System

Abstract

An object of the system according to the embodiment is to efficiently record a change in behavior or emotion of a kindergarten child and summarize the change as a daily report.SOLUTION: A system includes a face recognition part, a data collection part, a behavior analysis part, an emotion analysis part, and a daily report generation part. The face recognition unit recognizes a face of a preschooler. The data collection part collects action data on the basis of face recognition data recognized by the face recognition part. The behavior analysis unit analyzes the behavior data collected by the data collection unit. The emotion analysis unit analyzes ups and downs of emotions based on the behavior data. The daily report generator generates a daily report based on the behavior data and the emotional fluctuation data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to efficiently record changes in the behavior and emotions of children in daycare centers and kindergartens and compile the information into daily reports.

[0005] The system according to the embodiment aims to efficiently record changes in the behavior and emotions of children and compile them into a daily report. [Means for solving the problem]

[0006] The system according to the embodiment includes a face recognition unit, a data collection unit, a behavior analysis unit, an emotion analysis unit, and a daily report generation unit. The face recognition unit recognizes the faces of the children. The data collection unit collects behavioral data based on the face recognition data recognized by the face recognition unit. The behavior analysis unit analyzes the behavioral data collected by the data collection unit. The emotion analysis unit analyzes emotional fluctuations based on the behavioral data. The daily report generation unit generates a daily report based on the behavioral data and the emotional fluctuation data. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently record changes in the behavior and emotions of children and compile them into a daily report. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The kindergarten behavior recording system according to an embodiment of the present invention uses a camera to detect the movements and behavior of kindergarten children, and a generation AI outputs a daily record as text in a communication notebook. This allows the kindergarten behavior recording system to record changes in the behavior and emotions of kindergarten children in detail, and provide parents with accurate daily reports.

[0029] A kindergartener behavior recording system according to an embodiment includes a face recognition unit, a data collection unit, a behavior analysis unit, an emotion analysis unit, and a daily report generation unit. The face recognition unit recognizes the faces of kindergarteners. For example, a camera detects the faces of kindergarteners and identifies the faces to track the behavior of specific kindergarteners. The data collection unit collects behavioral data based on the facial recognition data recognized by the face recognition unit. For example, it records the behavior of kindergarteners in chronological order. The behavior analysis unit analyzes the behavioral data collected by the data collection unit. For example, it analyzes what kind of games the kindergarteners played and who they played with. The emotion analysis unit analyzes emotional fluctuations based on the behavioral data. For example, it analyzes emotional changes from the children's facial expressions and tone of voice. The daily report generation unit generates a daily report based on the behavioral data and the emotional fluctuation data. For example, it generates a sentence in the form of, "Today, I had fun playing with blocks in the morning. After lunch, I read picture books with my friends." As a result, the kindergartener behavior recording system according to the embodiment can record changes in the behavior and emotions of kindergarteners in detail and provide parents with accurate daily reports.

[0030] In addition to recognizing the faces of the children, the face recognition unit also analyzes their body movements and postures, making it possible to grasp detailed patterns of behavior. For example, the face recognition unit not only recognizes the faces of the children using a camera, but also simultaneously analyzes their body movements and postures. For example, it can identify whether the children are sitting, standing, or running, and grasp detailed patterns of behavior. This makes it possible to grasp the children's behavior patterns in more detail.

[0031] The face recognition unit can predict behavior based on the facial recognition data of the children by comparing them with their past behavioral history. For example, the face recognition unit analyzes the past behavioral history based on the facial recognition data of the children and predicts their future behavior. For example, if a particular child tends to play the same game at the same time every day, that behavior can be predicted. This makes it possible to predict the children's future behavior and take appropriate measures.

[0032] The facial recognition unit can add a function that allows parents to check how their children are doing in real time on their smartphones using the facial recognition data of the children. For example, the facial recognition unit can develop an app that allows parents to check how their children are doing in real time on their smartphones based on the facial recognition data of the children. For example, a function to stream camera footage can be added. This allows parents to check how their children are doing in real time.

