimal learning methods. For example, if a child is interested in dinosaurs, it suggests picture books, videos, and activities related to dinosaurs. Furthermore, the child growth recording system incorporates an AI-powered monitoring system to ensure the child's safety by notifying parents if there are unusual movements or sounds, or if the child returns home later than the designated time. In addition, even when the child is alone, the AI ​​can talk to them or play with them as needed to alleviate boredom. For example, when a child is alone, the AI ​​might say, "Let's draw together," and suggest an activity for the child to draw. This system allows parents to record their children's growth, discover their interests, provide appropriate education, and ensure their safety, even amidst busy daily routines.

[0068] The child growth recording system according to this embodiment comprises a recording unit, an analysis unit, a provision unit, a notification unit, and a dialogue unit. The recording unit records the child's daily life. The recording unit records the child's daily life in detail using, for example, a camera, sensors, and voice recognition. The recording unit records the child's movements using, for example, a fixed camera. The recording unit can also record the child's biometric data using wearable sensors. Furthermore, the recording unit can also record the child's conversations using voice recognition technology. For example, the recording unit records the child's playtime using a fixed camera. Wearable sensors record the child's heart rate and activity level. Voice recognition technology records the child's speech as text data. The analysis unit analyzes the data recorded by the recording unit to identify the child's interests. The analysis unit analyzes the data using, for example, machine learning algorithms. The analysis unit analyzes behavioral patterns to identify the child's interests. The analysis unit can also analyze speech content to identify the child's interests. Furthermore, the analysis unit can analyze the selected play and learning materials to identify the child's interests. For example, the analysis unit analyzes behavioral patterns to identify that the child is interested in dinosaurs. It analyzes the child's speech to identify that the child is interested in space. It analyzes the games and learning materials the child chooses to identify that the child is interested in music. The provision unit provides learning materials and activities based on the interests identified by the analysis unit. The provision unit provides, for example, online learning materials. The provision unit can also provide, for example, field activities. The provision unit can also provide games. For example, the provision unit provides online learning materials about dinosaurs. As a field activity, it suggests a visit to a dinosaur museum. As a game, it provides a dinosaur quiz. The notification unit detects unusual movements or sounds and notifies parents. The notification unit uses, for example, motion sensors to detect unusual movements. The notification unit uses, for example, speech recognition technology to detect unusual sounds. The notification unit can also notify if the child returns home later than a set time. For example, the notification unit uses motion sensors to detect unusual movements. It uses speech recognition technology to detect unusual sounds. It notifies parents if the child returns home later than a set time.The dialogue unit interacts with the child and suggests activities to alleviate boredom. The dialogue unit can, for example, engage in voice dialogue. The dialogue unit can also, for example, engage in text dialogue. Furthermore, the dialogue unit can suggest topics for dialogue. For example, the dialogue unit might use voice dialogue to say, "Let's draw a picture together," or use text dialogue to ask, "What do you want to do today?" It might suggest drawing as a topic for dialogue. In this way, the child growth record system according to this embodiment can record the child's daily life, identify their interests, provide appropriate learning materials and activities, detect abnormalities and notify parents, and engage in dialogue with the child.

[0069] The recording unit records the child's daily life. For example, it uses cameras, sensors, and voice recognition to meticulously record the child's daily activities. Specifically, when recording the child's movements using fixed cameras, the cameras are placed in corners or on the ceiling to cover a wide area. This allows for continuous recording of the child playing and learning. When recording the child's biometric data using wearable sensors, the sensors are worn on the child's body as wristwatch or band-type devices. This allows for real-time collection of data such as heart rate, activity level, and body temperature. Furthermore, when recording the child's conversations using voice recognition technology, microphones are placed in multiple locations in the room to capture the child's speech with high accuracy. The audio data is converted into text data for later analysis. For example, the recording unit records the child's playtime using fixed cameras, records the child's heart rate and activity level using wearable sensors, and records the child's speech as text data using voice recognition technology. This allows the recording unit to collect multifaceted data and gain a detailed understanding of the child's daily life. Additionally, the recording unit stores this data on a cloud server, making it accessible to parents and educators. The data is encrypted, ensuring privacy. Furthermore, the recording unit can adjust the frequency and accuracy of data collection, allowing for flexible responses to specific situations and conditions. This enables the recording unit to collect data efficiently and effectively, improving the overall system performance.

[0070] The analysis department analyzes data recorded by the recording department to identify children's interests. For example, the analysis department uses machine learning algorithms to analyze the data. Specifically, it builds behavioral prediction models based on past data to analyze behavioral patterns and identify children's interests. This model predicts what kinds of games and activities children prefer and identifies areas of interest. It also analyzes text data using natural language processing techniques to analyze spoken content and identify children's interests. For example, it extracts keywords that children frequently talk about and identifies interests related to those keywords. Furthermore, it analyzes selection history to analyze the games and learning materials children choose and identify their interests. For example, it analyzes what kinds of games and learning materials children choose and identifies areas of interest from their selection patterns. For example, the analysis department might analyze behavioral patterns to identify a child's interest in dinosaurs, analyze spoken content to identify a child's interest in space, and analyze chosen games and learning materials to identify a child's interest in music. This allows the analysis department to gain a multifaceted understanding of children's interests and provide foundational information for offering appropriate learning materials and activities. Furthermore, the analysis department can continuously update this data and respond to changes in children's interests. This allows the analysis department to always perform highly accurate analyses based on the latest information, supporting children's development.

