system

Abstract

The system according to the embodiment aims to efficiently grasp the attendance status of children and to promptly contact caregivers. [Solution] A system according to an embodiment includes a face recognition camera, a kindergarten attendance app, a generation unit, and a communication unit. The face recognition camera recognizes the child's face. The kindergarten attendance app records information recognized by the face recognition camera. The generation unit creates a message based on the information recorded by the kindergarten attendance app. The communication unit sends the message created by the generation unit to a caregiver.

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 had the problem of making it difficult to efficiently grasp children's attendance status and quickly contact caregivers.

[0005] The system according to the embodiment aims to efficiently grasp the attendance status of children and to promptly contact caregivers. [Means for solving the problem]

[0006] The system according to the embodiment includes a face recognition camera, a kindergarten attendance app, a generation unit, and a communication unit. The face recognition camera recognizes the child's face. The kindergarten attendance app records information recognized by the face recognition camera. The generation unit creates a message based on the information recorded by the kindergarten attendance app. The communication unit sends the message created by the generation unit to the caregiver. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently grasp the attendance status of children and quickly contact caregivers. [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 nursery school attendance management system according to an embodiment of the present invention is a system that links a facial recognition camera that recognizes the faces of children and a nursery school attendance app. This system installs a facial recognition camera at the entrance of the nursery school and recognizes the faces of children arriving at the nursery school. This camera is linked to the nursery school attendance app and records in real time whether or not a child has arrived at the nursery school. If a child has not arrived by the designated time, a generation AI creates a message to be sent to the caregiver. This message is sent to the caregiver via a messaging app (e.g., LINE (registered trademark)) or SMS. After receiving the message, the caregiver can reply with the reason for the child's absence. For example, a facial recognition camera installed at the entrance of the nursery school recognizes the face of a child arriving at the nursery school and records the message in the nursery school attendance app. If a child has not arrived by the designated time, the generation AI creates a message saying, "It seems your child has not arrived at the nursery school yet. Is there a problem?" and sends it to the caregiver via a messaging app or SMS. After receiving the message, the caregiver replies with the reason, such as, "I'm not feeling well, so I'm keeping him / her home." This system allows nurseries to grasp the attendance status of children in real time and facilitates smooth communication with caregivers. Also, nurseries can quickly communicate the reason why a child is not at school, so they can leave their children with peace of mind. This allows the nursery school attendance management system to grasp the attendance status of children in real time and facilitates smooth communication with caregivers.

[0029] A nursery school attendance management system according to an embodiment includes a face recognition camera, a nursery school attendance app, a generation unit, and a communication unit. The face recognition camera recognizes the faces of children. For example, the face recognition camera can perform highly accurate facial recognition using a deep learning algorithm. The face recognition camera can recognize the faces of children in real time and send information to the nursery school attendance app. For example, the face recognition camera recognizes the faces of children as they pass through the entrance to the nursery school and sends the information to the nursery school attendance app. The nursery school attendance app records the information recognized by the face recognition camera. For example, the nursery school attendance app can store the children's arrival times and attendance statuses in a database. The nursery school attendance app can provide the generation unit with information about children who have not arrived at the nursery school by a specified time. For example, the nursery school attendance app sends information about children whose arrival has not been confirmed even after a specified time has passed to the generation unit. The generation unit uses a generation AI to create a message based on the information about children who have not arrived at the nursery school by the specified time. For example, the generation AI can generate a message such as, "It seems your child has not arrived at the nursery school yet. Is there a problem?" The generation unit can also use the generation AI to customize the tone and content of the message to be sent to the caregiver. For example, the generation AI can estimate the childcare worker's emotions and generate a message in a gentle tone. The communication unit sends the message created by the generation unit to the childcare worker. For example, the communication unit can send the message using a messaging app or SMS. The communication unit can also select the optimal communication method taking into account the childcare worker's device information. For example, if the childcare worker is using a smartphone, the communication unit can preferentially select SMS or a messaging app. As a result, the nursery school attendance management system according to the embodiment can grasp the attendance status of children in real time and smoothly communicate with the childcare worker.

[0030] The facial recognition camera can recognize the child's face and send the information to the kindergarten attendance app. The facial recognition camera can perform highly accurate facial recognition using, for example, a deep learning algorithm. For example, the facial recognition camera recognizes the child's face as he or she passes through the entrance of the nursery school and sends the information to the kindergarten attendance app. The facial recognition camera can also perform facial recognition in real time and send the information to the kindergarten attendance app. For example, the facial recognition camera recognizes the child's face as he or she passes through the entrance of the nursery school and sends the information to the kindergarten attendance app in real time. By recognizing the child's face and sending the information to the kindergarten attendance app, the kindergarten attendance status can be monitored in real time. Some or all of the above-mentioned processing in the facial recognition camera may be performed using, for example, AI, or may be performed without AI. For example, the facial recognition camera can input image data for recognizing the child's face into a generation AI and have the generation AI perform facial recognition.

[0031] The kindergarten attendance app can provide the generation unit with information about children who have not arrived at kindergarten by a specified time. The kindergarten attendance app, for example, transmits information about children who have not been confirmed to have arrived at kindergarten even after a specified time has passed to the generation unit. For example, the kindergarten attendance app retrieves information about children who have not been confirmed to have arrived at kindergarten even after a specified time from a database and transmits it to the generation unit. The kindergarten attendance app can also provide the generation unit with information about children who have not been confirmed to have arrived at kindergarten even after a specified time in real time. For example, the kindergarten attendance app transmits information about children who have not been confirmed to have arrived at kindergarten even after a specified time in real time to the generation unit. This allows for prompt action by providing the generation unit with information about children who have not arrived at kindergarten by the specified time. Some or all of the above-described processing in the kindergarten attendance app may be performed using, or without, AI, for example. For example, the kindergarten attendance app can input information about children who have not been confirmed to have arrived at kindergarten even after a specified time into the generation AI and have the generation AI provide the information.

[0032] The generation unit can create a message based on information about children who have not arrived at school by the specified time. The generation unit, for example, uses a generation AI to create a message based on information about children who have not arrived at school by the specified time. For example, the generation AI can generate a message such as, "It seems your child has not arrived at school yet. Is there a problem?" The generation unit can also use the generation AI to customize the tone and content of the message to be sent to the caregiver. For example, the generation AI can estimate the caregiver's emotions and generate a message in a gentle tone. This allows the caregiver to be contacted quickly by creating a message based on information about children who have not arrived at school by the specified time. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input information about children who have not arrived at school by the specified time into the generation AI and have the generation AI create a message.

[0033] The communication unit can send the message created by the generation unit to the caregiver. The communication unit can send the message using, for example, a messaging app or SMS. For example, the communication unit sends the message created by the generation unit to the caregiver using a messaging app. The communication unit can also send the message created by the generation unit to the caregiver via SMS. This allows smooth communication with the caregiver by sending the message created by the generation unit to the caregiver. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the message created by the generation unit into the generation AI and cause the generation AI to send the message.

[0034] The communication unit can add specific communication means (e.g., email or telephone). The communication unit can, for example, send a message using email. For example, the communication unit sends a message created by the generation unit to the caregiver by email. The communication unit can also send a message using telephone. For example, the communication unit sends a message created by the generation unit to the caregiver by telephone. This diversifies the means of communication with the caregiver by adding communication means other than message apps and SMS. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input a message created by the generation unit into a generation AI and have the generation AI send the message.

[0035] The system may be equipped with a specific encryption method (e.g., AES encryption) and access control (e.g., password authentication). The system may encrypt data using, for example, AES encryption. For example, the system may protect children's attendance information and caregiver contact information using AES encryption. The system may also perform access control using password authentication. For example, the system may require password authentication when a caregiver accesses the attendance app. This improves the security of the system by providing data encryption and access control. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system may have a generating AI perform data encryption and access control.

[0036] The system can perform specific security checks (e.g., virus scans) and updates (e.g., software patches). The system can, for example, perform regular security checks using a virus scan. For example, the system runs a virus scan to detect viruses and malware in the system. The system can also perform regular updates using a software patch. For example, the system applies a software patch to fix system vulnerabilities. In this way, regular security checks and updates maintain the security of the system. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can have a generation AI perform security checks and updates.

[0037] The facial recognition camera can analyze a child's past attendance history and optimize the timing of facial recognition. For example, the facial recognition camera analyzes how often a child has been late in the past and strengthens facial recognition during times when lateness is likely. For example, the facial recognition camera retrieves from a database the frequency with which a child has been late in the past and strengthens facial recognition during times when lateness is likely. Furthermore, if a child is likely to be late on a particular day of the week, the facial recognition camera can increase the frequency of facial recognition on that day. For example, the facial recognition camera analyzes data on which children are likely to be late on a particular day of the week and increases the frequency of facial recognition on that day. Furthermore, if a child tends to arrive early, the facial recognition camera can strengthen facial recognition in the early morning. For example, the facial recognition camera retrieves from a database the tendency for children to arrive early and strengthens facial recognition in the early morning. In this way, the timing of facial recognition can be optimized by analyzing the child's past attendance history. Some or all of the above-described processing in the facial recognition camera may be performed using, for example, AI, or without AI. For example, a facial recognition camera can input a child's past attendance history into the generation AI, allowing the generation AI to optimize the timing of facial recognition.