[0033] The facial recognition unit can be equipped with a function that monitors the health status of children based on their facial recognition data and issues an alert if there is an abnormality. For example, the facial recognition unit can monitor changes in facial color and facial expression based on the facial recognition data of the children and analyze their health status. For example, an alert can be issued if the child looks pale or has a grim expression. This allows the health status of children to be monitored in real time and any abnormalities to be dealt with quickly.

[0034] The behavior analysis unit can use different camera angles and sensors for each type of behavior in order to record the children's behavior in detail. For example, the behavior analysis unit sets different camera angles for each type of behavior in order to record the children's behavior in detail. For example, cameras can be installed in play areas, eating areas, study areas, etc. to record the children's behavior in detail. This allows the children's behavior to be recorded in more detail.

[0035] The behavior analysis unit not only records the content of the behavior, but also analyzes the frequency and duration of the behavior, allowing for a detailed understanding of the behavioral patterns. For example, the behavior analysis unit not only records the content of the behavior of the kindergarten children, but also analyzes the frequency and duration of the behavior. For example, it records how often a particular play is performed and how long it lasts. This allows for a more detailed understanding of the behavioral patterns of the kindergarten children.

[0036] The behavior analysis unit can simultaneously collect voice data when recording the behavior of the children and analyze the relationship between the behavior and what is said. For example, the behavior analysis unit simultaneously collects voice data when recording the behavior of the children. For example, it records what is said during playtime and study time and analyzes the relationship between the behavior and what is said. This allows for a detailed analysis of the relationship between the behavior and what is said.

[0037] When recording the behavior of a child, the behavior analysis unit also analyzes interactions with other children, making it possible to grasp social relationships. For example, when recording the behavior of a child, the behavior analysis unit simultaneously records interactions with other children. For example, it records with whom a particular child often plays. This makes it possible to grasp the social relationships of the children in detail.

[0038] The daily report generation unit can generate an individually customized daily report by taking into consideration the interests and past feedback of the parent or guardian when generating the daily report. The daily report generation unit can generate an individually customized daily report by taking into consideration the interests and past feedback of the parent or guardian when generating the daily report. For example, the unit can give priority to writing information about specific behaviors and emotions. This allows the unit to generate an individually customized daily report by taking into consideration the interests and past feedback of the parent or guardian.

[0039] The daily report generation unit can include visual data (e.g., graphs and charts) related to changes in the behavior and emotions of the children when generating the daily report. For example, the daily report generation unit includes visual data related to changes in the behavior and emotions of the children when generating the daily report. For example, the changes in behavior and emotions are visually displayed in graphs and charts. This allows the visual data related to changes in the behavior and emotions of the children to be included in the daily report.

[0040] The daily report generation unit can include information about interactions with other children and social relationships when generating a daily report. For example, the daily report generation unit includes information about interactions with other children and social relationships when generating a daily report. For example, it describes with whom a particular child often plays. This makes it possible to include information about interactions with other children and social relationships in the daily report.

[0041] The system can improve security by strengthening data encryption and access control to maintain the integrity of information within the nursery. For example, the system can strengthen data encryption to maintain the integrity of information within the nursery. For example, the system can encrypt and store data of children to prevent unauthorized access from outside. This can strengthen data encryption and access control and improve security.

[0042] The system can strengthen the data backup and recovery functions to maintain the completeness of the information within the park, and ensure the safety of the data. The system, for example, strengthens the data backup functions to maintain the completeness of the information within the park. For example, data is backed up periodically to prevent data loss. This strengthens the data backup and recovery functions and ensures the safety of the data.

[0043] The system decentralizes data storage locations to maintain the completeness of information within the park, thereby improving data security. The system decentralizes data storage locations to maintain the completeness of information within the park, for example, by storing data in a distributed manner across multiple servers to prevent data loss. This decentralizes data storage locations and improves data security.

[0044] The system records detailed data access logs to maintain the completeness of in-park information and can prevent unauthorized access. The system records detailed data access logs to maintain the completeness of in-park information, for example, by recording who accessed which data and when, thereby preventing unauthorized access. This allows the system to record detailed data access logs and prevent unauthorized access.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The child behavior recording system can further include a voice recognition unit. The voice recognition unit analyzes the content of what the children say in real time and records it along with the behavioral data. For example, it can analyze what the children say while playing or what they say while studying and associate it with the behavioral data. This allows for a detailed understanding of the relationship between the children's behavior and what they say, and provides parents with more specific daily reports.

