system

Abstract

The system according to this embodiment aims to reduce the burden on childcare workers and to propose appropriate childcare plans by gaining a detailed understanding of each child's behavior. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, a distribution unit, and a proposal unit. The acquisition unit acquires video from a camera installed in the classroom. The analysis unit analyzes the video acquired by the acquisition unit to understand the children's daily activities. The distribution unit distributes activity reports to parents based on the activities understood by the analysis unit. The proposal unit accumulates daily records based on the activity reports distributed by the distribution unit and proposes childcare plans for each child to childcare workers.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method 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 in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the burden on nursery teachers is large and it is difficult to grasp the actions of each child in detail and make an appropriate childcare plan.

[0005] The system according to the embodiment aims to reduce the burden on nursery teachers, grasp the actions of each child in detail, and propose an appropriate childcare plan.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, a distribution unit, and a proposal unit. The acquisition unit acquires video from a camera installed in the classroom. The analysis unit analyzes the video acquired by the acquisition unit to understand the children's daily activities. The distribution unit distributes activity reports to parents based on the activities understood by the analysis unit. The proposal unit accumulates daily records based on the activity reports distributed by the distribution unit and proposes individual childcare plans to childcare workers. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the burden on childcare workers and allow for a detailed understanding of each child's behavior, enabling the proposal of an appropriate childcare plan. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The childcare support system according to an embodiment of the present invention is a system that uses AI to reduce the burden on childcare workers and enhance the care of each individual child. The childcare support system acquires video from cameras installed in the classroom and understands the children's daily activities. Next, the childcare support system automatically distributes activity reports to parents using AI. Furthermore, the childcare support system accumulates daily records and proposes individualized childcare plans to childcare workers. For example, the childcare support system acquires video from cameras installed in the classroom. This video includes children playing and learning, as well as meal and rest times. The childcare support system analyzes this video to understand the children's daily activities. For example, it analyzes what kind of play a particular child prefers, and at what times of day their concentration is highest. Next, the childcare support system automatically distributes activity reports to parents using AI. The activity reports include the children's daily activities, learning outcomes, and meal contents. Parents can check on their children in real time via smartphones or computers. This allows parents to monitor their children's growth and gain a sense of security. Furthermore, the childcare support system accumulates daily records and proposes individualized childcare plans to childcare workers. For example, if a particular child shows interest in a specific activity, the system will suggest a childcare plan tailored to that child. This allows childcare workers to provide individualized care and support the children's development. This system reduces the burden on childcare workers and enhances the care of each child. Childcare workers can provide more effective care based on the information provided by the AI. In addition, parents can check on their children in real time, giving them peace of mind. For example, even while at work, parents can check on their children via their smartphones, allowing them to concentrate on their work with peace of mind. In this way, the childcare support system reduces the burden on childcare workers and enhances the care of each child.

[0029] The childcare support system according to this embodiment comprises an acquisition unit, an analysis unit, a distribution unit, and a proposal unit. The acquisition unit acquires video from a camera installed in the classroom. The acquisition unit can acquire video of the classroom using, for example, a fixed camera or a PTZ camera. The acquisition unit can also acquire optimal video by adjusting the camera's installation location and height. For example, the acquisition unit can install a camera in the center of the classroom to grasp the overall situation. The acquisition unit can also adjust the camera's height to capture the children's faces. Furthermore, the acquisition unit can adjust the camera's angle to focus on a specific area. The analysis unit analyzes the video acquired by the acquisition unit to understand the children's daily activities. The analysis unit can, for example, use AI to analyze the video and classify the children's activities. For example, the analysis unit can analyze the children's play and learning to understand their behavioral patterns. The analysis unit can also analyze the children's meal and rest times to understand their activity rhythms. Furthermore, the analysis unit can classify the children's activities by time of day and generate a detailed activity history. The distribution department distributes activity reports to parents based on the behaviors identified by the analysis department. The distribution department can, for example, automatically generate and distribute activity reports using AI. For example, the distribution department can report on children's daily activities, learning outcomes, and meal contents. The distribution department can also distribute activity reports in real time via parents' smartphones and computers. Furthermore, the distribution department can adjust the level of detail in the reports based on the parents' interests. The proposal department accumulates daily records based on the activity reports distributed by the distribution department and proposes individualized childcare plans to childcare workers. The proposal department can, for example, analyze daily records using AI and generate individualized childcare plans. For example, if a particular child shows interest in a particular activity, the proposal department can propose a childcare plan suitable for that child. Furthermore, the proposal department can customize childcare plans based on the developmental stages of the children. Furthermore, the proposal department can estimate the emotions of childcare workers and adjust the way childcare plans are proposed to reduce their burden.As a result, the childcare support system according to this embodiment can reduce the burden on childcare workers and enhance the care of each individual child.

[0030] The acquisition unit acquires video footage from cameras installed in the classroom. For example, the acquisition unit can acquire video footage of the classroom using fixed cameras or PTZ cameras. Specifically, fixed cameras are installed to capture an overall view of the classroom, while PTZ cameras utilize pan, tilt, and zoom functions to track specific areas or the movements of children in detail. Furthermore, the acquisition unit can acquire optimal footage by adjusting the camera's placement and height. For example, placing a camera in the center of the classroom makes it easier to grasp the overall situation. Adjusting the camera's height allows for clearer shots of children's faces, enabling detailed observation of their expressions and actions. Adjusting the camera's angle allows for focusing on specific areas. For example, pointing the camera towards a play area or study space allows for more detailed recording of children's activities. Additionally, by linking multiple cameras, the acquisition unit can simultaneously acquire video footage from different viewpoints within the classroom, collecting more comprehensive data. This allows the acquisition unit to grasp various situations within the classroom in real time and provide the necessary data to the analysis and distribution units.

[0031] The analysis unit analyzes the video footage acquired by the acquisition unit to understand the children's daily activities. For example, the analysis unit can use AI to analyze the video and classify the children's activities. Specifically, the AI ​​uses image recognition technology to analyze the children's movements and facial expressions, automatically classifying activity patterns such as playing, learning, eating, and resting. For example, it can analyze children playing with blocks or reading picture books and identify each activity. The AI ​​can also classify activities by time of day and generate a detailed activity history. This allows for an understanding of the children's daily schedule and activity rhythms. Furthermore, the analysis unit can accumulate children's activity data and analyze long-term activity patterns and trends. For example, it can analyze how interested a particular child is in a particular activity, understanding that child's interests and concerns. This allows the analysis unit to provide data useful for developing childcare plans based on the individual needs and interests of the children. In addition, the analysis unit is useful for detecting abnormal behavior and safety management. For example, it can detect abnormal behavior, such as children engaging in dangerous behavior or a particular child staying in the same place for a long time, and respond quickly. This allows the analysis unit to improve the quality of childcare while ensuring the safety of the children.