[0038] A facial recognition camera can improve the accuracy of facial recognition by also using physical information such as a child's height and weight. For example, a facial recognition camera can improve the accuracy of facial recognition by adjusting the camera angle based on the child's height information. For example, a facial recognition camera can obtain the child's height information from a database and adjust the camera angle. A facial recognition camera can also improve the accuracy of facial recognition by correcting the facial contours based on the child's weight information. For example, a facial recognition camera can obtain the child's weight information from a database and correct the facial contours. A facial recognition camera can also optimize the facial recognition algorithm based on the child's physical information. For example, a facial recognition camera can optimize the facial recognition algorithm based on the child's physical information such as the child's height and weight. By using the child's physical information in combination, the accuracy of facial recognition is improved. Some or all of the above-described processing in a facial recognition camera may be performed using, or without, AI. For example, a facial recognition camera can input the child's physical information such as the height and weight into a generation AI and cause the generation AI to improve the recognition accuracy.

[0039] A facial recognition camera can improve the accuracy of facial recognition by taking into account a child's clothing and belongings. For example, if a child is wearing a specific uniform, the facial recognition camera improves the accuracy of recognition by taking into account the clothing. For example, if a child is wearing a specific uniform, the facial recognition camera detects the clothing and improves the accuracy of recognition. Furthermore, if a child is wearing a specific hat, the facial recognition camera can improve the accuracy of recognition by taking into account the child's belongings. For example, if a child is wearing a specific hat, the facial recognition camera detects the belongings and improves the accuracy of recognition. Furthermore, if a child is carrying a specific backpack, the facial recognition camera can improve the accuracy of recognition by taking into account the child's belongings. For example, if a child is carrying a specific backpack, the facial recognition camera detects the belongings and improves the accuracy of recognition. In this way, the accuracy of facial recognition is improved by taking into account the child's clothing and belongings. Some or all of the above-described processing in the facial recognition camera may be performed using, for example, AI, or may be performed without AI. For example, the facial recognition camera can input image data for detecting the child's clothing and belongings into a generation AI, and have the generation AI improve the accuracy of recognition.

[0040] When identifying faces, a facial recognition camera can improve the accuracy of identification by analyzing a child's behavioral patterns when commuting to kindergarten. For example, if a child takes a specific route to kindergarten, the facial recognition camera improves the accuracy of identification by taking into account that behavioral pattern. For example, when a child takes a specific route to kindergarten, the facial recognition camera detects that behavioral pattern and improves the accuracy of identification. Furthermore, if a child commutes to kindergarten at a specific time of day, the facial recognition camera can improve the accuracy of identification by taking into account that behavioral pattern. For example, when a child commutes to kindergarten at a specific time of day, the facial recognition camera detects that behavioral pattern and improves the accuracy of identification. Furthermore, if a child commutes to kindergarten with a specific friend, the facial recognition camera can improve the accuracy of identification by taking into account that behavioral pattern. For example, when a child commutes to kindergarten with a specific friend, the facial recognition camera detects that behavioral pattern and improves the accuracy of identification. In this way, the accuracy of facial recognition is improved by analyzing the behavioral patterns when commuting to kindergarten. Some or all of the above-described processing in the facial recognition camera may be performed using, for example, AI, or may be performed without using AI. For example, a facial recognition camera can input data to the generation AI to detect children's behavioral patterns when they arrive at school, allowing the generation AI to improve its recognition accuracy.

[0041] The kindergarten attendance app can add a function to record a child's health condition and allergy information. For example, the kindergarten attendance app may record a child's health condition daily and notify the caregiver. For example, the kindergarten attendance app may record a child's health condition daily and notify the caregiver. The kindergarten attendance app may also record a child's allergy information and notify the caregiver. For example, the kindergarten attendance app may record a child's allergy information and notify the caregiver. The kindergarten attendance app may also provide appropriate advice to the caregiver based on the child's health condition and allergy information. For example, the kindergarten attendance app may provide appropriate advice to the caregiver based on the child's health condition and allergy information. In this way, by recording the child's health condition and allergy information, appropriate information can be provided to the caregiver. Some or all of the above-mentioned processes in the kindergarten attendance app may be performed using, or without, AI. For example, the kindergarten attendance app may input a child's health condition and allergy information into a generation AI and have the generation AI record and notify the information.

[0042] The kindergarten attendance app can add a function to automatically save photos of children when they arrive at kindergarten. For example, the kindergarten attendance app automatically saves photos taken with a face recognition camera when a child arrives at kindergarten. For example, the kindergarten attendance app automatically saves photos taken with a face recognition camera when a child arrives at kindergarten. The kindergarten attendance app can also automatically save photos taken by a caregiver when a child arrives at kindergarten. For example, the kindergarten attendance app automatically saves photos taken by a caregiver when a child arrives at kindergarten. The kindergarten attendance app can also automatically take and save photos when a child arrives at kindergarten. For example, the kindergarten attendance app automatically takes and saves photos when a child arrives at kindergarten. In this way, by automatically saving photos when a child arrives at kindergarten, it is possible to record the child's attendance status. Some or all of the above-mentioned processing in the kindergarten attendance app may be performed using, or without, AI. For example, the kindergarten attendance app can input photos of a child when they arrive at kindergarten into a generation AI and have the generation AI save the photos.

[0043] The kindergarten attendance app can add a communication function between childcare workers. For example, the kindergarten attendance app can add a chat function that allows childcare workers to send messages to each other. For example, the kindergarten attendance app can add a chat function that allows childcare workers to send messages to each other. The kindergarten attendance app can also add a bulletin board function that allows childcare workers to share information with each other. For example, the kindergarten attendance app can add a bulletin board function that allows childcare workers to share information with each other. The kindergarten attendance app can also add a calendar function that allows childcare workers to share schedules with each other. For example, the kindergarten attendance app can add a calendar function that allows childcare workers to share schedules with each other. By adding this communication function between childcare workers, information sharing becomes smoother. Some or all of the above-mentioned processing in the kindergarten attendance app can be performed using, for example, AI, or can be performed without using AI. For example, the kindergarten attendance app can input the communication function between childcare workers into a generation AI and have the generation AI add the function.

[0044] The kindergarten attendance app can add a function to record an audio message when a child arrives at kindergarten. In the kindergarten attendance app, for example, a caregiver records an audio message when a child arrives at kindergarten and saves it in the app. For example, in the kindergarten attendance app, a caregiver records an audio message when a child arrives at kindergarten and saves it in the app. In addition, in the kindergarten attendance app, a facial recognition camera can record an audio message when a child arrives at kindergarten and save it in the app. For example, in the kindergarten attendance app, a facial recognition camera can record an audio message when a child arrives at kindergarten and save it in the app. In addition, in the kindergarten attendance app, the app can automatically record and save an audio message when a child arrives at kindergarten. For example, in the kindergarten attendance app, the app automatically records and saves an audio message when a child arrives at kindergarten. In this way, by recording an audio message when a child arrives at kindergarten, it is possible to record the child's attendance status in detail. Some or all of the above-mentioned processing in the kindergarten attendance app may be performed, for example, using AI or without using AI. For example, a kindergarten attendance app can input a voice message that a child will send when arriving at kindergarten into a generation AI, and have the generation AI record the voice message.

[0045] The generation unit can be added with a function to generate an optimal message by referring to past message history. The generation unit, for example, generates an optimal message based on messages received by the caregiver in the past. For example, the generation unit retrieves messages received by the caregiver in the past from a database and generates an optimal message. The generation unit can also generate an optimal message based on messages sent by the caregiver in the past. For example, the generation unit retrieves messages sent by the caregiver in the past from a database and generates an optimal message. The generation unit can also analyze the content of messages exchanged by the caregiver in the past and generate an optimal message. For example, the generation unit analyzes the content of messages exchanged by the caregiver in the past and generates an optimal message. In this way, a more appropriate message can be generated by referring to the past message history. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past message history into the generation AI and cause the generation AI to generate an optimal message.