[0071] The provision department provides learning materials and activities based on the interests identified by the analysis department. Specifically, when providing online materials, the provision department selects customized materials tailored to the child's interests and provides an interactive learning experience. For example, for a child interested in dinosaurs, online materials that teach about the history and types of dinosaurs would be provided. When providing field activities, the provision department would suggest appropriate field trips and workshops based on the child's interests. For example, a visit to a dinosaur museum would be suggested, providing an experience of actually viewing dinosaur fossils. Furthermore, when providing games, the provision department would select educational games based on the child's interests and provide an environment where learning is fun. For example, a dinosaur quiz game would be provided, allowing children to deepen their knowledge while having fun. In this way, the provision department can provide a variety of learning materials and activities that match the child's interests and enhance their motivation to learn. In addition, the provision department can monitor the effectiveness of these materials and activities and update the content as needed. For example, it can track the child's learning progress and suggest new materials or activities if their understanding in a particular area improves. The provision department can also collaborate with parents and educators to share the child's learning status, thereby strengthening support at home and school. This allows the service provider to comprehensively support children's growth and maximize learning effectiveness.

[0072] The notification unit detects unusual movements and sounds and notifies parents. Specifically, when motion sensors are used to detect unusual movements, sensors are installed in multiple locations in the room to continuously monitor the child's movements. If unusual movements are detected, the notification unit immediately sends an alert to the parents. When voice recognition technology is used to detect unusual sounds, microphones are installed in multiple locations in the room to capture unusual sounds with high accuracy. For example, if a child cries or something falls, the notification unit immediately notifies the parents. Furthermore, it has a function to notify if the child returns home later than a set time, and monitors the child's return time by tracking their location in real time. For example, if a child is late returning home from school, the notification unit sends an alert to the parents, urging them to check on the situation. In this way, the notification unit can ensure the child's safety and provide peace of mind to parents. In addition, the notification unit can customize the content of notifications, providing information tailored to the parents' needs. For example, it is possible to set notifications to be received only during specific times or situations, reducing unnecessary notifications. The notification unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the notification system to provide information to parents quickly and reliably, thus protecting the safety of children.

[0073] The dialogue unit interacts with children and suggests activities to alleviate boredom. Specifically, in voice interactions, the dialogue unit uses natural language processing technology to understand the child's statements and generate appropriate responses. For example, the dialogue unit suggests an activity by saying, "Let's draw a picture together." In text interactions, the dialogue unit interacts with children in the form of a chatbot and suggests activities that interest them. For example, it might ask, "What do you want to do today?" and suggest an appropriate activity based on the child's answer. Furthermore, the dialogue unit can also suggest topics for conversation, providing topics that match the child's interests. For example, if a child is interested in dinosaurs, it might suggest, "Let's research dinosaurs together." In this way, the dialogue unit can engage in conversations that match the child's interests and alleviate boredom. In addition, the dialogue unit can monitor the child's responses and continuously improve the content of the conversation. For example, if a child shows no interest in a particular activity, it will suggest an alternative activity. The dialogue unit can also collaborate with parents and educators, sharing the child's conversation content to strengthen support at home and school. In this way, the dialogue unit can comprehensively support the child's growth and maximize learning effectiveness.

[0074] The recording unit can record a child's daily life using cameras, sensors, and voice recognition. For example, the recording unit can record a child's movements using a camera. The recording unit can also record a child's biometric data using sensors. Furthermore, the recording unit can record a child's conversations using voice recognition technology. For example, the recording unit can record a child's playtime using a camera. Sensors can record a child's heart rate and activity level. Voice recognition technology records what the child says as text data. In this way, by using cameras, sensors, and voice recognition, a child's daily life can be recorded in detail. Some or all of the above-described processes in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input video data acquired by a camera into a generating AI and have the generating AI perform the conversion from video data to text data.

[0075] The analysis unit can analyze the data recorded by the recording unit to identify the child's interests. The analysis unit can analyze the data using, for example, machine learning algorithms. The analysis unit can analyze behavioral patterns to identify the child's interests. The analysis unit can also analyze the content of speech to identify the child's interests. Furthermore, the analysis unit can analyze the games and learning materials the child chooses to identify the child's interests. For example, the analysis unit can analyze behavioral patterns to identify that the child is interested in dinosaurs. It can analyze the content of speech to identify that the child is interested in space. It can analyze the games and learning materials the child chooses to identify that the child is interested in music. In this way, the child's interests can be identified by analyzing the recorded data. Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input the data recorded by the recording unit into a generating AI and have the generating AI perform the identification of interests.

[0076] The provisioning unit can provide learning materials and activities based on the interests identified by the analysis unit. For example, the provisioning unit can provide online learning materials. The provisioning unit can also provide field activities. Furthermore, the provisioning unit can provide games. For example, the provisioning unit can provide online learning materials about dinosaurs. As a field activity, it can suggest a visit to a dinosaur museum. As a game, it can provide a quiz about dinosaurs. This allows for the provision of appropriate learning materials and activities based on identified interests. Some or all of the above processing in the provisioning unit may be performed using AI, for example, or without AI. For example, the provisioning unit can input the interests identified by the analysis unit into a generating AI and have the generating AI perform the provision of learning materials and activities.

[0077] The notification unit can detect abnormal movements or sounds and notify parents. For example, the notification unit uses a motion sensor to detect abnormal movements. For example, the notification unit uses speech recognition technology to detect abnormal sounds. The notification unit can also notify if a child returns home later than a set time. For example, the notification unit uses a motion sensor to detect abnormal movements. It uses speech recognition technology to detect abnormal sounds. It notifies parents if a child returns home later than a set time. This ensures the safety of children by detecting abnormalities and notifying parents. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on abnormal movements or sounds into a generating AI and have the generating AI perform abnormality detection and notification.

