system

Abstract

The system according to this embodiment aims to efficiently communicate information about the child's condition to parents. [Solution] The system according to the embodiment comprises a shooting unit, a generation unit, and a writing unit. The shooting unit takes pictures of the children. The generation unit analyzes the pictures taken by the shooting unit and writes down the children's behavior. The writing unit writes the text generated by the generation unit into an electronic communication notebook.

Description

Technical Field

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

Background Art

[0002] [[ID=1,2]] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is time-consuming and difficult to efficiently fill in a contact book for communicating the situation of a kindergarten child to a guardian.

[0005] The system according to the embodiment aims to efficiently communicate the situation of a kindergarten child to a guardian.

Means for Solving the Problems

[0006] The system according to the embodiment includes a photographing unit, a generating unit, and an entry unit. The photographing unit photographs the situation of a kindergarten child. The generating unit analyzes the photograph taken by the photographing unit and converts the situation of the kindergarten child into text. The entry unit enters the text generated by the generating unit into an electronic contact book.

Effects of the Invention

[0007] The system according to this embodiment can efficiently communicate information about the child's condition to parents. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] <000009...The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The electronic communication log system for children according to an embodiment of the present invention is a system that automatically generates text using a generation AI in an electronic communication log for children used in nurseries and kindergartens. In this system, a childcare worker takes a picture of the child with a smartphone, the generation AI analyzes the picture and converts the child's activities into text, which is then automatically entered into the electronic communication log. This reduces the burden on childcare workers and facilitates smoother communication with parents. For example, a childcare worker takes a picture of the child with a smartphone. For example, they might take a picture of the child playing or eating. This picture is input into the generation AI. Next, the generation AI analyzes the input picture and converts the child's activities into text. The generation AI understands the content of the picture and converts what the child was doing, what expression they had, etc., into text. For example, a sentence such as "Today, XX-chan was playing in the sandbox. They seemed to be having a lot of fun." is generated. The generated text is automatically entered into the electronic communication log. The childcare worker can check the generated text and make corrections as needed. Once the corrections are complete, the communication log is sent to the parents. This system reduces the burden on childcare workers. Childcare workers only need to photograph the children; the AI ​​generates the entries for the communication log, significantly reducing the time spent creating the log. Parents can also learn more about their children's activities, providing topics for conversation at home. Furthermore, the AI ​​can periodically compile photos and log entries into albums. For example, albums can be created at the end of the school year or upon graduation. This streamlines administrative tasks for childcare workers. This AI-generated communication log allows childcare workers to dedicate more resources to important communications and communication with children and parents. For instance, important information, such as a child's injury, can be communicated directly to parents by the childcare worker without relying on the AI. Thus, utilizing the AI ​​reduces the burden on childcare workers and facilitates smoother communication with parents. Additionally, childcare workers can focus on important communications, improving the quality of childcare. In summary, this electronic communication log system for children reduces the burden on childcare workers and facilitates smoother communication with parents.

[0029] The electronic communication log system for kindergarten children according to this embodiment comprises a shooting unit, a generation unit, and a writing unit. The shooting unit takes pictures of the children. These pictures include, but are not limited to, playing, eating, or napping. The shooting unit can, for example, use a smartphone camera to take pictures of the children. The shooting unit can also take high-resolution photographs using a digital camera. Furthermore, the shooting unit can also record videos using a video camera. For example, the shooting unit can take pictures of the children playing using a smartphone camera. It can also take pictures of the children eating using a digital camera. It can also take pictures of the children napping using a video camera. The generation unit uses a generation AI to analyze the pictures taken by the shooting unit and convert the children's actions into text. For example, the generation AI understands the content of the pictures and converts what the children were doing, what expressions they were making, etc., into text. For example, the generation unit receives a prompt from the generation AI saying, "Please turn the content of this picture into text," and converts the content of the picture into text. Furthermore, the generation unit can use its generation AI to recognize people and objects in a photograph and generate text based on that recognition. It can also use its generation AI to analyze background information in a photograph and generate text based on that analysis. For example, the generation unit can use its generation AI to analyze a child's facial expression in a photograph and generate text such as, "Today, [child's name] was playing in the sandbox. They seemed to be having a lot of fun." The generation unit can also use its generation AI to recognize playground equipment in a photograph and generate text such as, "Today, [child's name] was playing on the swing." The generation unit can also use its generation AI to analyze the weather in the background of a photograph and generate text such as, "It was sunny today, and [child's name] was playing outside." The input unit enters the text generated by the generation unit into the electronic communication log. For example, the input unit can automatically enter the generated text into the electronic communication log. Alternatively, the input unit can allow childcare workers to review the generated text, make necessary corrections, and then enter it into the electronic communication log. Furthermore, the input unit has a function to send the generated text to parents. For example, the input unit can automatically enter the generated text into the electronic communication log.Childcare workers can review the generated text, make corrections as needed, and then enter it into the electronic communication log. The entry section can also send the generated text to parents via email. As a result, the electronic communication log system for children according to this embodiment can reduce the burden on childcare workers and facilitate smooth communication with parents.

[0030] The photography team will photograph the children's activities. These activities may include, but are not limited to, playing, eating, or napping. The photography team may use smartphone cameras to photograph the children, digital cameras to take high-resolution photos, or video cameras to record videos. For example, they could use smartphone cameras to photograph children playing, digital cameras to photograph children eating, or video cameras to photograph children napping. The photography team will carefully consider camera position and angle to capture the children in a relaxed state, ensuring they are in a natural state. For example, they might place cameras in inconspicuous locations so the children are unaware of their presence. They will also photograph at appropriate times based on the children's activities. For example, photographing during playtime or mealtimes can capture natural expressions and movements. Furthermore, the camera unit instantly saves captured photos and videos as digital data, making them available for subsequent processing. This allows the camera unit to record the children's activities as high-quality digital data, enabling smoother processing in subsequent generation and data entry units.

[0031] The generation unit uses a generation AI to analyze photos taken by the photography unit and translate the children's activities into text. For example, the generation unit's generation AI understands the content of the photo and converts what the children were doing, what expressions they were making, etc., into text. For example, the generation unit can receive a prompt such as "Please write text about the content of this photo" and convert the content of the photo into text. The generation unit can also have the generation AI recognize people and objects in the photo and generate text based on that. Furthermore, the generation unit can have the generation AI analyze background information in the photo and generate text based on that. For example, the generation unit can have the generation AI analyze a child's expression in the photo and generate text such as, "Today, XX was playing in the sandbox. She seemed to be having a lot of fun." The generation unit can also have the generation AI recognize playground equipment in the photo and generate text such as, "Today, XX was playing on the swings." The generation unit can also have the generation AI analyze the weather in the background of the photo and generate text such as, "It was sunny today, and XX was playing outside." The generation unit uses AI to analyze the facial expressions and actions of children in photographs in detail, enabling it to generate more specific text. For example, the AI ​​can analyze a child's smile and hand movements and generate text such as, "〇〇-chan was building a sandcastle in the sandbox. She was smiling and seemed to be having fun." The generation unit can also analyze multiple photographs in sequence and generate text summarizing a child's daily activities. This allows the generation unit to describe the child's activities in detail and specifically, making it easy for parents to understand.

[0032] The input unit enters the text generated by the generation unit into the electronic communication log. For example, the input unit automatically enters the generated text into the electronic communication log. Alternatively, the input unit allows the childcare worker to review the generated text, make corrections as needed, and then enter it into the electronic communication log. Furthermore, the input unit has a function to send the generated text to the parents. For example, the input unit automatically enters the generated text into the electronic communication log. Alternatively, the childcare worker can review the generated text, make corrections as needed, and then enter it into the electronic communication log. The input unit can also send the generated text to the parents via email. As a result, the electronic communication log system for children according to this embodiment can reduce the burden on childcare workers and facilitate communication with parents. The input unit provides an interface that allows childcare workers to easily correct or add to the generated text when entering it into the electronic communication log. For example, childcare workers can review the generated text and add comments or supplementary information as needed. The input unit also has a function that allows parents to select their preferred method of contact (email, SMS, app notification, etc.) when sending the generated text to parents. As a result, parents can receive updates on their child's condition in the way that is most suitable for them. Furthermore, the input section includes a function that allows childcare workers to perform a final check before sending the generated text to parents, reducing the risk of sending incorrect information. As a result, the input section can accurately and quickly communicate the child's condition to parents while reducing the burden on childcare workers.

[0033] The generation unit can analyze photographs using generation AI and convert the children's activities into text. For example, the generation unit's generation AI can understand the content of a photograph and convert what the children were doing, what expressions they were making, etc., into text. For example, the generation unit's generation AI can receive a prompt such as "Please write text about the content of this photograph" and convert the content of the photograph into text. The generation unit can also have the generation AI recognize people and objects in a photograph and generate text based on that. Furthermore, the generation unit can have the generation AI analyze the background information of a photograph and generate text based on that. For example, the generation unit can have the generation AI analyze a child's expression in a photograph and generate text such as, "Today, XX was playing in the sandbox. He seemed to be having a lot of fun." The generation unit can also have the generation AI recognize playground equipment in a photograph and generate text such as, "Today, XX was playing on the swings." The generation unit can also have the generation AI analyze the weather in the background of a photograph and generate text such as, "It was sunny today, and XX was playing outside." In this way, text generation from photographs is automated by using generation AI. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs a photograph into the generation AI, which analyzes the content of the photograph and generates text.

[0034] The input unit can automatically enter the generated text into the electronic communication log. For example, the input unit automatically enters the generated text into the electronic communication log. The input unit can also allow the childcare worker to review the generated text, make corrections as needed, and then enter it into the electronic communication log. Furthermore, the input unit has a function to send the generated text to the parents. For example, the input unit automatically enters the generated text into the electronic communication log. The childcare worker can also review the generated text, make corrections as needed, and then enter it into the electronic communication log. The input unit can also send the generated text to the parents via email. This streamlines the work of childcare workers by automatically entering the generated text into the electronic communication log. Some or all of the above processes in the input unit may be performed using AI or not. For example, the input unit inputs the generated text into AI, and the AI ​​automatically enters it into the electronic communication log.