[0047] The child behavior recording system can further include an environmental sensor unit. The environmental sensor unit measures the temperature, humidity, illuminance, and other factors of the environment in which the children are present in real time and records them along with the behavioral data. For example, if the temperature in the play area is high or the illuminance in the study area is low, the environmental data is recorded. This allows for a detailed understanding of the environmental conditions under which the children's behavior is occurring, and daily reports including environmental information can be provided to parents.

[0048] The child behavior recording system can further include a health monitoring unit. The health monitoring unit measures biological data such as a child's body temperature and heart rate in real time and records it along with the behavioral data. For example, if a child's body temperature rises or their heart rate becomes abnormally high, the data is recorded. This allows the system to grasp the child's health status in real time and provide parents with daily reports including health information.

[0049] The child behavior recording system can further include a location information unit. The location information unit tracks the child's location in real time and records it along with the behavior data. For example, it can grasp in real time which area the child is playing or studying in. This allows the child's range of activity to be grasped in detail and a daily report including location information to be provided to parents.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The face recognition unit recognizes the faces of the children. For example, a camera detects the faces of the children and identifies them to track the behavior of specific children. Step 2: The data collection unit collects behavioral data based on the facial recognition data obtained by the facial recognition unit. For example, it records the behavior of the children in chronological order. Step 3: The behavior analysis unit analyzes the behavior data collected by the data collection unit, for example, analyzing what kind of play the children were doing and who they were playing with. Step 4: The emotion analysis unit analyzes emotional fluctuations based on behavioral data. For example, it analyzes emotional changes from the children's facial expressions and tone of voice. Step 5: The daily report generator generates a daily report based on the behavioral data and emotional fluctuation data. For example, it generates sentences such as, "Today, I had fun playing with blocks in the morning. After lunch, I read picture books with my friends."

[0052] (Example 2) The kindergarten behavior recording system according to an embodiment of the present invention uses a camera to detect the movements and behavior of kindergarten children, and a generation AI outputs a daily record as text in a communication notebook. This allows the kindergarten behavior recording system to record changes in the behavior and emotions of kindergarten children in detail, and provide parents with accurate daily reports.

[0053] A kindergartener behavior recording system according to an embodiment includes a face recognition unit, a data collection unit, a behavior analysis unit, an emotion analysis unit, and a daily report generation unit. The face recognition unit recognizes the faces of kindergarteners. For example, a camera detects the faces of kindergarteners and identifies the faces to track the behavior of specific kindergarteners. The data collection unit collects behavioral data based on the facial recognition data recognized by the face recognition unit. For example, it records the behavior of kindergarteners in chronological order. The behavior analysis unit analyzes the behavioral data collected by the data collection unit. For example, it analyzes what kind of games the kindergarteners played and who they played with. The emotion analysis unit analyzes emotional fluctuations based on the behavioral data. For example, it analyzes emotional changes from the children's facial expressions and tone of voice. The daily report generation unit generates a daily report based on the behavioral data and the emotional fluctuation data. For example, it generates a sentence in the form of, "Today, I had fun playing with blocks in the morning. After lunch, I read picture books with my friends." As a result, the kindergartener behavior recording system according to the embodiment can record changes in the behavior and emotions of kindergarteners in detail and provide parents with accurate daily reports.

[0054] In addition to recognizing the faces of the children, the face recognition unit also analyzes their body movements and postures, making it possible to grasp detailed patterns of behavior. For example, the face recognition unit not only recognizes the faces of the children using a camera, but also simultaneously analyzes their body movements and postures. For example, it can identify whether the children are sitting, standing, or running, and grasp detailed patterns of behavior. This makes it possible to grasp the children's behavior patterns in more detail.

[0055] The face recognition unit can predict behavior based on the facial recognition data of the children by comparing them with their past behavioral history. For example, the face recognition unit analyzes the past behavioral history based on the facial recognition data of the children and predicts their future behavior. For example, if a particular child tends to play the same game at the same time every day, that behavior can be predicted. This makes it possible to predict the children's future behavior and take appropriate measures.

