system

Abstract

The system according to this embodiment aims to reduce the workload of childcare workers and improve the quality of childcare. [Solution] The system according to the embodiment comprises an identification unit, an analysis unit, a generation unit, a sharing unit, and a provision unit. The identification unit identifies each child. The analysis unit analyzes the behavior and situation of the child identified by the identification unit. The generation unit generates a communication log based on the behavior data analyzed by the analysis unit. The sharing unit shares the communication log generated by the generation unit among childcare workers. The provision unit provides the communication log shared by the sharing unit to the parents.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including 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 as a 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 were problems such as a large workload for nursery teachers and difficulty in filling out the contact book.

[0005] The system according to the embodiment aims to reduce the workload of nursery teachers and improve the quality of childcare.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an identification unit, an analysis unit, a generation unit, a sharing unit, and a provision unit. The identification unit identifies each child. The analysis unit analyzes the behavior and circumstances of the children identified by the identification unit. The generation unit generates a communication log based on the behavioral data analyzed by the analysis unit. The sharing unit shares the communication log generated by the generation unit among childcare workers. The provision unit provides the communication log shared by the sharing unit to the parents. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the workload of childcare workers and improve the quality of childcare. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F 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] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The childcare support system according to an embodiment of the present invention is a system that reduces the workload of childcare workers and improves the quality of childcare. This childcare support system identifies each child, and AI analyzes their behavior and situation in real time. Next, based on the behavioral data analyzed by the AI, it generates individually customized communication logs. Furthermore, the generated communication logs are used to enhance information sharing among childcare workers. This system eliminates the need for childcare workers to manually record the children's situations and behaviors, thereby reducing their workload. It also improves the quality of childcare and provides the necessary environment for children to grow up and develop safely. The quality of life for childcare workers is also improved, and it can support them in professional growth and lead richer and more fulfilling lives. This system supports the childcare field and improves operational efficiency. For example, the childcare support system identifies each child using a camera. AI analyzes the behavior and situation of the identified child in real time. AI generates individually customized communication logs based on the analyzed behavioral data. The generated communication logs are shared among childcare workers in real time. The shared communication logs are provided to parents. In this way, the childcare support system can reduce the workload of childcare workers and improve the quality of childcare. This allows the childcare support system to reduce the workload of childcare workers and improve the quality of childcare.

[0029] The childcare support system according to this embodiment comprises an identification unit, an analysis unit, a generation unit, a sharing unit, and a provision unit. The identification unit identifies each child. The identification unit can identify each child using, for example, facial recognition technology. The identification unit can also identify each child using RFID tags. The identification unit can also identify each child using voice recognition technology. The analysis unit analyzes the behavior and situation of the child identified by the identification unit. The analysis unit can analyze, for example, the type of play the child is engaged in. The analysis unit can also analyze the child's eating habits. The analysis unit can also analyze the child's health status. The generation unit generates a communication log based on the behavioral data analyzed by the analysis unit. The generation unit can generate, for example, a text-based communication log. The generation unit can also generate a communication log with images. The generation unit can also generate a communication log with videos. The sharing unit shares the communication log generated by the generation unit among childcare workers. The sharing unit can share the communication log in real time via, for example, the internet. The sharing unit can also share the communication log using a dedicated application. The sharing unit can also share the contact log during specific events. The providing unit provides the contact log shared by the sharing unit to the parents. The providing unit can provide the contact log to the parents, for example, via email. The providing unit can also provide the contact log to the parents via app notifications. The providing unit can also provide the contact log to the parents using paper copies. As a result, the childcare support system according to this embodiment can reduce the workload of childcare workers and improve the quality of childcare.

[0030] The identification unit identifies each child. For example, the identification unit can identify each child using facial recognition technology. Facial recognition technology analyzes the child's facial image captured by the camera and compares it with a pre-registered facial database to identify each child. Facial recognition technology uses advanced algorithms that can handle lighting conditions, face orientation, and changes in facial expression. The identification unit can also identify each child using RFID tags. RFID tags are attached to the child's clothing or belongings, and an RFID reader receives the signal transmitted from the tag for identification. RFID tags enable contactless identification and can process multiple children quickly even when they are identified simultaneously. The identification unit can also identify each child using voice recognition technology. Voice recognition technology records the child's voice and analyzes the voice pattern to identify each child. Voice recognition technology is designed to handle background noise and changes in voice tone. As a result, the identification unit can accurately and quickly identify each child by using facial recognition, RFID tags, voice recognition, or a combination of these.

[0031] The analysis unit analyzes the behavior and circumstances of children identified by the identification unit. For example, the analysis unit can analyze the types of play children engage in. By analyzing what kinds of play children prefer and how much time they spend on each type, it can understand the interests and concerns of individual children. The analysis unit can also analyze children's eating habits. It records meal times, amounts eaten, and whether or not food is left over to evaluate children's eating patterns and nutritional status. The analysis unit can also analyze children's health conditions. It collects health data such as body temperature, heart rate, and activity levels to check for abnormalities. The analysis unit processes this data in real time and can immediately issue an alert if an abnormality is detected. Furthermore, the analysis unit can accumulate historical data and analyze long-term trends and patterns. This allows the analysis unit to gain a detailed understanding of children's behavior and circumstances, providing information that enables childcare workers to take appropriate action.

[0032] The generation unit generates communication logs based on behavioral data analyzed by the analysis unit. For example, the generation unit can generate communication logs in text format. Text-format communication logs include detailed information about the child's daily activities, mealtimes, and health status. The generation unit can also generate communication logs with images. These logs include photos of the child playing, eating, and health check results. The generation unit can also generate communication logs with videos. These videos include footage of the child's activities and specific events, allowing parents to have a more realistic understanding of their child's day. The generation unit automatically generates these communication logs, significantly reducing the workload for childcare workers. Furthermore, the generation unit can customize the content of the communication logs, providing information tailored to the needs of parents and the characteristics of the children. This allows the generation unit to improve the efficiency of childcare workers' work and provide comprehensive information to parents.

[0033] The sharing unit shares the communication log generated by the generation unit among childcare workers. The sharing unit can, for example, share the communication log in real time via the internet. Childcare workers can access the communication log and check the information anytime, anywhere using an internet-connected device. The sharing unit can also share the communication log using a dedicated app. The dedicated app is designed to allow childcare workers to easily view the communication log and quickly obtain the necessary information. The sharing unit can also share the communication log during specific events. For example, during nursery school events or emergencies, relevant communication logs can be quickly shared, allowing childcare workers to share information. This enables the sharing unit to facilitate information sharing among childcare workers and improve the quality of childcare. Furthermore, the sharing unit manages the sharing history of the communication log and ensures information security by recording who accessed which information and when. This enables the sharing unit to strengthen collaboration among childcare workers, improve the efficiency of childcare operations, and achieve centralized information management.

[0034] The provisioning department provides parents with the communication log shared by the sharing department. The provisioning department can provide the communication log to parents using, for example, email. Email is a means of communication that parents use on a daily basis, and it can deliver the communication log quickly and reliably. The provisioning department can also provide the communication log to parents using app notifications. By notifying parents of the communication log through a dedicated app, parents can easily check the communication log on their smartphones or tablets. The provisioning department can also provide parents with the communication log using paper copies. Paper copies of the communication log are easy for parents to physically store and can be reviewed at any time as needed. This allows the provisioning department to provide the communication log in a flexible way that suits the needs and circumstances of the parents. Furthermore, the provisioning department can manage the history of communication log delivery and confirm whether parents have received the communication log. This allows the provisioning department to ensure that information is provided to parents and to facilitate smooth communication between the nursery school and parents.