[0078] The dialogue unit can interact with children and suggest activities to alleviate boredom. The dialogue unit can, for example, engage in voice dialogue. The dialogue unit can also, for example, engage in text dialogue. Furthermore, the dialogue unit can suggest topics for dialogue. For example, the dialogue unit might use voice dialogue to say, "Let's draw together." Using text dialogue, it might ask, "What do you want to do today?" It could then suggest drawing as a topic for dialogue. This allows the dialogue unit to engage with children and suggest activities to alleviate boredom, thereby keeping the children interested. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input the child's dialogue data into a generating AI and have the generating AI suggest dialogue content and topics.

[0079] The recording unit can estimate the child's emotions and adjust the frequency and timing of recording based on the estimated emotions. For example, if the child is excited, the recording unit will record frequently to ensure that important moments are not missed. If the child is relaxed, the recording unit will reduce the frequency of recording and prioritize natural behavior. The recording unit can also pause recording and prioritize rest if the child is tired. For example, if the child is excited, the recording unit will record frequently to ensure that important moments are not missed. If the child is relaxed, the recording unit will reduce the frequency of recording and prioritize natural behavior. If the child is tired, the recording unit will pause recording and prioritize rest. This allows for recording of important moments without missing them by adjusting the frequency and timing of recording based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the child's emotional data into a generating AI, which can then adjust the frequency and timing of the recordings.

[0080] The recording unit can automatically select different recording modes depending on the type of child's activity during recording. For example, if the child is playing, the recording unit will prioritize recording scenes with a lot of movement. If the child is studying, the recording unit will prioritize recording quiet scenes. The recording unit can also record details of the child eating. For example, if the child is playing, the recording unit will prioritize recording scenes with a lot of movement. If the child is studying, it will prioritize recording quiet scenes. If the child is eating, it will record details of the child eating. This allows for detailed recording by selecting the appropriate recording mode according to the type of child's activity. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the child's activity data into a generating AI and have the generating AI select the recording mode.

[0081] The recording unit can analyze the recorded data in real time and automatically tag important moments as highlights. For example, the recording unit can tag the moment a child speaks their first word as a highlight. For example, the recording unit can tag the moment a child starts a new game as a highlight. The recording unit can also tag the moment a child achieves a specific goal as a highlight. For example, the recording unit can tag the moment a child speaks their first word as a highlight. For example, the recording unit can tag the moment a child starts a new game as a highlight. For example, the recording unit can tag the moment a child achieves a specific goal as a highlight. By tagging important moments as highlights, they can be easily reviewed later. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the recorded data into a generating AI and have the generating AI perform the tagging of important moments.

[0082] The recording unit can estimate a child's emotions and determine the priority of data to record based on the estimated emotions. For example, if a child is excited, the recording unit prioritizes recording heightened emotions. If a child is relaxed, the recording unit prioritizes recording calm everyday moments. Furthermore, if a child is sad, the recording unit can prioritize recording events that caused that emotion. This allows for the priority of recording important data by determining the priority of data to record based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input children's emotional data into a generating AI and have the generating AI determine the priority of the data to be recorded.

[0083] The recording unit can prioritize recording activities at specific locations, taking into account the child's location information. For example, if the child is playing in a park, the recording unit will prioritize recording that activity. For example, if the child is studying at school, the recording unit will prioritize recording that activity. The recording unit can also prioritize recording activities when the child is at home. For example, if the child is playing in a park, the recording unit will prioritize recording that activity. If the child is studying at school, the recording unit will prioritize recording that activity. If the child is at home, the recording unit will prioritize recording that activity. This allows for detailed recording of important activities by prioritizing the recording of activities at specific locations. Some or all of the above processing in the recording unit may be performed using AI, for example, or without AI. For example, the recording unit can input the child's location information into a generating AI and have the generating AI record activities at specific locations.

[0084] The recording unit can automatically upload recorded data to the cloud, allowing parents to access it from anywhere. For example, the recording unit can upload recorded data to the cloud in real time, allowing parents to access it from their smartphones. Alternatively, the recording unit can periodically back up recorded data to the cloud, allowing parents to access it from their computers. Furthermore, the recording unit can encrypt recorded data and store it in the cloud, ensuring secure access for parents. For example, the recording unit can upload recorded data to the cloud in real time, allowing parents to access it from their smartphones. It can periodically back up recorded data to the cloud, allowing parents to access it from their computers. It can also encrypt recorded data and store it in the cloud, ensuring secure access for parents. This allows parents to access recorded data from anywhere by uploading it to the cloud. Some or all of the above-described processes in the recording unit may be performed using AI, or not. For example, the recording unit can input recorded data into a generating AI and have the generating AI perform the upload to the cloud.

[0085] The analysis unit can estimate a child's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if a child is excited, the analysis unit will focus on heightened emotions. If a child is relaxed, the analysis unit will focus on calm everyday moments. The analysis unit can also perform an analysis to identify the cause of sadness if the child is sad. For example, if a child is excited, the analysis unit will focus on heightened emotions. If a child is relaxed, the analysis unit will focus on calm everyday moments. If a child is sad, the analysis unit will identify the cause of sadness. By adjusting the analysis algorithm based on the child's emotions, a more accurate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input children's emotional data into a generating AI and have the generating AI adjust the analysis algorithm.