[0035] The verification unit allows childcare workers to review and correct the generated text. For example, the verification unit allows childcare workers to review the generated text and make corrections as needed. The verification unit also allows childcare workers to review the generated text and correct errors or inappropriate expressions. Furthermore, the verification unit allows childcare workers to review the generated text and add additional information. For example, the verification unit allows childcare workers to review the generated text and correct typos and grammatical errors. Childcare workers can also review the generated text and correct grammar and expressions. The verification unit can also add additional information to the generated text. This ensures that accurate information is conveyed to parents by allowing childcare workers to review and correct the generated text. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs the generated text into AI, which then detects and corrects typos and grammatical errors.

[0036] The album generation unit can periodically compile photos and communication log entries into albums. For example, the album generation unit can create albums of photos and entries at the end of the school year or upon graduation. It can also create albums at regular intervals, such as monthly or weekly. Furthermore, the album generation unit can create albums to coincide with specific events or occasions. For example, it can create an album of photos and entries at the end of the school year. It can also create albums summarizing monthly activities. It can also create albums to coincide with specific events or occasions. This streamlines the administrative work of childcare workers by compiling photos and communication log entries into albums. Some or all of the above-described processes in the album generation unit may be performed using AI, or not. For example, the album generation unit inputs photos and entries into AI, and the AI ​​automatically generates the albums.

[0037] The Direct Contact Department allows childcare workers to directly communicate important information to parents. For example, in cases where a child is injured or an emergency needs to be communicated, childcare workers can directly contact parents. The Direct Contact Department can also allow childcare workers to directly communicate information about important events and activities to parents. Furthermore, the Direct Contact Department has functions to enable childcare workers to communicate individually with parents. For example, the Direct Contact Department allows childcare workers to contact parents by phone or email. Childcare workers can also schedule meetings with parents. The Direct Contact Department has functions to enable childcare workers to quickly contact parents in emergencies. This ensures that important information is reliably conveyed by allowing childcare workers to communicate important information directly to parents. Some or all of the above processes in the Direct Contact Department may be performed using AI or not. For example, the Direct Contact Department may use AI to analyze the communication content entered by childcare workers and suggest appropriate communication methods.

[0038] The camera unit can automatically adjust the optimal shooting angle and distance according to the children's activities. For example, when children are playing, the AI ​​automatically sets the optimal shooting angle and distance. The camera unit can also take pictures at an appropriate distance and angle when children are eating. Furthermore, the camera unit can quietly take pictures at an optimal angle and distance when children are napping. For example, when children are playing, the AI ​​automatically sets the optimal shooting angle and distance. When children are eating, the AI ​​can also take pictures at an appropriate distance and angle. When children are napping, the AI ​​can also quietly take pictures at an optimal angle and distance. This ensures that optimal shooting is performed according to the children's activities. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit inputs the children's activities into the AI, and the AI ​​sets the optimal shooting angle and distance.

[0039] The camera unit can analyze the children's facial expressions and movements in real time during shooting and capture the most appropriate moment. For example, the camera unit's AI can analyze in real time the moment a child smiles and take a picture. The camera unit can also use AI to capture the moment a child makes a specific movement and take the optimal photo. Furthermore, the camera unit's AI can analyze the moment a child is excited and capture the best facial expression. For example, the camera unit's AI can analyze in real time the moment a child smiles and take a picture. The AI ​​can also capture the moment a child makes a specific movement and take the optimal photo. The AI ​​can also analyze the moment a child is excited and capture the best facial expression. This allows the camera unit to capture the child's best moment. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit inputs the child's facial expression data into the AI, and the AI ​​captures the most appropriate moment.

[0040] The camera crew can refer to a child's past activity history during filming to focus on specific activities. For example, the camera crew can use AI to refer to activities a child has enjoyed in the past and focus on filming them. The camera crew can also use AI to refer to activities a child has struggled with in the past and film improvements. Furthermore, the camera crew can use AI to refer to activities a child has been particularly interested in in the past and film them in detail. For example, the camera crew can use AI to refer to activities a child has enjoyed in the past and focus on filming them. The AI ​​can also refer to activities a child has struggled with in the past and film improvements. The AI ​​can also refer to activities a child has been particularly interested in in the past and film them in detail. This allows for focusing on specific activities by referring to a child's past activity history. Some or all of the above processing in the camera crew may be performed using AI or not. For example, the camera crew inputs the child's past activity history into the AI, and the AI ​​focuses on filming specific activities.

[0041] The filming unit can prioritize filming specific scenes based on the wishes of the child's parents during filming. For example, the filming unit can use AI to prioritize filming specific activity scenes requested by the parents. The filming unit can also use AI to prioritize filming scenes at specific times of day requested by the parents. Furthermore, the filming unit can use AI to prioritize filming specific facial expressions or movements requested by the parents. For example, the filming unit can use AI to prioritize filming specific activity scenes requested by the parents. The AI ​​can also prioritize filming scenes at specific times of day requested by the parents. The AI ​​can also prioritize filming specific facial expressions or movements requested by the parents. This allows for the filming of specific scenes based on the wishes of the child's parents. Some or all of the above processing in the filming unit may be performed using AI or not. For example, the filming unit inputs the parents' wishes into the AI, and the AI ​​prioritizes filming specific scenes.

[0042] The generation unit can generate more detailed text by referring to the child's past activity data during the generation process. For example, the generation unit can refer to the child's past activities, and the generation AI can add a detailed explanation. The generation unit can also generate text that includes specific episodes based on the child's past activity data. Furthermore, the generation unit can refer to the child's past activity history, and the generation AI can add relevant information. For example, the generation unit can refer to the child's past activities, and the generation AI can add a detailed explanation. The generation AI can also generate text that includes specific episodes based on the child's past activity data. The generation AI can also refer to the child's past activity history, and add relevant information. As a result, more detailed text is generated by referring to the child's past activity data. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the child's past activity data into the generation AI, and the generation AI generates a detailed text.

[0043] The generation unit can generate text that reflects the individual characteristics and interests of the children during the generation process. For example, the generation unit's AI can generate text that reflects activities that the children are particularly interested in. The generation unit can also consider the individual characteristics of the children and have the generation AI generate personalized text. Furthermore, the generation unit can have the generation AI generate text that includes specific anecdotes based on the children's interests and characteristics. For example, the generation unit's AI can generate text that reflects activities that the children are particularly interested in. The generation AI can also consider the individual characteristics of the children and have the generation AI generate personalized text. The generation AI can also generate text that includes specific anecdotes based on the children's interests and characteristics. This results in the generation of text that reflects the individual characteristics and interests of the children. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs data on the children's characteristics and interests into the generation AI, and the generation AI generates text based on that data.

[0044] The generation unit can customize the text during generation by reflecting feedback from the child's parents. For example, based on parental feedback, the generation AI can generate text that emphasizes specific information. The generation unit can also generate text with detailed explanations based on the parents' wishes. Furthermore, the generation unit can reflect parental feedback and generate text that focuses on specific activities. For example, based on parental feedback, the generation AI can generate text that emphasizes specific information. Based on the parents' wishes, the generation AI can also generate text with detailed explanations. The generation AI can also reflect parental feedback and generate text that focuses on specific activities. By customizing the text to reflect feedback from the child's parents, more appropriate text is generated. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs parental feedback data into the generation AI, and the generation AI generates text based on that.

[0045] The generation unit can apply different text generation algorithms depending on the child's activity during generation. For example, if the child is playing, the generation AI can apply an algorithm specialized for play to generate text. Similarly, if the child is eating, the generation AI can apply an algorithm specialized for eating to generate text. Furthermore, if the child is napping, the generation AI can apply an algorithm specialized for rest to generate text. This ensures that different text generation algorithms are applied depending on the child's activity. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs data on the child's activity into the generation AI, which then applies an appropriate algorithm to generate text.

[0046] The input section can select the optimal submission timing while considering the schedule of the child's parents. For example, the input section can refer to the parents' schedule, and the AI ​​can select the optimal submission timing. The input section can also have the AI ​​send the information at an appropriate time, avoiding busy times for the parents. Furthermore, the input section can have the AI ​​automatically set the optimal submission timing based on the parents' schedule. For example, the input section can refer to the parents' schedule, and the AI ​​can select the optimal submission timing. The AI ​​can also send the information at an appropriate time, avoiding busy times for the parents. The AI ​​can also automatically set the optimal submission timing based on the parents' schedule. This ensures that the optimal submission timing is selected while considering the schedule of the child's parents. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can input the parents' schedule data into the AI, and the AI ​​can select the optimal submission timing.

[0047] The entry section can apply different entry formats depending on the child's activity during entry. For example, if the child is playing, the AI ​​will apply an entry format specialized for play. Similarly, if the child is eating, the AI ​​can apply an entry format specialized for eating. Furthermore, if the child is napping, the AI ​​can apply an entry format specialized for resting. This ensures that different entry formats are applied depending on the child's activity. Some or all of the above processing in the entry section is performed using a generating AI. For example, the entry section inputs the child's activity data into the generating AI, which then applies the appropriate entry format based on that data.

[0048] The input section can highlight specific information based on the wishes of the child's parents during the input process. For example, the input section can use AI to highlight specific activities desired by the parents. The input section can also use AI to highlight information about specific time slots desired by the parents. Furthermore, the input section can use AI to highlight specific facial expressions or movements desired by the parents. For example, the input section can use AI to highlight specific activities desired by the parents. The AI ​​can also use AI to highlight information about specific time slots desired by the parents. The AI ​​can also use AI to highlight specific facial expressions or movements desired by the parents. This allows for the provision of more appropriate information by highlighting specific information based on the wishes of the child's parents. Some or all of the above processing in the input section is performed using a generating AI. For example, the input section inputs the parents' wish data into the generating AI, which then highlights and fills in specific information based on that data.

[0049] The input section can add relevant information by referring to the child's activity history when filling out the form. For example, the input section can refer to the child's past activity history, and the AI ​​can add relevant information. The input section can also have the AI ​​add specific episodes based on the child's past activity data. Furthermore, the input section can refer to the child's past activity content, and the AI ​​can add relevant information. For example, the input section can refer to the child's past activity history, and the AI ​​can add relevant information. The AI ​​can also add specific episodes based on the child's past activity data. The AI ​​can also add relevant information by referring to the child's past activity content. In this way, relevant information is added by referring to the child's activity history. Some or all of the above processing in the input section is performed using a generating AI. For example, the input section inputs the child's activity history data into the generating AI, and the generating AI adds relevant information based on that.