[0032] The distribution department delivers activity reports to parents based on the behaviors identified by the analysis department. The distribution department can, for example, automatically generate and deliver activity reports using AI. Specifically, the AI ​​generates reports summarizing children's daily activities, learning outcomes, and meal contents based on data provided by the analysis department. For example, it can detail what kind of play the children engaged in, what learning activities they participated in, the content and quantity of their meals, and their behavior during breaks. Furthermore, the distribution department can deliver activity reports in real time via parents' smartphones and computers. This allows parents to check on their children anytime, anywhere, providing them with peace of mind. The distribution department can also adjust the level of detail in the reports based on parents' interests. For example, if a particular parent is especially interested in their child's learning outcomes, they can be provided with detailed reports on learning activities. The distribution department can also collect feedback from parents and use it to improve the reports. For example, if a parent wants to know more about specific information, the report can be customized accordingly. This allows the distribution department to strengthen communication with parents and share in the children's growth.

[0033] The Proposal Department accumulates daily records based on activity reports distributed by the Distribution Department and proposes individualized childcare plans to childcare workers. For example, the Proposal Department can use AI to analyze daily records and generate individualized childcare plans. Specifically, the AI ​​analyzes children's behavioral data and interests to propose the most suitable childcare plan for each child. For example, if a particular child shows interest in a particular activity, it can suggest play and learning activities that are appropriate for that child. The Proposal Department can also customize childcare plans based on the developmental stages of the children. For example, it can suggest appropriate activities and learning content according to age and developmental stage to support the children's growth. Furthermore, the Proposal Department can estimate the emotions of childcare workers and adjust the way childcare plans are proposed to reduce their burden. For example, if a childcare worker is tired or stressed, it can propose a flexible childcare plan that is appropriate to the situation, reducing the burden on the childcare worker. In this way, the Proposal Department can reduce the burden on childcare workers while strengthening the care of each individual child. The Proposal Department can also monitor the implementation status of the childcare plan and revise the plan as needed. For example, if a particular activity is not suitable for the children, or if the plan does not proceed as intended, the plan can be revised accordingly to provide optimal childcare. This allows the proposal department to always provide flexible childcare plans based on the latest information, supporting the children's growth.

[0034] The acquisition unit can focus on a specific area within the classroom and acquire detailed information about the children's behavior. For example, the acquisition unit can focus on the classroom's play area and record in detail how the children are playing. It can also focus on the learning area and record in detail how the children are learning. Furthermore, the acquisition unit can focus on the dining area and record in detail how the children are eating. In this way, by focusing on a specific area, the children's behavior can be recorded in detail. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input video data focused on a specific area into a generating AI and have the generating AI perform a detailed analysis of the behavior.

[0035] The acquisition unit can acquire children's voices simultaneously with video acquisition and analyze the relationship between their actions and voices. For example, the acquisition unit can acquire laughter and conversations when children are playing and analyze the relationship between their actions and voices. It can also acquire statements and questions made during learning and analyze the progress of their learning. Furthermore, the acquisition unit can acquire conversations and reactions during meals and analyze the relationship between their eating habits and the voices. By analyzing the relationship between actions and voices, it becomes possible to gain a deeper understanding of children's behavior. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input video and audio data into a generating AI and have the generating AI perform the analysis of the relationship between actions and voices.

[0036] The acquisition unit can acquire optimal video by considering the classroom lighting and ambient noise during video acquisition. For example, if the classroom lighting is bright, the acquisition unit can adjust the brightness of the video to acquire the optimal video. Also, if the ambient noise is loud, the acquisition unit can remove noise from the audio to acquire clear audio. Furthermore, if the lighting is dim, the acquisition unit can correct the brightness of the video to acquire the optimal video. In this way, by acquiring the optimal video according to the classroom environment, clear video and audio can be provided. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input classroom lighting and ambient noise data into a generating AI and have the generating AI perform the acquisition of the optimal video.

[0037] The acquisition unit can adjust the camera angle based on the children's height and build when acquiring video. For example, the acquisition unit can adjust the camera angle to match the children's height so that everyone's face is visible. It can also adjust the camera angle based on their build so that everyone's movements are visible. Furthermore, the acquisition unit can adjust the camera angle to match the children's positions so that everyone's actions are recorded. In this way, by adjusting the camera angle according to the children's height and build, everyone's actions can be properly recorded. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the children's height and build into a generating AI and have the generating AI perform the camera angle adjustment.

[0038] The analysis unit can classify children's behavior patterns by time of day during analysis and generate detailed behavioral histories. For example, the analysis unit can analyze morning behavioral patterns and generate detailed behavioral histories. It can also analyze post-lunch behavioral patterns and generate detailed behavioral histories. Furthermore, it can analyze afternoon behavioral patterns and generate detailed behavioral histories. In this way, detailed behavioral histories can be generated by classifying behavioral patterns by time of day. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input time-based behavioral data into a generating AI and have the generating AI perform the generation of behavioral histories.

[0039] The analysis unit can analyze the relationship between children's behavior and environmental factors in the classroom (temperature, humidity, etc.) during the analysis. For example, if the classroom temperature is high, the analysis unit can analyze the relationship between children's behavior and temperature. Similarly, if the classroom humidity is high, the analysis unit can analyze the relationship between children's behavior and humidity. Furthermore, if the classroom temperature is low, the analysis unit can analyze the relationship between children's behavior and temperature. This allows for a deeper understanding of children's behavior by analyzing the relationship between environmental factors and behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input classroom environmental data into a generating AI and have the generating AI perform the analysis of the relationship between behavior and environmental factors.

[0040] The analysis unit can analyze children's behavior and interactions with other children during the analysis process to understand their social relationships. For example, the analysis unit can analyze interactions when children are playing to understand their social relationships. It can also analyze interactions during learning to understand their social relationships. Furthermore, it can analyze interactions during meals to understand their social relationships. In this way, by analyzing interactions, it is possible to understand the social relationships of children. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input children's interaction data into a generating AI and have the generating AI perform the analysis of social relationships.

[0041] The analysis unit can detect abnormal behavior by comparing children's current behavior with their past behavioral history during analysis. For example, the analysis unit can detect abnormal behavior by comparing children's current behavior with their past behavioral history. The analysis unit can also detect abnormal behavior by analyzing children's behavioral patterns. Furthermore, the analysis unit can detect abnormal behavior based on children's behavioral history. This allows for the detection of abnormal behavior by comparing it with past behavioral history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past behavioral history data into a generating AI and have the generating AI perform abnormal behavior detection.