[0086] The analysis unit can identify new changes in interests by comparing current data with past data during the analysis. For example, the analysis unit can identify themes that children have recently become interested in by comparing them with past data. For example, the analysis unit can identify themes that children have lost interest in by comparing them with past data. The analysis unit can also analyze changes in children's interests over time by comparing them with past data. For example, the analysis unit can identify themes that children have recently become interested in by comparing them with past data. For example, the analysis unit can identify themes that children have lost interest in by comparing them with past data. For example, the analysis unit can input past data into a generating AI and have the generating AI identify changes in new interests.

[0087] The analysis department can visualize the analysis results so that parents can understand them intuitively. For example, the analysis department can visualize the analysis results in graphs and charts so that parents can understand them intuitively. For example, the analysis department can visualize the analysis results in infographics so that parents can easily understand them. The analysis department can also visualize the analysis results in a dashboard format so that parents can check them in real time. For example, the analysis department can visualize the analysis results in graphs and charts so that parents can understand them intuitively. For example, the analysis department can visualize the analysis results in infographics so that parents can easily understand them. For example, the analysis department can visualize the analysis results in a dashboard format so that parents can check them in real time. This allows parents to understand the analysis results intuitively by visualizing them. Some or all of the above processing in the analysis department may be performed using AI, for example, or not using AI. For example, the analysis department can input the analysis results into a generating AI and have the generating AI perform the visualization.

[0088] The analysis unit can estimate a child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if a child is excited, the analysis unit provides a display method that emphasizes the heightened emotion. For example, if a child is relaxed, the analysis unit provides a calm display method. The analysis unit can also provide a display method to identify the cause of a child's sadness. For example, if a child is excited, the analysis unit provides a display method that emphasizes the heightened emotion. If a child is relaxed, it provides a calm display method. If a child is sad, it provides a display method to identify the cause of that emotion. By adjusting the display method based on the child's emotions, it becomes possible to provide a display that is easy for parents to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing 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 adjustment of the display method.

[0089] The analysis unit can identify interests by considering children's friendships and social interactions during analysis. For example, the analysis unit can analyze children's friendships to identify common interests. For example, the analysis unit can analyze children's social interactions to identify new interests. The analysis unit can also analyze children's friendships and social interactions over time to identify changes in interests. For example, the analysis unit can analyze children's friendships to identify common interests. For example, it can analyze children's social interactions to identify new interests. For example, it can analyze children's friendships to identify common interests. For example, it can analyze children's social interactions to identify new interests. For example, it can analyze children's friendships and social interactions over time to identify changes in interests. This makes it possible to identify interests more accurately by considering friendships and social interactions. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on children's friendships and social interactions into a generating AI and have the generating AI perform the identification of interests.

[0090] The analysis department can provide parents with analysis results in the form of regular reports. For example, the analysis department can provide parents with analysis results as a weekly report. For example, the analysis department can provide parents with analysis results as a monthly report. The analysis department can also provide parents with customized reports based on the analysis results. For example, the analysis department can provide parents with analysis results as a weekly report. For example, the analysis department can provide parents with analysis results as a monthly report. For example, the analysis department can provide parents with customized reports based on the analysis results. This allows parents to continuously monitor their child's development by providing reports regularly. Some or all of the above processes in the analysis department may be performed using AI, for example, or without AI. For example, the analysis department can input analysis results into a generating AI and have the generating AI create and provide the reports.

[0091] The service provider can estimate a child's emotions and adjust the content of the teaching materials and activities based on the estimated emotions. For example, if a child is excited, the service provider can provide teaching materials and activities that take advantage of their heightened emotions. If a child is relaxed, the service provider can provide calming teaching materials and activities. The service provider can also provide teaching materials and activities to alleviate a child's sadness. For example, if a child is excited, the service provider can provide teaching materials and activities that take advantage of their heightened emotions. If a child is relaxed, the service provider can provide calming teaching materials and activities. If a child is sad, the service provider can provide teaching materials and activities to alleviate their sadness. By adjusting the content of teaching materials and activities based on a child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 service provider may be performed using AI, for example, or without AI. For example, the service provider can input children's emotional data into a generating AI and have the AI ​​adjust the content of teaching materials and activities.

[0092] The provider can select different types of learning materials at the time of delivery, depending on the child's learning style. For example, if the child has a visual learning style, the provider will provide visual materials. For example, if the child has an auditory learning style, the provider will provide audio materials. The provider can also provide practical activities if the child has an experiential learning style. For example, if the provider has a visual learning style, the provider will provide visual materials. If the child has an auditory learning style, the provider will provide audio materials. If the child has an experiential learning style, the provider will provide practical activities. By selecting materials that match the learning style, the effectiveness of the child's learning can be enhanced. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the child's learning style data into a generating AI and have the generating AI select the learning materials.

[0093] The service provider can evaluate the effectiveness of the materials and activities they provide and reflect the results in future offerings. For example, the service provider can evaluate the effectiveness of the materials and activities they have provided and improve the content of future offerings. For example, the service provider can collect feedback on the materials and activities they have provided and reflect the results in future offerings. The service provider can also quantitatively evaluate the effectiveness of the materials and activities they have provided and optimize the content of future offerings. For example, the service provider can evaluate the effectiveness of the materials and activities they have provided and improve the content of future offerings. They can collect feedback on the materials and activities they have provided and reflect the results in future offerings. They can quantitatively evaluate the effectiveness of the materials and activities they have provided and optimize the content of future offerings. This allows them to improve the content of future offerings by evaluating the effectiveness of the materials and activities they have provided. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the effectiveness data of the materials and activities they have provided into a generating AI and have the generating AI perform the evaluation and improvement of the content of future offerings.