[0046] The generation unit can be added with a function to analyze a child's attendance history and generate an individually customized message. The generation unit generates an individually customized message, for example, based on how often the child has been late in the past. For example, the generation unit retrieves from a database the frequency with which the child has been late in the past and generates an individually customized message. Furthermore, if a child is prone to being late on a particular day of the week, the generation unit can generate a message tailored to that day. For example, the generation unit analyzes data on how often a child is prone to being late on a particular day of the week and generates a message tailored to that day. Furthermore, if a child tends to arrive early, the generation unit can generate a message tailored to early mornings. For example, the generation unit retrieves from a database the child's tendency to arrive early and generates a message tailored to early mornings. In this way, individually customized messages can be generated by analyzing a child's attendance history. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input a child's attendance history into the generation AI and cause the generation AI to generate an individually customized message.

[0047] The generation unit can add a function to generate multilingual messages according to the language setting of the caregiver. The generation unit automatically sets the language of the message based on, for example, the language setting of the caregiver's device. For example, the generation unit retrieves the language setting of the caregiver's device from a database and automatically sets the language of the message. The generation unit can also provide a language switching function when the caregiver uses multiple languages. For example, the generation unit provides a language switching function when the caregiver uses multiple languages. The generation unit can also generate messages in a specific language when the caregiver selects that language. For example, when the caregiver selects a specific language, the generation unit generates messages in that language. This enables more appropriate communication by generating multilingual messages according to the language setting of the caregiver. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the language setting of the caregiver into the generation AI and cause the generation AI to generate multilingual messages.

[0048] The generation unit can be added with a function to predict an optimal reply by analyzing the content of the childcare worker's past replies. The generation unit, for example, predicts an optimal reply based on the content of the childcare worker's past replies. For example, the generation unit retrieves the content of the childcare worker's past replies from a database and predicts an optimal reply. The generation unit can also analyze the content of messages exchanged by the childcare worker in the past and predict an optimal reply. For example, the generation unit analyzes the content of messages exchanged by the childcare worker in the past and predicts an optimal reply. The generation unit can also predict an optimal reply based on messages received by the childcare worker in the past. For example, the generation unit retrieves messages received by the childcare worker in the past from a database and predicts an optimal reply. In this way, by analyzing the content of the childcare worker's past replies, a more appropriate reply can be predicted. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the content of the childcare worker's past replies into the generation AI and cause the generation AI to predict an optimal reply.

[0049] The communication unit may be added with a function for determining the optimal timing of contact by referring to the childcare worker's past communication history. The communication unit determines the optimal timing of contact, for example, based on the time period in which the childcare worker previously replied. For example, the communication unit may retrieve the time period in which the childcare worker previously replied from a database and determine the optimal timing of contact. The communication unit may also determine the optimal timing of contact based on the time period in which the childcare worker previously received communication. For example, the communication unit may retrieve the time period in which the childcare worker previously received communication from a database and determine the optimal timing of contact. The communication unit may also determine the optimal timing of contact based on the frequency in which the childcare worker previously received communication. For example, the communication unit may retrieve the frequency in which the childcare worker previously received communication from a database and determine the optimal timing of contact. In this way, the optimal timing of contact can be determined by referring to the childcare worker's past communication history. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input the childcare worker's past communication history into a generation AI and have the generation AI determine the optimal timing of contact.

[0050] The communication unit can add a function to automatically update the contact information of the caregiver. For example, if the caregiver changes their contact information, the communication unit automatically reflects the changes in the system. For example, when the caregiver changes their contact information, the communication unit automatically reflects the changes in the system. The communication unit can also automatically reflect new contact information added by the caregiver in the system. For example, when the caregiver adds new contact information, the communication unit automatically reflects the changes in the system. The communication unit can also automatically delete contact information deleted by the caregiver from the system. For example, when the caregiver deletes contact information, the communication unit automatically deletes the information from the system. In this way, by automatically updating the contact information of the caregiver, the latest contact information can be maintained. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the contact information of the caregiver into the generation AI and have the generation AI update the information.

[0051] The communication unit may be added with a function to select the optimal means of communication by taking into consideration device information of the childcare worker. For example, when the childcare worker is using a smartphone, the communication unit may prioritize SMS or a messaging app. For example, when the childcare worker is using a smartphone, the communication unit may prioritize SMS or a messaging app. The communication unit may also prioritize email when the childcare worker is using a tablet. For example, when the childcare worker is using a tablet, the communication unit may prioritize email. The communication unit may also prioritize email or chat when the childcare worker is using a computer. For example, when the childcare worker is using a computer, the communication unit may prioritize email or chat. This allows the optimal means of communication to be selected by taking into consideration device information of the childcare worker. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit may input device information of the childcare worker into a generation AI and cause the generation AI to select the optimal means of communication.

[0052] The communication unit may add a function of analyzing the childcare worker's usage of communication methods and suggesting the optimal communication method. The communication unit, for example, suggests the optimal communication method based on the communication method used most frequently by the childcare worker in the past. For example, the communication unit may retrieve from a database the communication methods used most frequently by the childcare worker in the past and suggest the optimal communication method. The communication unit may also suggest the optimal communication method based on the communication method used by the childcare worker during a specific time period. For example, the communication unit may retrieve from a database the communication methods used by the childcare worker during a specific time period and suggest the optimal communication method. The communication unit may also suggest the optimal communication method based on the communication method used by the childcare worker on a specific day of the week. For example, the communication unit may retrieve from a database the communication methods used by the childcare worker on a specific day of the week and suggest the optimal communication method. In this way, the optimal communication method can be suggested by analyzing the childcare worker's usage of communication methods. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input the childcare worker's usage of communication methods into a generation AI and cause the generation AI to suggest the optimal communication method.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The nursery school attendance management system may further include a health management unit that monitors the health status of children. The health management unit may periodically measure a child's body temperature and heart rate and notify the caregiver if an abnormality is detected. For example, if a child's body temperature is high, the health management unit may send the caregiver a message saying, "Your child's body temperature seems high. Please check if there are any health problems." Similarly, if a child's heart rate is abnormally high, the health management unit may send the caregiver a message saying, "Your child's heart rate seems high. Please check if there are any health problems." Furthermore, the health management unit may record the child's health data over the long term and report any changes in the child's health status to the caregiver. For example, the health management unit may graph the child's body temperature and heart rate data and send the caregiver a message saying, "Please check for any changes in your child's health status." This allows for real-time monitoring of a child's health status and prompt notification to the caregiver, thereby enabling more effective health management of children.

[0055] The nursery school attendance management system may further include a behavior recording unit that records the behavior of children when they arrive at the nursery school. The behavior recording unit may record the behavior of children when they arrive at the nursery school using a camera and provide the information to caregivers. For example, the behavior recording unit may record video of children arriving at the nursery school and provide the video to caregivers along with a message such as, "Please check your child's behavior when arriving at the nursery school." The behavior recording unit may also analyze the behavioral patterns of children when they arrive at the nursery school and notify caregivers if an abnormality is detected. For example, if a child behaves differently than usual, the behavior recording unit may send a message such as, "Your child's behavior when arriving at the nursery school is abnormal. Please check." The behavior recording unit may also record the behavioral data of children when they arrive at the nursery school over the long term and report any changes in behavior to caregivers. For example, the behavior recording unit may graph the behavioral data of children when they arrive at the nursery school and send a message such as, "Please check any changes in your child's behavior when arriving at the nursery school." This allows for detailed recording of children's behavior when they arrive at the nursery school and provides the information to caregivers, thereby enabling more effective child safety management.

[0056] The nursery school attendance management system may further include a learning record unit that records the child's learning status and reports it to the caregiver. The learning record unit can record the child's learning status and report it to the caregiver. For example, the learning record unit can record the learning activities the child performed at the nursery school and report it to the caregiver along with a message such as, "Please check the learning activities your child performed today." The learning record unit can also analyze the child's learning progress and send the caregiver a message such as, "Please check your child's learning progress." The learning record unit can also record the child's learning data over the long term and report changes in learning to the caregiver. For example, the learning record unit can graph the child's learning data and send the caregiver a message such as, "Please check the changes in your child's learning." This allows the child's learning status to be recorded in detail and provided to the caregiver, thereby more effectively managing the child's learning.

[0057] The nursery school attendance management system may further include a meal recording unit that records the child's eating habits and reports them to the caregiver. The meal recording unit can record the child's eating habits and report them to the caregiver. For example, the meal recording unit can record the meals the child ate at the nursery school and report them to the caregiver along with a message such as, "Please check what your child ate today." The meal recording unit can also analyze the child's food intake and send the caregiver a message such as, "Please check how much your child ate." The meal recording unit can also record the child's dietary data over the long term and report any changes in the child's diet to the caregiver. For example, the meal recording unit can graph the child's dietary data and send the caregiver a message such as, "Please check any changes in your child's diet." This allows the child's dietary habits to be recorded in detail and provided to the caregiver, thereby enabling more effective dietary management of the child.