[0035] The identification unit can identify each child using a camera. For example, the identification unit can identify each child using a fixed camera. The identification unit can also identify each child using a mobile camera. The identification unit can also identify each child using a wide-angle camera. This allows for accurate identification of each child using a camera. Some or all of the above-described processing in the identification unit may be performed using AI or not. For example, the identification unit can input image data acquired by the camera into a generating AI and have the generating AI perform child identification from the image data.

[0036] The analysis unit can analyze the behavior and circumstances of identified children in real time. For example, the analysis unit can analyze the type of play a child is engaged in in real time. The analysis unit can also analyze a child's eating habits in real time. The analysis unit can also analyze a child's health status in real time. This allows for an immediate understanding of a child's behavior and circumstances through real-time analysis. Specific definitions and criteria for "real time" include, for example, seconds, minutes, and acceptable delays. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data acquired in real time into a generating AI and have the generating AI perform the data analysis.

[0037] The generation unit can generate individually customized contact logs based on the analyzed behavioral data. For example, the generation unit can customize according to the child's characteristics. The generation unit can also customize according to the requests of the parents. The generation unit can also customize based on the child's behavioral patterns. This allows for the generation of individually customized contact logs, providing information tailored to the child's situation. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the analyzed behavioral data into a generation AI and have the generation AI perform the generation of the contact log.

[0038] The sharing function allows the generated communication log to be shared among childcare workers in real time. The sharing function can share the communication log in real time, for example, via the internet. The sharing function can also share the communication log in real time using a dedicated app. The sharing function can also share the communication log in real time during specific events. This allows for rapid information sharing among childcare workers through real-time sharing. Some or all of the above processes in the sharing function may be performed using AI or not. For example, the sharing function can input the generated communication log into a generation AI and have the generation AI perform the sharing of the communication log.

[0039] The service provider can provide the shared contact notebook to the parents. The service provider can provide the contact notebook to the parents, for example, by email. The service provider can also provide the contact notebook to the parents by app notification. The service provider can also provide the contact notebook to the parents using paper. This allows the service provider to understand the child's situation by providing the contact notebook to the parents. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the shared contact notebook into a generating AI and have the generating AI perform the task of providing the contact notebook.

[0040] The identification unit can improve the accuracy of identification by referring to the child's past behavioral history during the identification process. For example, the identification unit learns the behavioral patterns of a particular child based on past behavioral data. The identification unit can also refer to past behavioral history and use it as a reference during identification. The identification unit can also analyze past behavioral data to improve the accuracy of identification. As a result, the accuracy of identification is improved by referring to past behavioral history. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0041] The identification unit can use the child's clothing and accessories as auxiliary information during identification. For example, the identification unit can identify the color and design of the child's clothing and use it as auxiliary information. The identification unit can also identify the child's accessories and use them as auxiliary information. The identification unit can also analyze the characteristics of the child's clothing and accessories to improve the accuracy of identification. As a result, the accuracy of identification is improved by using clothing and accessories as auxiliary information. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input image data of the child's clothing and accessories into a generating AI and use it as auxiliary information for identification.

[0042] The identification unit can improve the accuracy of identification by considering the child's geographical location information during identification. For example, the identification unit can acquire the child's current location information to improve the accuracy of identification. The identification unit can also refer to the child's movement history to improve the accuracy of identification. The identification unit can also update the child's location information in real time to improve the accuracy of identification. This improves the accuracy of identification by considering geographical location information. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input the child's location information data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0043] The identification unit can improve the accuracy of identification by referring to information about the child's parents or guardians during the identification process. For example, the identification unit can refer to information about parents or guardians and use it to identify the child. The identification unit can also analyze the characteristics of parents or guardians and use it to identify the child. The identification unit can also refer to the behavioral patterns of parents or guardians and use them to identify the child. In this way, the accuracy of identification is improved by referring to information about parents or guardians. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input parent or guardian information data into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0044] The analysis unit can improve the accuracy of its analysis by referring to the child's past behavioral data during the analysis. For example, the analysis unit learns the behavioral patterns of a particular child based on past behavioral data. The analysis unit can also refer to past behavioral history and use it as a reference during the analysis. The analysis unit can also analyze past behavioral data to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past behavioral data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0045] The analysis unit can perform behavioral analysis while considering the child's health status and physical condition. For example, the analysis unit can monitor the child's health status and reflect it in the analysis. The analysis unit can also improve the accuracy of behavioral analysis by considering the child's physical condition. The analysis unit can also refer to the child's health data and use it in behavioral analysis. This improves the accuracy of behavioral analysis by considering the child's health status and physical condition. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the child's health data into a generating AI and have the generating AI perform the task of improving the accuracy of behavioral analysis.

[0046] The analysis unit can perform behavioral analysis while considering the child's geographical location information. For example, the analysis unit can acquire the child's current location information and reflect it in the behavioral analysis. The analysis unit can also refer to the child's movement history and use it in the behavioral analysis. The analysis unit can also update the child's location information in real time and reflect it in the behavioral analysis. This improves the accuracy of the behavioral analysis by considering geographical location information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the child's location information data into a generating AI and have the generating AI perform improvements to the accuracy of the behavioral analysis.

[0047] The analysis unit can improve the accuracy of behavioral analysis by referring to information about the child's parents or guardians during the analysis. For example, the analysis unit can refer to information about parents or guardians and use it in behavioral analysis. The analysis unit can also analyze the characteristics of parents or guardians and reflect them in behavioral analysis. The analysis unit can also refer to the behavioral patterns of parents or guardians and use them in behavioral analysis. In this way, the accuracy of behavioral analysis is improved by referring to information about parents or guardians. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input parent or guardian information data into a generating AI and have the generating AI perform the improvement of behavioral analysis accuracy.

[0048] The generation unit can optimize the contents of the communication log by referring to the child's past communication log data during generation. For example, the generation unit can learn a specific child's behavioral patterns based on past communication log data. The generation unit can also optimize the contents of the communication log by referring to past communication log data. The generation unit can also analyze past communication log data and customize the contents of the communication log. As a result, the contents of the communication log are optimized by referring to past communication log data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past communication log data into a generation AI and have the generation AI perform the optimization of the communication log contents.

[0049] The generation unit can customize the contents of the communication log during generation, taking into account the child's health and physical condition. For example, the generation unit can monitor the child's health and reflect this in the contents of the communication log. The generation unit can also customize the contents of the communication log considering the child's physical condition. The generation unit can also optimize the contents of the communication log by referring to the child's health data. In this way, the contents of the communication log are customized by taking into account the child's health and physical condition. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's health data into a generation AI and have the generation AI perform the customization of the communication log contents.

[0050] The generation unit can adjust the contents of the contact book while considering the child's geographical location information during generation. For example, the generation unit can acquire the child's current location information and reflect it in the contents of the contact book. The generation unit can also refer to the child's movement history and adjust the contents of the contact book. The generation unit can also update the child's location information in real time and reflect it in the contents of the contact book. In this way, the contents of the contact book are adjusted by considering the geographical location information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the child's location information data into a generation AI and have the generation AI perform the adjustment of the contents of the contact book.

[0051] The generation unit can optimize the content of the communication notebook by referring to the information of the child's parents or guardians during generation. For example, the generation unit can refer to the information of parents or guardians and reflect it in the content of the communication notebook. The generation unit can also analyze the characteristics of parents or guardians and customize the content of the communication notebook. The generation unit can also refer to the behavioral patterns of parents or guardians and optimize the content of the communication notebook. In this way, the content of the communication notebook is optimized by referring to the information of parents or guardians. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input parent or guardian information data into a generation AI and have the generation AI perform the optimization of the communication notebook content.