[0042] The distribution unit can adjust the level of detail in the report based on the parents' interests during distribution. For example, if a parent is interested in a particular activity, the distribution unit can report details of that activity. If a parent is interested in the overall situation, the distribution unit can provide an overall activity report. Furthermore, if a parent is interested in a specific time period, the distribution unit can report details of that time period. By adjusting the level of detail in the report according to the parents' interests, parental satisfaction can be increased. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input parental interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in the report.

[0043] The distribution unit can customize the report content to match the parent's language and culture at the time of distribution. For example, the distribution unit can translate the activity report to match the parent's language. It can also adjust the content of the activity report to match the parent's culture. Furthermore, the distribution unit can provide the most suitable report format based on the parent's language and culture. This allows for a deeper understanding of the report by customizing the content to match the parent's language and culture. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input the parent's language and culture data into a generating AI and have the generating AI perform the customization of the report content.

[0044] The distribution unit can distribute reports in the most optimal format, taking into account the parent's device information. For example, if the parent is using a smartphone, the distribution unit can distribute the report in a format optimized for smartphones. Similarly, if the parent is using a tablet, the distribution unit can distribute the report in a format optimized for tablets. Furthermore, if the parent is using a computer, the distribution unit can distribute the report in a format optimized for computers. This improves the readability of reports by distributing them in the most optimal format according to the parent's device information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the parent's device information into a generating AI and have the generating AI determine the optimal format.

[0045] The distribution unit can optimize the report content by referring to the parent's past feedback at the time of distribution. For example, the distribution unit can adjust the report content based on feedback previously provided by the parent. Furthermore, the distribution unit can prioritize reporting on topics of interest based on the parent's past feedback. In addition, the distribution unit can analyze the parent's feedback and provide the most appropriate report content. This allows for the optimization of the report content by referring to the parent's past feedback. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the parent's past feedback data into a generating AI and have the generating AI perform the optimization of the report content.

[0046] The suggestion unit can generate an optimal childcare plan by referring to the children's past behavioral history when making a suggestion. For example, the suggestion unit can refer to the children's past play history and suggest an optimal play plan. It can also refer to the children's past learning history and suggest an optimal learning plan. Furthermore, it can refer to the children's past meal history and suggest an optimal meal plan. In this way, by referring to past behavioral history, it is possible to suggest an optimal childcare plan for each child. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past behavioral history data into a generation AI and have the generation AI execute the generation of the childcare plan.

[0047] The proposal unit can customize childcare plans based on the individual developmental stages of the children when making a proposal. For example, the proposal unit can propose an appropriate play plan based on the children's developmental stages. It can also propose an appropriate learning plan based on the children's developmental stages. Furthermore, it can propose an appropriate meal plan based on the children's developmental stages. This allows for addressing individual needs by proposing childcare plans that are tailored to the children's developmental stages. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input children's developmental stage data into a generating AI and have the generating AI perform the customization of the childcare plan.

[0048] The proposal unit can adjust the childcare plan when making a proposal, taking into account the children's home environment and the parents' requests. For example, the proposal unit can propose an appropriate childcare plan based on the children's home environment. It can also adjust the childcare plan based on the parents' requests. Furthermore, the proposal unit can propose the optimal childcare plan by comprehensively considering the children's home environment and the parents' requests. This allows for the proposal of a more appropriate childcare plan by considering the home environment and the parents' requests. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the home environment and the parents' requests into a generating AI and have the generating AI perform the adjustment of the childcare plan.

[0049] The proposal unit can optimize the childcare plan by considering the children's health status and allergy information when making a proposal. For example, the proposal unit can propose an appropriate childcare plan based on the children's health status. Furthermore, the proposal unit can adjust the childcare plan based on the children's allergy information. In addition, the proposal unit can propose the optimal childcare plan by comprehensively considering the children's health status and allergy information. This allows for the proposal of the most suitable childcare plan for each child by considering their health status and allergy information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input health status and allergy information data into a generating AI and have the generating AI perform the optimization of the childcare plan.

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

[0051] The childcare support system can also be equipped with a speech recognition unit. The speech recognition unit can acquire sounds in the classroom and analyze children's speech and conversations. For example, the speech recognition unit can analyze conversations while children are playing and understand what words they are using. It can also analyze speech during learning and evaluate the children's level of understanding. Furthermore, the speech recognition unit can analyze conversations during meals and understand how the children are eating. As a result, by using the speech recognition unit, it is possible to understand not only the children's actions but also the content of their speech and conversations, allowing for the proposal of more detailed childcare plans.

[0052] The childcare support system can also be equipped with an environmental sensor unit. The environmental sensor unit can acquire environmental information such as temperature, humidity, and illuminance in the classroom and analyze its relationship to children's behavior. For example, the environmental sensor unit can analyze how children's behavior changes when the temperature in the classroom is high. It can also analyze how children's concentration is affected when the humidity is high. Furthermore, it can analyze how children's activities change when the illuminance is low. By using the environmental sensor unit, it is possible to understand the relationship between environmental information in the classroom and children's behavior and propose a more appropriate childcare plan.

[0053] The childcare support system can also be equipped with a biometric information acquisition unit. This unit can acquire biometric information such as children's heart rate and body temperature, allowing for monitoring of their health status. For example, the unit can monitor children's heart rates and notify childcare workers if abnormalities are detected. It can also measure body temperature, enabling early intervention if signs of fever are detected. Furthermore, the unit can monitor heart rate during sleep and evaluate sleep quality. This allows for real-time monitoring of children's health status and the provision of appropriate care.

[0054] The childcare support system can also be equipped with a behavior prediction unit. This unit can predict children's future behavior based on past behavioral data. For example, it can predict how a particular child will behave at a specific time of day. It can also predict how a particular child will react in a specific situation. Furthermore, it can predict what kind of interest a particular child will show in a specific activity. Therefore, by using the behavior prediction unit, it is possible to predict children's future behavior and propose an appropriate childcare plan.

[0055] The childcare support system can also be equipped with a behavior modification unit. This unit can monitor children's behavior in real time and modify it as needed. For example, it can issue warnings if children are engaging in dangerous behavior. It can also remind children if they are not following rules. Furthermore, it can praise children when they are behaving appropriately. Thus, by using the behavior modification unit, children's behavior can be corrected in real time, promoting safe and appropriate behavior.

