System

Abstract

An object of a system according to an embodiment is to reduce the burden on a nursery teacher and effectively manage the safety and growth of a child.SOLUTION: A system includes a camera, an analysis unit, a generation unit, a distribution unit, an alert unit, and an analysis unit. The camera captures an image of the entire classroom. The analysis unit analyzes the video captured by the camera. The generation unit creates an address book or a daily childcare record based on the data analyzed by the analysis unit. The distribution unit distributes the address book or the daily childcare record created by the generation unit to the protector. The alert unit issues an alert based on the dangerous behavior detected by the analysis unit. The analysis unit analyzes the growth of each child based on the data collected by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of requiring childcare workers to take the time and effort to create communication notebooks and childcare diaries, and does not adequately detect risky behavior or analyze children's growth.

[0005] The system according to the embodiment aims to reduce the burden on childcare workers and effectively manage the safety and growth of children. [Means for solving the problem]

[0006] The system according to the embodiment includes a camera, an analysis unit, a generation unit, a distribution unit, an alert unit, and an analysis unit. The camera captures an image of the entire classroom. The analysis unit analyzes the image captured by the camera. The generation unit creates a communication book or a childcare diary based on the data analyzed by the analysis unit. The distribution unit distributes the communication book or childcare diary created by the generation unit to parents. The alert unit issues an alert based on risky behavior detected by the analysis unit. The analysis unit analyzes the growth of each child based on the data collected by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden on childcare workers and effectively manage the safety and growth of children. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A childcare support system according to an embodiment of the present invention uses AI to create communication notebooks and childcare diaries for childcare workers. This system uses a high-resolution fixed camera to capture images of the entire classroom, and AI analyzes the footage to automatically create communication notebooks and childcare diaries for each child. The system also automatically distributes the created content to parents and issues an alert to staff if it detects dangerous behavior that could lead to an accident. Furthermore, it analyzes the growth of each child based on daily records and provides advice on childcare policies to childcare workers. As a result, the childcare support system reduces the workload of childcare workers and supports the safety and growth of children.

[0029] A childcare support system according to an embodiment includes a camera, an analysis unit, a generation unit, a distribution unit, an alert unit, and an analysis unit. The camera captures the entire classroom. For example, a high-resolution fixed camera can be installed in the classroom to capture children's movements and behavior in detail. The analysis unit analyzes the video captured by the camera. For example, it uses an image analysis algorithm to analyze the children's behavior and comments. The generation unit creates a communication book or childcare diary based on the data analyzed by the analysis unit. For example, a generation AI automatically generates the communication book or childcare diary based on the children's behavior and comments. The distribution unit distributes the communication book or childcare diary created by the generation unit to parents. For example, via email or a dedicated app, parents can check on their children's condition at any time. The alert unit issues an alert based on dangerous behavior detected by the analysis unit. For example, it issues an alert to staff if a child tries to climb to a high place or nearly collide with another child. The analysis unit analyzes each child's growth based on the data collected by the analysis unit. For example, the system analyzes each child's growth in detail, such as language development and motor skill improvement, and provides childcare workers with advice on childcare policies. As a result, the childcare support system according to the embodiment can reduce the workload of childcare workers and support the safety and growth of children. For example, it can reduce the time childcare workers spend on creating communication notebooks and childcare diaries, allowing them to spend more time directly interacting with children. In addition, parents can grasp their children's growth in real time, which increases their sense of security. Furthermore, it can reduce the risk of accidents by detecting dangerous behavior and issuing alerts.

[0030] The camera is equipped with a voice recognition function and can analyze and record the content of children's conversations. For example, the camera is a high-resolution fixed camera with added voice recognition functionality, which analyzes the content of children's conversations in real time. For example, what children say while playing can be recorded as text data and later reflected in the childcare diary. The camera is also equipped with a voice recognition function, which analyzes the content of children's conversations to understand the background of their actions. For example, it can record conversations when children work together to create something, and the process of that collaboration can be recorded in detail in the childcare diary. The camera can also use the voice recognition function to analyze the content of children's conversations and extract specific keywords and phrases. For example, it can record conversations when children learn new words, and reflect their growth in the childcare diary. In this way, by recording the content of children's conversations, a more detailed childcare diary can be created.

[0031] The camera is equipped with an automatic viewpoint adjustment function, allowing it to capture important scenes in more detail. For example, the camera adds an automatic viewpoint adjustment function to a high-resolution fixed camera to automatically focus on important scenes. For example, when children start a particular activity, the camera automatically zooms in on that scene. The camera is also equipped with an automatic viewpoint adjustment function to capture children's important actions in detail. For example, when children start a new game, the camera tracks the scene. The camera also uses the automatic viewpoint adjustment function to capture children's important actions. For example, when children work together to create something, the camera captures the process in detail. This allows important scenes to be captured in detail, making for more accurate recording.

[0032] The camera is equipped with a temperature sensor and a humidity sensor, and can simultaneously record environmental data and analyze the correlation with children's behavior. For example, a high-resolution fixed camera equipped with temperature and humidity sensors can simultaneously record environmental data. For example, it can analyze how children behave when the temperature in the classroom is high. The camera can also record environmental data and build a system that analyzes the correlation with children's behavior. For example, it can analyze what types of play children prefer when the humidity is high. The camera can also record environmental data such as temperature and humidity and analyze children's behavior patterns. For example, it can record how children's behavior changes when the environment in the classroom changes. In this way, it is possible to analyze the correlation with children's behavior by recording environmental data.

[0033] Multiple cameras are installed and images from different angles are integrated to generate a 3D model, allowing for more detailed behavioral analysis. For example, multiple high-resolution cameras are installed in a classroom and images from different angles are integrated to generate a 3D model. For example, children's movements are analyzed in three dimensions and detailed behavioral patterns are recorded. In addition, a system is built using multiple cameras to integrate images from different angles and generate a 3D model. For example, children's play is recorded in three dimensions and used for behavioral analysis. In addition, cameras are integrated with images from different angles to generate a 3D model, allowing for more detailed behavioral analysis. For example, the movements of children working together to create something are recorded in three dimensions. In this way, more detailed behavioral analysis is possible by generating a 3D model.

[0034] The generation unit can infer the background and intentions of children's behavior and record them in the communication book or childcare diary. For example, the generation unit uses AI to analyze children's behavior, infer the background and intentions, and record them in the communication book or childcare diary. For example, it records the intentions when children work together to create something. The generation unit also builds a system in which AI analyzes children's behavior, infers the background and intentions, and records them. For example, it records the intentions when children start a new game. The generation unit also uses AI to infer the background and intentions of children's behavior and reflects this in the communication book or childcare diary. For example, it records the reasons why children behave in a certain way. This makes it possible to create a more detailed communication book or childcare diary by recording the background and intentions of children's behavior.

