system

Abstract

An object of the system according to the embodiment is to prevent a kindergarten child or an infant from being left behind on a bus or being left behind in a company, and to quickly notify a person concerned when an abnormality occurs.SOLUTION: A system according to an embodiment includes an AI camera with a human sensor, a watching kids mobile, and an alert system. The AI camera with a human sensor is installed in a specific area in a bus or a company, constantly monitors the motion of a person, and issues an alert when an abnormality is detected. The watching kids' mobile phone is a device that enables a kindergarten child or an infant to always grasp position information thereof by carrying the watching kids' mobile phone. The watching kids' mobile phone is equipped with a GPS function and can track the position of the child in real time. The alert system transmits an alert to a person concerned in a case where an abnormality is detected on the basis of a AI camera with a human sensor or a watching kids mobile.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 does not adequately provide systems to prevent kindergarten children and young children from being left behind on buses or inside companies, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent kindergarten children and small children from being left behind on buses or inside a company, and to quickly notify relevant parties if an abnormality occurs. [Means for solving the problem]

[0006] The system according to the embodiment comprises an AI camera with a motion sensor, a mobile phone for watching over kids, and an alert system. The AI ​​camera with a motion sensor is installed on buses or in specific areas within a company, constantly monitoring people's movements and issuing an alert if it detects an abnormality. The mobile phone for watching over kids is a device carried by kindergarteners and toddlers that constantly tracks their location and is equipped with a GPS function, allowing the child's location to be tracked in real time. The alert system uses data from the AI ​​camera with a motion sensor and the mobile phone for watching over kids to send an alert to relevant parties if an abnormality is detected. [Effects of the Invention]

[0007] The system according to the embodiment can prevent kindergarten children and small children from being left behind on buses or inside a company, and can quickly notify relevant parties if an abnormality occurs. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The child monitoring system according to the embodiment of the present invention is a system that prevents kindergarten children from being left behind on buses or infants left behind in the workplace, and in the unlikely event that such an incident occurs, protects children by alerting relevant parties or calling for help from outside the company. This makes it possible for the child monitoring system to prevent accidents from occurring and to respond quickly.

[0029] A child monitoring system according to an embodiment includes an AI camera with a motion sensor, a mobile phone for monitoring children, and an alert system. The AI ​​camera with a motion sensor is installed on a bus or in a specific area within a company to constantly monitor human movement and issue an alert if an abnormality is detected. For example, the AI ​​camera with a motion sensor detects human movement using an infrared sensor. The AI ​​camera with a motion sensor can also recognize human presence using deep learning. The AI ​​camera with a motion sensor can also detect human movement using an ultrasonic sensor. The mobile phone for monitoring children is a device carried by kindergarteners and toddlers that constantly monitors their location and is equipped with a GPS function, allowing real-time tracking of children's locations. For example, the mobile phone for monitoring children acquires location information using GPS. The mobile phone for monitoring children can also acquire location information using Wi-Fi. The mobile phone for monitoring children can also acquire location information using cellular communication. The alert system uses data from the AI ​​camera with a motion sensor and the mobile phone to send an alert to relevant parties if an abnormality is detected. For example, the alert system sends an alert via email. The alert system can also send alerts using SMS. The alert system can also send alerts using app notifications. As a result, the child monitoring system according to the embodiment can prevent kindergarten children from being left behind on buses or infants left behind in offices and can respond quickly. For example, the alert system can send an alert to drivers or nursery teachers when an abnormality is detected. The alert system can also send an alert to parents when an abnormality is detected. The alert system can also send an alert to the police or fire department when an abnormality is detected.

[0030] AI cameras with motion sensors can learn children's behavior patterns based on video data and predict abnormal behavior. For example, AI cameras with motion sensors can learn children's normal behavior patterns based on video data and predict abnormal behavior. For example, they can issue an alert if a child moves in a way that is different from normal. AI cameras with motion sensors also analyze video data and record children's behavior patterns over time. For example, if abnormal behavior is detected, they can compare it with past behavior patterns to evaluate the degree of abnormality. AI cameras with motion sensors also incorporate algorithms that learn children's behavior patterns in real time and predict abnormal behavior. For example, they can issue an alert if a child does not move for an extended period of time. This makes it possible to predict abnormal behavior and respond quickly.

[0031] AI cameras with motion sensors can monitor the health of children based on video data and detect abnormalities. For example, AI cameras with motion sensors monitor their health by analyzing the complexion and sluggishness of children's movements. For example, they will issue an alert if their complexion turns pale. AI cameras with motion sensors also analyze the speed and frequency of children's movements based on video data to detect abnormalities. For example, they will issue an alert if their movements become extremely slow. AI cameras with motion sensors also incorporate algorithms that monitor children's health in real time and detect abnormalities. For example, they will issue an alert if they detect changes in complexion or sluggish movements. This makes it possible to monitor children's health and quickly detect abnormalities.