[0056] The childcare support system can also be equipped with a behavioral evaluation unit. This unit can evaluate children's behavior and provide feedback to childcare workers. For example, it can evaluate children's play and provide feedback to childcare workers on what types of play are effective. It can also evaluate children's learning progress and provide feedback to childcare workers on what learning methods are effective. Furthermore, it can evaluate children's eating habits and provide feedback to childcare workers on what types of meals are healthy. Thus, by using the behavioral evaluation unit, it is possible to evaluate children's behavior and propose effective childcare methods to childcare workers.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The acquisition unit acquires video from cameras installed in the classroom. The acquisition unit can acquire video of the classroom using fixed cameras or PTZ cameras. Furthermore, by adjusting the camera's placement and height, optimal video can be acquired. For example, by placing the camera in the center of the classroom, the overall situation can be observed. The camera height can also be adjusted to capture the children's faces. In addition, the camera angle can be adjusted to focus on a specific area. Step 2: The analysis unit analyzes the video footage acquired by the acquisition unit to understand the children's daily activities. The analysis unit uses AI to analyze the video and classify the children's activities. For example, it can analyze the children's play and learning activities to understand their behavioral patterns. It can also analyze meal and rest times to understand their activity rhythms. Furthermore, it can classify activities by time of day to generate a detailed activity history. Step 3: The distribution unit distributes activity reports to parents based on the behavior identified by the analysis unit. The distribution unit can automatically generate and distribute activity reports using AI. For example, it can report on children's daily activities, learning outcomes, and meal contents. Furthermore, activity reports can be delivered in real time via parents' smartphones and computers. In addition, the level of detail in the reports can be adjusted based on the parents' interests. Step 4: The proposal department accumulates daily records based on activity reports distributed by the distribution department and proposes individual childcare plans to childcare workers. The proposal department can use AI to analyze daily records and generate individual childcare plans. For example, if a particular child shows interest in a specific activity, it can propose a childcare plan suitable for that child. It can also customize childcare plans based on the developmental stages of the children. Furthermore, it can estimate the emotions of childcare workers and adjust the way childcare plans are proposed to reduce the burden on childcare workers.

[0059] (Example of form 2) The childcare support system according to an embodiment of the present invention is a system that uses AI to reduce the burden on childcare workers and enhance the care of each individual child. The childcare support system acquires video from cameras installed in the classroom and understands the children's daily activities. Next, the childcare support system automatically distributes activity reports to parents using AI. Furthermore, the childcare support system accumulates daily records and proposes individualized childcare plans to childcare workers. For example, the childcare support system acquires video from cameras installed in the classroom. This video includes children playing and learning, as well as meal and rest times. The childcare support system analyzes this video to understand the children's daily activities. For example, it analyzes what kind of play a particular child prefers, and at what times of day their concentration is highest. Next, the childcare support system automatically distributes activity reports to parents using AI. The activity reports include the children's daily activities, learning outcomes, and meal contents. Parents can check on their children in real time via smartphones or computers. This allows parents to monitor their children's growth and gain a sense of security. Furthermore, the childcare support system accumulates daily records and proposes individualized childcare plans to childcare workers. For example, if a particular child shows interest in a specific activity, the system will suggest a childcare plan tailored to that child. This allows childcare workers to provide individualized care and support the children's development. This system reduces the burden on childcare workers and enhances the care of each child. Childcare workers can provide more effective care based on the information provided by the AI. In addition, parents can check on their children in real time, giving them peace of mind. For example, even while at work, parents can check on their children via their smartphones, allowing them to concentrate on their work with peace of mind. In this way, the childcare support system reduces the burden on childcare workers and enhances the care of each child.

[0060] The childcare support system according to this embodiment comprises an acquisition unit, an analysis unit, a distribution unit, and a proposal unit. The acquisition unit acquires video from a camera installed in the classroom. The acquisition unit can acquire video of the classroom using, for example, a fixed camera or a PTZ camera. The acquisition unit can also acquire optimal video by adjusting the camera's installation location and height. For example, the acquisition unit can install a camera in the center of the classroom to grasp the overall situation. The acquisition unit can also adjust the camera's height to capture the children's faces. Furthermore, the acquisition unit can adjust the camera's angle to focus on a specific area. The analysis unit analyzes the video acquired by the acquisition unit to understand the children's daily activities. The analysis unit can, for example, use AI to analyze the video and classify the children's activities. For example, the analysis unit can analyze the children's play and learning to understand their behavioral patterns. The analysis unit can also analyze the children's meal and rest times to understand their activity rhythms. Furthermore, the analysis unit can classify the children's activities by time of day and generate a detailed activity history. The distribution department distributes activity reports to parents based on the behaviors identified by the analysis department. The distribution department can, for example, automatically generate and distribute activity reports using AI. For example, the distribution department can report on children's daily activities, learning outcomes, and meal contents. The distribution department can also distribute activity reports in real time via parents' smartphones and computers. Furthermore, the distribution department can adjust the level of detail in the reports based on the parents' interests. The proposal department accumulates daily records based on the activity reports distributed by the distribution department and proposes individualized childcare plans to childcare workers. The proposal department can, for example, analyze daily records using AI and generate individualized childcare plans. For example, if a particular child shows interest in a particular activity, the proposal department can propose a childcare plan suitable for that child. Furthermore, the proposal department can customize childcare plans based on the developmental stages of the children. Furthermore, the proposal department can estimate the emotions of childcare workers and adjust the way childcare plans are proposed to reduce their burden.As a result, the childcare support system according to this embodiment can reduce the burden on childcare workers and enhance the care of each individual child.

[0061] The acquisition unit acquires video footage from cameras installed in the classroom. For example, the acquisition unit can acquire video footage of the classroom using fixed cameras or PTZ cameras. Specifically, fixed cameras are installed to capture an overall view of the classroom, while PTZ cameras utilize pan, tilt, and zoom functions to track specific areas or the movements of children in detail. Furthermore, the acquisition unit can acquire optimal footage by adjusting the camera's placement and height. For example, placing a camera in the center of the classroom makes it easier to grasp the overall situation. Adjusting the camera's height allows for clearer shots of children's faces, enabling detailed observation of their expressions and actions. Adjusting the camera's angle allows for focusing on specific areas. For example, pointing the camera towards a play area or study space allows for more detailed recording of children's activities. Additionally, by linking multiple cameras, the acquisition unit can simultaneously acquire video footage from different viewpoints within the classroom, collecting more comprehensive data. This allows the acquisition unit to grasp various situations within the classroom in real time and provide the necessary data to the analysis and distribution units.

[0062] The analysis unit analyzes the video footage acquired by the acquisition unit to understand the children's daily activities. For example, the analysis unit can use AI to analyze the video and classify the children's activities. Specifically, the AI ​​uses image recognition technology to analyze the children's movements and facial expressions, automatically classifying activity patterns such as playing, learning, eating, and resting. For example, it can analyze children playing with blocks or reading picture books and identify each activity. The AI ​​can also classify activities by time of day and generate a detailed activity history. This allows for an understanding of the children's daily schedule and activity rhythms. Furthermore, the analysis unit can accumulate children's activity data and analyze long-term activity patterns and trends. For example, it can analyze how interested a particular child is in a particular activity, understanding that child's interests and concerns. This allows the analysis unit to provide data useful for developing childcare plans based on the individual needs and interests of the children. In addition, the analysis unit is useful for detecting abnormal behavior and safety management. For example, it can detect abnormal behavior, such as children engaging in dangerous behavior or a particular child staying in the same place for a long time, and respond quickly. This allows the analysis unit to improve the quality of childcare while ensuring the safety of the children.