[0058] The nursery school attendance management system may further include a sleep recording unit that records the child's sleep status and reports it to the caregiver. The sleep recording unit can record the child's sleep status and report it to the caregiver. For example, the sleep recording unit can record how many hours the child slept at the nursery school and report it to the caregiver along with a message such as, "Please check how many hours your child slept today." The sleep recording unit can also analyze the child's sleep quality and send the caregiver a message such as, "Please check the quality of your child's sleep." The sleep recording unit can also record the child's sleep data over the long term and report changes in sleep to the caregiver. For example, the sleep recording unit can graph the child's sleep data and send the caregiver a message such as, "Please check changes in your child's sleep." This allows the child's sleep status to be recorded in detail and provided to the caregiver, thereby enabling more effective sleep management for the child.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The facial recognition camera recognizes the child's face. For example, the facial recognition camera can perform highly accurate facial recognition using a deep learning algorithm. The facial recognition camera can also recognize the child's face in real time and send information to the kindergarten attendance app. For example, the facial recognition camera can recognize the child's face as they pass through the entrance to the kindergarten and send that information to the kindergarten attendance app. Step 2: The kindergarten attendance app records the information recognized by the face recognition camera. For example, the kindergarten attendance app can save the child's kindergarten attendance time and attendance status in a database. The kindergarten attendance app can also provide the generation unit with information about children who have not arrived at kindergarten by a specified time. For example, the kindergarten attendance app sends information about children who have not been confirmed to have arrived at kindergarten by the specified time to the generation unit. Step 3: The generation unit uses the generation AI to create a message based on information about children who have not arrived at school by the specified time. For example, the generation AI can generate a message such as, "It seems your child has not arrived at school yet. Is there a problem?" The generation unit can also use the generation AI to customize the tone and content of the message to be sent to the caregiver. For example, the generation AI can estimate the caregiver's emotions and generate a message in a gentle tone. Step 4: The communication unit sends the message created by the generation unit to the caregiver. For example, the communication unit can send the message using a messaging app or SMS. The communication unit can also select the optimal means of communication taking into account the device information of the caregiver. For example, if the caregiver is using a smartphone, the communication unit can preferentially select SMS or a messaging app.

[0061] (Example 2) A nursery school attendance management system according to an embodiment of the present invention is a system that links a facial recognition camera that recognizes the faces of children and a nursery school attendance app. This system installs a facial recognition camera at the entrance of the nursery school and recognizes the faces of children arriving at the nursery school. This camera is linked to the nursery school attendance app and records in real time whether or not a child has arrived at the nursery school. Next, if a child has not arrived by the designated time, a generation AI creates a message to be sent to the caregiver. This message is sent to the caregiver via a messaging app (e.g., LINE) or SMS. After receiving the message, the caregiver can reply with the reason for the child's absence. For example, a facial recognition camera installed at the entrance of the nursery school recognizes the face of a child arriving at the nursery school and records the face in the nursery school attendance app. If a child has not arrived by the designated time, the generation AI creates a message saying, "It seems your child has not arrived at the nursery school yet. Is there a problem?" and sends it to the caregiver via a messaging app or SMS. After receiving the message, the caregiver replies with the reason, such as, "I'm not feeling well, so I'm keeping him / her home." This system allows nurseries to grasp the attendance status of children in real time and facilitates smooth communication with caregivers. Also, nurseries can quickly communicate the reason why a child is not at school, so they can leave their children with peace of mind. This allows the nursery school attendance management system to grasp the attendance status of children in real time and facilitates smooth communication with caregivers.

[0062] A nursery school attendance management system according to an embodiment includes a face recognition camera, a nursery school attendance app, a generation unit, and a communication unit. The face recognition camera recognizes the faces of children. For example, the face recognition camera can perform highly accurate facial recognition using a deep learning algorithm. The face recognition camera can recognize the faces of children in real time and send information to the nursery school attendance app. For example, the face recognition camera recognizes the faces of children as they pass through the entrance to the nursery school and sends the information to the nursery school attendance app. The nursery school attendance app records the information recognized by the face recognition camera. For example, the nursery school attendance app can store the children's arrival times and attendance statuses in a database. The nursery school attendance app can provide the generation unit with information about children who have not arrived at the nursery school by a specified time. For example, the nursery school attendance app sends information about children whose arrival has not been confirmed even after a specified time has passed to the generation unit. The generation unit uses a generation AI to create a message based on the information about children who have not arrived at the nursery school by the specified time. For example, the generation AI can generate a message such as, "It seems your child has not arrived at the nursery school yet. Is there a problem?" The generation unit can also use the generation AI to customize the tone and content of the message to be sent to the caregiver. For example, the generation AI can estimate the childcare worker's emotions and generate a message in a gentle tone. The communication unit sends the message created by the generation unit to the childcare worker. For example, the communication unit can send the message using a messaging app or SMS. The communication unit can also select the optimal communication method taking into account the childcare worker's device information. For example, if the childcare worker is using a smartphone, the communication unit can preferentially select SMS or a messaging app. As a result, the nursery school attendance management system according to the embodiment can grasp the attendance status of children in real time and smoothly communicate with the childcare worker.

[0063] The facial recognition camera can recognize the child's face and send the information to the kindergarten attendance app. The facial recognition camera can perform highly accurate facial recognition using, for example, a deep learning algorithm. For example, the facial recognition camera recognizes the child's face as he or she passes through the entrance of the nursery school and sends the information to the kindergarten attendance app. The facial recognition camera can also perform facial recognition in real time and send the information to the kindergarten attendance app. For example, the facial recognition camera recognizes the child's face as he or she passes through the entrance of the nursery school and sends the information to the kindergarten attendance app in real time. By recognizing the child's face and sending the information to the kindergarten attendance app, the kindergarten attendance status can be monitored in real time. Some or all of the above-mentioned processing in the facial recognition camera may be performed using, for example, AI, or may be performed without AI. For example, the facial recognition camera can input image data for recognizing the child's face into a generation AI and have the generation AI perform facial recognition.

[0064] The kindergarten attendance app can provide the generation unit with information about children who have not arrived at kindergarten by a specified time. The kindergarten attendance app, for example, transmits information about children who have not been confirmed to have arrived at kindergarten even after a specified time has passed to the generation unit. For example, the kindergarten attendance app retrieves information about children who have not been confirmed to have arrived at kindergarten even after a specified time from a database and transmits it to the generation unit. The kindergarten attendance app can also provide the generation unit with information about children who have not been confirmed to have arrived at kindergarten even after a specified time in real time. For example, the kindergarten attendance app transmits information about children who have not been confirmed to have arrived at kindergarten even after a specified time in real time to the generation unit. This allows for prompt action by providing the generation unit with information about children who have not arrived at kindergarten by the specified time. Some or all of the above-described processing in the kindergarten attendance app may be performed using, or without, AI, for example. For example, the kindergarten attendance app can input information about children who have not been confirmed to have arrived at kindergarten even after a specified time into the generation AI and have the generation AI provide the information.

[0065] The generation unit can create a message based on information about children who have not arrived at school by the specified time. The generation unit, for example, uses a generation AI to create a message based on information about children who have not arrived at school by the specified time. For example, the generation AI can generate a message such as, "It seems your child has not arrived at school yet. Is there a problem?" The generation unit can also use the generation AI to customize the tone and content of the message to be sent to the caregiver. For example, the generation AI can estimate the caregiver's emotions and generate a message in a gentle tone. This allows the caregiver to be contacted quickly by creating a message based on information about children who have not arrived at school by the specified time. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input information about children who have not arrived at school by the specified time into the generation AI and have the generation AI create a message.

[0066] The communication unit can send the message created by the generation unit to the caregiver. The communication unit can send the message using, for example, a messaging app or SMS. For example, the communication unit sends the message created by the generation unit to the caregiver using a messaging app. The communication unit can also send the message created by the generation unit to the caregiver via SMS. This allows smooth communication with the caregiver by sending the message created by the generation unit to the caregiver. Some or all of the above-mentioned processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the message created by the generation unit into the generation AI and cause the generation AI to send the message.

[0067] The communication unit can add specific communication means (e.g., email or telephone). The communication unit can, for example, send a message using email. For example, the communication unit sends a message created by the generation unit to the caregiver by email. The communication unit can also send a message using telephone. For example, the communication unit sends a message created by the generation unit to the caregiver by telephone. This diversifies the means of communication with the caregiver by adding communication means other than message apps and SMS. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input a message created by the generation unit into a generation AI and have the generation AI send the message.

[0068] The system may be equipped with a specific encryption method (e.g., AES encryption) and access control (e.g., password authentication). The system may encrypt data using, for example, AES encryption. For example, the system may protect children's attendance information and caregiver contact information using AES encryption. The system may also perform access control using password authentication. For example, the system may require password authentication when a caregiver accesses the attendance app. This improves the security of the system by providing data encryption and access control. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system may have a generating AI perform data encryption and access control.