[0052] The sharing unit can select the optimal sharing method by referring to past sharing history when sharing. For example, the sharing unit can propose the optimal sharing method based on past sharing history. The sharing unit can also optimize the sharing method by referring to past sharing history. The sharing unit can also analyze past sharing history and customize the sharing method. As a result, the optimal sharing method is selected by referring to past sharing history. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input past sharing history data into a generating AI and have the generating AI perform the selection of the optimal sharing method.

[0053] The sharing unit can adjust the timing of sharing, taking into account the work status of the childcare workers. For example, the sharing unit can monitor the work status of the childcare workers and adjust the timing of sharing. The sharing unit can also optimize the timing of sharing, taking into account the work status of the childcare workers. The sharing unit can also refer to the work status of the childcare workers and customize the timing of sharing. In this way, the timing of sharing is adjusted by taking the work status into consideration. Some or all of the above processes in the sharing unit may be performed using AI or not. For example, the sharing unit can input childcare worker work status data into a generating AI and have the generating AI perform the adjustment of the sharing timing.

[0054] The sharing unit can adjust the sharing method when sharing, taking into account the geographical location information of the childcare worker. For example, the sharing unit can acquire the childcare worker's current location information and adjust the sharing method. The sharing unit can also refer to the childcare worker's movement history and optimize the sharing method. The sharing unit can also update the childcare worker's location information in real time and adjust the sharing method. This ensures that the sharing method is adjusted by taking geographical location information into consideration. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the childcare worker's location information data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0055] The sharing function can improve the accuracy of sharing by referring to the past work history of childcare workers during the sharing process. For example, the sharing function can suggest the optimal sharing method based on past work history. The sharing function can also optimize the sharing method by referring to past work history. The sharing function can also analyze past work history and customize the sharing method. This improves the accuracy of sharing by referring to past work history. Some or all of the above processes in the sharing function may be performed using AI or not. For example, the sharing function can input past work history data into a generating AI and have the generating AI perform the task of improving the accuracy of sharing.

[0056] The delivery unit can select the optimal delivery method by referring to past delivery history at the time of delivery. For example, the delivery unit can propose the optimal delivery method based on past delivery history. The delivery unit can also optimize the delivery method by referring to past delivery history. The delivery unit can also analyze past delivery history and customize the delivery method. As a result, the optimal delivery method is selected by referring to past delivery history. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input past delivery history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0057] The delivery unit can adjust the timing of delivery by taking into account the parents' living situation. For example, the delivery unit can monitor the parents' living situation and adjust the timing of delivery. The delivery unit can also optimize the timing of delivery by taking into account the parents' living situation. The delivery unit can also customize the timing of delivery by referring to the parents' living situation. In this way, the timing of delivery is adjusted by taking into account the living situation. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input data on the parents' living situation into a generating AI and have the generating AI perform the adjustment of the timing of delivery.

[0058] The service provider can adjust its delivery method by considering the guardian's geographical location information at the time of delivery. For example, the service provider can acquire the guardian's current location information and adjust the delivery method. The service provider can also refer to the guardian's movement history and optimize the delivery method. The service provider can also update the guardian's location information in real time and adjust the delivery method. This ensures that the delivery method is adjusted by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the guardian's location information data into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0059] The service provider can improve the accuracy of its service provision by referring to the parent's past contact log viewing history at the time of provision. For example, the service provider can propose the optimal service provision method based on past contact log viewing history. The service provider can also optimize the service provision method by referring to past contact log viewing history. The service provider can also analyze past contact log viewing history and customize the service provision method. This improves the accuracy of service provision by referring to past contact log viewing history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past contact log viewing history data into a generating AI and have the generating AI perform the task of improving the accuracy of service provision.

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

[0061] The childcare support system can also be equipped with a voice recognition unit. The voice recognition unit recognizes the voices of childcare workers and children and converts the voice data into text data. For example, when a childcare worker verbally records a child's behavior, the voice recognition unit can automatically transcribe the content into text and reflect it in the communication log. It can also analyze what children say in real time and record it as behavior data. This reduces the workload for childcare workers and enables more accurate recording. The voice recognition unit can also receive instructions from childcare workers by voice and operate the system by voice. For example, if a childcare worker says, "Generate the communication log," the voice recognition unit can recognize the instruction and start generating the communication log.

[0062] The childcare support system can also be equipped with a temperature sensor. The temperature sensor measures the child's body temperature and monitors their health. For example, if a child's body temperature is abnormally high, the temperature sensor transmits this information to the analysis unit, which can detect an abnormality in their health. The temperature sensor can also measure the temperature of the childcare room and provide data for appropriate temperature management. This allows for real-time monitoring of the child's health and appropriate action to be taken. The temperature sensor can also reflect the child's body temperature data in the communication log. For example, if the child's temperature is high, this information can be notified to the parents, prompting early action.

[0063] The childcare support system can also be equipped with a location tracking unit. This unit tracks the child's location in real time, enhancing safety management. For example, if a child leaves the childcare room, the location tracking unit transmits this information to an analysis unit, which can detect the anomaly. The location tracking unit can also record the child's movement history and use it to analyze behavioral patterns. This helps ensure the child's safety and improves the accuracy of behavioral data. The location tracking unit can also reflect the child's location information in a communication log. For example, if a child frequently visits a particular location, this information can be provided to the parents, allowing them to understand the child's interests and concerns.

[0064] The childcare support system can also be equipped with a voice feedback unit. This unit provides voice feedback to both childcare workers and children. For example, when a childcare worker is recording a child's behavior, the voice feedback unit can notify them that "recording is complete." It can also provide voice feedback when a child performs a specific action. This improves the efficiency of childcare workers and encourages children's behavior. The voice feedback unit can also receive instructions from childcare workers via voice and operate the system using voice commands. For example, if a childcare worker says, "Start the next activity," the voice feedback unit can recognize the instruction and initiate the next activity.

[0065] The childcare support system can also be equipped with a behavior prediction unit. This unit predicts future behavior based on the child's past behavioral data. For example, if a child tends to want to play during a specific time period, the behavior prediction unit can transmit this information to the analysis unit and record the prediction data. Furthermore, if a child prefers a particular activity, the unit can suggest the next activity based on that information. This allows for understanding the child's behavioral patterns and taking appropriate action. The behavior prediction unit can also reflect the child's behavioral prediction data in a communication log. For example, if a child tends to want to play during a specific time period, this information can be provided to the parents, allowing them to understand the child's behavioral patterns.

[0066] The childcare support system can also be equipped with a behavior modification unit. This unit monitors children's behavior in real time and provides corrective instructions as needed. For example, if a child engages in dangerous behavior, the behavior modification unit can transmit this information to the analysis unit and issue a warning to the caregiver. It can also provide voice instructions to encourage children to behave appropriately. This helps ensure children's safety and promotes appropriate behavior. The behavior modification unit can also reflect the child's behavior modification data in a communication log. For example, if a specific behavior is modified, this information can be provided to the parents, allowing them to understand the progress of their child's behavioral improvement.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The identification unit identifies each child. The identification unit can identify each child using, for example, facial recognition technology, RFID tags, or voice recognition technology. Step 2: The analysis unit analyzes the child's behavior and circumstances identified by the identification unit. For example, the analysis unit can analyze the type of play the child is engaged in, their eating habits, and their health status. Step 3: The generation unit generates a contact log based on the behavioral data analyzed by the analysis unit. The generation unit can generate contact logs in, for example, text format, with images, or with videos. Step 4: The sharing unit shares the communication log generated by the generation unit among childcare workers. The sharing unit can, for example, share the communication log in real time via the internet using a dedicated app during specific events. Step 5: The providing department provides the contact book shared by the sharing department to the parents. The providing department can provide the contact book to the parents using, for example, email, app notifications, or paper copies.