[0035] The generation unit can refer to past data and track and record a child's growth or changes over the long term. For example, the generation unit uses AI to refer to past data and build a system that tracks and records a child's growth and changes over the long term. For example, it records improvements in language development and motor skills. The generation unit also uses AI to analyze past data, track a child's growth and changes, and reflect this in a communication book or childcare diary. For example, it records in detail the growth of a specific child. The generation unit also develops a system that uses AI to track a child's growth and changes over the long term based on past data. For example, it records changes in children's behavioral patterns. This makes it possible to track a child's growth and changes over the long term and record them in more detail.

[0036] The generation unit can automatically attach photos of children's creations or artworks. For example, the generation unit constructs a system in which AI automatically takes photos of children's creations or artworks and attaches them to a communication book or childcare diary. For example, it records photos of pictures and crafts made by children. The generation unit also adds a function to automatically attach photos of children's creations or artworks to the communication book or childcare diary. For example, it provides photos of artwork made by children to parents. The generation unit also uses AI to automatically take photos of children's creations or artworks and reflect them in the communication book or childcare diary. For example, it records photos of pictures and crafts made by children. In this way, by attaching photos of children's creations or artworks, parents can keep a closer track of their children's activities.

[0037] The generation unit can automatically add comments or advice from the childcare worker. The generation unit, for example, builds a system in which AI automatically generates comments and advice from childcare workers and adds them to a communication book or childcare diary. For example, advice for a specific child is recorded. The generation unit also adds a function to automatically add comments and advice from childcare workers to the communication book or childcare diary. For example, advice given by childcare workers to children is recorded. The generation unit also uses AI to automatically generate comments and advice from childcare workers and reflect them in the communication book or childcare diary. For example, advice given by childcare workers to children is recorded. In this way, by automatically adding comments and advice from childcare workers, parents can receive specific advice regarding their child's growth.

[0038] The distribution unit can customize the distribution content to suit the parents' preferences and prioritize the display of important information. For example, the distribution unit builds a system in which AI analyzes parents' preferences and customizes the distribution content. For example, it prioritizes the display of information that parents are particularly interested in. The distribution unit also adds a function to customize the distribution content to suit parents' preferences and prioritize the display of important information. For example, it prioritizes the delivery of information about children's growth that parents are interested in. The distribution unit also analyzes parents' preferences and customizes the distribution content. For example, it prioritizes the display of activities and events that parents are particularly interested in. In this way, by customizing the distribution content to suit parents' preferences, parents can receive the information they need with priority.

[0039] The distribution unit can automatically attach video clips of children's activities to the content of the broadcast. For example, the distribution unit builds a system in which AI automatically generates video clips of children's activities and attaches them to the content of the broadcast. For example, it distributes videos of children playing or learning. The distribution unit also adds a function to automatically attach video clips of children's activities to the content of the broadcast. For example, it distributes videos of children learning a new game. The distribution unit also uses AI to automatically generate video clips of children's activities and reflect them in the content of the broadcast. For example, it distributes videos of children working together to create something. In this way, by attaching video clips of children's activities, parents can understand their children's activities in more detail.

[0040] The distribution department will automatically translate the content into multiple languages, making it possible to accommodate foreign parents. For example, the distribution department will build a system in which AI will automatically translate the content into multiple languages. For example, the content will be translated to match the parent's native language, such as English, Spanish, or Chinese. The distribution department will also add a function to automatically translate the content into multiple languages ​​and accommodate foreign parents. For example, the content will be displayed in the language selected by the parent. The distribution department will also develop a system in which AI will automatically translate the content to accommodate foreign parents. For example, the content will be made available so that parents can view the content in their native language. This will allow the content to be automatically translated into multiple languages, making it possible to accommodate foreign parents.

[0041] The distribution unit can have a function to distribute the content at a specific time period selected by the guardian. For example, the distribution unit builds a system in which AI sends the content at a specific time period selected by the guardian. For example, the content is sent to coincide with the time when the guardian returns home from work. The distribution unit also adds a function to send the content at a specific time period selected by the guardian. For example, it makes it possible for the guardian to receive the content at a time period of their choice. The distribution unit also develops a system in which AI sends the content at a time period selected by the guardian. For example, it makes it possible for the guardian to receive the content in the morning. As a result, by distributing the content at a specific time period selected by the guardian, the guardian can receive the information at a time of their choice.

[0042] The alert unit improves the risky behavior detection algorithm and is able to detect danger earlier. For example, the alert unit improves the AI's risky behavior detection algorithm and builds a system that detects danger earlier. For example, it detects early on the movement of a child trying to climb to a high place. The alert unit also improves the risky behavior detection algorithm and adds a function that detects danger earlier. For example, it detects early on the movement of a child that is about to collide violently with another child. The alert unit also improves the AI's risky behavior detection algorithm and develops a system that detects danger earlier. For example, it detects early on the movement of a child trying to pick up a dangerous tool. In this way, by improving the risky behavior detection algorithm, danger can be detected earlier.

[0043] The alert unit can learn patterns of dangerous behavior and issue preventative alerts. For example, the alert unit will build a system in which AI learns patterns of dangerous behavior and issues preventative alerts. For example, it will issue an alert before a child tries to climb to a high place. The alert unit will also add a function to learn patterns of dangerous behavior and issue preventative alerts. For example, it will issue an alert before a child is about to collide violently with another child. The alert unit will also develop a system in which AI learns patterns of dangerous behavior and issues preventative alerts. For example, it will issue an alert before a child tries to pick up a dangerous tool. In this way, by learning patterns of dangerous behavior and issuing preventative alerts, the risk of accidents can be reduced.

[0044] The alert unit can suggest specific ways of responding to childcare workers when an alert is issued. For example, the alert unit will build a system in which AI suggests specific ways of responding to childcare workers when an alert is issued. For example, when a child tries to climb to a high place, the alert unit will suggest safe ways of responding to the childcare worker. The alert unit will also add a function to suggest specific ways of responding to childcare workers when an alert is issued. For example, when a child is about to collide violently with another child, the alert unit will suggest appropriate ways of responding to the childcare worker. The alert unit will also develop a system in which AI suggests specific ways of responding to childcare workers when an alert is issued. For example, when a child tries to pick up a dangerous tool, the alert unit will suggest safe ways of responding to the childcare worker. In this way, by suggesting specific ways of responding when an alert is issued, childcare workers can respond quickly and appropriately.