[0069] The system can perform specific security checks (e.g., virus scans) and updates (e.g., software patches). The system can, for example, perform regular security checks using a virus scan. For example, the system runs a virus scan to detect viruses and malware in the system. The system can also perform regular updates using a software patch. For example, the system applies a software patch to fix system vulnerabilities. In this way, regular security checks and updates maintain the security of the system. Some or all of the above-mentioned processing in the system may be performed using, for example, AI, or may be performed without using AI. For example, the system can have a generation AI perform security checks and updates.

[0070] The nursery school attendance management system further includes a face recognition camera that estimates a child's emotions and adjusts the accuracy of face recognition based on the estimated child's emotions. For example, if a child is nervous, the face recognition camera adjusts the recognition accuracy by taking into account changes in facial expression. For example, when a child is nervous, the face recognition camera detects subtle changes in facial expression and improves the recognition accuracy. Furthermore, if a child is smiling, the face recognition camera can also enhance the recognition accuracy by highlighting the features of the smile. For example, when a child is smiling, the face recognition camera enhances the recognition accuracy. Furthermore, if a child is tired, the face recognition camera can also adjust the recognition accuracy by taking into account the degree of eye opening and facial color. For example, when a child is tired, the face recognition camera detects the degree of eye opening and facial color and adjusts the recognition accuracy. In this way, the recognition accuracy is improved by adjusting the recognition accuracy based on the child's emotions. Some or all of the above-described processing in the face recognition camera may be performed using, for example, AI, or may be performed without using AI. For example, a facial recognition camera can input image data for estimating a child's emotions into a generation AI, allowing the generation AI to estimate emotions and adjust the accuracy of the recognition.

[0071] The facial recognition camera can analyze a child's past attendance history and optimize the timing of facial recognition. For example, the facial recognition camera analyzes how often a child has been late in the past and strengthens facial recognition during times when lateness is likely. For example, the facial recognition camera retrieves from a database the frequency with which a child has been late in the past and strengthens facial recognition during times when lateness is likely. Furthermore, if a child is likely to be late on a particular day of the week, the facial recognition camera can increase the frequency of facial recognition on that day. For example, the facial recognition camera analyzes data on which children are likely to be late on a particular day of the week and increases the frequency of facial recognition on that day. Furthermore, if a child tends to arrive early, the facial recognition camera can strengthen facial recognition in the early morning. For example, the facial recognition camera retrieves from a database the tendency for children to arrive early and strengthens facial recognition in the early morning. In this way, the timing of facial recognition can be optimized by analyzing the child's past attendance history. Some or all of the above-described processing in the facial recognition camera may be performed using, for example, AI, or without AI. For example, a facial recognition camera can input a child's past attendance history into the generation AI, allowing the generation AI to optimize the timing of facial recognition.

[0072] A facial recognition camera can improve the accuracy of facial recognition by also using physical information such as a child's height and weight. For example, a facial recognition camera can improve the accuracy of facial recognition by adjusting the camera angle based on the child's height information. For example, a facial recognition camera can obtain the child's height information from a database and adjust the camera angle. A facial recognition camera can also improve the accuracy of facial recognition by correcting the facial contours based on the child's weight information. For example, a facial recognition camera can obtain the child's weight information from a database and correct the facial contours. A facial recognition camera can also optimize the facial recognition algorithm based on the child's physical information. For example, a facial recognition camera can optimize the facial recognition algorithm based on the child's physical information such as the child's height and weight. By using the child's physical information in combination, the accuracy of facial recognition is improved. Some or all of the above-described processing in a facial recognition camera may be performed using, or without, AI. For example, a facial recognition camera can input the child's physical information such as the height and weight into a generation AI and cause the generation AI to improve the recognition accuracy.

[0073] The face recognition camera can estimate a child's emotions and determine a priority for face recognition based on the estimated child's emotions. For example, if a child is feeling anxious, the face recognition camera sets a high priority for face recognition. For example, if a child is feeling anxious, the face recognition camera detects a change in facial expression and sets a high priority for face recognition. The face recognition camera can also set a low priority for face recognition if a child is relaxed. For example, if a child is relaxed, the face recognition camera detects a change in facial expression and sets a low priority for face recognition. The face recognition camera can also set a medium priority for face recognition if a child is excited. For example, if a child is excited, the face recognition camera detects a change in facial expression and sets a medium priority for face recognition. In this way, by determining the priority for face recognition based on the child's emotions, the efficiency of recognition is improved. Some or all of the above-described processing in the face recognition camera may be performed using, for example, AI, or may be performed without using AI. For example, a facial recognition camera can input image data for estimating a child's emotions into a generation AI, allowing the generation AI to estimate emotions and determine classification priorities.

[0074] A facial recognition camera can improve the accuracy of facial recognition by taking into account a child's clothing and belongings. For example, if a child is wearing a specific uniform, the facial recognition camera improves the accuracy of recognition by taking into account the clothing. For example, if a child is wearing a specific uniform, the facial recognition camera detects the clothing and improves the accuracy of recognition. Furthermore, if a child is wearing a specific hat, the facial recognition camera can improve the accuracy of recognition by taking into account the child's belongings. For example, if a child is wearing a specific hat, the facial recognition camera detects the belongings and improves the accuracy of recognition. Furthermore, if a child is carrying a specific backpack, the facial recognition camera can improve the accuracy of recognition by taking into account the child's belongings. For example, if a child is carrying a specific backpack, the facial recognition camera detects the belongings and improves the accuracy of recognition. In this way, the accuracy of facial recognition is improved by taking into account the child's clothing and belongings. Some or all of the above-described processing in the facial recognition camera may be performed using, for example, AI, or may be performed without AI. For example, the facial recognition camera can input image data for detecting the child's clothing and belongings into a generation AI, and have the generation AI improve the accuracy of recognition.

[0075] When identifying faces, a facial recognition camera can improve the accuracy of identification by analyzing a child's behavioral patterns when commuting to kindergarten. For example, if a child takes a specific route to kindergarten, the facial recognition camera improves the accuracy of identification by taking into account that behavioral pattern. For example, when a child takes a specific route to kindergarten, the facial recognition camera detects that behavioral pattern and improves the accuracy of identification. Furthermore, if a child commutes to kindergarten at a specific time of day, the facial recognition camera can improve the accuracy of identification by taking into account that behavioral pattern. For example, when a child commutes to kindergarten at a specific time of day, the facial recognition camera detects that behavioral pattern and improves the accuracy of identification. Furthermore, if a child commutes to kindergarten with a specific friend, the facial recognition camera can improve the accuracy of identification by taking into account that behavioral pattern. For example, when a child commutes to kindergarten with a specific friend, the facial recognition camera detects that behavioral pattern and improves the accuracy of identification. In this way, the accuracy of facial recognition is improved by analyzing the behavioral patterns when commuting to kindergarten. Some or all of the above-described processing in the facial recognition camera may be performed using, for example, AI, or may be performed without using AI. For example, a facial recognition camera can input data to the generation AI to detect children's behavioral patterns when they arrive at school, allowing the generation AI to improve its recognition accuracy.

[0076] The kindergarten attendance app can estimate the emotions of the caregiver and adjust the notification method of the app based on the estimated emotions of the caregiver. For example, the kindergarten attendance app provides a simple notification method when the caregiver is stressed. For example, the kindergarten attendance app provides a simple notification method when the caregiver is stressed. The kindergarten attendance app can also provide a detailed notification method when the caregiver is relaxed. For example, the kindergarten attendance app provides a detailed notification method when the caregiver is relaxed. The kindergarten attendance app can also provide a quick notification method when the caregiver is in a hurry. For example, the kindergarten attendance app provides a quick notification method when the caregiver is in a hurry. In this way, by adjusting the notification method based on the emotions of the caregiver, the effectiveness of the notification is improved. Some or all of the above-mentioned processing in the kindergarten attendance app may be performed using AI, for example, or may be performed without using AI. For example, the kindergarten attendance app can input data for estimating the emotions of the caregiver into a generation AI and have the generation AI adjust the notification method.

[0077] The kindergarten attendance app can add a function to record a child's health condition and allergy information. For example, the kindergarten attendance app may record a child's health condition daily and notify the caregiver. For example, the kindergarten attendance app may record a child's health condition daily and notify the caregiver. The kindergarten attendance app may also record a child's allergy information and notify the caregiver. For example, the kindergarten attendance app may record a child's allergy information and notify the caregiver. The kindergarten attendance app may also provide appropriate advice to the caregiver based on the child's health condition and allergy information. For example, the kindergarten attendance app may provide appropriate advice to the caregiver based on the child's health condition and allergy information. In this way, by recording the child's health condition and allergy information, appropriate information can be provided to the caregiver. Some or all of the above-mentioned processes in the kindergarten attendance app may be performed using, or without, AI. For example, the kindergarten attendance app may input a child's health condition and allergy information into a generation AI and have the generation AI record and notify the information.