[0050] The verification unit can determine the need for correction by referring to past contact log data during verification. For example, the verification unit can refer to past contact log data, and the AI ​​can determine the need for correction. The verification unit can also compare past contact log data with current data, and the AI ​​can detect inconsistencies and propose corrections. Furthermore, the verification unit can perform corrections based on past contact log data to provide consistent information. For example, the verification unit can refer to past contact log data, and the AI ​​can determine the need for correction. It can also compare past contact log data with current data, and the AI ​​can detect inconsistencies and propose corrections. It can also perform corrections based on past contact log data to provide consistent information. This allows the need for correction to be determined by referring to past contact log data. Some or all of the above processing in the verification unit is performed using a generating AI. For example, the verification unit inputs past contact log data into the generating AI, and the generating AI determines the need for correction based on that data.

[0051] The verification unit can revise the text during the verification process, reflecting feedback from the child's parents. For example, the verification unit can use AI to revise specific information based on parental feedback. The verification unit can also revise the text by adding detailed explanations based on the parents' requests. Furthermore, the verification unit can revise the text by reflecting parental feedback and focusing on specific activities. For example, the verification unit can use AI to revise specific information based on parental feedback. The AI ​​can also revise the text by adding detailed explanations based on the parents' requests. The AI ​​can also revise the text by reflecting parental feedback and focusing on specific activities. This ensures that more appropriate information is provided by revising the text to reflect feedback from the child's parents. Some or all of the above processing in the verification unit is performed using a generative AI. For example, the verification unit inputs parental feedback data into the generative AI, which then revises the text based on that data.

[0052] The verification unit can highlight specific information based on the wishes of the child's parents during the verification process. For example, the verification unit can use AI to highlight and verify specific activities that the parents desire. The verification unit can also use AI to highlight and verify information about specific time slots that the parents desire. Furthermore, the verification unit can use AI to highlight and verify specific facial expressions or movements that the parents desire. For example, the verification unit can use AI to highlight and verify specific activities that the parents desire. The verification unit can also use AI to highlight and verify information about specific time slots that the parents desire. The verification unit can also use AI to highlight and verify specific facial expressions or movements that the parents desire. This allows for the provision of more appropriate information by highlighting specific information based on the wishes of the child's parents. Some or all of the above processing in the verification unit is performed using a generating AI. For example, the verification unit inputs the parents' preference data into the generating AI, which then highlights and verifies specific information based on that input.

[0053] The verification unit can add relevant information by referring to the child's activity history during verification. For example, the verification unit can refer to the child's past activity history, and the AI ​​can add relevant information. The verification unit can also add specific episodes based on the child's past activity data. Furthermore, the verification unit can refer to the child's past activity content, and the AI ​​can add relevant information. For example, the verification unit can refer to the child's past activity history, and the AI ​​can add relevant information. The AI ​​can also add specific episodes based on the child's past activity data. The AI ​​can also add relevant information by referring to the child's past activity content. In this way, relevant information is added by referring to the child's activity history. Some or all of the above processing in the verification unit is performed using a generating AI. For example, the verification unit inputs the child's activity history data into the generating AI, and the generating AI adds relevant information based on that.

[0054] The album generation unit can apply different layouts depending on the child's activity during album generation. For example, if a child is playing, the AI ​​will apply a layout optimized for play. Similarly, if a child is eating, the AI ​​can apply a layout optimized for eating. Furthermore, if a child is napping, the AI ​​can apply a layout optimized for rest. This ensures that different layouts are applied depending on the child's activity. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs data on the child's activities into the generation AI, which then applies an appropriate layout based on that data.

[0055] The album generation unit can emphasize specific photos and texts based on the wishes of the child's parents when generating an album. For example, the album generation unit's AI can prioritize including specific activity scenes requested by the parents in the album. It can also prioritize including scenes from specific time periods requested by the parents. Furthermore, the album generation unit's AI can prioritize including specific facial expressions and movements requested by the parents in the album. For example, the album generation unit's AI can prioritize including specific activity scenes requested by the parents in the album. It can also prioritize including scenes from specific time periods requested by the parents in the album. It can also prioritize including specific facial expressions and movements requested by the parents in the album. This allows for the generation of more appropriate albums by emphasizing specific photos and texts based on the wishes of the child's parents. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs parent preference data into the generation AI, which then emphasizes and includes specific photos and texts in the album based on that data.

[0056] The album generation unit can add relevant photos and text by referencing the child's past activity data when generating an album. For example, the album generation unit can refer to the child's past activity data and the AI ​​can add relevant photos. The album generation unit can also add text containing specific episodes based on the child's past activity history. Furthermore, the album generation unit can refer to the child's past activity content and the AI ​​can add relevant information. For example, the album generation unit can refer to the child's past activity data and the AI ​​can add relevant photos. The AI ​​can also add text containing specific episodes based on the child's past activity history. The AI ​​can also refer to the child's past activity content and add relevant information. As a result, relevant photos and text are added by referencing the child's past activity data. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs the child's past activity data into the generation AI, and the generation AI adds relevant photos and text based on that.

[0057] The album generation unit can customize the layout during album generation by reflecting feedback from the child's parents. For example, based on parental feedback, the AI ​​generates a layout that highlights specific information. The album generation unit can also generate a layout with detailed explanations based on the parents' wishes. Furthermore, the album generation unit can reflect parental feedback and generate a layout that focuses on specific activities. For example, based on parental feedback, the AI ​​generates a layout that highlights specific information. Based on the parents' wishes, the AI ​​can also generate a layout with detailed explanations. The AI ​​can also reflect parental feedback and generate a layout that focuses on specific activities. By customizing the layout to reflect feedback from the child's parents, a more appropriate album is generated. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs parental feedback data into the generation AI, and the generation AI customizes the layout based on that data.

[0058] The direct contact unit can select the most suitable contact method when contacting parents, taking into account their schedules. For example, the direct contact unit can refer to the parents' schedules, and the AI ​​can select the most suitable contact method. The direct contact unit can also select an appropriate contact method that avoids busy times for parents. Furthermore, the direct contact unit can automatically set the most suitable contact method based on the parents' schedules. For example, the direct contact unit can refer to the parents' schedules, and the AI ​​can select the most suitable contact method. The AI ​​can also select an appropriate contact method that avoids busy times for parents. The AI ​​can also automatically set the most suitable contact method based on the parents' schedules. This ensures that the most suitable contact method is selected considering the parents' schedules. Some or all of the above processing in the direct contact unit is performed using a generating AI. For example, the direct contact unit inputs the parents' schedule data into the generating AI, and the generating AI selects the most suitable contact method based on that data.

[0059] The direct contact unit can emphasize specific information based on the wishes of the child's parents when making contact. For example, the direct contact unit can use AI to emphasize specific activities that the parents desire. The direct contact unit can also use AI to emphasize information about specific time slots that the parents desire. Furthermore, the direct contact unit can use AI to emphasize specific facial expressions or movements that the parents desire. For example, the direct contact unit can use AI to emphasize specific activities that the parents desire. The direct contact unit can also use AI to emphasize information about specific time slots that the parents desire. The direct contact unit can also use AI to emphasize specific facial expressions or movements that the parents desire. This allows for the provision of more appropriate information by emphasizing specific information based on the wishes of the child's parents. Some or all of the above processing in the direct contact unit is performed using a generative AI. For example, the direct contact unit inputs parent preference data into the generative AI, which then emphasizes specific information based on that input.

[0060] The direct contact unit can add relevant information by referring to the child's activity history when making contact. For example, the direct contact unit can refer to the child's past activity history, and the AI ​​can add relevant information. The direct contact unit can also add specific episodes based on the child's past activity data. Furthermore, the direct contact unit can refer to the child's past activity content, and the AI ​​can add relevant information. For example, the direct contact unit can refer to the child's past activity history, and the AI ​​can add relevant information. The AI ​​can also add specific episodes based on the child's past activity data. The AI ​​can also refer to the child's past activity content, and the AI ​​can add relevant information. In this way, relevant information is added by referring to the child's activity history. Some or all of the above processing in the direct contact unit is performed using a generating AI. For example, the direct contact unit inputs the child's activity history data into the generating AI, and the generating AI adds relevant information based on that.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The electronic communication system for preschool children can also be equipped with a translation function. This function can translate generated text into multiple languages. For example, if a parent speaks a foreign language, the generated text can be translated into that language and provided to them. The translation function can also accommodate situations where childcare workers record information in a foreign language, with the AI ​​translating that content into Japanese. Furthermore, the translation function can automatically translate according to the parent's preferred language. This enables multilingual support and facilitates smoother intercultural communication.

[0063] The electronic communication system for preschool children can also include a notification function. This function can notify parents of important information in real time. For example, it can immediately send notifications to parents if a child is injured or if an emergency contact is necessary. The notification function can also send periodic notifications at times requested by parents. Furthermore, the notification function can prioritize notifications for specific information that parents wish to receive. This facilitates smoother communication with parents and ensures that important information is conveyed quickly.

[0064] The electronic communication system for preschool children can also include a health management section. This section can record the children's health status and provide this information to their parents. For example, it can record the children's body temperature, meals, and sleep duration, and a generating AI can analyze this data to assess their health. Furthermore, the health management section can receive questions from parents regarding their child's health, and the generating AI can provide appropriate answers. In addition, the health management section can send alerts to parents if any abnormalities are detected in the child's health. This allows for more effective health management of the children.

[0065] The electronic communication system for preschool children can also include a learning record section. This section can record the children's learning activities and provide this information to parents. For example, it can record what learning activities the children undertook and what results they achieved, and a generating AI can analyze this data to evaluate their learning progress. Furthermore, the learning record section can also provide appropriate answers to questions parents have about their child's learning. In addition, based on the children's learning activities, the learning record section can offer suggestions to parents for home learning support. This allows for more effective support of the children's learning activities.