[0094] The delivery unit can estimate the child's emotions and adjust the timing of delivery based on the estimated emotions. For example, if the child is excited, the delivery unit will deliver at a time that takes advantage of the heightened emotion. If the child is relaxed, the delivery unit will deliver at a calm time. The delivery unit can also deliver at a time that alleviates the child's sadness. For example, if the child is excited, the delivery unit will deliver at a time that takes advantage of the heightened emotion. If the child is relaxed, the delivery unit will deliver at a calm time. If the child is sad, the delivery unit will deliver at a time that alleviates the child's sadness. By adjusting the timing of delivery based on the child's emotions, more effective learning becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the child's emotion data into the generative AI and have the generative AI perform the adjustment of the delivery timing.

[0095] The service provider can select the most suitable learning materials at the time of delivery, taking into account the child's past learning history. For example, the service provider can analyze the child's past learning history and select the most suitable materials. For example, the service provider can select materials that match the child's interests, taking into account the child's past learning history. The service provider can also select materials that match the child's learning progress, based on the child's past learning history. For example, the service provider can analyze the child's past learning history and select the most suitable materials. For example, the service provider can select materials that match the child's interests, taking into account the child's past learning history. For example, the service provider can select materials that match the child's learning progress, based on the child's past learning history. In this way, by taking into account the child's past learning history, the service provider can provide the child with the most suitable learning materials. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's past learning history data into a generating AI and have the generating AI select the most suitable learning materials.

[0096] The service provider can customize the materials and activities it offers to meet the individual needs of each child. For example, the service provider can provide customized materials based on the child's interests. For example, the service provider can provide customized activities according to the child's learning style. The service provider can also provide customized materials and activities according to the child's learning progress. For example, the service provider can provide customized materials based on the child's interests. For example, the service provider can provide customized activities according to the child's learning style. For example, the service provider can provide customized materials and activities according to the child's learning progress. In this way, by customizing the materials and activities, the service provider can meet the individual needs of each child. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input data on the child's individual needs into a generating AI and have the generating AI perform the customization of materials and activities.

[0097] The notification unit can estimate the child's emotions and adjust the content and method of the notification based on the estimated emotions. For example, if the child is excited, the notification unit will send a notification that reflects the heightened emotions. If the child is relaxed, the notification unit will send a calm notification. The notification unit can also send a notification to alleviate the child's sadness. For example, if the child is excited, the notification unit will send a notification that reflects the heightened emotions. If the child is relaxed, the notification unit will send a calm notification. If the child is sad, the notification unit will send a notification to alleviate the child's sadness. This allows for more appropriate notifications by adjusting the content and method of the notification based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input the child's emotion data into the generative AI and have the generative AI adjust the content and method of the notification.

[0098] The notification unit can select different notification methods depending on the type of anomaly when it issues a notification. For example, if abnormal movement is detected, the notification unit will issue an emergency notification. For example, if an abnormal sound is detected, the notification unit will issue an audio notification. The notification unit can also issue a text notification if the person returns home later than a set time. For example, if abnormal movement is detected, the notification unit will issue an emergency notification. If an abnormal sound is detected, it will issue an audio notification. If the person returns home later than a set time, it will issue a text notification. This allows for more effective notifications by selecting the appropriate notification method according to the type of anomaly. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input anomaly type data into a generating AI and have the generating AI select the notification method.

[0099] The notification unit can save notification history, allowing parents to review past notifications. For example, the notification unit can save notification history to the cloud, making it accessible to parents at any time. The notification unit can also periodically back up notification history to ensure data security. Furthermore, the notification unit can visualize notification history, allowing parents to intuitively review it. For example, the notification unit can save notification history to the cloud, making it accessible to parents at any time. It can periodically back up notification history to ensure data security. It can visualize notification history to allow parents to intuitively review it. This allows parents to review past notifications by saving notification history. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input notification history data into a generating AI and have the generating AI perform saving and visualization.

[0100] The notification unit can estimate the child's emotions and determine the priority of notifications based on the estimated emotions. For example, if the child is excited, the notification unit will prioritize notifications that reflect heightened emotions. If the child is relaxed, the notification unit will prioritize calming notifications. The notification unit can also prioritize notifications that alleviate sadness if the child is sad. For example, if the child is excited, the notification unit will prioritize notifications that reflect heightened emotions. If the child is relaxed, it will prioritize calming notifications. If the child is sad, it will prioritize notifications that alleviate sadness. This allows important notifications to be prioritized by determining the priority of notifications based on 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 notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input the child's emotion data into a generative AI and have the generative AI determine the priority of notifications.

[0101] The notification unit can select a notification method considering the parent's current situation when sending a notification. For example, if the parent is at work, the notification unit will select a quiet notification method. For example, if the parent is driving, the notification unit will select an audio notification. The notification unit can also select a more detailed notification method if the parent is at home. For example, if the parent is at work, the notification unit will select a quiet notification method. If the parent is driving, it will select an audio notification. If the parent is at home, it will select a more detailed notification method. This allows for more appropriate notifications by selecting a notification method according to the parent's situation. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can input data on the parent's current situation into a generating AI and have the generating AI select the notification method.

[0102] The notification unit can make notification content multilingual and accommodate parents who speak different languages. For example, the notification unit can automatically translate notification content based on the parent's language settings. For example, the notification unit can provide notification content in multiple languages ​​and allow parents to choose. The notification unit can also make notification content concise and easy to understand across language barriers. For example, the notification unit can automatically translate notification content based on the parent's language settings. For example, it can provide notification content in multiple languages ​​and allow parents to choose. For example, it can make notification content concise and easy to understand across language barriers. This allows the system to accommodate parents who speak different languages ​​by providing multilingual notification content. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can input notification content into a generation AI and have the generation AI perform multilingual translation.