[0069] (Example of form 2) The childcare support system according to an embodiment of the present invention is a system that reduces the workload of childcare workers and improves the quality of childcare. This childcare support system identifies each child, and AI analyzes their behavior and situation in real time. Next, based on the behavioral data analyzed by the AI, it generates individually customized communication logs. Furthermore, the generated communication logs are used to enhance information sharing among childcare workers. This system eliminates the need for childcare workers to manually record the children's situations and behaviors, thereby reducing their workload. It also improves the quality of childcare and provides the necessary environment for children to grow up and develop safely. The quality of life for childcare workers is also improved, and it can support them in professional growth and lead richer and more fulfilling lives. This system supports the childcare field and improves operational efficiency. For example, the childcare support system identifies each child using a camera. AI analyzes the behavior and situation of the identified child in real time. AI generates individually customized communication logs based on the analyzed behavioral data. The generated communication logs are shared among childcare workers in real time. The shared communication logs are provided to parents. In this way, the childcare support system can reduce the workload of childcare workers and improve the quality of childcare. This allows the childcare support system to reduce the workload of childcare workers and improve the quality of childcare.

[0070] The childcare support system according to this embodiment comprises an identification unit, an analysis unit, a generation unit, a sharing unit, and a provision unit. The identification unit identifies each child. The identification unit can identify each child using, for example, facial recognition technology. The identification unit can also identify each child using RFID tags. The identification unit can also identify each child using voice recognition technology. The analysis unit analyzes the behavior and situation of the child identified by the identification unit. The analysis unit can analyze, for example, the type of play the child is engaged in. The analysis unit can also analyze the child's eating habits. The analysis unit can also analyze the child's health status. The generation unit generates a communication log based on the behavioral data analyzed by the analysis unit. The generation unit can generate, for example, a text-based communication log. The generation unit can also generate a communication log with images. The generation unit can also generate a communication log with videos. The sharing unit shares the communication log generated by the generation unit among childcare workers. The sharing unit can share the communication log in real time via, for example, the internet. The sharing unit can also share the communication log using a dedicated application. The sharing unit can also share the contact log during specific events. The providing unit provides the contact log shared by the sharing unit to the parents. The providing unit can provide the contact log to the parents, for example, via email. The providing unit can also provide the contact log to the parents via app notifications. The providing unit can also provide the contact log to the parents using paper copies. As a result, the childcare support system according to this embodiment can reduce the workload of childcare workers and improve the quality of childcare.

[0071] The identification unit identifies each child. For example, the identification unit can identify each child using facial recognition technology. Facial recognition technology analyzes the child's facial image captured by the camera and compares it with a pre-registered facial database to identify each child. Facial recognition technology uses advanced algorithms that can handle lighting conditions, face orientation, and changes in facial expression. The identification unit can also identify each child using RFID tags. RFID tags are attached to the child's clothing or belongings, and an RFID reader receives the signal transmitted from the tag for identification. RFID tags enable contactless identification and can process multiple children quickly even when they are identified simultaneously. The identification unit can also identify each child using voice recognition technology. Voice recognition technology records the child's voice and analyzes the voice pattern to identify each child. Voice recognition technology is designed to handle background noise and changes in voice tone. As a result, the identification unit can accurately and quickly identify each child by using facial recognition, RFID tags, voice recognition, or a combination of these.

[0072] The analysis unit analyzes the behavior and circumstances of children identified by the identification unit. For example, the analysis unit can analyze the types of play children engage in. By analyzing what kinds of play children prefer and how much time they spend on each type, it can understand the interests and concerns of individual children. The analysis unit can also analyze children's eating habits. It records meal times, amounts eaten, and whether or not food is left over to evaluate children's eating patterns and nutritional status. The analysis unit can also analyze children's health conditions. It collects health data such as body temperature, heart rate, and activity levels to check for abnormalities. The analysis unit processes this data in real time and can immediately issue an alert if an abnormality is detected. Furthermore, the analysis unit can accumulate historical data and analyze long-term trends and patterns. This allows the analysis unit to gain a detailed understanding of children's behavior and circumstances, providing information that enables childcare workers to take appropriate action.

[0073] The generation unit generates communication logs based on behavioral data analyzed by the analysis unit. For example, the generation unit can generate communication logs in text format. Text-format communication logs include detailed information about the child's daily activities, mealtimes, and health status. The generation unit can also generate communication logs with images. These logs include photos of the child playing, eating, and health check results. The generation unit can also generate communication logs with videos. These videos include footage of the child's activities and specific events, allowing parents to have a more realistic understanding of their child's day. The generation unit automatically generates these communication logs, significantly reducing the workload for childcare workers. Furthermore, the generation unit can customize the content of the communication logs, providing information tailored to the needs of parents and the characteristics of the children. This allows the generation unit to improve the efficiency of childcare workers' work and provide comprehensive information to parents.

[0074] The sharing unit shares the communication log generated by the generation unit among childcare workers. The sharing unit can, for example, share the communication log in real time via the internet. Childcare workers can access the communication log and check the information anytime, anywhere using an internet-connected device. The sharing unit can also share the communication log using a dedicated app. The dedicated app is designed to allow childcare workers to easily view the communication log and quickly obtain the necessary information. The sharing unit can also share the communication log during specific events. For example, during nursery school events or emergencies, relevant communication logs can be quickly shared, allowing childcare workers to share information. This enables the sharing unit to facilitate information sharing among childcare workers and improve the quality of childcare. Furthermore, the sharing unit manages the sharing history of the communication log and ensures information security by recording who accessed which information and when. This enables the sharing unit to strengthen collaboration among childcare workers, improve the efficiency of childcare operations, and achieve centralized information management.

[0075] The provisioning department provides parents with the communication log shared by the sharing department. The provisioning department can provide the communication log to parents using, for example, email. Email is a means of communication that parents use on a daily basis, and it can deliver the communication log quickly and reliably. The provisioning department can also provide the communication log to parents using app notifications. By notifying parents of the communication log through a dedicated app, parents can easily check the communication log on their smartphones or tablets. The provisioning department can also provide parents with the communication log using paper copies. Paper copies of the communication log are easy for parents to physically store and can be reviewed at any time as needed. This allows the provisioning department to provide the communication log in a flexible way that suits the needs and circumstances of the parents. Furthermore, the provisioning department can manage the history of communication log delivery and confirm whether parents have received the communication log. This allows the provisioning department to ensure that information is provided to parents and to facilitate smooth communication between the nursery school and parents.

[0076] The identification unit can identify each child using a camera. For example, the identification unit can identify each child using a fixed camera. The identification unit can also identify each child using a mobile camera. The identification unit can also identify each child using a wide-angle camera. This allows for accurate identification of each child using a camera. Some or all of the above-described processing in the identification unit may be performed using AI or not. For example, the identification unit can input image data acquired by the camera into a generating AI and have the generating AI perform child identification from the image data.