[0056] The emotion analysis unit can use the emotion estimation function to estimate emotions from the children's facial expressions in real time and collect this emotion data along with their behavioral data. The emotion analysis unit performs facial expression analysis based on, for example, the children's facial recognition data, and estimates their emotions in real time. For example, it identifies emotions such as smile, anger, and sadness, and collects that data. This allows the emotions of the children to be grasped in real time and collected along with their behavioral data.

[0057] The facial recognition unit can add a function that allows parents to check how their children are doing in real time on their smartphones using the facial recognition data of the children. For example, the facial recognition unit can develop an app that allows parents to check how their children are doing in real time on their smartphones based on the facial recognition data of the children. For example, a function to stream camera footage can be added. This allows parents to check how their children are doing in real time.

[0058] The facial recognition unit can be equipped with a function that monitors the health status of children based on their facial recognition data and issues an alert if there is an abnormality. For example, the facial recognition unit can monitor changes in facial color and facial expression based on the facial recognition data of the children and analyze their health status. For example, an alert can be issued if the child looks pale or has a grim expression. This allows the health status of children to be monitored in real time and any abnormalities to be dealt with quickly.

[0059] The emotion analysis unit can add a function to use the emotion estimation function to analyze how a child feels about a specific activity and provide feedback to the parents. The emotion analysis unit, for example, uses the emotion estimation function to analyze in real time how a child feels about a specific activity. For example, it analyzes emotions during playtime and study time. This makes it possible to understand how a child feels about a specific activity and provide feedback to the parents.

[0060] The behavior analysis unit can use different camera angles and sensors for each type of behavior in order to record the children's behavior in detail. For example, the behavior analysis unit sets different camera angles for each type of behavior in order to record the children's behavior in detail. For example, cameras can be installed in play areas, eating areas, study areas, etc. to record the children's behavior in detail. This allows the children's behavior to be recorded in more detail.

[0061] The behavior analysis unit not only records the content of the behavior, but also analyzes the frequency and duration of the behavior, allowing for a detailed understanding of the behavioral patterns. For example, the behavior analysis unit not only records the content of the behavior of the kindergarten children, but also analyzes the frequency and duration of the behavior. For example, it records how often a particular play is performed and how long it lasts. This allows for a more detailed understanding of the behavioral patterns of the kindergarten children.

[0062] The behavior analysis unit can use the emotion estimation function to analyze the association between behavior content and emotion, and record the impact of changes in emotion on behavior. The behavior analysis unit, for example, uses the emotion estimation function to analyze the association between the behavior content and emotion of the kindergartener. For example, it analyzes whether a specific behavior is associated with a specific emotion. This makes it possible to record in detail the impact changes in emotion have on behavior.

[0063] The behavior analysis unit can simultaneously collect voice data when recording the behavior of the children and analyze the relationship between the behavior and what is said. For example, the behavior analysis unit simultaneously collects voice data when recording the behavior of the children. For example, it records what is said during playtime and study time and analyzes the relationship between the behavior and what is said. This allows for a detailed analysis of the relationship between the behavior and what is said.

[0064] When recording the behavior of a child, the behavior analysis unit also analyzes interactions with other children, making it possible to grasp social relationships. For example, when recording the behavior of a child, the behavior analysis unit simultaneously records interactions with other children. For example, it records with whom a particular child often plays. This makes it possible to grasp the social relationships of the children in detail.

[0065] The behavior analysis unit can be equipped with a function that uses the emotion estimation function to analyze how a specific behavior affects a child's emotions and provide feedback to the parents. The behavior analysis unit, for example, uses the emotion estimation function to analyze how a specific behavior affects a child's emotions. For example, it analyzes emotional changes during playtime and study time. This allows for a detailed analysis of the impact that a specific behavior has on a child's emotions and provides feedback to the parents.