[0045] The alert unit can also send a notification to parents when an alert is issued, allowing the situation to be shared. For example, the alert unit will build a system in which, when an alert is issued by AI, a notification is sent to parents as well, allowing the situation to be shared. For example, if a child tries to climb to a high place, a notification is also sent to the parents. The alert unit will also add a function in which, when an alert is issued, a notification is sent to parents as well, allowing the situation to be shared. For example, if a child is about to collide violently with another child, a notification is also sent to the parents. The alert unit will also develop a system in which, when an alert is issued by AI, a notification is sent to parents as well, allowing the situation to be shared. For example, if a child tries to pick up a dangerous tool, a notification is also sent to the parents. This allows the parents to quickly understand the situation of their child by sending a notification to parents as well when an alert is issued and sharing the situation.

[0046] The analysis unit can add evaluations of children's social skills and cooperation. For example, the analysis unit builds a system in which AI evaluates children's social skills and cooperation and reflects this in growth analysis. For example, it evaluates how well children cooperate when working together to create something. The analysis unit also develops a function to add evaluations of children's social skills and cooperation to growth analysis. For example, it evaluates how well children cooperate when playing with friends. The analysis unit also develops a system in which AI evaluates children's social skills and cooperation and reflects this in growth analysis. For example, it evaluates how well children cooperate when participating in group activities. In this way, adding evaluations of children's social skills and cooperation makes it possible to perform more detailed growth analysis.

[0047] The analysis unit can visualize the results of the growth analysis in a format that is easy for childcare workers to understand. For example, the analysis unit builds a system in which AI visualizes the results of the growth analysis and provides them in a format that is easy for childcare workers to understand. For example, it visually displays a child's growth using graphs and charts. The analysis unit also develops a function to visualize the results of the growth analysis in a format that is easy for childcare workers to understand. For example, it generates infographics showing a child's growth. The analysis unit also develops a system in which AI visualizes the results of the growth analysis and provides them in a format that is easy for childcare workers to understand. For example, it creates a dashboard showing a child's growth. In this way, visualizing the results of the growth analysis makes it easier for childcare workers to understand a child's growth.

[0048] The analysis unit shares the results of the growth analysis with parents, allowing them to use it to inform child-rearing policies at home. For example, the analysis unit may build a system in which AI shares the results of the growth analysis with parents. For example, it may provide information that will help parents use their child's growth to inform their child-rearing policies at home. The analysis unit may also develop a function to share the results of the growth analysis with parents, allowing them to use it to inform their child-rearing policies at home. For example, it may provide information in a format that makes it easy for parents to understand their child's growth. The analysis unit may also develop a system in which AI shares the results of the growth analysis with parents, allowing them to use it to inform their child-rearing policies at home. For example, it may provide advice to parents so that they can reflect their child's growth in their child-rearing policies at home. In this way, sharing the results of the growth analysis with parents will allow them to use it to inform their child-rearing policies at home.

[0049] The analysis department can share the results of the growth analysis with other childcare facilities to learn best practices. For example, the analysis department builds a system in which the AI ​​shares the results of the growth analysis with other childcare facilities. For example, it shares information with other childcare facilities to learn best practices. The analysis department also develops a function to share the results of the growth analysis with other childcare facilities to learn best practices. For example, it learns ways to cooperate with other childcare facilities to promote children's growth. The analysis department also develops a system in which the AI ​​shares the results of the growth analysis with other childcare facilities to learn best practices. For example, it shares information with other childcare facilities to learn effective childcare policies. In this way, by sharing the results of the growth analysis with other childcare facilities, it is possible to learn best practices and improve the quality of childcare.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The childcare support system can further include a voice recognition unit. The voice recognition unit can analyze the content of children's conversations in real time and extract specific keywords and phrases. For example, it can record conversations when children learn new words and reflect their growth in the childcare diary. The voice recognition unit can also analyze the content of children's conversations to understand the background to their actions. For example, it can record conversations when children work together to create something and describe the process of their collaboration in detail in the childcare diary. Furthermore, the voice recognition unit can record the content of children's conversations as text data and later reflect it in the childcare diary. In this way, by recording the content of children's conversations, a more detailed childcare diary can be created.

[0052] The childcare support system can further include an automatic viewpoint adjustment unit. The automatic viewpoint adjustment unit can automatically focus on important scenes and capture them in detail. For example, when children start a particular activity, the camera automatically zooms in on that scene. The automatic viewpoint adjustment unit can also capture important children's actions in detail. For example, when children start a new game, the camera tracks that scene. The automatic viewpoint adjustment unit can also capture in detail the process of children working together to create something. This allows for more accurate recording by capturing important scenes in detail.

[0053] The childcare support system can further include a temperature sensor and a humidity sensor. These sensors can simultaneously record environmental data and analyze the correlation with children's behavior. For example, it can analyze how children behave when the temperature in the classroom is high. It can also analyze what kind of play children prefer when the humidity is high. Furthermore, environmental data such as temperature and humidity can be recorded and children's behavior patterns can be analyzed. This makes it possible to analyze the correlation with children's behavior by recording environmental data.

[0054] Childcare support systems can also be equipped with multiple cameras and integrate footage from different angles to generate 3D models, enabling more detailed behavioral analysis. For example, children's movements can be analyzed in three dimensions and detailed behavioral patterns can be recorded. Systems can also be built that use multiple cameras to integrate footage from different angles and generate 3D models, enabling more detailed behavioral analysis. For example, children's play can be recorded in three dimensions and used for behavioral analysis. Furthermore, footage from different angles can be integrated to generate 3D models, enabling more detailed behavioral analysis.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The camera captures the entire classroom. For example, a high-resolution fixed camera can be installed in the classroom to capture the children's movements and behavior in detail. Step 2: The analysis unit analyzes the video captured by the camera. For example, it uses an image analysis algorithm to analyze the children's behavior and speech. Step 3: The generation unit creates a communication book or a childcare diary based on the data analyzed by the analysis unit. For example, the generation AI automatically generates a communication book or a childcare diary based on the children's behavior and comments. Step 4: The distribution unit distributes the communication book or childcare diary created by the generation unit to parents, for example, via email or a dedicated app, so that parents can check how their children are doing at any time. Step 5: The alert unit issues an alert based on the dangerous behavior detected by the analysis unit. For example, if a child tries to climb to a high place or nearly collide with another child, it will issue an alert to staff. Step 6: The analysis unit analyzes the growth of each child based on the data collected by the analysis unit. For example, it analyzes in detail the growth of each child, such as language development and improvement in motor skills, and provides advice to childcare workers on childcare policies.