[0078] The kindergarten attendance app can add a function to automatically save photos of children when they arrive at kindergarten. For example, the kindergarten attendance app automatically saves photos taken with a face recognition camera when a child arrives at kindergarten. For example, the kindergarten attendance app automatically saves photos taken with a face recognition camera when a child arrives at kindergarten. The kindergarten attendance app can also automatically save photos taken by a caregiver when a child arrives at kindergarten. For example, the kindergarten attendance app automatically saves photos taken by a caregiver when a child arrives at kindergarten. The kindergarten attendance app can also automatically take and save photos when a child arrives at kindergarten. For example, the kindergarten attendance app automatically takes and saves photos when a child arrives at kindergarten. In this way, by automatically saving photos when a child arrives at kindergarten, it is possible to record the child's attendance status. Some or all of the above-mentioned processing in the kindergarten attendance app may be performed using, or without, AI. For example, the kindergarten attendance app can input photos of a child when they arrive at kindergarten into a generation AI and have the generation AI save the photos.

[0079] The kindergarten attendance app can estimate the emotions of the caregiver and customize the app interface based on the estimated emotions of the caregiver. For example, when the caregiver is nervous, the kindergarten attendance app provides an interface with subdued colors. For example, when the caregiver is nervous, the kindergarten attendance app provides an interface with subdued colors. Furthermore, when the caregiver is having fun, the kindergarten attendance app can provide an interface with bright colors. For example, when the caregiver is having fun, the kindergarten attendance app can provide an interface with bright colors. Furthermore, when the caregiver is tired, the kindergarten attendance app can provide an interface with simple, high visibility. For example, when the caregiver is tired, the kindergarten attendance app provides an interface with simple, high visibility. This improves usability by customizing the interface based on the emotions of the caregiver. Some or all of the above-described processing in the kindergarten attendance app may be performed using AI, for example, or may be performed without using AI. For example, the kindergarten attendance app can input data for estimating the emotions of the caregiver into a generation AI and have the generation AI customize the interface.

[0080] The kindergarten attendance app can add a communication function between childcare workers. For example, the kindergarten attendance app can add a chat function that allows childcare workers to send messages to each other. For example, the kindergarten attendance app can add a chat function that allows childcare workers to send messages to each other. The kindergarten attendance app can also add a bulletin board function that allows childcare workers to share information with each other. For example, the kindergarten attendance app can add a bulletin board function that allows childcare workers to share information with each other. The kindergarten attendance app can also add a calendar function that allows childcare workers to share schedules with each other. For example, the kindergarten attendance app can add a calendar function that allows childcare workers to share schedules with each other. By adding this communication function between childcare workers, information sharing becomes smoother. Some or all of the above-mentioned processing in the kindergarten attendance app can be performed using, for example, AI, or can be performed without using AI. For example, the kindergarten attendance app can input the communication function between childcare workers into a generation AI and have the generation AI add the function.

[0081] The kindergarten attendance app can add a function to record an audio message when a child arrives at kindergarten. In the kindergarten attendance app, for example, a caregiver records an audio message when a child arrives at kindergarten and saves it in the app. For example, in the kindergarten attendance app, a caregiver records an audio message when a child arrives at kindergarten and saves it in the app. In addition, in the kindergarten attendance app, a facial recognition camera can record an audio message when a child arrives at kindergarten and save it in the app. For example, in the kindergarten attendance app, a facial recognition camera can record an audio message when a child arrives at kindergarten and save it in the app. In addition, in the kindergarten attendance app, the app can automatically record and save an audio message when a child arrives at kindergarten. For example, in the kindergarten attendance app, the app automatically records and saves an audio message when a child arrives at kindergarten. In this way, by recording an audio message when a child arrives at kindergarten, it is possible to record the child's attendance status in detail. Some or all of the above-mentioned processing in the kindergarten attendance app may be performed, for example, using AI or without using AI. For example, a kindergarten attendance app can input a voice message that a child will send when arriving at kindergarten into a generation AI, and have the generation AI record the voice message.

[0082] The generation unit can estimate the caregiver's emotions and adjust the tone of the message based on the estimated caregiver's emotions. For example, when the caregiver is stressed, the generation unit generates a message in a gentle tone. For example, when the caregiver is stressed, the generation unit generates a message in a gentle tone. The generation unit can also generate a message in a friendly tone when the caregiver is relaxed. For example, when the caregiver is relaxed, the generation unit generates a message in a friendly tone. The generation unit can also generate a message in a concise and quick tone when the caregiver is in a hurry. For example, when the caregiver is in a hurry, the generation unit generates a message in a concise and quick tone. This enables more appropriate communication by adjusting the tone of the message based on the caregiver's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data for estimating the caregiver's emotions into the generation AI and cause the generation AI to adjust the tone of the message.

[0083] The generation unit can be added with a function to generate an optimal message by referring to past message history. The generation unit, for example, generates an optimal message based on messages received by the caregiver in the past. For example, the generation unit retrieves messages received by the caregiver in the past from a database and generates an optimal message. The generation unit can also generate an optimal message based on messages sent by the caregiver in the past. For example, the generation unit retrieves messages sent by the caregiver in the past from a database and generates an optimal message. The generation unit can also analyze the content of messages exchanged by the caregiver in the past and generate an optimal message. For example, the generation unit analyzes the content of messages exchanged by the caregiver in the past and generates an optimal message. In this way, a more appropriate message can be generated by referring to the past message history. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input past message history into the generation AI and cause the generation AI to generate an optimal message.

[0084] The generation unit can be added with a function to analyze a child's attendance history and generate an individually customized message. The generation unit generates an individually customized message, for example, based on how often the child has been late in the past. For example, the generation unit retrieves from a database the frequency with which the child has been late in the past and generates an individually customized message. Furthermore, if a child is prone to being late on a particular day of the week, the generation unit can generate a message tailored to that day. For example, the generation unit analyzes data on how often a child is prone to being late on a particular day of the week and generates a message tailored to that day. Furthermore, if a child tends to arrive early, the generation unit can generate a message tailored to early mornings. For example, the generation unit retrieves from a database the child's tendency to arrive early and generates a message tailored to early mornings. In this way, individually customized messages can be generated by analyzing a child's attendance history. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input a child's attendance history into the generation AI and cause the generation AI to generate an individually customized message.

[0085] The generation unit can estimate the caregiver's emotions and adjust the length of the message based on the estimated caregiver's emotions. For example, when the caregiver is stressed, the generation unit generates a short and to-the-point message. For example, when the caregiver is stressed, the generation unit generates a short and to-the-point message. The generation unit can also generate a longer message including detailed explanations when the caregiver is relaxed. For example, when the caregiver is relaxed, the generation unit generates a longer message including detailed explanations. The generation unit can also generate a concise and quick message when the caregiver is in a hurry. For example, when the caregiver is in a hurry, the generation unit generates a concise and quick message. This enables more appropriate communication by adjusting the length of the message based on the caregiver's emotions. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit may input data for estimating the caregiver's emotions into the generation AI and cause the generation AI to adjust the length of the message.

[0086] The generation unit can add a function to generate multilingual messages according to the language setting of the caregiver. The generation unit automatically sets the language of the message based on, for example, the language setting of the caregiver's device. For example, the generation unit retrieves the language setting of the caregiver's device from a database and automatically sets the language of the message. The generation unit can also provide a language switching function when the caregiver uses multiple languages. For example, the generation unit provides a language switching function when the caregiver uses multiple languages. The generation unit can also generate messages in a specific language when the caregiver selects that language. For example, when the caregiver selects a specific language, the generation unit generates messages in that language. This enables more appropriate communication by generating multilingual messages according to the language setting of the caregiver. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the language setting of the caregiver into the generation AI and cause the generation AI to generate multilingual messages.

[0087] The generation unit can be added with a function to predict an optimal reply by analyzing the content of the childcare worker's past replies. The generation unit, for example, predicts an optimal reply based on the content of the childcare worker's past replies. For example, the generation unit retrieves the content of the childcare worker's past replies from a database and predicts an optimal reply. The generation unit can also analyze the content of messages exchanged by the childcare worker in the past and predict an optimal reply. For example, the generation unit analyzes the content of messages exchanged by the childcare worker in the past and predicts an optimal reply. The generation unit can also predict an optimal reply based on messages received by the childcare worker in the past. For example, the generation unit retrieves messages received by the childcare worker in the past from a database and predicts an optimal reply. In this way, by analyzing the content of the childcare worker's past replies, a more appropriate reply can be predicted. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input the content of the childcare worker's past replies into the generation AI and cause the generation AI to predict an optimal reply.