[0077] The analysis unit can analyze the behavior and circumstances of identified children in real time. For example, the analysis unit can analyze the type of play a child is engaged in in real time. The analysis unit can also analyze a child's eating habits in real time. The analysis unit can also analyze a child's health status in real time. This allows for an immediate understanding of a child's behavior and circumstances through real-time analysis. Specific definitions and criteria for "real time" include, for example, seconds, minutes, and acceptable delays. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input data acquired in real time into a generating AI and have the generating AI perform the data analysis.

[0078] The generation unit can generate individually customized contact logs based on the analyzed behavioral data. For example, the generation unit can customize according to the child's characteristics. The generation unit can also customize according to the requests of the parents. The generation unit can also customize based on the child's behavioral patterns. This allows for the generation of individually customized contact logs, providing information tailored to the child's situation. Some or all of the above-described processes in the generation unit may be performed using AI or not. For example, the generation unit can input the analyzed behavioral data into a generation AI and have the generation AI perform the generation of the contact log.

[0079] The sharing function allows the generated communication log to be shared among childcare workers in real time. The sharing function can share the communication log in real time, for example, via the internet. The sharing function can also share the communication log in real time using a dedicated app. The sharing function can also share the communication log in real time during specific events. This allows for rapid information sharing among childcare workers through real-time sharing. Some or all of the above processes in the sharing function may be performed using AI or not. For example, the sharing function can input the generated communication log into a generation AI and have the generation AI perform the sharing of the communication log.

[0080] The service provider can provide the shared contact notebook to the parents. The service provider can provide the contact notebook to the parents, for example, by email. The service provider can also provide the contact notebook to the parents by app notification. The service provider can also provide the contact notebook to the parents using paper. This allows the service provider to understand the child's situation by providing the contact notebook to the parents. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the shared contact notebook into a generating AI and have the generating AI perform the task of providing the contact notebook.

[0081] The identification unit can estimate a child's emotions and improve identification accuracy based on the estimated emotions. For example, the identification unit can analyze a child's facial expressions captured by a camera and estimate their emotions. The identification unit can also analyze a child's tone of voice and speaking style and estimate their emotions. The identification unit can also analyze a child's behavioral patterns and estimate their emotions. This allows for more accurate identification by improving identification accuracy based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input image data of a child captured by a camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0082] The identification unit can improve the accuracy of identification by referring to the child's past behavioral history during the identification process. For example, the identification unit learns the behavioral patterns of a particular child based on past behavioral data. The identification unit can also refer to past behavioral history and use it as a reference during identification. The identification unit can also analyze past behavioral data to improve the accuracy of identification. As a result, the accuracy of identification is improved by referring to past behavioral history. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0083] The identification unit can use the child's clothing and accessories as auxiliary information during identification. For example, the identification unit can identify the color and design of the child's clothing and use it as auxiliary information. The identification unit can also identify the child's accessories and use them as auxiliary information. The identification unit can also analyze the characteristics of the child's clothing and accessories to improve the accuracy of identification. As a result, the accuracy of identification is improved by using clothing and accessories as auxiliary information. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input image data of the child's clothing and accessories into a generating AI and use it as auxiliary information for identification.

[0084] The identification unit can estimate a child's emotions and determine the priority of identification based on the estimated emotions. For example, the identification unit may prioritize identifying children with unstable emotions. It may also postpone identifying children with calm emotions. It may also prioritize identifying children with rapidly changing emotions. By determining the priority of identification based on the child's emotions, more appropriate identification becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input image data of a child captured by a camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0085] The identification unit can improve the accuracy of identification by considering the child's geographical location information during identification. For example, the identification unit can acquire the child's current location information to improve the accuracy of identification. The identification unit can also refer to the child's movement history to improve the accuracy of identification. The identification unit can also update the child's location information in real time to improve the accuracy of identification. This improves the accuracy of identification by considering geographical location information. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input the child's location information data into a generating AI and have the generating AI perform the improvement of identification accuracy.

[0086] The identification unit can improve the accuracy of identification by referring to information about the child's parents or guardians during the identification process. For example, the identification unit can refer to information about parents or guardians and use it to identify the child. The identification unit can also analyze the characteristics of parents or guardians and use it to identify the child. The identification unit can also refer to the behavioral patterns of parents or guardians and use them to identify the child. In this way, the accuracy of identification is improved by referring to information about parents or guardians. Some or all of the above processing in the identification unit may be performed using AI or not. For example, the identification unit can input parent or guardian information data into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0087] The analysis unit can estimate a child's emotions and improve the accuracy of behavioral analysis based on the estimated emotions. For example, the analysis unit can analyze a child's facial expressions and estimate their emotions. The analysis unit can also analyze a child's tone of voice and speaking style and estimate their emotions. The analysis unit can also analyze a child's behavioral patterns and estimate their emotions. This allows for more accurate analysis by improving the accuracy of behavioral analysis based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input child facial expression data into a generative AI and have the generative AI perform the improvement of behavioral analysis accuracy.

[0088] The analysis unit can improve the accuracy of its analysis by referring to the child's past behavioral data during the analysis. For example, the analysis unit learns the behavioral patterns of a particular child based on past behavioral data. The analysis unit can also refer to past behavioral history and use it as a reference during the analysis. The analysis unit can also analyze past behavioral data to improve the accuracy of the analysis. In this way, the accuracy of the analysis is improved by referring to past behavioral data. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input past behavioral data into a generating AI and have the generating AI perform the task of improving the accuracy of the analysis.

[0089] The analysis unit can perform behavioral analysis while considering the child's health status and physical condition. For example, the analysis unit can monitor the child's health status and reflect it in the analysis. The analysis unit can also improve the accuracy of behavioral analysis by considering the child's physical condition. The analysis unit can also refer to the child's health data and use it in behavioral analysis. This improves the accuracy of behavioral analysis by considering the child's health status and physical condition. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the child's health data into a generating AI and have the generating AI perform the task of improving the accuracy of behavioral analysis.

[0090] The analysis unit can estimate a child's emotions and determine the priority of behavioral analysis based on the estimated emotions. For example, the analysis unit may prioritize analyzing the behavior of a child with unstable emotions. The analysis unit may also postpone analyzing the behavior of a child with calm emotions. The analysis unit may also prioritize analyzing the behavior of a child with rapidly changing emotions. By determining the priority of behavioral analysis based on the child's emotions, more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input child emotion data into a generative AI and have the generative AI perform the determination of behavioral analysis priorities.

[0091] The analysis unit can perform behavioral analysis while considering the child's geographical location information. For example, the analysis unit can acquire the child's current location information and reflect it in the behavioral analysis. The analysis unit can also refer to the child's movement history and use it in the behavioral analysis. The analysis unit can also update the child's location information in real time and reflect it in the behavioral analysis. This improves the accuracy of the behavioral analysis by considering geographical location information. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the child's location information data into a generating AI and have the generating AI perform improvements to the accuracy of the behavioral analysis.

[0092] The analysis unit can improve the accuracy of behavioral analysis by referring to information about the child's parents or guardians during the analysis. For example, the analysis unit can refer to information about parents or guardians and use it in behavioral analysis. The analysis unit can also analyze the characteristics of parents or guardians and reflect them in behavioral analysis. The analysis unit can also refer to the behavioral patterns of parents or guardians and use them in behavioral analysis. In this way, the accuracy of behavioral analysis is improved by referring to information about parents or guardians. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input parent or guardian information data into a generating AI and have the generating AI perform the improvement of behavioral analysis accuracy.