[0057] (Example 2) A childcare support system according to an embodiment of the present invention uses AI to create communication notebooks and childcare diaries for childcare workers. This system uses a high-resolution fixed camera to capture images of the entire classroom, and AI analyzes the footage to automatically create communication notebooks and childcare diaries for each child. The system also automatically distributes the created content to parents and issues an alert to staff if it detects dangerous behavior that could lead to an accident. Furthermore, it analyzes the growth of each child based on daily records and provides advice on childcare policies to childcare workers. As a result, the childcare support system reduces the workload of childcare workers and supports the safety and growth of children.

[0058] A childcare support system according to an embodiment includes a camera, an analysis unit, a generation unit, a distribution unit, an alert unit, and an analysis unit. The camera captures the entire classroom. For example, a high-resolution fixed camera can be installed in the classroom to capture children's movements and behavior in detail. The analysis unit analyzes the video captured by the camera. For example, it uses an image analysis algorithm to analyze the children's behavior and comments. The generation unit creates a communication book or childcare diary based on the data analyzed by the analysis unit. For example, a generation AI automatically generates the communication book or childcare diary based on the children's behavior and comments. The distribution unit distributes the communication book or childcare diary created by the generation unit to parents. For example, via email or a dedicated app, parents can check on their children's condition at any time. The alert unit issues an alert based on dangerous behavior detected by the analysis unit. For example, it issues an alert to staff if a child tries to climb to a high place or nearly collide with another child. The analysis unit analyzes each child's growth based on the data collected by the analysis unit. For example, the system analyzes each child's growth in detail, such as language development and motor skill improvement, and provides childcare workers with advice on childcare policies. As a result, the childcare support system according to the embodiment can reduce the workload of childcare workers and support the safety and growth of children. For example, it can reduce the time childcare workers spend on creating communication notebooks and childcare diaries, allowing them to spend more time directly interacting with children. In addition, parents can grasp their children's growth in real time, which increases their sense of security. Furthermore, it can reduce the risk of accidents by detecting dangerous behavior and issuing alerts.

[0059] The camera is equipped with a voice recognition function and can analyze and record the content of children's conversations. For example, the camera is a high-resolution fixed camera with added voice recognition functionality, which analyzes the content of children's conversations in real time. For example, what children say while playing can be recorded as text data and later reflected in the childcare diary. The camera is also equipped with a voice recognition function, which analyzes the content of children's conversations to understand the background of their actions. For example, it can record conversations when children work together to create something, and the process of that collaboration can be recorded in detail in the childcare diary. The camera can also use the voice recognition function to analyze the content of children's conversations and extract specific keywords and phrases. For example, it can record conversations when children learn new words, and reflect their growth in the childcare diary. In this way, by recording the content of children's conversations, a more detailed childcare diary can be created.

[0060] The camera is equipped with an automatic viewpoint adjustment function, allowing it to capture important scenes in more detail. For example, the camera adds an automatic viewpoint adjustment function to a high-resolution fixed camera to automatically focus on important scenes. For example, when children start a particular activity, the camera automatically zooms in on that scene. The camera is also equipped with an automatic viewpoint adjustment function to capture children's important actions in detail. For example, when children start a new game, the camera tracks the scene. The camera also uses the automatic viewpoint adjustment function to capture children's important actions. For example, when children work together to create something, the camera captures the process in detail. This allows important scenes to be captured in detail, making for more accurate recording.

[0061] The camera is equipped with an emotion estimation function, which can infer emotions from children's facial expressions and tone of voice and reflect that information in the records. For example, the camera is a high-resolution fixed camera with the emotion estimation function added, which can infer emotions from children's facial expressions and tone of voice. For example, it can record scenes where children are having fun and reflect those emotions in the childcare diary. The camera also uses the emotion estimation function to analyze emotions from children's facial expressions and tone of voice and reflect those emotions in the records. For example, it can record scenes where children are in trouble and record those emotions in the childcare diary. The camera is also equipped with an emotion estimation function, which can analyze children's emotions in real time. For example, it can record scenes where children are surprised or happy and reflect those emotions in the childcare diary. In this way, by reflecting children's emotions in the records, a more detailed childcare diary can be created.

[0062] The camera is equipped with a temperature sensor and a humidity sensor, and can simultaneously record environmental data and analyze the correlation with children's behavior. For example, a high-resolution fixed camera equipped with temperature and humidity sensors can simultaneously record environmental data. For example, it can analyze how children behave when the temperature in the classroom is high. The camera can also record environmental data and build a system that analyzes the correlation with children's behavior. For example, it can analyze what types of play children prefer when the humidity is high. The camera can also record environmental data such as temperature and humidity and analyze children's behavior patterns. For example, it can record how children's behavior changes when the environment in the classroom changes. In this way, it is possible to analyze the correlation with children's behavior by recording environmental data.

[0063] Multiple cameras are installed and images from different angles are integrated to generate a 3D model, allowing for more detailed behavioral analysis. For example, multiple high-resolution cameras are installed in a classroom and images from different angles are integrated to generate a 3D model. For example, children's movements are analyzed in three dimensions and detailed behavioral patterns are recorded. In addition, a system is built using multiple cameras to integrate images from different angles and generate a 3D model. For example, children's play is recorded in three dimensions and used for behavioral analysis. In addition, cameras are integrated with images from different angles to generate a 3D model, allowing for more detailed behavioral analysis. For example, the movements of children working together to create something are recorded in three dimensions. In this way, more detailed behavioral analysis is possible by generating a 3D model.

[0064] The camera is equipped with an emotion estimation function and can monitor changes in children's emotions in real time and provide feedback to childcare workers. For example, the camera is a high-resolution fixed camera with an emotion estimation function added, which monitors changes in children's emotions in real time. For example, it detects scenes in which children are feeling anxious and provides feedback to childcare workers. The camera also uses the emotion estimation function to build a system that analyzes changes in children's emotions in real time and provides feedback to childcare workers. For example, it detects scenes in which children are having fun and notifies childcare workers. The camera is also equipped with an emotion estimation function and monitors changes in children's emotions in real time. For example, it detects scenes in which children are in trouble and prompts childcare workers to take action. This makes it possible to monitor changes in children's emotions in real time and provide feedback to childcare workers, enabling rapid response.