[0066] The emotion analysis unit analyzes voice data and body movements in addition to facial expression data of the children, allowing for a multifaceted understanding of emotional ups and downs. For example, the emotion analysis unit analyzes voice data in addition to facial expression data of the children, allowing for a multifaceted understanding of emotional ups and downs. For example, it analyzes changes in tone and volume of voice to understand changes in emotions. This allows for a multifaceted understanding of emotional ups and downs.

[0067] When analyzing emotional fluctuations, the emotion analysis unit can compare the emotional data with past emotional data and track long-term changes in emotions. For example, the emotion analysis unit collects emotional data from kindergarten children over the long term and compares it with past data to track changes in emotions. For example, it analyzes changes in emotions over a specific period of time. This makes it possible to track changes in emotions over the long term.

[0068] The emotion analysis unit can use the emotion estimation function to analyze whether a change in a child's emotion is related to a specific event or activity. The emotion analysis unit, for example, uses the emotion estimation function to analyze whether a change in a child's emotion is related to a specific event or activity. For example, it analyzes the impact of a specific event on emotions. This makes it possible to analyze whether a change in emotion is related to a specific event or activity.

[0069] The emotion analysis unit can add a function to analyze the impact of emotional changes on the health condition based on the emotional data of the children and provide feedback to the parents. The emotion analysis unit, for example, analyzes the impact of emotional changes on the health condition based on the emotional data of the children. For example, it analyzes whether the emotional changes are related to signs of poor health or stress. This makes it possible to analyze the impact of emotional changes on the health condition and provide feedback to the parents.

[0070] The emotion analysis unit analyzes the impact of emotional changes on learning effectiveness based on the emotional data of the children, and can use this information to improve the educational program. The emotion analysis unit, for example, analyzes the impact of emotional changes on learning effectiveness based on the emotional data of the children. For example, it analyzes how a specific emotion affects learning effectiveness. This allows the analysis of the impact of emotional changes on learning effectiveness to be used to improve the educational program.

[0071] The emotion analysis unit can add a function to use the emotion estimation function to analyze how a child feels about a specific activity and provide feedback to the parents. The emotion analysis unit, for example, uses the emotion estimation function to analyze in real time how a child feels about a specific activity. For example, it analyzes emotions during playtime and study time. This makes it possible to understand how a child feels about a specific activity and provide feedback to the parents.

[0072] The daily report generation unit can include detailed analysis results regarding changes in the behavior and emotions of the children in the daily report it generates. For example, the daily report generation unit includes detailed analysis results regarding changes in the behavior and emotions of the children in the daily report generated by the generation AI. For example, changes in behavior and emotions during specific time periods can be described in detail. This allows the detailed analysis results regarding changes in the behavior and emotions of the children to be included in the daily report.

[0073] The daily report generation unit can generate an individually customized daily report by taking into consideration the interests and past feedback of the parent or guardian when generating the daily report. The daily report generation unit can generate an individually customized daily report by taking into consideration the interests and past feedback of the parent or guardian when generating the daily report. For example, the unit can give priority to writing information about specific behaviors and emotions. This allows the unit to generate an individually customized daily report by taking into consideration the interests and past feedback of the parent or guardian.

[0074] The daily report generation unit can use the emotion estimation function to include in the daily report the detailed analysis results regarding changes in the emotions of the children. The daily report generation unit, for example, uses the emotion estimation function to include in the daily report the detailed analysis results regarding changes in the emotions of the children. For example, changes in emotions during a specific time period are described in detail. This allows the detailed analysis results regarding changes in the emotions of the children to be included in the daily report.

[0075] The daily report generation unit can include visual data (e.g., graphs and charts) related to changes in the behavior and emotions of the children when generating the daily report. For example, the daily report generation unit includes visual data related to changes in the behavior and emotions of the children when generating the daily report. For example, the changes in behavior and emotions are visually displayed in graphs and charts. This allows the visual data related to changes in the behavior and emotions of the children to be included in the daily report.

[0076] The daily report generation unit can include information about interactions with other children and social relationships when generating a daily report. For example, the daily report generation unit includes information about interactions with other children and social relationships when generating a daily report. For example, it describes with whom a particular child often plays. This makes it possible to include information about interactions with other children and social relationships in the daily report.