[0063] The distribution department delivers activity reports to parents based on the behaviors identified by the analysis department. The distribution department can, for example, automatically generate and deliver activity reports using AI. Specifically, the AI ​​generates reports summarizing children's daily activities, learning outcomes, and meal contents based on data provided by the analysis department. For example, it can detail what kind of play the children engaged in, what learning activities they participated in, the content and quantity of their meals, and their behavior during breaks. Furthermore, the distribution department can deliver activity reports in real time via parents' smartphones and computers. This allows parents to check on their children anytime, anywhere, providing them with peace of mind. The distribution department can also adjust the level of detail in the reports based on parents' interests. For example, if a particular parent is especially interested in their child's learning outcomes, they can be provided with detailed reports on learning activities. The distribution department can also collect feedback from parents and use it to improve the reports. For example, if a parent wants to know more about specific information, the report can be customized accordingly. This allows the distribution department to strengthen communication with parents and share in the children's growth.

[0064] The Proposal Department accumulates daily records based on activity reports distributed by the Distribution Department and proposes individualized childcare plans to childcare workers. For example, the Proposal Department can use AI to analyze daily records and generate individualized childcare plans. Specifically, the AI ​​analyzes children's behavioral data and interests to propose the most suitable childcare plan for each child. For example, if a particular child shows interest in a particular activity, it can suggest play and learning activities that are appropriate for that child. The Proposal Department can also customize childcare plans based on the developmental stages of the children. For example, it can suggest appropriate activities and learning content according to age and developmental stage to support the children's growth. Furthermore, the Proposal Department can estimate the emotions of childcare workers and adjust the way childcare plans are proposed to reduce their burden. For example, if a childcare worker is tired or stressed, it can propose a flexible childcare plan that is appropriate to the situation, reducing the burden on the childcare worker. In this way, the Proposal Department can reduce the burden on childcare workers while strengthening the care of each individual child. The Proposal Department can also monitor the implementation status of the childcare plan and revise the plan as needed. For example, if a particular activity is not suitable for the children, or if the plan does not proceed as intended, the plan can be revised accordingly to provide optimal childcare. This allows the proposal department to always provide flexible childcare plans based on the latest information, supporting the children's growth.

[0065] The acquisition unit can estimate the child's emotions and adjust the timing of video acquisition based on the estimated emotions. For example, if the child is excited, the acquisition unit can increase the frequency of video acquisition to capture that moment using AI. If the child is calm, the acquisition unit can reduce the frequency of video acquisition, recording only important moments. Furthermore, if the child is tired, the acquisition unit can prioritize capturing video during rest periods, recording the child in a relaxed state. This allows for the capture of important moments by adjusting the timing of video acquisition according to 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 acquisition unit may be performed using AI, or not. For example, the acquisition unit can input video data acquired by the camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0066] The acquisition unit can focus on a specific area within the classroom and acquire detailed information about the children's behavior. For example, the acquisition unit can focus on the classroom's play area and record in detail how the children are playing. It can also focus on the learning area and record in detail how the children are learning. Furthermore, the acquisition unit can focus on the dining area and record in detail how the children are eating. In this way, by focusing on a specific area, the children's behavior can be recorded in detail. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input video data focused on a specific area into a generating AI and have the generating AI perform a detailed analysis of the behavior.

[0067] The acquisition unit can acquire children's voices simultaneously with video acquisition and analyze the relationship between their actions and voices. For example, the acquisition unit can acquire laughter and conversations when children are playing and analyze the relationship between their actions and voices. It can also acquire statements and questions made during learning and analyze the progress of their learning. Furthermore, the acquisition unit can acquire conversations and reactions during meals and analyze the relationship between their eating habits and the voices. By analyzing the relationship between actions and voices, it becomes possible to gain a deeper understanding of children's behavior. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input video and audio data into a generating AI and have the generating AI perform the analysis of the relationship between actions and voices.

[0068] The acquisition unit can estimate the child's emotions and determine the priority of the video footage to acquire based on the estimated emotions. For example, if the child is excited, the acquisition unit can prioritize capturing that moment. If the child is calm, the acquisition unit can prioritize capturing other important moments. Furthermore, if the child is tired, the acquisition unit can prioritize capturing resting times. In this way, important footage can be acquired preferentially by determining the priority of the video footage according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 acquisition unit may be performed using AI, or not using AI. For example, the acquisition unit can input video data acquired by the camera into a generative AI and have the generative AI determine the priority of the video footage.

[0069] The acquisition unit can acquire optimal video by considering the classroom lighting and ambient noise during video acquisition. For example, if the classroom lighting is bright, the acquisition unit can adjust the brightness of the video to acquire the optimal video. Also, if the ambient noise is loud, the acquisition unit can remove noise from the audio to acquire clear audio. Furthermore, if the lighting is dim, the acquisition unit can correct the brightness of the video to acquire the optimal video. In this way, by acquiring the optimal video according to the classroom environment, clear video and audio can be provided. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input classroom lighting and ambient noise data into a generating AI and have the generating AI perform the acquisition of the optimal video.

[0070] The acquisition unit can adjust the camera angle based on the children's height and build when acquiring video. For example, the acquisition unit can adjust the camera angle to match the children's height so that everyone's face is visible. It can also adjust the camera angle based on their build so that everyone's movements are visible. Furthermore, the acquisition unit can adjust the camera angle to match the children's positions so that everyone's actions are recorded. In this way, by adjusting the camera angle according to the children's height and build, everyone's actions can be properly recorded. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the children's height and build into a generating AI and have the generating AI perform the camera angle adjustment.

[0071] The analysis unit can estimate a child's emotions and adjust the behavioral analysis algorithm based on the estimated emotions. For example, if a child is excited, the analysis unit can adjust the behavioral analysis algorithm to identify the cause of the excitement. Similarly, if a child is calm, the analysis unit can adjust the behavioral analysis algorithm to identify the reason for the calmness. Furthermore, if a child is tired, the analysis unit can adjust the behavioral analysis algorithm to identify the cause of the fatigue. By adjusting the behavioral analysis algorithm according to the child's emotions, more accurate behavioral analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generative AI. The generative AI is, but is not limited to, 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 using AI. For example, the analysis unit can input child emotion data into a generative AI and have the generative AI adjust the behavioral analysis algorithm.