[0066] The electronic communication system for preschool children can also be equipped with a behavior prediction unit. This unit analyzes past behavioral data of the children and predicts future behavior. For example, if a child tends to exhibit certain behaviors at specific times, the system can predict these behaviors and suggest appropriate responses to the caregivers in advance. The behavior prediction unit can also suggest methods for home care to parents based on the child's behavioral patterns. Furthermore, the behavior prediction unit can send alerts to caregivers and parents if any abnormalities are detected in the child's behavior. This enables appropriate responses to the children's behavior.

[0067] The electronic communication system for preschool children can also include an activity record section. This section can record the child's activities in detail and provide this information to parents. For example, it can record what kind of play the child engaged in and what actions the child took, and a generating AI can analyze this data to evaluate behavioral patterns. The activity record section can also provide appropriate answers to questions parents have about their child's behavior using the generating AI. Furthermore, based on the child's behavior, the activity record section can suggest ways for parents to handle the situation at home. This allows for a more detailed record of the child's activities and provides parents with appropriate information.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The photography team will photograph the children. This includes scenes of the children playing, eating, napping, etc. The photography team can take photos and videos using smartphone cameras, digital cameras, video cameras, etc. Step 2: The generation unit uses a generation AI to analyze the photos taken by the photography unit and transcribe the children's activities into text. The generation AI understands the content of the photos and generates text based on the children's activities, expressions, background information, etc. Step 3: The input unit enters the text generated by the generation unit into the electronic communication log. The input unit has functions to automatically enter the generated text into the electronic communication log, to allow childcare workers to review and correct the text before entering it, and to send the generated text to the parents.

[0070] (Example of form 2) The electronic communication log system for children according to an embodiment of the present invention is a system that automatically generates text using a generation AI in an electronic communication log for children used in nurseries and kindergartens. In this system, a childcare worker takes a picture of the child with a smartphone, the generation AI analyzes the picture and converts the child's activities into text, which is then automatically entered into the electronic communication log. This reduces the burden on childcare workers and facilitates smoother communication with parents. For example, a childcare worker takes a picture of the child with a smartphone. For example, they might take a picture of the child playing or eating. This picture is input into the generation AI. Next, the generation AI analyzes the input picture and converts the child's activities into text. The generation AI understands the content of the picture and converts what the child was doing, what expression they had, etc., into text. For example, a sentence such as "Today, XX-chan was playing in the sandbox. They seemed to be having a lot of fun." is generated. The generated text is automatically entered into the electronic communication log. The childcare worker can check the generated text and make corrections as needed. Once the corrections are complete, the communication log is sent to the parents. This system reduces the burden on childcare workers. Childcare workers only need to photograph the children; the AI ​​generates the entries for the communication log, significantly reducing the time spent creating the log. Parents can also learn more about their children's activities, providing topics for conversation at home. Furthermore, the AI ​​can periodically compile photos and log entries into albums. For example, albums can be created at the end of the school year or upon graduation. This streamlines administrative tasks for childcare workers. This AI-generated communication log allows childcare workers to dedicate more resources to important communications and communication with children and parents. For instance, important information, such as a child's injury, can be communicated directly to parents by the childcare worker without relying on the AI. Thus, utilizing the AI ​​reduces the burden on childcare workers and facilitates smoother communication with parents. Additionally, childcare workers can focus on important communications, improving the quality of childcare. In summary, this electronic communication log system for children reduces the burden on childcare workers and facilitates smoother communication with parents.

[0071] The electronic communication log system for kindergarten children according to this embodiment comprises a shooting unit, a generation unit, and a writing unit. The shooting unit takes pictures of the children. These pictures include, but are not limited to, playing, eating, or napping. The shooting unit can, for example, use a smartphone camera to take pictures of the children. The shooting unit can also take high-resolution photographs using a digital camera. Furthermore, the shooting unit can also record videos using a video camera. For example, the shooting unit can take pictures of the children playing using a smartphone camera. It can also take pictures of the children eating using a digital camera. It can also take pictures of the children napping using a video camera. The generation unit uses a generation AI to analyze the pictures taken by the shooting unit and convert the children's actions into text. For example, the generation AI understands the content of the pictures and converts what the children were doing, what expressions they were making, etc., into text. For example, the generation unit receives a prompt from the generation AI saying, "Please turn the content of this picture into text," and converts the content of the picture into text. Furthermore, the generation unit can use its generation AI to recognize people and objects in a photograph and generate text based on that recognition. It can also use its generation AI to analyze background information in a photograph and generate text based on that analysis. For example, the generation unit can use its generation AI to analyze a child's facial expression in a photograph and generate text such as, "Today, [child's name] was playing in the sandbox. They seemed to be having a lot of fun." The generation unit can also use its generation AI to recognize playground equipment in a photograph and generate text such as, "Today, [child's name] was playing on the swing." The generation unit can also use its generation AI to analyze the weather in the background of a photograph and generate text such as, "It was sunny today, and [child's name] was playing outside." The input unit enters the text generated by the generation unit into the electronic communication log. For example, the input unit can automatically enter the generated text into the electronic communication log. Alternatively, the input unit can allow childcare workers to review the generated text, make necessary corrections, and then enter it into the electronic communication log. Furthermore, the input unit has a function to send the generated text to parents. For example, the input unit can automatically enter the generated text into the electronic communication log.Childcare workers can review the generated text, make corrections as needed, and then enter it into the electronic communication log. The entry section can also send the generated text to parents via email. As a result, the electronic communication log system for children according to this embodiment can reduce the burden on childcare workers and facilitate smooth communication with parents.

[0072] The photography team will photograph the children's activities. These activities may include, but are not limited to, playing, eating, or napping. The photography team may use smartphone cameras to photograph the children, digital cameras to take high-resolution photos, or video cameras to record videos. For example, they could use smartphone cameras to photograph children playing, digital cameras to photograph children eating, or video cameras to photograph children napping. The photography team will carefully consider camera position and angle to capture the children in a relaxed state, ensuring they are in a natural state. For example, they might place cameras in inconspicuous locations so the children are unaware of their presence. They will also photograph at appropriate times based on the children's activities. For example, photographing during playtime or mealtimes can capture natural expressions and movements. Furthermore, the camera unit instantly saves captured photos and videos as digital data, making them available for subsequent processing. This allows the camera unit to record the children's activities as high-quality digital data, enabling smoother processing in subsequent generation and data entry units.

[0073] The generation unit uses a generation AI to analyze photos taken by the photography unit and translate the children's activities into text. For example, the generation unit's generation AI understands the content of the photo and converts what the children were doing, what expressions they were making, etc., into text. For example, the generation unit can receive a prompt such as "Please write text about the content of this photo" and convert the content of the photo into text. The generation unit can also have the generation AI recognize people and objects in the photo and generate text based on that. Furthermore, the generation unit can have the generation AI analyze background information in the photo and generate text based on that. For example, the generation unit can have the generation AI analyze a child's expression in the photo and generate text such as, "Today, XX was playing in the sandbox. She seemed to be having a lot of fun." The generation unit can also have the generation AI recognize playground equipment in the photo and generate text such as, "Today, XX was playing on the swings." The generation unit can also have the generation AI analyze the weather in the background of the photo and generate text such as, "It was sunny today, and XX was playing outside." The generation unit uses AI to analyze the facial expressions and actions of children in photographs in detail, enabling it to generate more specific text. For example, the AI ​​can analyze a child's smile and hand movements and generate text such as, "〇〇-chan was building a sandcastle in the sandbox. She was smiling and seemed to be having fun." The generation unit can also analyze multiple photographs in sequence and generate text summarizing a child's daily activities. This allows the generation unit to describe the child's activities in detail and specifically, making it easy for parents to understand.

[0074] The input unit enters the text generated by the generation unit into the electronic communication log. For example, the input unit automatically enters the generated text into the electronic communication log. Alternatively, the input unit allows the childcare worker to review the generated text, make corrections as needed, and then enter it into the electronic communication log. Furthermore, the input unit has a function to send the generated text to the parents. For example, the input unit automatically enters the generated text into the electronic communication log. Alternatively, the childcare worker can review the generated text, make corrections as needed, and then enter it into the electronic communication log. The input unit can also send the generated text to the parents via email. As a result, the electronic communication log system for children according to this embodiment can reduce the burden on childcare workers and facilitate communication with parents. The input unit provides an interface that allows childcare workers to easily correct or add to the generated text when entering it into the electronic communication log. For example, childcare workers can review the generated text and add comments or supplementary information as needed. The input unit also has a function that allows parents to select their preferred method of contact (email, SMS, app notification, etc.) when sending the generated text to parents. As a result, parents can receive updates on their child's condition in the way that is most suitable for them. Furthermore, the input section includes a function that allows childcare workers to perform a final check before sending the generated text to parents, reducing the risk of sending incorrect information. As a result, the input section can accurately and quickly communicate the child's condition to parents while reducing the burden on childcare workers.

[0075] The generation unit can analyze photographs using generation AI and convert the children's activities into text. For example, the generation unit's generation AI can understand the content of a photograph and convert what the children were doing, what expressions they were making, etc., into text. For example, the generation unit's generation AI can receive a prompt such as "Please write text about the content of this photograph" and convert the content of the photograph into text. The generation unit can also have the generation AI recognize people and objects in a photograph and generate text based on that. Furthermore, the generation unit can have the generation AI analyze the background information of a photograph and generate text based on that. For example, the generation unit can have the generation AI analyze a child's expression in a photograph and generate text such as, "Today, XX was playing in the sandbox. He seemed to be having a lot of fun." The generation unit can also have the generation AI recognize playground equipment in a photograph and generate text such as, "Today, XX was playing on the swings." The generation unit can also have the generation AI analyze the weather in the background of a photograph and generate text such as, "It was sunny today, and XX was playing outside." In this way, text generation from photographs is automated by using generation AI. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit inputs a photograph into the generation AI, which analyzes the content of the photograph and generates text.