[0093] The generation unit can estimate a child's emotions and adjust the content of the communication log based on the estimated emotions. For example, if the child is having fun, the generation unit will write positive content in the communication log that reflects that emotion. If the child is feeling anxious, the generation unit can also write supportive content in the communication log that reflects that emotion. If the child is excited, the generation unit can also write activity details in the communication log that reflect that emotion. By adjusting the content of the communication log based on the child's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input child emotion data into a generation AI and have the generation AI perform the adjustment of the communication log content.

[0094] The generation unit can optimize the contents of the communication log by referring to the child's past communication log data during generation. For example, the generation unit can learn a specific child's behavioral patterns based on past communication log data. The generation unit can also optimize the contents of the communication log by referring to past communication log data. The generation unit can also analyze past communication log data and customize the contents of the communication log. As a result, the contents of the communication log are optimized by referring to past communication log data. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input past communication log data into a generation AI and have the generation AI perform the optimization of the communication log contents.

[0095] The generation unit can customize the contents of the communication log during generation, taking into account the child's health and physical condition. For example, the generation unit can monitor the child's health and reflect this in the contents of the communication log. The generation unit can also customize the contents of the communication log considering the child's physical condition. The generation unit can also optimize the contents of the communication log by referring to the child's health data. In this way, the contents of the communication log are customized by taking into account the child's health and physical condition. Some or all of the above processes in the generation unit may be performed using AI or not. For example, the generation unit can input the child's health data into a generation AI and have the generation AI perform the customization of the communication log contents.

[0096] The generation unit can estimate a child's emotions and determine the priority of the communication notebook based on the estimated emotions. For example, the generation unit can prioritize generating communication notebooks for children with unstable emotions. The generation unit can also postpone generating communication notebooks for children with calm emotions. The generation unit can also prioritize generating communication notebooks for children with rapidly changing emotions. This allows for more appropriate information to be provided by prioritizing the communication notebook based on the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input child emotion data into the generation AI and have the generation AI perform the task of determining the priority of the communication notebook.

[0097] The generation unit can adjust the contents of the contact book while considering the child's geographical location information during generation. For example, the generation unit can acquire the child's current location information and reflect it in the contents of the contact book. The generation unit can also refer to the child's movement history and adjust the contents of the contact book. The generation unit can also update the child's location information in real time and reflect it in the contents of the contact book. In this way, the contents of the contact book are adjusted by considering the geographical location information. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input the child's location information data into a generation AI and have the generation AI perform the adjustment of the contents of the contact book.

[0098] The generation unit can optimize the content of the communication notebook by referring to the information of the child's parents or guardians during generation. For example, the generation unit can refer to the information of parents or guardians and reflect it in the content of the communication notebook. The generation unit can also analyze the characteristics of parents or guardians and customize the content of the communication notebook. The generation unit can also refer to the behavioral patterns of parents or guardians and optimize the content of the communication notebook. In this way, the content of the communication notebook is optimized by referring to the information of parents or guardians. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input parent or guardian information data into a generation AI and have the generation AI perform the optimization of the communication notebook content.

[0099] The sharing function can estimate the emotions of childcare workers and adjust the method of sharing the communication log based on the estimated emotions. For example, if a childcare worker is feeling stressed, the sharing function can provide a simple sharing method. If a childcare worker is relaxed, the sharing function can also provide more detailed sharing options. If a childcare worker is in a hurry, the sharing function can also provide a method for quick sharing. This allows for more appropriate sharing by adjusting the sharing method based on the emotions of the childcare workers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing function may be performed using AI or not. For example, the sharing function can input childcare worker emotion data into a generative AI and have the generative AI adjust the method of sharing the communication log.

[0100] The sharing unit can select the optimal sharing method by referring to past sharing history when sharing. For example, the sharing unit can propose the optimal sharing method based on past sharing history. The sharing unit can also optimize the sharing method by referring to past sharing history. The sharing unit can also analyze past sharing history and customize the sharing method. As a result, the optimal sharing method is selected by referring to past sharing history. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input past sharing history data into a generating AI and have the generating AI perform the selection of the optimal sharing method.

[0101] The sharing unit can adjust the timing of sharing, taking into account the work status of the childcare workers. For example, the sharing unit can monitor the work status of the childcare workers and adjust the timing of sharing. The sharing unit can also optimize the timing of sharing, taking into account the work status of the childcare workers. The sharing unit can also refer to the work status of the childcare workers and customize the timing of sharing. In this way, the timing of sharing is adjusted by taking the work status into consideration. Some or all of the above processes in the sharing unit may be performed using AI or not. For example, the sharing unit can input childcare worker work status data into a generating AI and have the generating AI perform the adjustment of the sharing timing.

[0102] The sharing unit can estimate the emotions of childcare workers and determine the sharing priority of communication notebooks based on the estimated emotions. For example, the sharing unit may prioritize sharing communication notebooks of childcare workers with unstable emotions. It may also postpone sharing communication notebooks of childcare workers with calm emotions. It may also prioritize sharing communication notebooks of childcare workers whose emotions fluctuate rapidly. This allows for more appropriate information sharing by determining sharing priorities based on the emotions of childcare workers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input childcare worker emotion data into a generative AI and have the generative AI determine the sharing priority of communication notebooks.

[0103] The sharing unit can adjust the sharing method when sharing, taking into account the geographical location information of the childcare worker. For example, the sharing unit can acquire the childcare worker's current location information and adjust the sharing method. The sharing unit can also refer to the childcare worker's movement history and optimize the sharing method. The sharing unit can also update the childcare worker's location information in real time and adjust the sharing method. This ensures that the sharing method is adjusted by taking geographical location information into consideration. Some or all of the above processing in the sharing unit may be performed using AI or not. For example, the sharing unit can input the childcare worker's location information data into a generating AI and have the generating AI perform the adjustment of the sharing method.

[0104] The sharing function can improve the accuracy of sharing by referring to the past work history of childcare workers during the sharing process. For example, the sharing function can suggest the optimal sharing method based on past work history. The sharing function can also optimize the sharing method by referring to past work history. The sharing function can also analyze past work history and customize the sharing method. This improves the accuracy of sharing by referring to past work history. Some or all of the above processes in the sharing function may be performed using AI or not. For example, the sharing function can input past work history data into a generating AI and have the generating AI perform the task of improving the accuracy of sharing.

[0105] The delivery unit can estimate the parent's emotions and adjust the method of delivering the communication log based on the estimated emotions. For example, if the parent is stressed, the delivery unit can provide a simple delivery method. If the parent is relaxed, the delivery unit can also provide detailed delivery options. If the parent is in a hurry, the delivery unit can also provide a method that allows for quick delivery. This allows for more appropriate information delivery by adjusting the delivery method based on the parent's emotions. Emotion estimation is achieved using an emotion estimation function, such as 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 delivery unit may be performed using AI or not. For example, the delivery unit can input parent emotion data into a generative AI and have the generative AI perform the adjustment of the communication log delivery method.

[0106] The delivery unit can select the optimal delivery method by referring to past delivery history at the time of delivery. For example, the delivery unit can propose the optimal delivery method based on past delivery history. The delivery unit can also optimize the delivery method by referring to past delivery history. The delivery unit can also analyze past delivery history and customize the delivery method. As a result, the optimal delivery method is selected by referring to past delivery history. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input past delivery history data into a generating AI and have the generating AI perform the selection of the optimal delivery method.

[0107] The delivery unit can adjust the timing of delivery by taking into account the parents' living situation. For example, the delivery unit can monitor the parents' living situation and adjust the timing of delivery. The delivery unit can also optimize the timing of delivery by taking into account the parents' living situation. The delivery unit can also customize the timing of delivery by referring to the parents' living situation. In this way, the timing of delivery is adjusted by taking into account the living situation. Some or all of the above processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input data on the parents' living situation into a generating AI and have the generating AI perform the adjustment of the timing of delivery.