[0072] The analysis unit can classify children's behavior patterns by time of day during analysis and generate detailed behavioral histories. For example, the analysis unit can analyze morning behavioral patterns and generate detailed behavioral histories. It can also analyze post-lunch behavioral patterns and generate detailed behavioral histories. Furthermore, it can analyze afternoon behavioral patterns and generate detailed behavioral histories. In this way, detailed behavioral histories can be generated by classifying behavioral patterns by time of day. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input time-based behavioral data into a generating AI and have the generating AI perform the generation of behavioral histories.

[0073] The analysis unit can analyze the relationship between children's behavior and environmental factors in the classroom (temperature, humidity, etc.) during the analysis. For example, if the classroom temperature is high, the analysis unit can analyze the relationship between children's behavior and temperature. Similarly, if the classroom humidity is high, the analysis unit can analyze the relationship between children's behavior and humidity. Furthermore, if the classroom temperature is low, the analysis unit can analyze the relationship between children's behavior and temperature. This allows for a deeper understanding of children's behavior by analyzing the relationship between environmental factors and behavior. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input classroom environmental data into a generating AI and have the generating AI perform the analysis of the relationship between behavior and environmental factors.

[0074] The analysis unit can estimate a child's emotions and adjust the order in which the behavioral analysis results are displayed based on the estimated emotions. For example, if a child is excited, the analysis unit can prioritize displaying that behavioral analysis result. If a child is calm, the analysis unit can prioritize displaying other important behavioral analysis results. Furthermore, if a child is tired, the analysis unit can prioritize displaying behavioral analysis results related to rest time. In this way, important information can be prioritized by adjusting the display order of behavioral analysis results according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input child emotion data into a generative AI and have the generative AI adjust the display order of the behavioral analysis results.

[0075] The analysis unit can analyze children's behavior and interactions with other children during the analysis process to understand their social relationships. For example, the analysis unit can analyze interactions when children are playing to understand their social relationships. It can also analyze interactions during learning to understand their social relationships. Furthermore, it can analyze interactions during meals to understand their social relationships. In this way, by analyzing interactions, it is possible to understand the social relationships of children. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input children's interaction data into a generating AI and have the generating AI perform the analysis of social relationships.

[0076] The analysis unit can detect abnormal behavior by comparing children's current behavior with their past behavioral history during analysis. For example, the analysis unit can detect abnormal behavior by comparing children's current behavior with their past behavioral history. The analysis unit can also detect abnormal behavior by analyzing children's behavioral patterns. Furthermore, the analysis unit can detect abnormal behavior based on children's behavioral history. This allows for the detection of abnormal behavior by comparing it with past behavioral history. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input past behavioral history data into a generating AI and have the generating AI perform abnormal behavior detection.

[0077] The distribution unit can estimate the parent's emotions and adjust the content of the activity report based on the estimated emotions. For example, if the parent is worried, the distribution unit can provide a detailed activity report. If the parent is relaxed, the distribution unit can provide a concise activity report. Furthermore, if the parent is busy, the distribution unit can provide a summary activity report. In this way, adjusting the content of the activity report according to the parent's emotions can increase the parent's sense of security. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input parent's emotion data into a generative AI and have the generative AI adjust the content of the activity report.

[0078] The distribution unit can adjust the level of detail in the report based on the parents' interests during distribution. For example, if a parent is interested in a particular activity, the distribution unit can report details of that activity. If a parent is interested in the overall situation, the distribution unit can provide an overall activity report. Furthermore, if a parent is interested in a specific time period, the distribution unit can report details of that time period. By adjusting the level of detail in the report according to the parents' interests, parental satisfaction can be increased. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input parental interest data into a generating AI and have the generating AI perform the adjustment of the level of detail in the report.

[0079] The distribution unit can customize the report content to match the parent's language and culture at the time of distribution. For example, the distribution unit can translate the activity report to match the parent's language. It can also adjust the content of the activity report to match the parent's culture. Furthermore, the distribution unit can provide the most suitable report format based on the parent's language and culture. This allows for a deeper understanding of the report by customizing the content to match the parent's language and culture. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not. For example, the distribution unit can input the parent's language and culture data into a generating AI and have the generating AI perform the customization of the report content.

[0080] The distribution unit can estimate the emotions of parents and adjust the timing of report delivery based on the estimated emotions. For example, if a parent is worried, the distribution unit can deliver the activity report earlier. If a parent is relaxed, the distribution unit can deliver the activity report at the usual time. Furthermore, if a parent is busy, the distribution unit can deliver the activity report at an appropriate time. In this way, adjusting the timing of report delivery according to the emotions of parents can increase their sense of security. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the distribution unit may be performed using AI, for example, or not using AI. For example, the distribution unit can input parent emotion data into a generative AI and have the generative AI adjust the timing of report delivery.

[0081] The distribution unit can distribute reports in the most optimal format, taking into account the parent's device information. For example, if the parent is using a smartphone, the distribution unit can distribute the report in a format optimized for smartphones. Similarly, if the parent is using a tablet, the distribution unit can distribute the report in a format optimized for tablets. Furthermore, if the parent is using a computer, the distribution unit can distribute the report in a format optimized for computers. This improves the readability of reports by distributing them in the most optimal format according to the parent's device information. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the parent's device information into a generating AI and have the generating AI determine the optimal format.

[0082] The distribution unit can optimize the report content by referring to the parent's past feedback at the time of distribution. For example, the distribution unit can adjust the report content based on feedback previously provided by the parent. Furthermore, the distribution unit can prioritize reporting on topics of interest based on the parent's past feedback. In addition, the distribution unit can analyze the parent's feedback and provide the most appropriate report content. This allows for the optimization of the report content by referring to the parent's past feedback. Some or all of the above processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input the parent's past feedback data into a generating AI and have the generating AI perform the optimization of the report content.

[0083] The suggestion unit can estimate the emotions of childcare workers and adjust the method of suggesting childcare plans based on the estimated emotions. For example, if a childcare worker is tired, the suggestion unit can suggest a simple and easy-to-implement childcare plan. If the childcare worker is relaxed, the suggestion unit can suggest a detailed childcare plan. Furthermore, if the childcare worker is busy, the suggestion unit can suggest a prioritized childcare plan. In this way, the burden on childcare workers can be reduced by adjusting the method of suggesting childcare plans according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input childcare worker emotion data into a generative AI and have the generative AI adjust the method of suggesting childcare plans.

[0084] The suggestion unit can generate an optimal childcare plan by referring to the children's past behavioral history when making a suggestion. For example, the suggestion unit can refer to the children's past play history and suggest an optimal play plan. It can also refer to the children's past learning history and suggest an optimal learning plan. Furthermore, it can refer to the children's past meal history and suggest an optimal meal plan. In this way, by referring to past behavioral history, it is possible to suggest an optimal childcare plan for each child. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input past behavioral history data into a generation AI and have the generation AI execute the generation of the childcare plan.