[0032] AI cameras with motion sensors can be installed not only on buses, but also in kindergartens and homes, enabling widespread monitoring. AI cameras with motion sensors can be installed not only on buses, but also in classrooms and playgrounds within kindergartens, enabling widespread monitoring. For example, they can detect abnormal behavior in classrooms. AI cameras with motion sensors can also be installed in homes to monitor the safety of kindergarten children after they return home. For example, they can detect abnormal behavior at home. AI cameras with motion sensors can also be installed in multiple locations both inside and outside kindergartens, creating a system for integrated data analysis. For example, data from inside the kindergarten and at home can be linked to detect abnormalities. This enables widespread monitoring and ensures the safety of kindergarten children.

[0033] AI cameras with motion sensors can add voice recognition functionality to detect children's voices and crying and issue an alert. AI cameras with motion sensors can add voice recognition functionality to detect children's voices and crying. For example, an alert will be issued if crying is detected. AI cameras with motion sensors can also analyze audio data to detect abnormal voice tones and content from children. For example, an alert will be issued if a cry for help is detected. Furthermore, voice recognition algorithms can be introduced to analyze children's voices and crying in real time. For example, an alert will be issued if prolonged crying is detected. This allows for the detection of children's voices and crying and rapid response.

[0034] The Mimamori Kids Mobile Phone is equipped with sensors that measure heart rate and body temperature, allowing health monitoring. The Mimamori Kids Mobile Phone is equipped with, for example, a heart rate sensor, and monitors the heart rate of the children in real time. For example, an alert is issued if an abnormal heart rate is detected. The Mimamori Kids Mobile Phone is also equipped with a body temperature sensor, and monitors the body temperature of the children in real time. For example, an alert is issued if an abnormal body temperature is detected. The Mimamori Kids Mobile Phone also analyzes heart rate and body temperature data in an integrated manner, creating a system that monitors the health of the children. For example, an alert is issued if abnormal data is detected. This allows the health of the children to be monitored in real time and abnormalities to be detected quickly.

[0035] The Mimamori Kids mobile phone is equipped with an emergency button that can be pressed by a child when they sense danger, sending an immediate alert. The Mimamori Kids mobile phone is equipped with an emergency button that can be pressed by a child when they sense danger, sending an alert. For example, pressing the button sends a notification to parents and other relevant parties. In addition, when the emergency button is pressed, the Mimamori Kids mobile phone automatically sends location information to identify the child's current location. For example, this allows parents and other relevant parties to respond quickly. In addition, when the emergency button is pressed, the Mimamori Kids mobile phone automatically generates a voice message explaining the child's situation. For example, it provides detailed information about the dangerous situation. This allows for a quick alert to be sent when a child senses danger.

[0036] The mobile phone for watching over kids can be equipped with a camera function, which allows it to take pictures of the situation around the children and send them to their parents. The mobile phone for watching over kids can be equipped with a camera function, for example, to take pictures of the situation around the children. For example, if an abnormal situation is detected, the mobile phone can automatically take pictures and send them to their parents. The mobile phone for watching over kids can also use the camera function to monitor the behavior of the children in real time. For example, it can allow parents to check the situation of their children on their smartphones. The mobile phone for watching over kids can also use the camera function to periodically take pictures of the situation around the children and send them to their parents. For example, it can automatically take pictures and send them at regular intervals. This allows the situation around the children to be sent to their parents, allowing for a quick response.

[0037] The Mimamori Kids Mobile Phone can be equipped with games and educational apps, allowing kindergarteners to have fun while using it. For example, the Mimamori Kids Mobile Phone can be equipped with educational apps, allowing kindergarteners to learn while having fun. For example, it can provide games for learning the alphabet and numbers. The Mimamori Kids Mobile Phone can also be equipped with game apps, allowing kindergarteners to have fun while using it. For example, it can provide simple puzzle games or action games. The Mimamori Kids Mobile Phone can also regularly update its educational and game apps, allowing kindergarteners to use it without getting bored. For example, new content can be added. This allows kindergarteners to have fun using the Mimamori Kids Mobile Phone.

[0038] The alert system uses AI to automatically set the priority of alerts, giving priority to alerts with higher importance. For example, the alert system may introduce AI to automatically set the priority of alerts. For example, it may give priority to alerts with higher urgency. The alert system may also introduce an algorithm in which AI analyzes the content of the alert and evaluates its importance. For example, it may give priority to alerts related to the health status of kindergarten children. The alert system may also adjust the priority of alerts in real time, building a system that quickly notifies high-priority alerts. For example, when multiple alerts occur, it may give priority to the most important alert. This allows high-priority alerts to be notified first, enabling a quick response.

[0039] The alert system can be linked with a smartwatch or smart speaker, allowing alerts to be received on multiple devices. The alert system can be linked with a smartwatch, for example, to allow alerts to be received. For example, an alert notification can be displayed on the smartwatch. The alert system can also be linked with a smart speaker to notify the alert by voice. For example, in an emergency, the smart speaker can explain the alert content by voice. The alert system can also be built to allow alerts to be received on multiple devices. For example, alerts can be received on a smartphone, smartwatch, and smart speaker simultaneously. This allows alerts to be received on multiple devices, allowing for quick response.