[0108] The service provider can estimate the emotions of parents and determine the priority of providing contact notebooks based on the estimated emotions. For example, the service provider may prioritize providing contact notebooks to parents with unstable emotions. The service provider may also postpone providing contact notebooks to parents with calm emotions. The service provider may also prioritize providing contact notebooks to parents with rapidly changing emotions. By determining the priority of provision based on the emotions of parents, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input parent emotion data into a generative AI and have the generative AI perform the determination of the priority of providing contact notebooks.

[0109] The service provider can adjust its delivery method by considering the guardian's geographical location information at the time of delivery. For example, the service provider can acquire the guardian's current location information and adjust the delivery method. The service provider can also refer to the guardian's movement history and optimize the delivery method. The service provider can also update the guardian's location information in real time and adjust the delivery method. This ensures that the delivery method is adjusted by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the guardian's location information data into a generating AI and have the generating AI perform the adjustment of the delivery method.

[0110] The service provider can improve the accuracy of its service provision by referring to the parent's past contact log viewing history at the time of provision. For example, the service provider can propose the optimal service provision method based on past contact log viewing history. The service provider can also optimize the service provision method by referring to past contact log viewing history. The service provider can also analyze past contact log viewing history and customize the service provision method. This improves the accuracy of service provision by referring to past contact log viewing history. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past contact log viewing history data into a generating AI and have the generating AI perform the task of improving the accuracy of service provision.

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

[0112] The childcare support system can also be equipped with a voice recognition unit. The voice recognition unit recognizes the voices of childcare workers and children and converts the voice data into text data. For example, when a childcare worker verbally records a child's behavior, the voice recognition unit can automatically transcribe the content into text and reflect it in the communication log. It can also analyze what children say in real time and record it as behavior data. This reduces the workload for childcare workers and enables more accurate recording. The voice recognition unit can also receive instructions from childcare workers by voice and operate the system by voice. For example, if a childcare worker says, "Generate the communication log," the voice recognition unit can recognize the instruction and start generating the communication log.

[0113] The childcare support system can also be equipped with a temperature sensor. The temperature sensor measures the child's body temperature and monitors their health. For example, if a child's body temperature is abnormally high, the temperature sensor transmits this information to the analysis unit, which can detect an abnormality in their health. The temperature sensor can also measure the temperature of the childcare room and provide data for appropriate temperature management. This allows for real-time monitoring of the child's health and appropriate action to be taken. The temperature sensor can also reflect the child's body temperature data in the communication log. For example, if the child's temperature is high, this information can be notified to the parents, prompting early action.

[0114] The childcare support system can also be equipped with a location tracking unit. This unit tracks the child's location in real time, enhancing safety management. For example, if a child leaves the childcare room, the location tracking unit transmits this information to an analysis unit, which can detect the anomaly. The location tracking unit can also record the child's movement history and use it to analyze behavioral patterns. This helps ensure the child's safety and improves the accuracy of behavioral data. The location tracking unit can also reflect the child's location information in a communication log. For example, if a child frequently visits a particular location, this information can be provided to the parents, allowing them to understand the child's interests and concerns.

[0115] The childcare support system can also be equipped with an emotion estimation unit. The emotion estimation unit analyzes the child's facial expressions and tone of voice to estimate their emotions. For example, if a child is laughing, the emotion estimation unit can send that information to the analysis unit and record a positive emotion. If a child is crying, the information can be sent to the childcare worker to encourage a quick response. This allows for real-time understanding of the child's emotional state and appropriate responses. The emotion estimation unit can also reflect the child's emotional data in a communication log. For example, if a child is enjoying a particular activity, this information can be provided to the parents, allowing them to understand the child's interests and concerns.

[0116] The childcare support system can also be equipped with a voice feedback unit. This unit provides voice feedback to both childcare workers and children. For example, when a childcare worker is recording a child's behavior, the voice feedback unit can notify them that "recording is complete." It can also provide voice feedback when a child performs a specific action. This improves the efficiency of childcare workers and encourages children's behavior. The voice feedback unit can also receive instructions from childcare workers via voice and operate the system using voice commands. For example, if a childcare worker says, "Start the next activity," the voice feedback unit can recognize the instruction and initiate the next activity.

[0117] The childcare support system can also be equipped with an emotional feedback unit. This unit estimates the emotions of caregivers and children and provides feedback based on those emotions. For example, if a caregiver is feeling stressed, the emotional feedback unit can notify them by voice, "Please relax." Similarly, if a child is feeling anxious, it can provide reassuring feedback such as, "It's okay," based on that emotion. This allows for real-time monitoring of the emotional state of caregivers and children and the provision of appropriate feedback. The emotional feedback unit can also reflect the emotional data of caregivers and children in a communication log. For example, if a caregiver is relaxed during a particular activity, this information can be provided to parents, allowing them to understand the caregiver's work situation.

[0118] The childcare support system can also be equipped with a behavior prediction unit. This unit predicts future behavior based on the child's past behavioral data. For example, if a child tends to want to play during a specific time period, the behavior prediction unit can transmit this information to the analysis unit and record the prediction data. Furthermore, if a child prefers a particular activity, the unit can suggest the next activity based on that information. This allows for understanding the child's behavioral patterns and taking appropriate action. The behavior prediction unit can also reflect the child's behavioral prediction data in a communication log. For example, if a child tends to want to play during a specific time period, this information can be provided to the parents, allowing them to understand the child's behavioral patterns.

[0119] The childcare support system can also be equipped with an emotion prediction unit. This unit predicts future emotions based on the child's past emotional data. For example, if a child tends to feel anxious during certain times of the day, the emotion prediction unit can transmit this information to the analysis unit and record the prediction data. Furthermore, if a child prefers a particular activity, the unit can suggest the next activity based on that information. This allows for understanding the child's emotional patterns and taking appropriate action. The emotion prediction unit can also reflect the child's predicted emotional data in the communication log. For example, if a child tends to feel anxious during certain times of the day, this information can be provided to the parents, allowing them to understand the child's emotional patterns.

[0120] The childcare support system can also be equipped with a behavior modification unit. This unit monitors children's behavior in real time and provides corrective instructions as needed. For example, if a child engages in dangerous behavior, the behavior modification unit can transmit this information to the analysis unit and issue a warning to the caregiver. It can also provide voice instructions to encourage children to behave appropriately. This helps ensure children's safety and promotes appropriate behavior. The behavior modification unit can also reflect the child's behavior modification data in a communication log. For example, if a specific behavior is modified, this information can be provided to the parents, allowing them to understand the progress of their child's behavioral improvement.

[0121] The childcare support system can also be equipped with an emotion modification unit. This unit monitors the child's emotions in real time and provides modification instructions as needed. For example, if a child is feeling anxious, the emotion modification unit can send that information to the analysis unit and instruct the caregiver to provide reassurance. It can also issue instructions to calm a child who is overly excited. This allows for real-time monitoring of the child's emotional state and appropriate responses. The emotion modification unit can also reflect the child's emotion modification data in the communication log. For example, if a specific emotion is modified, this information can be provided to the parents, allowing them to understand the child's emotional improvement.