[0065] The generation unit can infer the background and intentions of children's behavior and record them in the communication book or childcare diary. For example, the generation unit uses AI to analyze children's behavior, infer the background and intentions, and record them in the communication book or childcare diary. For example, it records the intentions when children work together to create something. The generation unit also builds a system in which AI analyzes children's behavior, infers the background and intentions, and records them. For example, it records the intentions when children start a new game. The generation unit also uses AI to infer the background and intentions of children's behavior and reflects this in the communication book or childcare diary. For example, it records the reasons why children behave in a certain way. This makes it possible to create a more detailed communication book or childcare diary by recording the background and intentions of children's behavior.

[0066] The generation unit can refer to past data and track and record a child's growth or changes over the long term. For example, the generation unit uses AI to refer to past data and build a system that tracks and records a child's growth and changes over the long term. For example, it records improvements in language development and motor skills. The generation unit also uses AI to analyze past data, track a child's growth and changes, and reflect this in a communication book or childcare diary. For example, it records in detail the growth of a specific child. The generation unit also develops a system that uses AI to track a child's growth and changes over the long term based on past data. For example, it records changes in children's behavioral patterns. This makes it possible to track a child's growth and changes over the long term and record them in more detail.

[0067] The generation unit is equipped with an emotion estimation function and can record changes in children's emotions and provide parents with emotional highlights of the day. For example, the generation unit adds an emotion estimation function to AI to record changes in children's emotions. For example, it records scenes in which children are having fun or in trouble and provides parents with emotional highlights of the day. The generation unit also uses the emotion estimation function to analyze changes in children's emotions and reflects them in a communication book or childcare diary. For example, it records scenes in which children are surprised or happy and provides these to parents. The generation unit also builds a system in which AI records changes in children's emotions and provides parents with emotional highlights of the day. For example, it records scenes in which children are feeling anxious and notifies parents. In this way, by recording changes in children's emotions and providing parents with emotional highlights of the day, parents can have a more detailed understanding of how their children are doing throughout the day.

[0068] The generation unit can automatically attach photos of children's creations or artworks. For example, the generation unit constructs a system in which AI automatically takes photos of children's creations or artworks and attaches them to a communication book or childcare diary. For example, it records photos of pictures and crafts made by children. The generation unit also adds a function to automatically attach photos of children's creations or artworks to the communication book or childcare diary. For example, it provides photos of artwork made by children to parents. The generation unit also uses AI to automatically take photos of children's creations or artworks and reflect them in the communication book or childcare diary. For example, it records photos of pictures and crafts made by children. In this way, by attaching photos of children's creations or artworks, parents can keep a closer track of their children's activities.

[0069] The generation unit can automatically add comments or advice from the childcare worker. The generation unit, for example, builds a system in which AI automatically generates comments and advice from childcare workers and adds them to a communication book or childcare diary. For example, advice for a specific child is recorded. The generation unit also adds a function to automatically add comments and advice from childcare workers to the communication book or childcare diary. For example, advice given by childcare workers to children is recorded. The generation unit also uses AI to automatically generate comments and advice from childcare workers and reflect them in the communication book or childcare diary. For example, advice given by childcare workers to children is recorded. In this way, by automatically adding comments and advice from childcare workers, parents can receive specific advice regarding their child's growth.

[0070] The generation unit has an emotion estimation function and can collect emotional reactions when parents view the communication book or childcare diary and reflect them in the next content. The generation unit, for example, uses the emotion estimation function to collect emotional reactions when parents view the communication book or childcare diary. For example, it detects scenes in which the parents are happy and reflects them in the next content. The generation unit also builds a system that analyzes the parents' emotional reactions and reflects them in the next content of the communication book or childcare diary. For example, it detects scenes in which the parents are feeling anxious and adjusts the next content. The generation unit also uses the emotion estimation function to collect emotional reactions in real time when parents view the communication book or childcare diary and reflect them in the next content. For example, it records scenes in which the parents are surprised or moved and reflects them in the next content. In this way, by collecting the parents' emotional reactions and reflecting them in the next content, it is possible to provide a communication book or childcare diary that meets the needs of parents.

[0071] The distribution unit can customize the distribution content to suit the parents' preferences and prioritize the display of important information. For example, the distribution unit builds a system in which AI analyzes parents' preferences and customizes the distribution content. For example, it prioritizes the display of information that parents are particularly interested in. The distribution unit also adds a function to customize the distribution content to suit parents' preferences and prioritize the display of important information. For example, it prioritizes the delivery of information about children's growth that parents are interested in. The distribution unit also analyzes parents' preferences and customizes the distribution content. For example, it prioritizes the display of activities and events that parents are particularly interested in. In this way, by customizing the distribution content to suit parents' preferences, parents can receive the information they need with priority.

[0072] The distribution unit can automatically attach video clips of children's activities to the content of the broadcast. For example, the distribution unit builds a system in which AI automatically generates video clips of children's activities and attaches them to the content of the broadcast. For example, it distributes videos of children playing or learning. The distribution unit also adds a function to automatically attach video clips of children's activities to the content of the broadcast. For example, it distributes videos of children learning a new game. The distribution unit also uses AI to automatically generate video clips of children's activities and reflect them in the content of the broadcast. For example, it distributes videos of children working together to create something. In this way, by attaching video clips of children's activities, parents can understand their children's activities in more detail.

[0073] The distribution unit is equipped with an emotion estimation function and can analyze the emotional reactions of parents when they view the communication book or childcare diary, and optimize the content of the next distribution. The distribution unit, for example, uses the emotion estimation function to build a system that analyzes the emotional reactions of parents when they view the distribution content. For example, it detects scenes in which the parents are happy, and optimizes the content of the next distribution. The distribution unit also develops a system that analyzes the emotional reactions of parents and optimizes the content of the next distribution. For example, it detects scenes in which the parents are feeling anxious, and adjusts the content of the next distribution. The distribution unit also uses the emotion estimation function to collect the emotional reactions of parents when they view the distribution content in real time, and optimize the content of the next distribution. For example, it records scenes in which the parents are surprised or moved, and reflects this in the content of the next distribution. In this way, by analyzing the emotional reactions of parents and optimizing the content of the next distribution, it is possible to provide information that meets the needs of parents.

[0074] The distribution department will automatically translate the content into multiple languages, making it possible to accommodate foreign parents. For example, the distribution department will build a system in which AI will automatically translate the content into multiple languages. For example, the content will be translated to match the parent's native language, such as English, Spanish, or Chinese. The distribution department will also add a function to automatically translate the content into multiple languages ​​and accommodate foreign parents. For example, the content will be displayed in the language selected by the parent. The distribution department will also develop a system in which AI will automatically translate the content to accommodate foreign parents. For example, the content will be made available so that parents can view the content in their native language. This will allow the content to be automatically translated into multiple languages, making it possible to accommodate foreign parents.