[0077] The daily report generation unit can add a function to use the emotion estimation function to analyze how a child feels about a specific activity and provide feedback to the parent. The daily report generation unit, for example, uses the emotion estimation function to analyze in real time how a child feels about a specific activity. For example, it analyzes emotions during playtime and study time. This makes it possible to understand how a child feels about a specific activity and provide feedback to the parent.

[0078] The system can improve security by strengthening data encryption and access control to maintain the integrity of information within the nursery. For example, the system can strengthen data encryption to maintain the integrity of information within the nursery. For example, the system can encrypt and store data of children to prevent unauthorized access from outside. This can strengthen data encryption and access control and improve security.

[0079] The system can strengthen the data backup and recovery functions to maintain the completeness of the information within the park, and ensure the safety of the data. The system, for example, strengthens the data backup functions to maintain the completeness of the information within the park. For example, data is backed up periodically to prevent data loss. This strengthens the data backup and recovery functions and ensures the safety of the data.

[0080] The system uses the emotion estimation function to keep all data, including children's emotion data, within the nursery school, preventing data leakage to the outside. The system, for example, uses the emotion estimation function to keep all data, including children's emotion data, within the nursery school. For example, the data is managed on an in-school server, preventing data leakage to the outside. This keeps all data, including children's emotion data, within the nursery school, preventing data leakage to the outside.

[0081] The system decentralizes data storage locations to maintain the completeness of information within the park, thereby improving data security. The system decentralizes data storage locations to maintain the completeness of information within the park, for example, by storing data in a distributed manner across multiple servers to prevent data loss. This decentralizes data storage locations and improves data security.

[0082] The system records detailed data access logs to maintain the completeness of in-park information and can prevent unauthorized access. The system records detailed data access logs to maintain the completeness of in-park information, for example, by recording who accessed which data and when, thereby preventing unauthorized access. This allows the system to record detailed data access logs and prevent unauthorized access.

[0083] The system uses the emotion estimation function to keep all data, including children's emotion data, within the nursery school, preventing data leakage to the outside. The system, for example, uses the emotion estimation function to keep all data, including children's emotion data, within the nursery school. For example, the data is managed on an in-school server, preventing data leakage to the outside. This keeps all data, including children's emotion data, within the nursery school, preventing data leakage to the outside.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The child behavior recording system can further include a voice recognition unit. The voice recognition unit analyzes the content of what the children say in real time and records it along with the behavioral data. For example, it can analyze what the children say while playing or what they say while studying and associate it with the behavioral data. This allows for a detailed understanding of the relationship between the children's behavior and what they say, and provides parents with more specific daily reports.

[0086] The child behavior recording system can further include an environmental sensor unit. The environmental sensor unit measures the temperature, humidity, illuminance, and other factors of the environment in which the children are present in real time and records them along with the behavioral data. For example, if the temperature in the play area is high or the illuminance in the study area is low, the environmental data is recorded. This allows for a detailed understanding of the environmental conditions under which the children's behavior is occurring, and daily reports including environmental information can be provided to parents.

[0087] The child behavior recording system can further include a health monitoring unit. The health monitoring unit measures biological data such as a child's body temperature and heart rate in real time and records it along with the behavioral data. For example, if a child's body temperature rises or their heart rate becomes abnormally high, the data is recorded. This allows the system to grasp the child's health status in real time and provide parents with daily reports including health information.

[0088] The child behavior recording system can further include a location information unit. The location information unit tracks the child's location in real time and records it along with the behavior data. For example, it can grasp in real time which area the child is playing or studying in. This allows the child's range of activity to be grasped in detail and a daily report including location information to be provided to parents.

[0089] The kindergarten behavior recording system can further include an activity recommendation unit. The activity recommendation unit recommends appropriate activities based on the kindergarten children's past behavioral data and emotional data. For example, if a particular kindergarten child tends to prefer a particular activity at a particular time, that activity is recommended. This makes it possible to recommend appropriate activities so that the kindergarten children can spend their time more enjoyably.