[0088] The communication unit can estimate the emotions of the caregiver and select a means of communication based on the estimated emotions of the caregiver. For example, the communication unit provides a simple means of communication when the caregiver is stressed. For example, the communication unit provides a simple means of communication when the caregiver is stressed. The communication unit can also provide a detailed means of communication when the caregiver is relaxed. For example, the communication unit provides a detailed means of communication when the caregiver is relaxed. The communication unit can also provide a quick means of communication when the caregiver is in a hurry. For example, the communication unit provides a quick means of communication when the caregiver is in a hurry. This enables more appropriate communication by selecting a means of communication based on the emotions of the caregiver. Some or all of the above-mentioned processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit can input data for estimating the emotions of the caregiver into a generation AI and have the generation AI select a means of communication.

[0089] The communication unit may be added with a function for determining the optimal timing of contact by referring to the childcare worker's past communication history. The communication unit determines the optimal timing of contact, for example, based on the time period in which the childcare worker previously replied. For example, the communication unit may retrieve the time period in which the childcare worker previously replied from a database and determine the optimal timing of contact. The communication unit may also determine the optimal timing of contact based on the time period in which the childcare worker previously received communication. For example, the communication unit may retrieve the time period in which the childcare worker previously received communication from a database and determine the optimal timing of contact. The communication unit may also determine the optimal timing of contact based on the frequency in which the childcare worker previously received communication. For example, the communication unit may retrieve the frequency in which the childcare worker previously received communication from a database and determine the optimal timing of contact. In this way, the optimal timing of contact can be determined by referring to the childcare worker's past communication history. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input the childcare worker's past communication history into a generation AI and have the generation AI determine the optimal timing of contact.

[0090] The communication unit can add a function to automatically update the contact information of the caregiver. For example, if the caregiver changes their contact information, the communication unit automatically reflects the changes in the system. For example, when the caregiver changes their contact information, the communication unit automatically reflects the changes in the system. The communication unit can also automatically reflect new contact information added by the caregiver in the system. For example, when the caregiver adds new contact information, the communication unit automatically reflects the changes in the system. The communication unit can also automatically delete contact information deleted by the caregiver from the system. For example, when the caregiver deletes contact information, the communication unit automatically deletes the information from the system. In this way, by automatically updating the contact information of the caregiver, the latest contact information can be maintained. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input the contact information of the caregiver into the generation AI and have the generation AI update the information.

[0091] The communication unit can estimate the emotions of the caregiver and customize the communication content based on the estimated emotions of the caregiver. For example, if the caregiver is feeling stressed, the communication unit customizes the communication content in a gentle tone. For example, if the caregiver is feeling stressed, the communication unit customizes the communication content in a gentle tone. The communication unit can also customize the communication content in a friendly tone if the caregiver is relaxed. For example, if the caregiver is relaxed, the communication unit customizes the communication content in a friendly tone. The communication unit can also customize the communication content in a concise and quick tone if the caregiver is in a hurry. For example, if the caregiver is in a hurry, the communication unit customizes the communication content in a concise and quick tone. This enables more appropriate communication by customizing the communication content based on the emotions of the caregiver. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit can input data for estimating the emotions of the caregiver into a generation AI and cause the generation AI to customize the communication content.

[0092] The communication unit may be added with a function to select the optimal means of communication by taking into consideration device information of the childcare worker. For example, when the childcare worker is using a smartphone, the communication unit may prioritize SMS or a messaging app. For example, when the childcare worker is using a smartphone, the communication unit may prioritize SMS or a messaging app. The communication unit may also prioritize email when the childcare worker is using a tablet. For example, when the childcare worker is using a tablet, the communication unit may prioritize email. The communication unit may also prioritize email or chat when the childcare worker is using a computer. For example, when the childcare worker is using a computer, the communication unit may prioritize email or chat. This allows the optimal means of communication to be selected by taking into consideration device information of the childcare worker. Some or all of the above-described processing in the communication unit may be performed using AI, for example, or may be performed without using AI. For example, the communication unit may input device information of the childcare worker into a generation AI and cause the generation AI to select the optimal means of communication.

[0093] The communication unit may add a function of analyzing the childcare worker's usage of communication methods and suggesting the optimal communication method. The communication unit, for example, suggests the optimal communication method based on the communication method used most frequently by the childcare worker in the past. For example, the communication unit may retrieve from a database the communication methods used most frequently by the childcare worker in the past and suggest the optimal communication method. The communication unit may also suggest the optimal communication method based on the communication method used by the childcare worker during a specific time period. For example, the communication unit may retrieve from a database the communication methods used by the childcare worker during a specific time period and suggest the optimal communication method. The communication unit may also suggest the optimal communication method based on the communication method used by the childcare worker on a specific day of the week. For example, the communication unit may retrieve from a database the communication methods used by the childcare worker on a specific day of the week and suggest the optimal communication method. In this way, the optimal communication method can be suggested by analyzing the childcare worker's usage of communication methods. Some or all of the above-described processing in the communication unit may be performed using, for example, AI, or may be performed without using AI. For example, the communication unit may input the childcare worker's usage of communication methods into a generation AI and cause the generation AI to suggest the optimal communication method. === Hard Collateral 1-1 === Each of the multiple elements, including the face recognition camera, the kindergarten attendance app, the generation unit, and the communication unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the face recognition camera is realized by the camera 42 of the smart device 14 and recognizes the child's face. The kindergarten attendance app is realized by the control unit 46A of the smart device 14 and records information from the face recognition camera. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a message based on information about a child who has not arrived at kindergarten by a specified time. The communication unit is realized by the communication I / F 44 of the smart device 14 and sends a message to the caregiver. The face recognition camera can also estimate the child's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the child is nervous, the face recognition camera adjusts the recognition accuracy taking into account changes in facial expression. === Hard Collateral 1-2 === Each of the multiple elements, including the face recognition camera, the kindergarten attendance app, the generation unit, and the communication unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the face recognition camera is realized by the camera 42 of the smart glasses 214 and recognizes the child's face. The kindergarten attendance app is realized by the control unit 46A of the smart glasses 214 and records information from the face recognition camera. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a message based on information about a child who has not arrived at kindergarten by a specified time. The communication unit is realized by the communication I / F 44 of the smart glasses 214 and sends a message to the caregiver. The face recognition camera can also estimate the child's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the child is nervous, the face recognition camera adjusts the recognition accuracy taking into account changes in facial expression. === Hard Collateral 1-3 === Each of the multiple elements, including the face recognition camera, the kindergarten attendance app, the generation unit, and the communication unit, described above, is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the face recognition camera is realized by the camera 42 of the headset terminal 314 and recognizes the child's face. The kindergarten attendance app is realized by the control unit 46A of the headset terminal 314 and records information from the face recognition camera. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a message based on information about a child who has not arrived at kindergarten by a specified time. The communication unit is realized by the communication I / F 44 of the headset terminal 314 and sends a message to the caregiver. The face recognition camera can also estimate the child's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the child is nervous, the face recognition camera adjusts the recognition accuracy taking into account changes in facial expression. === Hard Collateral 1-4 === Each of the multiple elements, including the face recognition camera, the kindergarten attendance app, the generation unit, and the communication unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the face recognition camera is realized by the camera 42 of the robot 414 and recognizes the child's face. The kindergarten attendance app is realized by the control unit 46A of the robot 414 and records information from the face recognition camera. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and creates a message based on information about a child who has not arrived at kindergarten by a specified time. The communication unit is realized by the communication I / F 44 of the robot 414 and sends a message to the caregiver. The face recognition camera can also estimate the child's emotions and adjust the recognition accuracy based on the estimated emotions. For example, if the child is nervous, the face recognition camera adjusts the recognition accuracy taking into account changes in facial expression.

[0094] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0095] The nursery school attendance management system may further include a health management unit that monitors the health status of children. The health management unit may periodically measure a child's body temperature and heart rate and notify the caregiver if an abnormality is detected. For example, if a child's body temperature is high, the health management unit may send the caregiver a message saying, "Your child's body temperature seems high. Please check if there are any health problems." Similarly, if a child's heart rate is abnormally high, the health management unit may send the caregiver a message saying, "Your child's heart rate seems high. Please check if there are any health problems." Furthermore, the health management unit may record the child's health data over the long term and report any changes in the child's health status to the caregiver. For example, the health management unit may graph the child's body temperature and heart rate data and send the caregiver a message saying, "Please check for any changes in your child's health status." This allows for real-time monitoring of a child's health status and prompt notification to the caregiver, thereby enabling more effective health management of children.

[0096] The nursery school attendance management system may further include a behavior recording unit that records the behavior of children when they arrive at the nursery school. The behavior recording unit may record the behavior of children when they arrive at the nursery school using a camera and provide the information to caregivers. For example, the behavior recording unit may record video of children arriving at the nursery school and provide the video to caregivers along with a message such as, "Please check your child's behavior when arriving at the nursery school." The behavior recording unit may also analyze the behavioral patterns of children when they arrive at the nursery school and notify caregivers if an abnormality is detected. For example, if a child behaves differently than usual, the behavior recording unit may send a message such as, "Your child's behavior when arriving at the nursery school is abnormal. Please check." The behavior recording unit may also record the behavioral data of children when they arrive at the nursery school over the long term and report any changes in behavior to caregivers. For example, the behavior recording unit may graph the behavioral data of children when they arrive at the nursery school and send a message such as, "Please check any changes in your child's behavior when arriving at the nursery school." This allows for detailed recording of children's behavior when they arrive at the nursery school and provides the information to caregivers, thereby enabling more effective child safety management.