[0075] The distribution unit can have a function to distribute the content at a specific time period selected by the guardian. For example, the distribution unit builds a system in which AI sends the content at a specific time period selected by the guardian. For example, the content is sent to coincide with the time when the guardian returns home from work. The distribution unit also adds a function to send the content at a specific time period selected by the guardian. For example, it makes it possible for the guardian to receive the content at a time period of their choice. The distribution unit also develops a system in which AI sends the content at a time period selected by the guardian. For example, it makes it possible for the guardian to receive the content in the morning. As a result, by distributing the content at a specific time period selected by the guardian, the guardian can receive the information at a time of their choice.

[0076] The distribution unit is equipped with an emotion estimation function and can monitor the emotions of parents when they view the communication book or childcare diary in real time and provide feedback. The distribution unit, for example, uses the emotion estimation function to build a system that monitors the emotions of parents when they view the distributed content in real time. For example, it detects scenes in which the parents are happy and provides feedback. The distribution unit also develops a system that monitors the emotions of parents in real time and provides feedback. For example, it detects scenes in which the parents are feeling anxious and provides appropriate feedback. The distribution unit also uses the emotion estimation function to collect the emotions of parents when they view the distributed content in real time and provide feedback. For example, it records scenes in which the parents are surprised or moved and provides appropriate feedback. In this way, by monitoring the emotions of parents in real time and providing feedback, it becomes possible to respond to the needs of parents.

[0077] The alert unit improves the risky behavior detection algorithm and is able to detect danger earlier. For example, the alert unit improves the AI's risky behavior detection algorithm and builds a system that detects danger earlier. For example, it detects early on the movement of a child trying to climb to a high place. The alert unit also improves the risky behavior detection algorithm and adds a function that detects danger earlier. For example, it detects early on the movement of a child that is about to collide violently with another child. The alert unit also improves the AI's risky behavior detection algorithm and develops a system that detects danger earlier. For example, it detects early on the movement of a child trying to pick up a dangerous tool. In this way, by improving the risky behavior detection algorithm, danger can be detected earlier.

[0078] The alert unit can learn patterns of dangerous behavior and issue preventative alerts. For example, the alert unit will build a system in which AI learns patterns of dangerous behavior and issues preventative alerts. For example, it will issue an alert before a child tries to climb to a high place. The alert unit will also add a function to learn patterns of dangerous behavior and issue preventative alerts. For example, it will issue an alert before a child is about to collide violently with another child. The alert unit will also develop a system in which AI learns patterns of dangerous behavior and issues preventative alerts. For example, it will issue an alert before a child tries to pick up a dangerous tool. In this way, by learning patterns of dangerous behavior and issuing preventative alerts, the risk of accidents can be reduced.

[0079] The alert unit is equipped with an emotion estimation function and can detect stress and anxiety in children and detect signs of risky behavior early. The alert unit, for example, uses the emotion estimation function to detect stress and anxiety in children and build a system that detects signs of risky behavior early. For example, it detects situations in which children are feeling anxious and detects signs of risky behavior. The alert unit also analyzes children's stress and anxiety and develops a system that detects signs of risky behavior early. For example, it detects situations in which children are feeling stressed and detects signs of risky behavior. The alert unit also uses the emotion estimation function to monitor children's stress and anxiety in real time and detect signs of risky behavior early. For example, it records situations in which children are feeling anxious and detects signs of risky behavior. In this way, the risk of accidents can be reduced by detecting stress and anxiety in children and detecting signs of risky behavior early.

[0080] The alert unit can suggest specific ways of responding to childcare workers when an alert is issued. For example, the alert unit will build a system in which AI suggests specific ways of responding to childcare workers when an alert is issued. For example, when a child tries to climb to a high place, the alert unit will suggest safe ways of responding to the childcare worker. The alert unit will also add a function to suggest specific ways of responding to childcare workers when an alert is issued. For example, when a child is about to collide violently with another child, the alert unit will suggest appropriate ways of responding to the childcare worker. The alert unit will also develop a system in which AI suggests specific ways of responding to childcare workers when an alert is issued. For example, when a child tries to pick up a dangerous tool, the alert unit will suggest safe ways of responding to the childcare worker. In this way, by suggesting specific ways of responding when an alert is issued, childcare workers can respond quickly and appropriately.

[0081] The alert unit can also send a notification to parents when an alert is issued, allowing the situation to be shared. For example, the alert unit will build a system in which, when an alert is issued by AI, a notification is sent to parents as well, allowing the situation to be shared. For example, if a child tries to climb to a high place, a notification is also sent to the parents. The alert unit will also add a function in which, when an alert is issued, a notification is sent to parents as well, allowing the situation to be shared. For example, if a child is about to collide violently with another child, a notification is also sent to the parents. The alert unit will also develop a system in which, when an alert is issued by AI, a notification is sent to parents as well, allowing the situation to be shared. For example, if a child tries to pick up a dangerous tool, a notification is also sent to the parents. This allows the parents to quickly understand the situation of their child by sending a notification to parents as well when an alert is issued and sharing the situation.

[0082] The alert unit has an emotion estimation function and can record and analyze changes in children's emotions before and after the occurrence of risky behavior. The alert unit, for example, uses the emotion estimation function to build a system that records changes in children's emotions before and after the occurrence of risky behavior. For example, it records and analyzes changes in emotions before and after a child tries to climb to a high place. The alert unit also adds a function to record and analyze changes in children's emotions before and after the occurrence of risky behavior. For example, it records and analyzes changes in emotions before and after a child almost hits another child. The alert unit also uses the emotion estimation function to record and analyze changes in children's emotions before and after the occurrence of risky behavior in real time. For example, it records and analyzes changes in emotions before and after a child tries to pick up a dangerous tool. In this way, by recording and analyzing changes in children's emotions before and after the occurrence of risky behavior, it is possible to more accurately grasp signs of risky behavior.

[0083] The analysis unit can add evaluations of children's social skills and cooperation. For example, the analysis unit builds a system in which AI evaluates children's social skills and cooperation and reflects this in growth analysis. For example, it evaluates how well children cooperate when working together to create something. The analysis unit also develops a function to add evaluations of children's social skills and cooperation to growth analysis. For example, it evaluates how well children cooperate when playing with friends. The analysis unit also develops a system in which AI evaluates children's social skills and cooperation and reflects this in growth analysis. For example, it evaluates how well children cooperate when participating in group activities. In this way, adding evaluations of children's social skills and cooperation makes it possible to perform more detailed growth analysis.