[0090] The kindergarten behavior recording system can further include a stress analysis unit. The stress analysis unit analyzes the stress level based on the behavioral and emotional data of the kindergarten children. For example, it analyzes whether a specific behavior is causing stress and records that data. This allows for a detailed understanding of the children's stress levels and provides parents with daily reports that include stress information.

[0091] The child behavior recording system may further include an emotion feedback unit. The emotion feedback unit provides parents with feedback regarding changes in emotion based on the child's emotion data. For example, it may provide detailed information about changes in emotion during a specific time period and provide advice regarding the changes in emotion to parents. This allows parents to understand the changes in their child's emotion and take appropriate action.

[0092] The kindergarten behavior recording system can further include an emotion prediction unit. The emotion prediction unit predicts future emotions based on the child's past emotion data. For example, if a child tends to feel a certain emotion at a certain time of day, the emotion is predicted. This makes it possible to predict the child's future emotions and take appropriate measures.

[0093] The child behavior recording system can further include an emotion training unit. The emotion training unit provides training to learn emotion control based on the child's emotion data. For example, it provides games or activities to control specific emotions. This allows the child to learn emotion control and acquire healthier emotional expression.

[0094] The kindergarten behavior recording system can further include an emotion sharing unit. The emotion sharing unit shares the child's emotion data with parents and educators, and they can jointly respond to changes in the child's emotions. For example, if there is a specific change in emotion, the data is notified to the parents and educators. This allows parents and educators to jointly respond to changes in the child's emotions.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The face recognition unit recognizes the faces of the children. For example, a camera detects the faces of the children and identifies them to track the behavior of specific children. Step 2: The data collection unit collects behavioral data based on the facial recognition data obtained by the facial recognition unit. For example, it records the behavior of the children in chronological order. Step 3: The behavior analysis unit analyzes the behavior data collected by the data collection unit, for example, analyzing what kind of play the children were doing and who they were playing with. Step 4: The emotion analysis unit analyzes emotional fluctuations based on behavioral data. For example, it analyzes emotional changes from the children's facial expressions and tone of voice. Step 5: The daily report generator generates a daily report based on the behavioral data and emotional fluctuation data. For example, it generates sentences such as, "Today, I had fun playing with blocks in the morning. After lunch, I read picture books with my friends."

[0097] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0098] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0100] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0101] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0102] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0103] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0104] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0105] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0106] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0107] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0108] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0109] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0110] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0111] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0114] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0115] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0116] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0117] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0118] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0119] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0121] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0122] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0123] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0124] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0125] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0126] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0127] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0129] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0133] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0134] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0135] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0136] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0137] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0138] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0139] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0140] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0141] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0142] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0143] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0144] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0145] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0146] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0147] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0148] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0149] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0150] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0151] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0152] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0153] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0154] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0155] 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.

[0156] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0157] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0158] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0159] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0160] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0161] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0162] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0163] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0164] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A face recognition unit that recognizes the faces of the children; a data collection unit that collects behavioral data based on face recognition data recognized by the face recognition unit; a behavior analysis unit that analyzes the behavior data collected by the data collection unit; an emotion analysis unit that analyzes emotional fluctuations based on the behavioral data; a daily report generation unit that generates a daily report based on the behavior data and the emotional fluctuation data. A system characterized by:

2. The face recognition unit In addition to recognizing children's faces, the system also analyzes their body movements and postures to understand detailed patterns of behavior.

2. The system of claim 1.

3. The behavior analysis unit To record the children's activities in detail, different camera angles and sensors are used for each type of activity.

2. The system of claim 1.

4. The emotion analysis unit In addition to facial expression data, voice data and physical movements are also analyzed to understand emotional fluctuations from multiple angles.

2. The system of claim 1.

5. The daily report generation unit Generate daily reports that include detailed analysis of changes in children's behavior and emotions 2. The system of claim 1.

6. The emotion analysis unit Estimate emotions from children's facial expressions in real time and collect this emotional data along with their behavioral data.

2. The system of claim 1.

7. The behavior analysis unit Analyze the relationship between behavior and emotions, and record the impact that changes in emotions have on behavior.

2. The system of claim 1.

8. The system comprises: All data, including children's emotional data, is kept within the school to prevent data leakage to the outside.

2. The system of claim 1.

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