[0076] The input unit can automatically enter the generated text into the electronic communication log. For example, the input unit automatically enters the generated text into the electronic communication log. The input unit can also allow the childcare worker to review the generated text, make corrections as needed, and then enter it into the electronic communication log. Furthermore, the input unit has a function to send the generated text to the parents. For example, the input unit automatically enters the generated text into the electronic communication log. The childcare worker can also review the generated text, make corrections as needed, and then enter it into the electronic communication log. The input unit can also send the generated text to the parents via email. This streamlines the work of childcare workers by automatically entering the generated text into the electronic communication log. Some or all of the above processes in the input unit may be performed using AI or not. For example, the input unit inputs the generated text into AI, and the AI ​​automatically enters it into the electronic communication log.

[0077] The verification unit allows childcare workers to review and correct the generated text. For example, the verification unit allows childcare workers to review the generated text and make corrections as needed. The verification unit also allows childcare workers to review the generated text and correct errors or inappropriate expressions. Furthermore, the verification unit allows childcare workers to review the generated text and add additional information. For example, the verification unit allows childcare workers to review the generated text and correct typos and grammatical errors. Childcare workers can also review the generated text and correct grammar and expressions. The verification unit can also add additional information to the generated text. This ensures that accurate information is conveyed to parents by allowing childcare workers to review and correct the generated text. Some or all of the above processes in the verification unit may be performed using AI or not. For example, the verification unit inputs the generated text into AI, which then detects and corrects typos and grammatical errors.

[0078] The album generation unit can periodically compile photos and communication log entries into albums. For example, the album generation unit can create albums of photos and entries at the end of the school year or upon graduation. It can also create albums at regular intervals, such as monthly or weekly. Furthermore, the album generation unit can create albums to coincide with specific events or occasions. For example, it can create an album of photos and entries at the end of the school year. It can also create albums summarizing monthly activities. It can also create albums to coincide with specific events or occasions. This streamlines the administrative work of childcare workers by compiling photos and communication log entries into albums. Some or all of the above-described processes in the album generation unit may be performed using AI, or not. For example, the album generation unit inputs photos and entries into AI, and the AI ​​automatically generates the albums.

[0079] The Direct Contact Department allows childcare workers to directly communicate important information to parents. For example, in cases where a child is injured or an emergency needs to be communicated, childcare workers can directly contact parents. The Direct Contact Department can also allow childcare workers to directly communicate information about important events and activities to parents. Furthermore, the Direct Contact Department has functions to enable childcare workers to communicate individually with parents. For example, the Direct Contact Department allows childcare workers to contact parents by phone or email. Childcare workers can also schedule meetings with parents. The Direct Contact Department has functions to enable childcare workers to quickly contact parents in emergencies. This ensures that important information is reliably conveyed by allowing childcare workers to communicate important information directly to parents. Some or all of the above processes in the Direct Contact Department may be performed using AI or not. For example, the Direct Contact Department may use AI to analyze the communication content entered by childcare workers and suggest appropriate communication methods.

[0080] The camera unit can estimate the emotions of childcare workers and adjust the shooting timing based on the estimated emotions. For example, if a childcare worker is stressed, the AI ​​will automatically select the shooting timing to reduce the burden on the childcare worker. Also, if the childcare worker is relaxed, the AI ​​can adjust the shooting timing according to the childcare worker's instructions. Furthermore, if the childcare worker is busy, the AI ​​can automatically take pictures at the optimal time. For example, if a childcare worker is stressed, the AI ​​will automatically select the shooting timing to reduce the burden on the childcare worker. If the childcare worker is relaxed, the AI ​​can adjust the shooting timing according to the childcare worker's instructions. If the childcare worker is busy, the AI ​​can automatically take pictures at the optimal time. In this way, the burden on childcare workers is reduced by adjusting the shooting timing according to their emotions. Emotion estimation is achieved using emotion estimation functions, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the filming unit may be performed using AI or not. For example, the filming unit inputs the childcare worker's facial expression data into a generating AI, which estimates the childcare worker's emotions and adjusts the filming timing accordingly.

[0081] The camera unit can automatically adjust the optimal shooting angle and distance according to the children's activities. For example, when children are playing, the AI ​​automatically sets the optimal shooting angle and distance. The camera unit can also take pictures at an appropriate distance and angle when children are eating. Furthermore, the camera unit can quietly take pictures at an optimal angle and distance when children are napping. For example, when children are playing, the AI ​​automatically sets the optimal shooting angle and distance. When children are eating, the AI ​​can also take pictures at an appropriate distance and angle. When children are napping, the AI ​​can also quietly take pictures at an optimal angle and distance. This ensures that optimal shooting is performed according to the children's activities. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit inputs the children's activities into the AI, and the AI ​​sets the optimal shooting angle and distance.

[0082] The camera unit can analyze the children's facial expressions and movements in real time during shooting and capture the most appropriate moment. For example, the camera unit's AI can analyze in real time the moment a child smiles and take a picture. The camera unit can also use AI to capture the moment a child makes a specific movement and take the optimal photo. Furthermore, the camera unit's AI can analyze the moment a child is excited and capture the best facial expression. For example, the camera unit's AI can analyze in real time the moment a child smiles and take a picture. The AI ​​can also capture the moment a child makes a specific movement and take the optimal photo. The AI ​​can also analyze the moment a child is excited and capture the best facial expression. This allows the camera unit to capture the child's best moment. Some or all of the above processing in the camera unit may be performed using AI or not. For example, the camera unit inputs the child's facial expression data into the AI, and the AI ​​captures the most appropriate moment.

[0083] The camera unit can estimate the emotions of childcare workers and determine the priority of which children to photograph based on the estimated emotions. For example, if a childcare worker is tired, the AI ​​will automatically determine the priority of the children and take photographs. Also, if a childcare worker is relaxed, the AI ​​can determine the priority of the children based on the childcare worker's instructions. Furthermore, if a childcare worker is busy, the AI ​​can determine the priority of the children and take photographs based on their activities. For example, if a childcare worker is tired, the AI ​​will automatically determine the priority of the children and take photographs. If a childcare worker is relaxed, the AI ​​can determine the priority of the children based on the childcare worker's instructions. If a childcare worker is busy, the AI ​​can determine the priority of the children and take photographs based on their activities. This reduces the burden on childcare workers by determining the priority of which children to photograph according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the processing described above in the filming unit may be performed using AI or not. For example, the filming unit inputs facial expression data of the childcare worker into the generation AI, which estimates the childcare worker's emotions and determines the priority of the children.

[0084] The camera crew can refer to a child's past activity history during filming to focus on specific activities. For example, the camera crew can use AI to refer to activities a child has enjoyed in the past and focus on filming them. The camera crew can also use AI to refer to activities a child has struggled with in the past and film improvements. Furthermore, the camera crew can use AI to refer to activities a child has been particularly interested in in the past and film them in detail. For example, the camera crew can use AI to refer to activities a child has enjoyed in the past and focus on filming them. The AI ​​can also refer to activities a child has struggled with in the past and film improvements. The AI ​​can also refer to activities a child has been particularly interested in in the past and film them in detail. This allows for focusing on specific activities by referring to a child's past activity history. Some or all of the above processing in the camera crew may be performed using AI or not. For example, the camera crew inputs the child's past activity history into the AI, and the AI ​​focuses on filming specific activities.

[0085] The filming unit can prioritize filming specific scenes based on the wishes of the child's parents during filming. For example, the filming unit can use AI to prioritize filming specific activity scenes requested by the parents. The filming unit can also use AI to prioritize filming scenes at specific times of day requested by the parents. Furthermore, the filming unit can use AI to prioritize filming specific facial expressions or movements requested by the parents. For example, the filming unit can use AI to prioritize filming specific activity scenes requested by the parents. The AI ​​can also prioritize filming scenes at specific times of day requested by the parents. The AI ​​can also prioritize filming specific facial expressions or movements requested by the parents. This allows for the filming of specific scenes based on the wishes of the child's parents. Some or all of the above processing in the filming unit may be performed using AI or not. For example, the filming unit inputs the parents' wishes into the AI, and the AI ​​prioritizes filming specific scenes.

[0086] The generation unit can estimate the emotions of the childcare worker and adjust the tone and style of the text based on the estimated emotions. For example, if the childcare worker is relaxed, the generation AI will generate text in a soft tone. The generation unit can also generate concise and to-the-point text if the childcare worker is stressed. Furthermore, if the childcare worker is having fun, the generation AI can generate text in a bright and cheerful tone. This allows for the generation of more appropriate text by adjusting the tone and style according to the childcare worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit inputs emotional data of the childcare worker into the generation AI, which estimates the childcare worker's emotions and adjusts the tone and style of the text.

[0087] The generation unit can generate more detailed text by referring to the child's past activity data during the generation process. For example, the generation unit can refer to the child's past activities, and the generation AI can add a detailed explanation. The generation unit can also generate text that includes specific episodes based on the child's past activity data. Furthermore, the generation unit can refer to the child's past activity history, and the generation AI can add relevant information. For example, the generation unit can refer to the child's past activities, and the generation AI can add a detailed explanation. The generation AI can also generate text that includes specific episodes based on the child's past activity data. The generation AI can also refer to the child's past activity history, and add relevant information. As a result, more detailed text is generated by referring to the child's past activity data. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the child's past activity data into the generation AI, and the generation AI generates a detailed text.

[0088] The generation unit can generate text that reflects the individual characteristics and interests of the children during the generation process. For example, the generation unit's AI can generate text that reflects activities that the children are particularly interested in. The generation unit can also consider the individual characteristics of the children and have the generation AI generate personalized text. Furthermore, the generation unit can have the generation AI generate text that includes specific anecdotes based on the children's interests and characteristics. For example, the generation unit's AI can generate text that reflects activities that the children are particularly interested in. The generation AI can also consider the individual characteristics of the children and have the generation AI generate personalized text. The generation AI can also generate text that includes specific anecdotes based on the children's interests and characteristics. This results in the generation of text that reflects the individual characteristics and interests of the children. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit inputs data on the children's characteristics and interests into the generation AI, and the generation AI generates text based on that data.

[0089] The generation unit can estimate the emotions of the childcare worker and adjust the length of the text based on the estimated emotions. For example, if the childcare worker is relaxed, the generation AI can generate a detailed, long text. If the childcare worker is stressed, the generation AI can also generate a concise, short text. Furthermore, if the childcare worker is busy, the generation AI can also generate a short, to-the-point text. For example, if the childcare worker is relaxed, the generation AI can generate a detailed, long text. If the childcare worker is stressed, the generation AI can also generate a concise, short text. If the childcare worker is busy, the generation AI can also generate a short, to-the-point text. By adjusting the length of the text according to the emotions of the childcare worker, more appropriate text is generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit inputs emotional data from childcare workers into the generation AI, which estimates the childcare workers' emotions and adjusts the length of the text accordingly.