[0103] The dialogue unit can estimate a child's emotions and adjust the content and tone of the dialogue based on the estimated emotions. For example, if the child is excited, the dialogue unit will engage in dialogue that reflects the heightened emotions. If the child is relaxed, the dialogue unit will engage in dialogue in a calm tone. The dialogue unit can also engage in dialogue to alleviate the child's sadness. For example, if the child is excited, the dialogue unit will engage in dialogue that reflects the heightened emotions. If the child is relaxed, the dialogue unit will engage in dialogue in a calm tone. If the child is sad, the dialogue unit will engage in dialogue to alleviate the child's sadness. This allows for more appropriate dialogue by adjusting the content and tone of the dialogue based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's emotion data into the generative AI and have the generative AI adjust the content and tone of the dialogue.

[0104] The dialogue unit can use different dialogue scripts depending on the child's age and developmental stage during a conversation. For example, the dialogue unit will use simple words and short sentences when interacting with toddlers. For example, the dialogue unit will use slightly more complex words and sentences when interacting with elementary school children. Furthermore, the dialogue unit can also use more advanced words and sentences when interacting with middle school students and older. For example, the dialogue unit will use simple words and short sentences when interacting with toddlers. For elementary school children, it will use slightly more complex words and sentences. For middle school students and older, it will use more advanced words and sentences. This allows for conversations appropriate to the child by using dialogue scripts that match their age and developmental stage. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or not. For example, the dialogue unit can input the child's age and developmental stage data into a generating AI and have the generating AI select a dialogue script.

[0105] The dialogue unit can save the dialogue history and reflect it in the next dialogue. For example, the dialogue unit can save the dialogue history to the cloud and reflect it in the next dialogue. For example, the dialogue unit can analyze the dialogue history and optimize the content of the next dialogue. The dialogue unit can also visualize the dialogue history so that parents can review it. For example, the dialogue unit can save the dialogue history to the cloud and reflect it in the next dialogue. It can analyze the dialogue history and optimize the content of the next dialogue. It can visualize the dialogue history so that parents can review it. This allows the dialogue history to be saved and reflected in the next dialogue. Some or all of the above processes in the dialogue unit may be performed using AI, for example, or not using AI. For example, the dialogue unit can input dialogue history data into a generating AI and have the generating AI perform saving and reflecting it in the next dialogue.

[0106] The dialogue unit can estimate the child's emotions and adjust the frequency of dialogue based on the estimated emotions. For example, if the child is excited, the dialogue unit will engage in dialogue frequently to support the heightened emotions. If the child is relaxed, the dialogue unit will reduce the frequency of dialogue and prioritize natural behavior. The dialogue unit can also engage in dialogue frequently if the child is sad to alleviate those emotions. For example, if the child is excited, the dialogue unit will engage in dialogue frequently to support the heightened emotions. If the child is relaxed, the dialogue unit will reduce the frequency of dialogue and prioritize natural behavior. If the child is sad, the dialogue unit will engage in dialogue frequently to alleviate those emotions. By adjusting the frequency of dialogue based on the child's emotions, more appropriate dialogue becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a 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 dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input child emotional data into a generating AI and have the AI ​​adjust the frequency of the dialogue.

[0107] The dialogue unit can suggest relevant activities based on the child's interests during the conversation. For example, if the child is interested in dinosaurs, the dialogue unit will suggest activities related to dinosaurs. If the child is interested in space, the dialogue unit will suggest activities related to space. The dialogue unit can also suggest activities related to music if the child is interested in music. For example, if the dialogue unit is interested in dinosaurs, it will suggest activities related to dinosaurs. If the child is interested in space, it will suggest activities related to space. If the child is interested in music, it will suggest activities related to music. This allows the dialogue unit to keep the child interested by suggesting activities based on their interests. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input the child's interest data into a generating AI and have the generating AI suggest activities.

[0108] The dialogue unit can customize the dialogue content to meet the individual needs of children. For example, the dialogue unit can conduct customized dialogue based on the child's interests. For example, the dialogue unit can conduct customized dialogue according to the child's learning style. Furthermore, the dialogue unit can conduct customized dialogue according to the child's developmental stage. For example, the dialogue unit can conduct customized dialogue based on the child's interests. For example, the dialogue unit can conduct customized dialogue according to the child's learning style. For example, the dialogue unit can conduct customized dialogue according to the child's developmental stage. In this way, by customizing the dialogue content, the individual needs of children can be met. Some or all of the above processing in the dialogue unit may be performed using AI, for example, or without AI. For example, the dialogue unit can input data on the child's individual needs into a generating AI and have the generating AI perform the customization of the dialogue content.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The child growth record system can also include a health management section. This section monitors the child's health and notifies parents if any abnormalities are detected. For example, it can regularly measure the child's body temperature and heart rate and issue an alert if abnormal values ​​are detected. It can also record the child's diet and exercise and provide advice to support healthy lifestyle habits. Furthermore, it can monitor the child's sleep patterns and suggest ways to ensure adequate sleep. This allows for comprehensive management of the child's health, giving parents peace of mind as they watch their child grow.