[0097] The nursery school attendance management system may further include an advice unit that estimates a child's emotions and provides advice to a caregiver based on the estimated emotions. The advice unit can estimate a child's emotions and provide appropriate advice to a caregiver. For example, if a child is feeling anxious, the advice unit may provide advice such as, "Your child seems anxious. Try suggesting a game that your child likes to help them relax." If a child is excited, the advice unit may provide advice such as, "Your child seems excited. Please provide a quiet environment to calm them down." If a child is tired, the advice unit may provide advice such as, "Your child seems tired. Please let him take a break." This allows caregivers to provide appropriate advice based on the child's emotions, thereby more effectively caring for children.

[0098] The nursery school attendance management system may further include a refreshment suggestion unit that estimates the emotions of the caregiver and suggests a refreshment method to the caregiver based on the estimated emotions. The refreshment suggestion unit can estimate the emotions of the caregiver and suggest an appropriate refreshment method. For example, if the caregiver is feeling stressed, the refreshment suggestion unit may suggest, "Take a short break and take a deep breath to refresh yourself." In addition, if the caregiver is tired, the refreshment suggestion unit may suggest, "Take a short walk to refresh yourself." Furthermore, if the caregiver is feeling tense, the refreshment suggestion unit may suggest, "Try listening to your favorite music to relax." In this way, by suggesting an appropriate refreshment method based on the emotions of the caregiver, stress management for the caregiver can be more effectively performed.

[0099] The nursery school attendance management system may further include a learning record unit that records the child's learning status and reports it to the caregiver. The learning record unit can record the child's learning status and report it to the caregiver. For example, the learning record unit can record the learning activities the child performed at the nursery school and report it to the caregiver along with a message such as, "Please check the learning activities your child performed today." The learning record unit can also analyze the child's learning progress and send the caregiver a message such as, "Please check your child's learning progress." The learning record unit can also record the child's learning data over the long term and report changes in learning to the caregiver. For example, the learning record unit can graph the child's learning data and send the caregiver a message such as, "Please check the changes in your child's learning." This allows the child's learning status to be recorded in detail and provided to the caregiver, thereby more effectively managing the child's learning.

[0100] The nursery school attendance management system may further include a mental health care unit that estimates the emotions of the caregiver and provides mental health care advice to the caregiver based on the estimated emotions. The mental health care unit can estimate the emotions of the caregiver and provide appropriate mental health care advice. For example, if the caregiver is feeling stressed, the mental health care unit can provide advice such as, "Take time to relax to reduce stress." If the caregiver is tired, the mental health care unit can also provide advice such as, "It's important to get enough rest." If the caregiver is feeling anxious, the mental health care unit can also provide advice such as, "Take a deep breath and relax to reduce anxiety." In this way, by providing appropriate mental health care advice based on the emotions of the caregiver, mental health management of the caregiver can be more effective.

[0101] The nursery school attendance management system may further include a meal recording unit that records the child's eating habits and reports them to the caregiver. The meal recording unit can record the child's eating habits and report them to the caregiver. For example, the meal recording unit can record the meals the child ate at the nursery school and report them to the caregiver along with a message such as, "Please check what your child ate today." The meal recording unit can also analyze the child's food intake and send the caregiver a message such as, "Please check how much your child ate." The meal recording unit can also record the child's dietary data over the long term and report any changes in the child's diet to the caregiver. For example, the meal recording unit can graph the child's dietary data and send the caregiver a message such as, "Please check any changes in your child's diet." This allows the child's dietary habits to be recorded in detail and provided to the caregiver, thereby enabling more effective dietary management of the child.

[0102] The nursery school attendance management system may further include a care suggestion unit that estimates a child's emotions and suggests to the caregiver how to care for the child based on the estimated emotions. The care suggestion unit can estimate a child's emotions and suggest an appropriate care method to the caregiver. For example, if a child is sad, the care suggestion unit may suggest, "Your child seems sad. Please talk to him / her gently." If a child is angry, the care suggestion unit may suggest, "Your child seems angry. Please provide a quiet environment to calm him / her down." If a child is having fun, the care suggestion unit may suggest, "Your child seems to be having fun. Please suggest more fun activities." In this way, by suggesting an appropriate care method to the caregiver based on the child's emotions, child care can be performed more effectively.

[0103] The nursery school attendance management system may further include a relaxation suggestion unit that estimates the emotions of the caregiver and suggests relaxation methods to the caregiver based on the estimated emotions. The relaxation suggestion unit can estimate the emotions of the caregiver and suggest appropriate relaxation methods. For example, if the caregiver is feeling stressed, the relaxation suggestion unit may suggest, "Take a deep breath and relax to reduce stress." If the caregiver is tired, the relaxation suggestion unit may suggest, "Take a short break and relax." If the caregiver is tense, the relaxation suggestion unit may suggest, "Try listening to your favorite music to relax." In this way, by suggesting appropriate relaxation methods based on the emotions of the caregiver, stress management for the caregiver can be more effectively performed.

[0104] The nursery school attendance management system may further include a sleep recording unit that records the child's sleep status and reports it to the caregiver. The sleep recording unit can record the child's sleep status and report it to the caregiver. For example, the sleep recording unit can record how many hours the child slept at the nursery school and report it to the caregiver along with a message such as, "Please check how many hours your child slept today." The sleep recording unit can also analyze the child's sleep quality and send the caregiver a message such as, "Please check the quality of your child's sleep." The sleep recording unit can also record the child's sleep data over the long term and report changes in sleep to the caregiver. For example, the sleep recording unit can graph the child's sleep data and send the caregiver a message such as, "Please check changes in your child's sleep." This allows the child's sleep status to be recorded in detail and provided to the caregiver, thereby enabling more effective sleep management for the child.

[0105] The processing flow of the second embodiment will be briefly explained below.

[0106] Step 1: The facial recognition camera recognizes the child's face. For example, the facial recognition camera can perform highly accurate facial recognition using a deep learning algorithm. The facial recognition camera can also recognize the child's face in real time and send information to the kindergarten attendance app. For example, the facial recognition camera can recognize the child's face as they pass through the entrance to the kindergarten and send that information to the kindergarten attendance app. Step 2: The kindergarten attendance app records the information recognized by the face recognition camera. For example, the kindergarten attendance app can save the child's kindergarten attendance time and attendance status in a database. The kindergarten attendance app can also provide the generation unit with information about children who have not arrived at kindergarten by a specified time. For example, the kindergarten attendance app sends information about children who have not been confirmed to have arrived at kindergarten by the specified time to the generation unit. Step 3: The generation unit uses the generation AI to create a message based on information about children who have not arrived at school by the specified time. For example, the generation AI can generate a message such as, "It seems your child has not arrived at school yet. Is there a problem?" The generation unit can also use the generation AI to customize the tone and content of the message to be sent to the caregiver. For example, the generation AI can estimate the caregiver's emotions and generate a message in a gentle tone. Step 4: The communication unit sends the message created by the generation unit to the caregiver. For example, the communication unit can send the message using a messaging app or SMS. The communication unit can also select the optimal means of communication taking into account the device information of the caregiver. For example, if the caregiver is using a smartphone, the communication unit can preferentially select SMS or a messaging app.

[0107] 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.

[0108] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0109] 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.

[0110] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0111] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0112] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0125] 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.

[0126] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0141] 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.

[0142] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0143] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0144] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0155] 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.

[0156] 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.

[0157] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0158] 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.

[0159] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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."

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] [Explanation of symbols]

[0179] 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 facial recognition camera that recognizes the child's face, a kindergarten attendance app that records information recognized by the face recognition camera; a generation unit that generates a message based on information recorded by the kindergarten attendance app; a communication unit that transmits the message created by the creation unit to a childcare provider; Equipped with A system characterized by:

2. The face recognition camera Recognizes the child's face and sends the information to the kindergarten attendance app 2. The system of claim 1.

3. The kindergarten app is Provide information on children who have not arrived at the kindergarten by the designated time to the generation unit.

2. The system of claim 1.

4. The generation unit Create a message based on information about children who have not arrived by the specified time 2. The system of claim 1.

5. The communication unit The message created by the creation unit is sent to the childcare provider.

2. The system of claim 1.

6. The generation unit Estimate the caregiver's emotions and adjust the length of the message based on the estimated emotions.

2. The system of claim 1.

Images