[0090] The generation unit can customize the text during generation by reflecting feedback from the child's parents. For example, based on parental feedback, the generation AI can generate text that emphasizes specific information. The generation unit can also generate text with detailed explanations based on the parents' wishes. Furthermore, the generation unit can reflect parental feedback and generate text that focuses on specific activities. For example, based on parental feedback, the generation AI can generate text that emphasizes specific information. Based on the parents' wishes, the generation AI can also generate text with detailed explanations. The generation AI can also reflect parental feedback and generate text that focuses on specific activities. By customizing the text to reflect feedback from the child's parents, more appropriate text is generated. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs parental feedback data into the generation AI, and the generation AI generates text based on that.

[0091] The generation unit can apply different text generation algorithms depending on the child's activity during generation. For example, if the child is playing, the generation AI can apply an algorithm specialized for play to generate text. Similarly, if the child is eating, the generation AI can apply an algorithm specialized for eating to generate text. Furthermore, if the child is napping, the generation AI can apply an algorithm specialized for rest to generate text. This ensures that different text generation algorithms are applied depending on the child's activity. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs data on the child's activity into the generation AI, which then applies an appropriate algorithm to generate text.

[0092] The entry section can estimate the emotions of the childcare worker and adjust the timing of entry based on the estimated emotions. For example, if the childcare worker is relaxed, the AI ​​will prompt them to enter information at an appropriate time. If the childcare worker is stressed, the AI ​​can also delay the entry. Furthermore, if the childcare worker is busy, the AI ​​can automatically enter information at the optimal time. For example, if the childcare worker is relaxed, the AI ​​will prompt them to enter information at an appropriate time. If the childcare worker is stressed, the AI ​​can also delay the entry. If the childcare worker is busy, the AI ​​can also automatically enter information at the optimal time. This reduces the burden on childcare workers by adjusting the timing of entry according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the entry section may be performed using AI or not. For example, the entry section inputs the childcare worker's emotional data into a generating AI, which then estimates the childcare worker's emotions and adjusts the timing of entry.

[0093] The input section can select the optimal submission timing while considering the schedule of the child's parents. For example, the input section can refer to the parents' schedule, and the AI ​​can select the optimal submission timing. The input section can also have the AI ​​send the information at an appropriate time, avoiding busy times for the parents. Furthermore, the input section can have the AI ​​automatically set the optimal submission timing based on the parents' schedule. For example, the input section can refer to the parents' schedule, and the AI ​​can select the optimal submission timing. The AI ​​can also send the information at an appropriate time, avoiding busy times for the parents. The AI ​​can also automatically set the optimal submission timing based on the parents' schedule. This ensures that the optimal submission timing is selected while considering the schedule of the child's parents. Some or all of the above processing in the input section may be performed using AI, or not. For example, the input section can input the parents' schedule data into the AI, and the AI ​​can select the optimal submission timing.

[0094] The entry section can apply different entry formats depending on the child's activity during entry. For example, if the child is playing, the AI ​​will apply an entry format specialized for play. Similarly, if the child is eating, the AI ​​can apply an entry format specialized for eating. Furthermore, if the child is napping, the AI ​​can apply an entry format specialized for resting. This ensures that different entry formats are applied depending on the child's activity. Some or all of the above processing in the entry section is performed using a generating AI. For example, the entry section inputs the child's activity data into the generating AI, which then applies the appropriate entry format based on that data.

[0095] The input section can estimate the emotions of the childcare worker and prioritize the content to be entered based on the estimated emotions. For example, if the childcare worker is relaxed, the AI ​​will prioritize entering detailed information. If the childcare worker is stressed, the AI ​​can also prioritize entering concise information. Furthermore, if the childcare worker is busy, the AI ​​can also prioritize entering important information. This reduces the burden on childcare workers by prioritizing the content to be entered according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the entry section may be performed using AI or not. For example, the entry section inputs the childcare worker's emotional data into a generating AI, which estimates the childcare worker's emotions and determines the priority of the entry content.

[0096] The input section can highlight specific information based on the wishes of the child's parents during the input process. For example, the input section can use AI to highlight specific activities desired by the parents. The input section can also use AI to highlight information about specific time slots desired by the parents. Furthermore, the input section can use AI to highlight specific facial expressions or movements desired by the parents. For example, the input section can use AI to highlight specific activities desired by the parents. The AI ​​can also use AI to highlight information about specific time slots desired by the parents. The AI ​​can also use AI to highlight specific facial expressions or movements desired by the parents. This allows for the provision of more appropriate information by highlighting specific information based on the wishes of the child's parents. Some or all of the above processing in the input section is performed using a generating AI. For example, the input section inputs the parents' wish data into the generating AI, which then highlights and fills in specific information based on that data.

[0097] The input section can add relevant information by referring to the child's activity history when filling out the form. For example, the input section can refer to the child's past activity history, and the AI ​​can add relevant information. The input section can also have the AI ​​add specific episodes based on the child's past activity data. Furthermore, the input section can refer to the child's past activity content, and the AI ​​can add relevant information. For example, the input section can refer to the child's past activity history, and the AI ​​can add relevant information. The AI ​​can also add specific episodes based on the child's past activity data. The AI ​​can also add relevant information by referring to the child's past activity content. In this way, relevant information is added by referring to the child's activity history. Some or all of the above processing in the input section is performed using a generating AI. For example, the input section inputs the child's activity history data into the generating AI, and the generating AI adds relevant information based on that.

[0098] The confirmation unit can estimate the emotions of the childcare worker and adjust the timing of confirmation based on the estimated emotions. For example, if the childcare worker is relaxed, the AI ​​will prompt for confirmation at an appropriate time. The confirmation unit can also delay the timing of confirmation if the childcare worker is stressed. Furthermore, if the childcare worker is busy, the AI ​​can automatically perform confirmation at the optimal time. For example, if the childcare worker is relaxed, the AI ​​will prompt for confirmation at an appropriate time. If the childcare worker is stressed, the AI ​​can delay the timing of confirmation. If the childcare worker is busy, the AI ​​can automatically perform confirmation at the optimal time. This reduces the burden on childcare workers by adjusting the timing of confirmation according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the confirmation unit may be performed using AI or not. For example, the verification unit inputs the childcare worker's emotional data into a generating AI, which then estimates the childcare worker's emotions and adjusts the timing of the verification.

[0099] The verification unit can determine the need for correction by referring to past contact log data during verification. For example, the verification unit can refer to past contact log data, and the AI ​​can determine the need for correction. The verification unit can also compare past contact log data with current data, and the AI ​​can detect inconsistencies and propose corrections. Furthermore, the verification unit can perform corrections based on past contact log data to provide consistent information. For example, the verification unit can refer to past contact log data, and the AI ​​can determine the need for correction. It can also compare past contact log data with current data, and the AI ​​can detect inconsistencies and propose corrections. It can also perform corrections based on past contact log data to provide consistent information. This allows the need for correction to be determined by referring to past contact log data. Some or all of the above processing in the verification unit is performed using a generating AI. For example, the verification unit inputs past contact log data into the generating AI, and the generating AI determines the need for correction based on that data.

[0100] The verification unit can revise the text during the verification process, reflecting feedback from the child's parents. For example, the verification unit can use AI to revise specific information based on parental feedback. The verification unit can also revise the text by adding detailed explanations based on the parents' requests. Furthermore, the verification unit can revise the text by reflecting parental feedback and focusing on specific activities. For example, the verification unit can use AI to revise specific information based on parental feedback. The AI ​​can also revise the text by adding detailed explanations based on the parents' requests. The AI ​​can also revise the text by reflecting parental feedback and focusing on specific activities. This ensures that more appropriate information is provided by revising the text to reflect feedback from the child's parents. Some or all of the above processing in the verification unit is performed using a generative AI. For example, the verification unit inputs parental feedback data into the generative AI, which then revises the text based on that data.

[0101] The verification unit can estimate the emotions of childcare workers and determine the priority of the verification content based on the estimated emotions. For example, if the childcare worker is relaxed, the AI ​​will prioritize checking detailed information. If the childcare worker is stressed, the AI ​​can also prioritize checking concise information. Furthermore, if the childcare worker is busy, the AI ​​can also prioritize checking important information. This reduces the burden on childcare workers by determining the priority of verification content according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the verification unit may be performed using AI or not. For example, the verification unit inputs the childcare worker's emotional data into a generating AI, which estimates the childcare worker's emotions and determines the priority of the verification items.

[0102] The verification unit can highlight specific information based on the wishes of the child's parents during the verification process. For example, the verification unit can use AI to highlight and verify specific activities that the parents desire. The verification unit can also use AI to highlight and verify information about specific time slots that the parents desire. Furthermore, the verification unit can use AI to highlight and verify specific facial expressions or movements that the parents desire. For example, the verification unit can use AI to highlight and verify specific activities that the parents desire. The verification unit can also use AI to highlight and verify information about specific time slots that the parents desire. The verification unit can also use AI to highlight and verify specific facial expressions or movements that the parents desire. This allows for the provision of more appropriate information by highlighting specific information based on the wishes of the child's parents. Some or all of the above processing in the verification unit is performed using a generating AI. For example, the verification unit inputs the parents' preference data into the generating AI, which then highlights and verifies specific information based on that input.

[0103] The verification unit can add relevant information by referring to the child's activity history during verification. For example, the verification unit can refer to the child's past activity history, and the AI ​​can add relevant information. The verification unit can also add specific episodes based on the child's past activity data. Furthermore, the verification unit can refer to the child's past activity content, and the AI ​​can add relevant information. For example, the verification unit can refer to the child's past activity history, and the AI ​​can add relevant information. The AI ​​can also add specific episodes based on the child's past activity data. The AI ​​can also add relevant information by referring to the child's past activity content. In this way, relevant information is added by referring to the child's activity history. Some or all of the above processing in the verification unit is performed using a generating AI. For example, the verification unit inputs the child's activity history data into the generating AI, and the generating AI adds relevant information based on that.