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

[0123] Step 1: The identification unit identifies each child. The identification unit can identify each child using, for example, facial recognition technology, RFID tags, or voice recognition technology. Step 2: The analysis unit analyzes the child's behavior and circumstances identified by the identification unit. For example, the analysis unit can analyze the type of play the child is engaged in, their eating habits, and their health status. Step 3: The generation unit generates a contact log based on the behavioral data analyzed by the analysis unit. The generation unit can generate contact logs in, for example, text format, with images, or with videos. Step 4: The sharing unit shares the communication log generated by the generation unit among childcare workers. The sharing unit can, for example, share the communication log in real time via the internet using a dedicated app during specific events. Step 5: The providing department provides the contact book shared by the sharing department to the parents. The providing department can provide the contact book to the parents using, for example, email, app notifications, or paper copies.

[0124] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

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

[0127] Each of the multiple elements described above, including the identification unit, analysis unit, generation unit, sharing unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the identification unit identifies each child using the camera 42 of the smart device 14 and performs identification processing using the control unit 46A. The analysis unit analyzes the behavior and situation of the identified child in real time using the identification processing unit 290 of the data processing unit 12. The generation unit generates a contact log based on the analyzed behavior data and is implemented by the identification processing unit 290 of the data processing unit 12. The sharing unit shares the generated contact log among childcare workers in real time via the internet and is implemented by the control unit 46A of the smart device 14. The provision unit provides the shared contact log to the parents and is implemented by the control unit 46A of the smart device 14. 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.

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

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0136] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0140] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0142] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the identification unit, analysis unit, generation unit, sharing unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the identification unit identifies each child using the camera 42 of the smart glasses 214 and performs identification processing using the control unit 46A. The analysis unit analyzes the behavior and situation of the child identified by the identification processing unit 290 of the data processing unit 12 in real time. The generation unit generates a contact log based on the analyzed behavior data and is implemented by the identification processing unit 290 of the data processing unit 12. The sharing unit shares the generated contact log among childcare workers in real time via the internet and is implemented by the control unit 46A of the smart glasses 214. The provision unit provides the shared contact log to the parents and is implemented by the control unit 46A of the smart glasses 214. 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.

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

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

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

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

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

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

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

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

[0152] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

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

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

[0158] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the identification unit, analysis unit, generation unit, sharing unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the identification unit identifies each child using the camera 42 of the headset terminal 314 and performs identification processing using the control unit 46A. The analysis unit analyzes the behavior and situation of the identified child in real time using the identification processing unit 290 of the data processing unit 12. The generation unit generates a contact log based on the analyzed behavior data and is implemented by the identification processing unit 290 of the data processing unit 12. The sharing unit shares the generated contact log among childcare workers in real time via the internet and is implemented by the control unit 46A of the headset terminal 314. The provision unit provides the shared contact log to the parents and is implemented by the control unit 46A of the headset terminal 314. 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.

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

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

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

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

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

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

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

[0167] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

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

[0169] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0173] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0175] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the identification unit, analysis unit, generation unit, sharing unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the identification unit identifies each child using the camera 42 of the robot 414 and performs the identification process using the control unit 46A. The analysis unit analyzes the behavior and situation of the child identified by the identification processing unit 290 of the data processing unit 12 in real time. The generation unit generates a communication log based on the analyzed behavior data and is implemented by the identification processing unit 290 of the data processing unit 12. The sharing unit shares the generated communication log among childcare workers in real time via the internet and is implemented by the control unit 46A of the robot 414. The provision unit provides the shared communication log to the parents and is implemented by the control unit 46A of the robot 414. 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.

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

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

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

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

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

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

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

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

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

[0186] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0187] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

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

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

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

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

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

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

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

[0195] (Note 1) An identification unit for identifying each child, An analysis unit analyzes the behavior and circumstances of the child identified by the aforementioned identification unit, A generation unit generates a contact log based on the behavioral data analyzed by the analysis unit, A sharing unit for sharing the communication log generated by the generation unit among childcare workers, The system includes a provisioning unit that provides the contact book shared by the aforementioned shared unit to the guardian. A system characterized by the following features. (Note 2) The aforementioned identification unit is Identifying each child using a camera The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the behavior and circumstances of identified children in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is Generate individually customized contact lists based on analyzed behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned shared portion is, The generated communication log is shared among childcare workers in real time. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide the shared communication log to the parents. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned identification unit is It estimates children's emotions and improves identification accuracy based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned identification unit is During identification, the accuracy of the identification is improved by referring to the child's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned identification unit is During identification, the clothing and accessories the child is wearing are used as supplementary identification information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned identification unit is The system estimates the child's emotions and determines the priority of identification based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned identification unit is When identifying children, consider their geographical location to improve identification accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned identification unit is During identification, we refer to information about the child's parents or guardians to improve the accuracy of the identification. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, This system estimates children's emotions and improves the accuracy of behavioral analysis based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, we refer to the child's past behavioral data to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the child's health and physical condition will be taken into consideration when performing the behavioral analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the child's emotions and determines the priority of behavioral analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the child's geographical location information is taken into consideration when performing behavioral analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to information about the child's parents and guardians to improve the accuracy of behavioral analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is Estimate the child's emotions and adjust the contents of the communication notebook based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the content of the contact book is optimized by referring to the child's past contact book data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating the communication log, the contents are customized to take into account the child's health and physical condition. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is The system estimates the child's emotions and determines the priority of the communication notebook based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the contents of the contact book are adjusted to take into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the content of the contact book is optimized by referencing information about the child's parents or guardians. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned shared portion is, The system estimates the emotions of childcare workers and adjusts the method of sharing communication logs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned shared portion is, When sharing, refer to past sharing history to select the most suitable sharing method. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned shared portion is, When sharing information, adjust the timing of the sharing to take into consideration the workload of the childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned shared portion is, The system estimates the emotions of childcare workers and determines the priority of sharing communication logs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned shared portion is, When sharing, the sharing method will be adjusted to take into account the geographical location of the childcare worker. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned shared portion is, When sharing information, we improve the accuracy of the sharing process by referring to the childcare worker's past work history. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, We estimate the feelings of parents and adjust the method of providing communication notebooks based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected by referring to past delivery history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, we will adjust the timing of the service considering the parents' living situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned supply unit is, The system estimates the feelings of parents and determines the priority for providing communication notebooks based on those estimated feelings. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned supply unit is, When providing the service, we will adjust the delivery method considering the guardian's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing information, we refer to the parent's past contact log viewing history to improve the accuracy of the information provided. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

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

Claims

1. An identification unit for identifying each child, An analysis unit analyzes the behavior and circumstances of the child identified by the aforementioned identification unit, A generation unit generates a contact log based on the behavioral data analyzed by the analysis unit, A sharing unit for sharing the communication log generated by the generation unit among childcare workers, The system includes a provisioning unit that provides the contact book shared by the aforementioned shared unit to the guardian. A system characterized by the following features.

2. The aforementioned identification unit is Identifying each child using a camera The system according to feature 1.

3. The aforementioned analysis unit, Analyze the behavior and circumstances of identified children in real time. The system according to feature 1.

4. The generating unit is Generate individually customized contact lists based on analyzed behavioral data. The system according to feature 1.

5. The aforementioned shared portion is, The generated communication log is shared among childcare workers in real time. The system according to feature 1.

6. The aforementioned supply unit is, Provide the shared communication log to the parents. The system according to feature 1.

7. The aforementioned identification unit is It estimates children's emotions and improves identification accuracy based on the estimated emotions. The system according to feature 1.

8. The aforementioned identification unit is During identification, the accuracy of the identification is improved by referring to the child's past behavioral history. The system according to feature 1.

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