[0111] The child growth record system can also include a schedule management unit. This unit manages the child's learning and play schedules, supporting efficient time allocation. For example, it can register the child's school timetable and extracurricular activity schedules and send reminders at appropriate times. It can also suggest activities to help children make the most of their free time. Furthermore, it can link with the parent's schedule to coordinate the entire family's plans. This supports the child's time management and helps maintain an efficient balance between learning and play.

[0112] The child growth record system can also include a communication section. This section supports communication between children and parents, strengthening family bonds. For example, the communication section allows parents to report on their child's activities and what they learned during the day. It also allows parents to send messages to their children, who can then respond. Furthermore, the communication section can create shared diaries and albums for the whole family to record memories. This promotes family communication and allows families to share in the child's growth.

[0113] The child growth record system can also include a learning progress management unit. This unit monitors the child's learning progress and provides appropriate feedback. For example, it records the results of assignments and tests the child is working on, visualizing their progress. It can also analyze the child's strengths and weaknesses and propose individually customized learning plans. Furthermore, it can report the child's learning status to parents and advise on how to support them at home. This effectively supports the child's learning and maximizes learning outcomes.

[0114] The child development record system can also include an emotional diary section. This section records the child's emotions daily and tracks emotional changes. For example, the emotional diary records how the child felt about the day's events. It can also analyze the child's emotions in response to specific events and identify emotional patterns. Furthermore, the emotional diary provides the child with tools to express their emotions and deepen their understanding of them. This allows for tracking changes in the child's emotions and providing appropriate support.

[0115] The child development recording system can also be equipped with an emotional feedback unit. This unit provides real-time feedback on the child's emotions, supporting emotional control. For example, if the child is agitated, the emotional feedback unit can suggest ways to relax. It can also suggest activities to cheer up the child if they are sad. Furthermore, if the child is angry, the emotional feedback unit can teach techniques to calm down. This allows children to understand and appropriately control their emotions.

[0116] The child growth record system can also include an emotion sharing section. This section allows children to share their emotions with family and friends and receive support. For example, the emotion sharing section allows children to report happy events to their family. It also allows children to receive advice from friends and family when they are struggling. Furthermore, the emotion sharing section provides a platform for children to express their emotions and deepen their understanding of them. This allows children to share their emotions and receive support, thereby promoting emotional stability.

[0117] The child growth record system can also be equipped with an emotion prediction unit. This unit predicts future emotions based on the child's past emotional data. For example, it can predict how a child will feel about a specific event. It can also predict how a child's emotions will change in a given situation. Furthermore, based on the predicted emotions, the unit can suggest appropriate countermeasures. This allows for proactive understanding of a child's emotional changes and the provision of appropriate support.

[0118] The child development record system can also include an emotional education section. This section educates children on understanding and expressing emotions. For example, it teaches children about different types of emotions and their meanings. It can also guide children on how to express their emotions appropriately. Furthermore, it can cultivate skills in children to understand and empathize with the emotions of others. This allows children to develop the ability to understand and express emotions appropriately.

[0119] The child growth record system can also be equipped with an emotional reflection section. This section supports children in reflecting on past emotions and deepening their self-understanding. For example, the emotional reflection section records and reflects on how a child felt about past events. It can also help children analyze patterns of past emotions and deepen their self-understanding. Furthermore, the emotional reflection section can provide advice to help children plan future actions based on their past emotions. This allows children to deepen their self-understanding and grow.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The recording unit records the child's daily life. The recording unit uses cameras, sensors, and voice recognition to record the child's daily life in detail. For example, it uses a fixed camera to record the child's movements and wearable sensors to record the child's biometric data. Furthermore, it uses voice recognition technology to record the child's conversations. Step 2: The analysis unit analyzes the data recorded by the recording unit to identify the child's interests. The analysis unit uses machine learning algorithms to analyze the data, identifying the child's interests by analyzing behavioral patterns, statements, and the types of games and learning materials they choose. Step 3: The provision department provides learning materials and activities based on the interests identified by the analysis department. The provision department provides online materials, field activities, games, etc., such as online materials about dinosaurs, museum visits, and quizzes. Step 4: The notification unit detects abnormal movements or sounds and notifies the parent or guardian. The notification unit uses motion sensors and voice recognition technology to detect abnormalities and also notifies if the child returns home later than the designated time. Step 5: The dialogue team engages with the child and suggests activities to alleviate boredom. The dialogue team uses voice and text dialogue to suggest topics for conversation. For example, they might say, "Let's draw a picture together," or ask, "What do you want to do today?"