[0085] The proposal unit can customize childcare plans based on the individual developmental stages of the children when making a proposal. For example, the proposal unit can propose an appropriate play plan based on the children's developmental stages. It can also propose an appropriate learning plan based on the children's developmental stages. Furthermore, it can propose an appropriate meal plan based on the children's developmental stages. This allows for addressing individual needs by proposing childcare plans that are tailored to the children's developmental stages. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input children's developmental stage data into a generating AI and have the generating AI perform the customization of the childcare plan.

[0086] The suggestion unit can estimate the emotions of childcare workers and determine the priority of childcare plans based on the estimated emotions. For example, if a childcare worker is tired, the suggestion unit can prioritize simple and easy-to-implement plans. If a childcare worker is relaxed, the suggestion unit can prioritize detailed plans. Furthermore, if a childcare worker is busy, the suggestion unit can prioritize important plans. This reduces the burden on childcare workers by determining the priority of childcare plans according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input childcare worker emotion data into a generative AI and have the generative AI determine the priority of childcare plans.

[0087] The proposal unit can adjust the childcare plan when making a proposal, taking into account the children's home environment and the parents' requests. For example, the proposal unit can propose an appropriate childcare plan based on the children's home environment. It can also adjust the childcare plan based on the parents' requests. Furthermore, the proposal unit can propose the optimal childcare plan by comprehensively considering the children's home environment and the parents' requests. This allows for the proposal of a more appropriate childcare plan by considering the home environment and the parents' requests. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input data on the home environment and the parents' requests into a generating AI and have the generating AI perform the adjustment of the childcare plan.

[0088] The proposal unit can optimize the childcare plan by considering the children's health status and allergy information when making a proposal. For example, the proposal unit can propose an appropriate childcare plan based on the children's health status. Furthermore, the proposal unit can adjust the childcare plan based on the children's allergy information. In addition, the proposal unit can propose the optimal childcare plan by comprehensively considering the children's health status and allergy information. This allows for the proposal of the most suitable childcare plan for each child by considering their health status and allergy information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input health status and allergy information data into a generating AI and have the generating AI perform the optimization of the childcare plan.

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

[0090] The childcare support system can also be equipped with a speech recognition unit. The speech recognition unit can acquire sounds in the classroom and analyze children's speech and conversations. For example, the speech recognition unit can analyze conversations while children are playing and understand what words they are using. It can also analyze speech during learning and evaluate the children's level of understanding. Furthermore, the speech recognition unit can analyze conversations during meals and understand how the children are eating. As a result, by using the speech recognition unit, it is possible to understand not only the children's actions but also the content of their speech and conversations, allowing for the proposal of more detailed childcare plans.

[0091] The childcare support system can also be equipped with an environmental sensor unit. The environmental sensor unit can acquire environmental information such as temperature, humidity, and illuminance in the classroom and analyze its relationship to children's behavior. For example, the environmental sensor unit can analyze how children's behavior changes when the temperature in the classroom is high. It can also analyze how children's concentration is affected when the humidity is high. Furthermore, it can analyze how children's activities change when the illuminance is low. By using the environmental sensor unit, it is possible to understand the relationship between environmental information in the classroom and children's behavior and propose a more appropriate childcare plan.

[0092] The childcare support system can also be equipped with a biometric information acquisition unit. This unit can acquire biometric information such as children's heart rate and body temperature, allowing for monitoring of their health status. For example, the unit can monitor children's heart rates and notify childcare workers if abnormalities are detected. It can also measure body temperature, enabling early intervention if signs of fever are detected. Furthermore, the unit can monitor heart rate during sleep and evaluate sleep quality. This allows for real-time monitoring of children's health status and the provision of appropriate care.

[0093] The childcare support system can also be equipped with an emotion estimation unit. This unit can analyze children's facial expressions and behavior to estimate their emotions. For example, it can detect children's smiles and estimate whether they are having fun. It can also detect crying expressions and estimate whether they are sad. Furthermore, it can detect focused expressions and estimate whether they are concentrating on learning. By using this emotion estimation unit, it becomes possible to understand children's emotions in real time and respond appropriately.

[0094] The childcare support system can also be equipped with a behavior prediction unit. This unit can predict children's future behavior based on past behavioral data. For example, it can predict how a particular child will behave at a specific time of day. It can also predict how a particular child will react in a specific situation. Furthermore, it can predict what kind of interest a particular child will show in a specific activity. Therefore, by using the behavior prediction unit, it is possible to predict children's future behavior and propose an appropriate childcare plan.

[0095] The childcare support system can also be equipped with an emotional feedback unit. This unit can provide feedback to caregivers and parents about the children's emotional state. For example, it can notify caregivers when children are enjoying themselves, encouraging them to continue the activity. It can also notify parents when children are feeling anxious, encouraging them to take appropriate action. Furthermore, it can notify caregivers when children are concentrating, encouraging them to support that activity. In this way, the emotional feedback unit allows for real-time monitoring of children's emotional state and appropriate responses.

[0096] The childcare support system can also be equipped with a behavior modification unit. This unit can monitor children's behavior in real time and modify it as needed. For example, it can issue warnings if children are engaging in dangerous behavior. It can also remind children if they are not following rules. Furthermore, it can praise children when they are behaving appropriately. Thus, by using the behavior modification unit, children's behavior can be corrected in real time, promoting safe and appropriate behavior.

[0097] The childcare support system can also be equipped with an emotional recording unit. This unit can record children's emotional states and track long-term emotional changes. For example, it can record situations in which children are enjoying themselves, situations in which they feel anxious, and situations in which they are concentrating. By using this emotional recording unit, it becomes possible to track changes in children's emotional states over the long term and propose appropriate childcare plans.

[0098] The childcare support system can also be equipped with a behavioral evaluation unit. This unit can evaluate children's behavior and provide feedback to childcare workers. For example, it can evaluate children's play and provide feedback to childcare workers on what types of play are effective. It can also evaluate children's learning progress and provide feedback to childcare workers on what learning methods are effective. Furthermore, it can evaluate children's eating habits and provide feedback to childcare workers on what types of meals are healthy. Thus, by using the behavioral evaluation unit, it is possible to evaluate children's behavior and propose effective childcare methods to childcare workers.

[0099] The childcare support system can also be equipped with an emotion prediction unit. This unit can predict children's future emotional states based on past emotional data. For example, it can predict what emotions a particular child will feel at a specific time of day. It can also predict what emotions a particular child will feel under specific circumstances. Furthermore, it can predict what emotions a particular child will feel in response to specific activities. Therefore, by using the emotion prediction unit, it is possible to predict children's future emotional states and propose appropriate childcare plans.

[0100] The following briefly describes the processing flow for example form 2.