[0084] The analysis unit can visualize the results of the growth analysis in a format that is easy for childcare workers to understand. For example, the analysis unit builds a system in which AI visualizes the results of the growth analysis and provides them in a format that is easy for childcare workers to understand. For example, it visually displays a child's growth using graphs and charts. The analysis unit also develops a function to visualize the results of the growth analysis in a format that is easy for childcare workers to understand. For example, it generates infographics showing a child's growth. The analysis unit also develops a system in which AI visualizes the results of the growth analysis and provides them in a format that is easy for childcare workers to understand. For example, it creates a dashboard showing a child's growth. In this way, visualizing the results of the growth analysis makes it easier for childcare workers to understand a child's growth.

[0085] The analysis unit is equipped with an emotion estimation function, and can reflect changes in children's emotions in growth analysis and utilize them in advising childcare policies. The analysis unit, for example, uses the emotion estimation function to build a system that reflects changes in children's emotions in growth analysis. For example, it records scenes in which children are having fun and utilizes them in advising childcare policies. The analysis unit also develops a function that reflects changes in children's emotions in growth analysis and utilizes them in advising childcare policies. For example, it records scenes in which children feel anxious and utilizes them in advising childcare policies. The analysis unit also develops a system that uses the emotion estimation function to record changes in children's emotions in real time and reflects them in growth analysis. For example, it records scenes in which children are surprised or moved and utilizes them in advising childcare policies. In this way, changes in children's emotions can be reflected in growth analysis and utilized in advising childcare policies, thereby providing more appropriate childcare policies.

[0086] The analysis unit shares the results of the growth analysis with parents, allowing them to use it to inform child-rearing policies at home. For example, the analysis unit may build a system in which AI shares the results of the growth analysis with parents. For example, it may provide information that will help parents use their child's growth to inform their child-rearing policies at home. The analysis unit may also develop a function to share the results of the growth analysis with parents, allowing them to use it to inform their child-rearing policies at home. For example, it may provide information in a format that makes it easy for parents to understand their child's growth. The analysis unit may also develop a system in which AI shares the results of the growth analysis with parents, allowing them to use it to inform their child-rearing policies at home. For example, it may provide advice to parents so that they can reflect their child's growth in their child-rearing policies at home. In this way, sharing the results of the growth analysis with parents will allow them to use it to inform their child-rearing policies at home.

[0087] The analysis department can share the results of the growth analysis with other childcare facilities to learn best practices. For example, the analysis department builds a system in which the AI ​​shares the results of the growth analysis with other childcare facilities. For example, it shares information with other childcare facilities to learn best practices. The analysis department also develops a function to share the results of the growth analysis with other childcare facilities to learn best practices. For example, it learns ways to cooperate with other childcare facilities to promote children's growth. The analysis department also develops a system in which the AI ​​shares the results of the growth analysis with other childcare facilities to learn best practices. For example, it shares information with other childcare facilities to learn effective childcare policies. In this way, by sharing the results of the growth analysis with other childcare facilities, it is possible to learn best practices and improve the quality of childcare.

[0088] The analysis unit is equipped with an emotion estimation function and can monitor children's emotional reactions after childcare policy advice is implemented and evaluate the effectiveness of the advice. The analysis unit, for example, uses the emotion estimation function to build a system that monitors children's emotional reactions after childcare policy advice is implemented. For example, it records scenes in which children are having fun and evaluates the effectiveness of the advice. The analysis unit also develops a function that monitors children's emotional reactions after childcare policy advice is implemented and evaluates the effectiveness of the advice. For example, it records scenes in which children feel anxious and evaluates the effectiveness of the advice. The analysis unit also uses the emotion estimation function to develop a system that monitors children's emotional reactions in real time after childcare policy advice is implemented and evaluates the effectiveness of the advice. For example, it records scenes in which children are surprised or moved and evaluates the effectiveness of the advice. In this way, by monitoring children's emotional reactions after childcare policy advice is implemented and evaluating the effectiveness of the advice, more effective childcare policies can be provided.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] The childcare support system can further include a voice recognition unit. The voice recognition unit can analyze the content of children's conversations in real time and extract specific keywords and phrases. For example, it can record conversations when children learn new words and reflect their growth in the childcare diary. The voice recognition unit can also analyze the content of children's conversations to understand the background to their actions. For example, it can record conversations when children work together to create something and describe the process of their collaboration in detail in the childcare diary. Furthermore, the voice recognition unit can record the content of children's conversations as text data and later reflect it in the childcare diary. In this way, by recording the content of children's conversations, a more detailed childcare diary can be created.

[0091] The childcare support system can further include an automatic viewpoint adjustment unit. The automatic viewpoint adjustment unit can automatically focus on important scenes and capture them in detail. For example, when children start a particular activity, the camera automatically zooms in on that scene. The automatic viewpoint adjustment unit can also capture important children's actions in detail. For example, when children start a new game, the camera tracks that scene. The automatic viewpoint adjustment unit can also capture in detail the process of children working together to create something. This allows for more accurate recording by capturing important scenes in detail.

[0092] The childcare support system can further include a temperature sensor and a humidity sensor. These sensors can simultaneously record environmental data and analyze the correlation with children's behavior. For example, it can analyze how children behave when the temperature in the classroom is high. It can also analyze what kind of play children prefer when the humidity is high. Furthermore, environmental data such as temperature and humidity can be recorded and children's behavior patterns can be analyzed. This makes it possible to analyze the correlation with children's behavior by recording environmental data.

[0093] Childcare support systems can also be equipped with multiple cameras and integrate footage from different angles to generate 3D models, enabling more detailed behavioral analysis. For example, children's movements can be analyzed in three dimensions and detailed behavioral patterns can be recorded. Systems can also be built that use multiple cameras to integrate footage from different angles and generate 3D models, enabling more detailed behavioral analysis. For example, children's play can be recorded in three dimensions and used for behavioral analysis. Furthermore, footage from different angles can be integrated to generate 3D models, enabling more detailed behavioral analysis.