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the recording unit, analysis unit, provision unit, notification unit, and dialogue unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the recording unit records the child's daily life using the camera 42 and sensors of the smart device 14. The analysis unit analyzes the recorded data using the identification processing unit 290 of the data processing unit 12 to identify the child's interests. The provision unit provides learning materials and activities based on the interests identified by the identification processing unit 290 of the data processing unit 12. The notification unit detects abnormalities using the motion sensors and voice recognition technology of the smart device 14 and notifies the guardian. The dialogue unit interacts with the child using the voice dialogue function of the smart device 14 and suggests activities to alleviate boredom. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the recording unit, analysis unit, provision unit, notification unit, and dialogue unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the recording unit records the child's daily life using the camera 42 and sensors of the smart glasses 214. The analysis unit analyzes the recorded data using the identification processing unit 290 of the data processing unit 12 to identify the child's interests. The provision unit provides learning materials and activities based on the interests identified by the identification processing unit 290 of the data processing unit 12. The notification unit detects abnormalities using the motion sensors and voice recognition technology of the smart glasses 214 and notifies the guardian. The dialogue unit interacts with the child using the voice dialogue function of the smart glasses 214 and suggests activities to alleviate boredom. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the recording unit, analysis unit, provision unit, notification unit, and dialogue unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the recording unit records the child's daily life using the camera 42 and sensors of the headset terminal 314. The analysis unit analyzes the recorded data using the identification processing unit 290 of the data processing unit 12 to identify the child's interests. The provision unit provides learning materials and activities based on the interests identified by the identification processing unit 290 of the data processing unit 12. The notification unit detects abnormalities using the motion sensor and voice recognition technology of the headset terminal 314 and notifies the guardian. The dialogue unit interacts with the child using the voice dialogue function of the headset terminal 314 and suggests activities to alleviate boredom. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the recording unit, analysis unit, provision unit, notification unit, and dialogue unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the recording unit records the child's daily life using the camera 42 and sensors of the robot 414. The analysis unit analyzes the recorded data by the identification processing unit 290 of the data processing unit 12 to identify the child's interests. The provision unit provides learning materials and activities based on the interests identified by the identification processing unit 290 of the data processing unit 12. The notification unit detects abnormalities using the motion sensors and voice recognition technology of the robot 414 and notifies the guardian. The dialogue unit interacts with the child using the voice dialogue function of the robot 414 and suggests activities to alleviate boredom. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A recording department that documents the children's daily lives, An analysis unit analyzes the data recorded by the recording unit and identifies the child's interests and concerns. A provisioning unit provides learning materials and activities based on the interests identified by the aforementioned analysis unit, A notification unit that detects abnormal movements or sounds and notifies the guardian, It includes a dialogue section for interacting with children. A system characterized by the following features. (Note 2) The aforementioned recording unit is Record children's daily lives using cameras, sensors, and voice recognition. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The data recorded by the recording unit is analyzed to identify the child's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, We provide learning materials and activities based on the interests identified by the analysis department. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, It detects unusual movements and sounds and notifies parents. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, Engage with children and suggest activities to alleviate boredom. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned recording unit is The system estimates the child's emotions and adjusts the frequency and timing of recordings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned recording unit is During recording, the system automatically selects a different recording mode depending on the type of activity the child is engaged in. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned recording unit is The system analyzes recorded data in real time and automatically tags important moments as highlights. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned recording unit is The system estimates the child's emotions and prioritizes the data to record based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned recording unit is When recording, prioritize recording activities at specific locations, taking into account the child's location information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned recording unit is The recorded data is automatically uploaded to the cloud, allowing parents to access it from anywhere. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is The system estimates the child's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During the analysis, identify new changes in interests by comparing them with past data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is Visualize the analysis results so that parents can understand them intuitively. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is The system estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During the analysis, we identify children's interests by considering their friendships and social interactions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is The analysis results will be provided to parents in regular reports. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the child's emotions and adjust the content of the teaching materials and activities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing the materials, we select different formats of learning materials according to the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, We will evaluate the effectiveness of the teaching materials and activities we provide and reflect the results in future offerings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, We estimate the child's emotions and adjust the timing of what we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing materials, the most suitable materials are selected considering the child's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, We customize the teaching materials and activities we provide to meet the individual needs of each child. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned notification unit, The system estimates the child's emotions and adjusts the content and method of notifications based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, When a notification is sent, a different notification method is selected depending on the type of anomaly. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, Save notification history so parents can review past notifications. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned notification unit, The system estimates the child's emotions and prioritizes notifications based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, When notifying, the notification method will be selected considering the current circumstances of the parents. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, The notification content will be made available in multiple languages ​​to accommodate parents who speak different languages. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned dialogue unit, The system estimates the child's emotions and adjusts the content and tone of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned dialogue unit, During conversations, use different dialogue scripts depending on the child's age and developmental stage. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned dialogue unit, Save the conversation history and apply it to the next conversation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned dialogue unit, The system estimates the child's emotions and adjusts the frequency of interaction based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned dialogue unit, During the conversation, suggest relevant activities based on the child's interests. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned dialogue unit, Customize the dialogue content to meet the individual needs of each child. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0194] 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. A recording department that documents the children's daily lives, An analysis unit analyzes the data recorded by the recording unit and identifies the child's interests and concerns. A provisioning unit provides learning materials and activities based on the interests identified by the aforementioned analysis unit, A notification unit that detects abnormal movements or sounds and notifies the guardian, It includes a dialogue section for interacting with children. A system characterized by the following features.

2. The aforementioned recording unit is Record children's daily lives using cameras, sensors, and voice recognition. The system according to feature 1.

3. The aforementioned analysis unit is The data recorded by the aforementioned recording unit is analyzed to identify the child's interests and concerns. The system according to feature 1.

4. The aforementioned supply unit is, Based on the interests identified by the aforementioned analysis unit, learning materials and activities are provided. The system according to feature 1.

5. The aforementioned notification unit, It detects unusual movements and sounds and notifies parents. The system according to feature 1.

6. The aforementioned dialogue unit, Engage with children and suggest activities to alleviate boredom. The system according to feature 1.

7. The aforementioned recording unit is The system estimates the child's emotions and adjusts the frequency and timing of recordings based on the estimated emotions. The system according to feature 1.

8. The aforementioned recording unit is During recording, the system automatically selects a different recording mode depending on the type of activity the child is engaged in. The system according to feature 1.

9. The aforementioned recording unit is The system analyzes recorded data in real time and automatically tags important moments as highlights. The system according to feature 1.

10. The aforementioned recording unit is The system estimates the child's emotions and prioritizes the data to record based on those estimated emotions. The system according to feature 1.

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