[0101] Step 1: The acquisition unit acquires video from cameras installed in the classroom. The acquisition unit can acquire video of the classroom using fixed cameras or PTZ cameras. Furthermore, by adjusting the camera's placement and height, optimal video can be acquired. For example, by placing the camera in the center of the classroom, the overall situation can be observed. The camera height can also be adjusted to capture the children's faces. In addition, the camera angle can be adjusted to focus on a specific area. Step 2: The analysis unit analyzes the video footage acquired by the acquisition unit to understand the children's daily activities. The analysis unit uses AI to analyze the video and classify the children's activities. For example, it can analyze the children's play and learning activities to understand their behavioral patterns. It can also analyze meal and rest times to understand their activity rhythms. Furthermore, it can classify activities by time of day to generate a detailed activity history. Step 3: The distribution unit distributes activity reports to parents based on the behavior identified by the analysis unit. The distribution unit can automatically generate and distribute activity reports using AI. For example, it can report on children's daily activities, learning outcomes, and meal contents. Furthermore, activity reports can be delivered in real time via parents' smartphones and computers. In addition, the level of detail in the reports can be adjusted based on the parents' interests. Step 4: The proposal department accumulates daily records based on activity reports distributed by the distribution department and proposes individual childcare plans to childcare workers. The proposal department can use AI to analyze daily records and generate individual childcare plans. For example, if a particular child shows interest in a specific activity, it can propose a childcare plan suitable for that child. It can also customize childcare plans based on the developmental stages of the children. Furthermore, it can estimate the emotions of childcare workers and adjust the way childcare plans are proposed to reduce the burden on childcare workers.

[0102] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0103] 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.

[0104] 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.

[0105] Each of the multiple elements described above, including the acquisition unit, analysis unit, distribution unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the acquisition unit acquires video footage of the classroom using the camera 42 of the smart device 14. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the acquired video footage to understand the children's behavior. The distribution unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and distributes activity reports to parents based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and accumulates daily records and proposes childcare plans for each child to childcare workers. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0106] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] Each of the multiple elements described above, including the acquisition unit, analysis unit, distribution unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit acquires images of the classroom using the camera 42 of the smart glasses 214. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the acquired images to understand the children's behavior. The distribution unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and distributes activity reports to parents based on the analysis results. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and accumulates daily records and proposes childcare plans for each child to childcare workers. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0122] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] Each of the multiple elements described above, including the acquisition unit, analysis unit, distribution unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit acquires video footage of the classroom using the camera 42 of the headset terminal 314. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the acquired video footage to understand the children's behavior. The distribution unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and distributes activity reports to parents based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and accumulates daily records and proposes childcare plans for each child to childcare workers. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0138] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the acquisition unit, analysis unit, distribution unit, and proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit acquires video footage of the classroom using the camera 42 of the robot 414. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the acquired video footage to understand the children's behavior. The distribution unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and distributes activity reports to parents based on the analysis results. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and accumulates daily records and proposes childcare plans for each child to childcare workers. The correspondence between each unit and the devices and control units is not limited to the example described above, and various changes are possible.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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."

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] (Note 1) An acquisition unit that acquires images from a camera installed in the classroom, The analysis unit analyzes the video footage acquired by the acquisition unit to understand the children's daily activities, Based on the actions identified by the aforementioned analysis unit, a distribution unit distributes activity reports to parents. The system includes a proposal unit that accumulates daily records based on activity reports distributed by the aforementioned distribution unit and proposes childcare plans for each child to childcare workers. A system characterized by the following features. (Note 2) The acquisition unit is, The system estimates the child's emotions and adjusts the timing of video acquisition based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Focus on specific areas within the classroom to obtain detailed information about children's behavior. The system described in Appendix 1, characterized by the features described herein. (Note 4) The acquisition unit is, When acquiring video footage, the children's voices are also captured simultaneously, and the relationship between their actions and voices is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The acquisition unit is, The system estimates the child's emotions and determines the priority of the video footage to be captured based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, When acquiring video footage, the optimal video is selected by considering the classroom lighting and ambient noise. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, When acquiring video footage, the camera angle is adjusted based on the children's height and build. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the behavioral analysis algorithm based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, children's behavioral patterns are classified by time of day, and a detailed behavioral history is generated. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During the analysis, we will examine the relationship between children's behavior and environmental factors within the classroom. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the order in which the behavioral analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, we analyze the children's behavior and their interactions with other children to understand their social relationships. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, children's current behavior is compared with their past behavioral history to detect abnormal behavior. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned distribution unit, We estimate the parents' emotions and adjust the content of the activity report based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned distribution unit, When distributing reports, the level of detail in the reports will be adjusted based on the parents' interests. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned distribution unit, When distributing reports, the content is customized to suit the parents' language and culture. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned distribution unit, We estimate the emotions of parents and adjust the timing of report delivery based on the estimated emotions of the parents. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned distribution unit, When distributing reports, the system will take into account the parent's device information and distribute them in the most appropriate format. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned distribution unit, When delivering reports, we optimize the content by referring to past feedback from parents. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, The system estimates the emotions of childcare workers and adjusts the method of proposing childcare plans based on the estimated emotions of the childcare workers. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, the optimal childcare plan is generated by referring to the children's past behavioral history. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, customize the childcare plan based on the individual developmental stage of each child. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, The system estimates the emotions of childcare workers and determines the priorities of the childcare plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we adjust the childcare plan taking into consideration the children's home environment and the wishes of their parents. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making a proposal, we optimize the childcare plan by taking into account the children's health status and allergy information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0174] 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 acquisition unit that acquires images from a camera installed in the classroom, The analysis unit analyzes the video footage acquired by the acquisition unit to understand the children's daily activities, Based on the actions identified by the aforementioned analysis unit, a distribution unit distributes activity reports to parents. The system includes a proposal unit that accumulates daily records based on activity reports distributed by the aforementioned distribution unit and proposes childcare plans for each child to childcare workers. A system characterized by the following features.

2. The acquisition unit is, The system estimates the child's emotions and adjusts the timing of video acquisition based on the estimated emotions. The system according to feature 1.

3. The acquisition unit is, Focus on specific areas within the classroom to obtain detailed information about children's behavior. The system according to feature 1.

4. The acquisition unit is, When acquiring video footage, the children's voices are also captured simultaneously, and the relationship between their actions and voices is analyzed. The system according to feature 1.

5. The acquisition unit is, The system estimates the child's emotions and determines the priority of the video footage to be captured based on the estimated emotions. The system according to feature 1.

6. The acquisition unit is, When acquiring video footage, the optimal video is selected by considering the classroom lighting and ambient noise. The system according to feature 1.

7. The acquisition unit is, When acquiring video footage, the camera angle is adjusted based on the children's height and build. The system according to feature 1.

8. The aforementioned analysis unit, The system estimates the child's emotions and adjusts the behavioral analysis algorithm based on those estimated emotions. The system according to feature 1.

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