[0094] The childcare support system also has an emotion estimation function that can estimate children's emotions from their facial expressions and tone of voice and reflect that information in the records. For example, scenes in which children are having fun can be recorded and their emotions can be reflected in the childcare diary. The emotion estimation function can also be used to analyze children's emotions from their facial expressions and tone of voice and reflect that information in the records. For example, scenes in which children are in trouble can be recorded and their emotions can be recorded in the childcare diary. Furthermore, the emotion estimation function can analyze children's emotions in real time. For example, scenes in which children are surprised or happy can be recorded and their emotions can be reflected in the childcare diary. By reflecting children's emotions in the records, a more detailed childcare diary can be created.

[0095] The childcare support system is further equipped with an emotion estimation function, which can monitor changes in children's emotions in real time and provide feedback to childcare workers. For example, it can detect when children are feeling anxious and provide feedback to childcare workers. It is also possible to build a system using the emotion estimation function to analyze changes in children's emotions in real time and provide feedback to childcare workers. For example, it can detect when children are having fun and notify the childcare workers. Furthermore, it is equipped with an emotion estimation function, which can monitor changes in children's emotions in real time. For example, it can detect when children are in trouble and prompt the childcare workers to take action. This allows changes in children's emotions to be monitored in real time and feedback to childcare workers, enabling rapid response.

[0096] The childcare support system can also be equipped with an emotion estimation function, which records changes in children's emotions and provides parents with emotional highlights for the day. For example, it can record scenes in which children are having fun or in trouble and provide parents with emotional highlights for the day. The emotion estimation function can also be used to analyze changes in children's emotions and reflect them in a communication book or childcare diary. For example, it can record scenes in which children are surprised or happy and provide these to parents. Furthermore, a system can be built that is equipped with an emotion estimation function, records changes in children's emotions, and provides parents with emotional highlights for the day. For example, it can record scenes in which children are feeling anxious and notify parents. In this way, by recording changes in children's emotions and providing parents with emotional highlights for the day, parents can have a more detailed understanding of how their children are doing throughout the day.

[0097] The childcare support system is further equipped with an emotion estimation function, which can collect emotional responses from parents when they view the communication book or childcare diary and reflect them in the next content. For example, it can detect scenes in which parents are happy and reflect them in the next content. In addition, using the emotion estimation function, it is possible to build a system that analyzes parents' emotional responses and reflects them in the next content of the communication book or childcare diary. For example, it can detect scenes in which parents are feeling anxious and adjust the next content. Furthermore, using the emotion estimation function, it is possible to collect emotional responses from parents when they view the communication book or childcare diary in real time and reflect them in the next content. For example, it can record scenes in which parents are surprised or moved and reflect them in the next content. In this way, by collecting parents' emotional responses and reflecting them in the next content, it is possible to provide communication books and childcare diaries that meet the needs of parents.

[0098] The childcare support system is further equipped with an emotion estimation function, which can monitor the emotions of parents when they view the communication book or childcare diary in real time and provide feedback. For example, it can detect when a parent is happy and provide feedback. Furthermore, using the emotion estimation function, it is possible to develop a system that monitors the emotions of parents in real time and provides feedback. For example, it can detect when a parent is feeling anxious and provide appropriate feedback. Furthermore, using the emotion estimation function, it is possible to collect the emotions of parents when they view the communication book or childcare diary in real time and provide feedback. For example, it can record when a parent is surprised or moved and provide appropriate feedback. In this way, by monitoring the emotions of parents in real time and providing feedback, it is possible to respond to parents' needs.

[0099] The childcare support system is further equipped with an emotion estimation function, which can monitor children's emotional reactions after childcare policy advice is implemented and evaluate the effectiveness of the advice. For example, scenes in which children are having fun can be recorded and the effectiveness of the advice can be evaluated. Furthermore, using the emotion estimation function, a system can be developed that monitors children's emotional reactions after childcare policy advice is implemented and evaluates the effectiveness of the advice. For example, scenes in which children feel anxious can be recorded and the effectiveness of the advice can be evaluated. Furthermore, using the emotion estimation function, children's emotional reactions can be monitored in real time after childcare policy advice is implemented and the effectiveness of the advice can be evaluated. For example, scenes in which children are surprised or moved can be recorded and the effectiveness of the advice can be evaluated. In this way, by monitoring children's emotional reactions after childcare policy advice is implemented and evaluating the effectiveness of the advice, more effective childcare policies can be provided.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The camera captures the entire classroom. For example, a high-resolution fixed camera can be installed in the classroom to capture the children's movements and behavior in detail. Step 2: The analysis unit analyzes the video captured by the camera. For example, it uses an image analysis algorithm to analyze the children's behavior and speech. Step 3: The generation unit creates a communication book or a childcare diary based on the data analyzed by the analysis unit. For example, the generation AI automatically generates a communication book or a childcare diary based on the children's behavior and comments. Step 4: The distribution unit distributes the communication book or childcare diary created by the generation unit to parents, for example, via email or a dedicated app, so that parents can check how their children are doing at any time. Step 5: The alert unit issues an alert based on the dangerous behavior detected by the analysis unit. For example, if a child tries to climb to a high place or nearly collide with another child, it will issue an alert to staff. Step 6: The analysis unit analyzes the growth of each child based on the data collected by the analysis unit. For example, it analyzes in detail the growth of each child, such as language development and improvement in motor skills, and provides advice to childcare workers on childcare policies.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0114] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0115] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0122] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0123] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0124] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0125] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0126] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0127] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0130] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0144] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0146] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0152] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0153] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0154] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0155] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0156] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0157] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0158] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0159] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0160] 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.

[0161] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0162] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0163] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0164] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0165] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0166] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0167] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0168] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A camera that captures the entire classroom, an analysis unit that analyzes the video captured by the camera; a generation unit that generates a communication book or a childcare diary based on the data analyzed by the analysis unit; a distribution unit that distributes the communication book or the childcare diary created by the generation unit to parents; an alert unit that issues an alert based on the risky behavior detected by the analysis unit; an analysis unit that analyzes the growth of each child based on the data collected by the analysis unit; A system characterized by:

2. The camera is Equipped with voice recognition function, it will analyze and record the content of children's conversations. The system of claim 1 .

3. The camera is Equipped with an automatic viewpoint adjustment function, it can capture important scenes in more detail. The system of claim 1 .

4. The camera is Equipped with an emotion estimation function, it estimates children's emotions from their facial expressions and tone of voice, and reflects this information in the records. The system of claim 1 .

5. The camera is Equipped with temperature and humidity sensors, it will simultaneously record environmental data and analyze its relationship with children's behavior. The system of claim 1 .

6. The camera is Multiple cameras will be installed, and images from different angles will be integrated to generate 3D models for more detailed behavioral analysis. The system of claim 1 .

Images