[0104] The album generation unit can estimate the emotions of the childcare worker and adjust the album layout based on the estimated emotions. For example, if the childcare worker is relaxed, the AI ​​will generate a layout with a soft tone. The album generation unit can also generate a simple and highly visible layout if the childcare worker is stressed. Furthermore, if the childcare worker is having fun, the AI ​​will generate a bright and cheerful layout. This allows for the generation of more appropriate albums by adjusting the album layout according to the childcare worker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. Generative AIs include, but are not limited to, text generation AIs (e.g., LLM) and multimodal generation AIs. Some or all of the above-described processes in the album generation unit may be performed using AI or not. For example, the album generation unit inputs emotional data of the childcare worker into a generating AI, which estimates the childcare worker's emotions and adjusts the album layout accordingly.

[0105] The album generation unit can apply different layouts depending on the child's activity during album generation. For example, if a child is playing, the AI ​​will apply a layout optimized for play. Similarly, if a child is eating, the AI ​​can apply a layout optimized for eating. Furthermore, if a child is napping, the AI ​​can apply a layout optimized for rest. This ensures that different layouts are applied depending on the child's activity. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs data on the child's activities into the generation AI, which then applies an appropriate layout based on that data.

[0106] The album generation unit can emphasize specific photos and texts based on the wishes of the child's parents when generating an album. For example, the album generation unit's AI can prioritize including specific activity scenes requested by the parents in the album. It can also prioritize including scenes from specific time periods requested by the parents. Furthermore, the album generation unit's AI can prioritize including specific facial expressions and movements requested by the parents in the album. For example, the album generation unit's AI can prioritize including specific activity scenes requested by the parents in the album. It can also prioritize including scenes from specific time periods requested by the parents in the album. It can also prioritize including specific facial expressions and movements requested by the parents in the album. This allows for the generation of more appropriate albums by emphasizing specific photos and texts based on the wishes of the child's parents. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs parent preference data into the generation AI, which then emphasizes and includes specific photos and texts in the album based on that data.

[0107] The album generation unit can estimate the emotions of the childcare worker and adjust the number of pages in the album based on the estimated emotions. For example, if the childcare worker is relaxed, the AI ​​will generate a multi-page album containing detailed information. If the childcare worker is stressed, the AI ​​can also generate a concise, short-page album. Furthermore, if the childcare worker is busy, the AI ​​can generate a concise, short-page album that gets straight to the point. By adjusting the number of pages in the album according to the emotions of the childcare worker, a more appropriate album can be generated. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the album generation unit may be performed using AI or not. For example, the album generation unit inputs emotional data of childcare workers into a generating AI, which estimates the childcare workers' emotions and adjusts the number of pages in the album.

[0108] The album generation unit can add relevant photos and text by referencing the child's past activity data when generating an album. For example, the album generation unit can refer to the child's past activity data and the AI ​​can add relevant photos. The album generation unit can also add text containing specific episodes based on the child's past activity history. Furthermore, the album generation unit can refer to the child's past activity content and the AI ​​can add relevant information. For example, the album generation unit can refer to the child's past activity data and the AI ​​can add relevant photos. The AI ​​can also add text containing specific episodes based on the child's past activity history. The AI ​​can also refer to the child's past activity content and add relevant information. As a result, relevant photos and text are added by referencing the child's past activity data. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs the child's past activity data into the generation AI, and the generation AI adds relevant photos and text based on that.

[0109] The album generation unit can customize the layout during album generation by reflecting feedback from the child's parents. For example, based on parental feedback, the AI ​​generates a layout that highlights specific information. The album generation unit can also generate a layout with detailed explanations based on the parents' wishes. Furthermore, the album generation unit can reflect parental feedback and generate a layout that focuses on specific activities. For example, based on parental feedback, the AI ​​generates a layout that highlights specific information. Based on the parents' wishes, the AI ​​can also generate a layout with detailed explanations. The AI ​​can also reflect parental feedback and generate a layout that focuses on specific activities. By customizing the layout to reflect feedback from the child's parents, a more appropriate album is generated. Some or all of the above processing in the album generation unit is performed using a generation AI. For example, the album generation unit inputs parental feedback data into the generation AI, and the generation AI customizes the layout based on that data.

[0110] The direct communication unit can estimate the emotions of childcare workers and adjust the timing of communication based on the estimated emotions. For example, if a childcare worker is relaxed, the AI ​​in the direct communication unit will prompt communication at an appropriate time. The direct communication unit can also delay communication if the childcare worker is stressed. Furthermore, if the childcare worker is busy, the AI ​​in the direct communication unit can automatically make communication at the optimal time. For example, if a childcare worker is relaxed, the AI ​​in the direct communication unit will prompt communication at an appropriate time. If the childcare worker is stressed, the AI ​​can delay communication. If the childcare worker is busy, the AI ​​can automatically make communication at the optimal time. This reduces the burden on childcare workers by adjusting the timing of communication according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the direct communication unit may be performed using AI or not. For example, the direct communication department inputs emotional data from childcare workers into a generating AI, which then estimates the childcare worker's emotions and adjusts the timing of communication.

[0111] The direct contact unit can select the most suitable contact method when contacting parents, taking into account their schedules. For example, the direct contact unit can refer to the parents' schedules, and the AI ​​can select the most suitable contact method. The direct contact unit can also select an appropriate contact method that avoids busy times for parents. Furthermore, the direct contact unit can automatically set the most suitable contact method based on the parents' schedules. For example, the direct contact unit can refer to the parents' schedules, and the AI ​​can select the most suitable contact method. The AI ​​can also select an appropriate contact method that avoids busy times for parents. The AI ​​can also automatically set the most suitable contact method based on the parents' schedules. This ensures that the most suitable contact method is selected considering the parents' schedules. Some or all of the above processing in the direct contact unit is performed using a generating AI. For example, the direct contact unit inputs the parents' schedule data into the generating AI, and the generating AI selects the most suitable contact method based on that data.

[0112] The direct communication unit can estimate the emotions of childcare workers and prioritize communication based on those emotions. For example, if a childcare worker is relaxed, the AI ​​will prioritize sending detailed information. If a childcare worker is stressed, the AI ​​can prioritize sending concise information. Furthermore, if a childcare worker is busy, the AI ​​can prioritize sending important information. This reduces the burden on childcare workers by prioritizing communication according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-mentioned processes in the direct communication department may be performed using AI or not. For example, the direct communication department inputs emotional data of childcare workers into a generating AI, which estimates the childcare workers' emotions and determines the priority of communication content.

[0113] The direct contact unit can emphasize specific information based on the wishes of the child's parents when making contact. For example, the direct contact unit can use AI to emphasize specific activities that the parents desire. The direct contact unit can also use AI to emphasize information about specific time slots that the parents desire. Furthermore, the direct contact unit can use AI to emphasize specific facial expressions or movements that the parents desire. For example, the direct contact unit can use AI to emphasize specific activities that the parents desire. The direct contact unit can also use AI to emphasize information about specific time slots that the parents desire. The direct contact unit can also use AI to emphasize specific facial expressions or movements that the parents desire. This allows for the provision of more appropriate information by emphasizing specific information based on the wishes of the child's parents. Some or all of the above processing in the direct contact unit is performed using a generative AI. For example, the direct contact unit inputs parent preference data into the generative AI, which then emphasizes specific information based on that input.

[0114] The direct contact unit can add relevant information by referring to the child's activity history when making contact. For example, the direct contact unit can refer to the child's past activity history, and the AI ​​can add relevant information. The direct contact unit can also add specific episodes based on the child's past activity data. Furthermore, the direct contact unit can refer to the child's past activity content, and the AI ​​can add relevant information. For example, the direct contact unit can refer to the child's past activity history, and the AI ​​can add relevant information. The AI ​​can also add specific episodes based on the child's past activity data. The AI ​​can also refer to the child's past activity content, and the AI ​​can add relevant information. In this way, relevant information is added by referring to the child's activity history. Some or all of the above processing in the direct contact unit is performed using a generating AI. For example, the direct contact unit inputs the child's activity history data into the generating AI, and the generating AI adds relevant information based on that.

[0115] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0116] The electronic communication system for preschool children can also be equipped with a voice input function. This function allows caregivers to record the children's activities using voice. For example, a caregiver can describe a child's behavior verbally, and an AI can analyze this voice data and transcribe it into text. Furthermore, the voice input function allows caregivers to easily record information even when their hands are busy. Additionally, the voice input function can reflect the caregiver's emotions in the generated text, allowing for a more natural and nuanced recording of the children's activities.

[0117] The electronic communication system for preschool children can also be equipped with a translation function. This function can translate generated text into multiple languages. For example, if a parent speaks a foreign language, the generated text can be translated into that language and provided to them. The translation function can also accommodate situations where childcare workers record information in a foreign language, with the AI ​​translating that content into Japanese. Furthermore, the translation function can automatically translate according to the parent's preferred language. This enables multilingual support and facilitates smoother intercultural communication.

[0118] The electronic communication system for preschool children can also include a notification function. This function can notify parents of important information in real time. For example, it can immediately send notifications to parents if a child is injured or if an emergency contact is necessary. The notification function can also send periodic notifications at times requested by parents. Furthermore, the notification function can prioritize notifications for specific information that parents wish to receive. This facilitates smoother communication with parents and ensures that important information is conveyed quickly.

[0119] The electronic communication log system for preschool children can also be equipped with an emotion analysis unit. This unit can estimate the emotions parents feel when viewing the log and provide feedback based on those emotions. For example, if a parent is happy after viewing the log, the system can detect this emotion and provide positive feedback to the caregiver. Similarly, if a parent is feeling anxious, the system can detect this emotion and suggest additional explanations or support to the caregiver. This allows for responses tailored to the parents' emotions, leading to better communication.

[0120] The electronic communication system for preschool children can also include a health management section. This section can record the children's health status and provide this information to their parents. For example, it can record the children's body temperature, meals, and sleep duration, and a generating AI can analyze this data to assess their health. Furthermore, the health management section can receive questions from parents regarding their child's health, and the generating AI can provide appropriate answers. In addition, the health management section can send alerts to parents if any abnormalities are detected in the child's health. This allows for more effective health management of the children.

[0121] The electronic communication system for preschool children can also include a learning record section. This section can record the children's learning activities and provide this information to parents. For example, it can record what learning activities the children undertook and what results they achieved, and a generating AI can analyze this data to evaluate their learning progress. Furthermore, the learning record section can also provide appropriate answers to questions parents have about their child's learning. In addition, based on the children's learning activities, the learning record section can offer suggestions to parents for home learning support. This allows for more effective support of the children's learning activities.

[0122] The electronic communication system for preschool children can also be equipped with an emotion estimation unit. This unit can analyze a child's facial expressions and behavior to estimate their emotions. For example, if a child is smiling, the system can estimate that emotion as "joy" and provide this information to the parents. Similarly, if a child is crying, the system can estimate that emotion as "sadness" and provide this information to the parents. Furthermore, the emotion estimation unit can also suggest appropriate responses to childcare workers based on the child's emotions. This enables responses that are in line with the child's emotions, leading to better childcare.

[0123] The electronic communication system for preschool children can also be equipped with a behavior prediction unit. This unit analyzes past behavioral data of the children and predicts future behavior. For example, if a child tends to exhibit certain behaviors at specific times, the system can predict these behaviors and suggest appropriate responses to the caregivers in advance. The behavior prediction unit can also suggest methods for home care to parents based on the child's behavioral patterns. Furthermore, the behavior prediction unit can send alerts to caregivers and parents if any abnormalities are detected in the child's behavior. This enables appropriate responses to the children's behavior.

[0124] The electronic communication system for preschool children can also be equipped with an emotion-sharing function. This function allows caregivers and parents to share their feelings with each other. For example, caregivers can input the emotions they feel when recording a child's behavior into the system and communicate those emotions to the parents. Similarly, parents can input the emotions they feel when viewing the communication log and communicate those emotions to the caregivers. Furthermore, the emotion-sharing function enables better communication by allowing caregivers and parents to understand each other's feelings. This promotes the sharing of emotions between caregivers and parents and deepens their relationship of trust.

[0125] The electronic communication system for preschool children can also include an activity record section. This section can record the child's activities in detail and provide this information to parents. For example, it can record what kind of play the child engaged in and what actions the child took, and a generating AI can analyze this data to evaluate behavioral patterns. The activity record section can also provide appropriate answers to questions parents have about their child's behavior using the generating AI. Furthermore, based on the child's behavior, the activity record section can suggest ways for parents to handle the situation at home. This allows for a more detailed record of the child's activities and provides parents with appropriate information.

[0126] The following briefly describes the processing flow for example form 2.

[0127] Step 1: The photography team will photograph the children. This includes scenes of the children playing, eating, napping, etc. The photography team can take photos and videos using smartphone cameras, digital cameras, video cameras, etc. Step 2: The generation unit uses a generation AI to analyze the photos taken by the photography unit and transcribe the children's activities into text. The generation AI understands the content of the photos and generates text based on the children's activities, expressions, background information, etc. Step 3: The input unit enters the text generated by the generation unit into the electronic communication log. The input unit has functions to automatically enter the generated text into the electronic communication log, to allow childcare workers to review and correct the text before entering it, and to send the generated text to the parents.

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0129] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0130] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the shooting unit, generation unit, entry unit, confirmation unit, album generation unit, and direct communication unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart device 14 to photograph the children. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the photos and generates text. The entry unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 as a processing unit that enters the generated text into an electronic communication notebook. The confirmation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12 as a processing unit for childcare workers to confirm and correct the generated text. The album generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that combines photos and text to create an album. The direct communication unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12, as a processing unit that allows childcare workers to directly convey important information to parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0133] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the shooting unit, generation unit, entry unit, confirmation unit, album generation unit, and direct communication unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the smart glasses 214 to photograph the children. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the photos and generates text. The entry unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 as a processing unit that enters the generated text into an electronic communication notebook. The confirmation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12 as a processing unit for childcare workers to confirm and correct the generated text. The album generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that combines photos and text to create an album. The direct communication unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12, as a processing unit that allows childcare workers to directly convey important information to parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0149] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the shooting unit, generation unit, entry unit, confirmation unit, album generation unit, and direct communication unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the headset terminal 314 to photograph the children. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the photos and generates text. The entry unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 as a processing unit that enters the generated text into an electronic communication notebook. The confirmation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12 as a processing unit for childcare workers to confirm and correct the generated text. The album generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that combines photos and text to create an album. The direct communication unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12, as a processing unit that allows childcare workers to directly convey important information to parents. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0165] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0166] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0167] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0168] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0169] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0170] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0171] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0172] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0173] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0174] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0175] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0176] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0177] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0178] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0179] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0180] Each of the multiple elements described above, including the shooting unit, generation unit, entry unit, confirmation unit, album generation unit, and direct communication unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the shooting unit uses the camera 42 of the robot 414 to photograph the children. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12, where a generation AI analyzes the photos and generates text. The entry unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 as a processing unit that enters the generated text into an electronic communication notebook. The confirmation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12 as a processing unit for childcare workers to confirm and correct the generated text. The album generation unit is implemented by the specific processing unit 290 of the data processing unit 12 as a processing unit that combines photos and text to create an album. The direct communication unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, as a processing unit that allows childcare workers to directly convey important information to parents. The correspondence between each unit and the devices or control units is not limited to the example described above and can be modified in various ways.

[0181] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0182] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0183] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0184] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0185] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0186] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0187] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0188] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0189] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0190] 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.

[0191] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0192] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0193] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0194] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0195] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0196] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0197] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0198] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0199] (Note 1) The photography team will be in charge of filming the children, The aforementioned camera unit analyzes the photographs taken and generates text describing the children's behavior, The system includes an input unit for entering the text generated by the generation unit into an electronic contact book. A system characterized by the following features. (Note 2) The generating unit is The AI ​​analyzes photos and translates descriptions of the children's behavior into text. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned entry section is, The generated text will be automatically entered into the electronic contact book. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a verification section where childcare workers can check and correct the generated text. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features an album generation unit that periodically compiles photos and contact log entries into albums. The system described in Appendix 1, characterized by the features described herein. (Note 6) A direct communication department is in place where childcare workers directly convey important information to parents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned imaging unit is The system estimates the emotions of the childcare workers and adjusts the timing of filming based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned imaging unit is The camera automatically adjusts the optimal shooting angle and distance according to the children's activities. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned imaging unit is During filming, the system analyzes the children's facial expressions and movements in real time to capture the most appropriate moment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned imaging unit is The system estimates the emotions of the childcare workers and determines the priority of which children to photograph based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned imaging unit is During filming, refer to the children's past activity records and focus on filming specific activities. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned imaging unit is During filming, specific scenes are prioritized based on the wishes of the children's parents. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is The system estimates the emotions of childcare workers and adjusts the tone and style of the text based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the system references the children's past activity data to generate more detailed text. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, the text is designed to reflect the individual characteristics and interests of each child. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is The system estimates the emotions of the childcare worker and adjusts the length of the text based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the text is customized to reflect feedback from the parents of the children. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, different text generation algorithms are applied depending on the children's activities. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned entry section is, The system estimates the emotions of the childcare workers and adjusts the timing of entries based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned entry section is, When filling out the form, consider the schedules of the child's parents to select the optimal time for submission. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned entry section is, When filling out the form, apply a different entry format depending on the child's activity. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned entry section is, The system estimates the emotions of the childcare workers and determines the priority of the information to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned entry section is, When filling out the form, highlight specific information based on the wishes of the child's parents. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned entry section is, When filling out the form, refer to the child's activity history and add relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned verification unit is The system estimates the emotions of the childcare workers and adjusts the timing of checks based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned verification unit is During the verification process, we will refer to past contact log data to determine if corrections are necessary. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned verification unit is During the review process, the text will be revised to reflect feedback from the parents of the children. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned verification unit is The system estimates the emotions of the childcare workers and prioritizes the items to be checked based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned verification unit is During the verification process, specific information will be emphasized based on the wishes of the child's parents. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned verification unit is During verification, refer to the child's activity history and add relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned album generation unit, The system estimates the emotions of the childcare workers and adjusts the album layout based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned album generation unit, When creating the album, different layouts are applied depending on the children's activities. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned album generation unit, When creating the album, highlight specific photos and text based on the wishes of the child's parents. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned album generation unit, The system estimates the emotions of the childcare workers and adjusts the number of pages in the album based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned album generation unit, When creating an album, the system references the children's past activity data to add relevant photos and text. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned album generation unit, When creating the album, the layout will be customized to reflect feedback from the children's parents. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned direct communication section is, The system estimates the emotions of childcare workers and adjusts the timing of communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned direct communication section is, When contacting parents, we will select the most suitable method of contact, taking into consideration the schedules of the parents of the children. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned direct communication section is, The system estimates the emotions of childcare workers and prioritizes communication based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned direct communication section is, When contacting parents, we emphasize specific information based on their preferences. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned direct communication section is, When contacting the child, refer to their activity history and add relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0200] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The photography team takes pictures of the children, The aforementioned camera unit analyzes the photographs taken and generates text describing the children's behavior, The system includes an input unit for entering the text generated by the generation unit into an electronic contact book. A system characterized by the following features.

2. The generating unit is The AI ​​analyzes photos and translates descriptions of the children's behavior into text. The system according to feature 1.

3. The aforementioned entry section is, The generated text will be automatically entered into the electronic contact book. The system according to feature 1.

4. It includes a review section where childcare workers can check and correct the generated text. The system according to feature 1.

5. It features an album generation unit that periodically compiles photos and contact log entries into albums. The system according to feature 1.

6. A direct communication department is in place where childcare workers directly convey important information to parents. The system according to feature 1.

7. The aforementioned imaging unit is The system estimates the emotions of the childcare workers and adjusts the timing of filming based on those estimated emotions. The system according to feature 1.

8. The aforementioned imaging unit is The camera automatically adjusts the optimal shooting angle and distance according to the children's activities. The system according to feature 1.

Images