[0040] The alert system can add a chatbot function to enable relevant parties to respond to alerts immediately. For example, the chatbot function can be added to the alert system to enable relevant parties to respond to alerts immediately. For example, the chatbot can suggest a response method. The alert system also uses the chatbot to analyze the content of the alert and instruct relevant parties on an appropriate response. For example, the chatbot can guide relevant parties on the procedures required in an emergency. The alert system also uses the chatbot function to build a system where relevant parties can ask questions or confirm things about alerts. For example, the chatbot can respond in real time. This allows relevant parties to respond to alerts immediately.

[0041] The function for requesting external help uses AI to automatically prioritize emergency contacts, allowing for quick contact with the most appropriate contact. The function for requesting external help, for example, uses AI to automatically prioritize emergency contacts. For example, it prioritizes the contact who can respond most quickly. The function for requesting external help also employs an algorithm that analyzes emergency contact data and automatically selects the most appropriate contact. For example, it selects a contact based on past response history. The function for requesting external help also adjusts the priority of emergency contacts in real time, building a system that quickly contacts the most appropriate contact. For example, it selects the most appropriate contact when there are multiple contacts. This allows for quick contact with the most appropriate emergency contact.

[0042] The function for requesting help from outside the company can generate an automated voice message to explain the situation in detail. The function for requesting help from outside the company can, for example, generate an automated voice message to explain the situation in detail when contacting an outside party. For example, a voice message can be automatically generated to contact an outside party in an emergency. The function for requesting help from outside the company can also introduce an automated voice message generation algorithm to provide detailed explanations according to the situation. For example, the current situation of the children and the necessary response can be explained in voice. The function for requesting help from outside the company can also generate automated voice messages in real time, building a system to be used immediately when contacting an outside party. For example, this allows for a quick response in an emergency. This makes it possible to generate an automated voice message to explain the situation in detail in an emergency.

[0043] The function for requesting help from outside the company can be linked with a smart home system, allowing emergency calls to be made from devices within the home. The function for requesting help from outside the company can be linked with a smart home system, for example, allowing emergency calls to be made from devices within the home. For example, an emergency call can be made from a smart speaker. The function for requesting help from outside the company can also be linked with smart home devices to create a system that allows emergency calls to be made from multiple devices within the home. For example, calls can be made from smartphones and tablets. The function for requesting help from outside the company can also generate an automated voice message that explains the situation in detail when an emergency call is made from a device within the home through the smart home system. For example, this allows for a quick response in the event of an emergency. This allows emergency calls to be made from devices within the home.

[0044] Data analysis and feedback uses AI to perform a detailed analysis of the causes of accidents based on collected data and propose specific preventive measures. Data analysis and feedback, for example, uses AI to perform a detailed analysis of the causes of accidents based on collected data. For example, past data is analyzed to identify common causes. Data analysis and feedback also introduces algorithms that analyze the causes of accidents and propose specific preventive measures. For example, preventive measures are proposed when specific actions or situations cause accidents. Data analysis and feedback also builds a system that analyzes collected data in real time and identifies the causes of accidents. For example, preventive measures are proposed when abnormal data is detected. This makes it possible to perform a detailed analysis of the causes of accidents and propose specific preventive measures.

[0045] Data analysis and feedback can improve the accuracy of a system by automatically providing feedback on improvements to the system based on the results of data analysis. For example, data analysis and feedback can automatically provide feedback on improvements to the system based on the results of data analysis. For example, system settings can be adjusted based on the analysis results. Data analysis and feedback can also be used to build a system that provides feedback on data analysis results in real time to improve the accuracy of the system. For example, the system can be adjusted when abnormal data is detected. Data analysis and feedback can also be used to introduce an algorithm that identifies improvements to the system based on the results of data analysis and provides automatic feedback. For example, system parameters can be adjusted based on the analysis results. This allows for automatic feedback to improve the accuracy of the system.

[0046] Data analysis and feedback can share the results of data analysis with other educational institutions and childcare facilities, making it possible to propose widespread prevention measures. Data analysis and feedback, for example, can build a system for sharing data analysis results with other educational institutions and childcare facilities and proposing widespread prevention measures. For example, common problems can be identified and countermeasures can be shared. Data analysis and feedback can also be used to work with other facilities to propose prevention measures based on the analysis results. For example, data can be shared and effective countermeasures can be jointly developed. Data analysis and feedback can also be used to share data analysis results in real time, making it possible to build a system for working with other facilities to implement prevention measures. For example, countermeasures can be shared immediately if abnormal data is detected. This makes it possible to share data analysis results and propose widespread prevention measures.

[0047] Data analysis and feedback can develop educational programs based on the results of data analysis, thereby raising the awareness of those involved. Data analysis and feedback can, for example, develop educational programs based on the results of data analysis. For example, a program can be provided to teach the causes of accidents and preventative measures. Data analysis and feedback can also develop training programs based on the analysis results to raise the awareness of those involved. For example, practical training can be provided using simulations. Data analysis and feedback can also build a system that reflects the results of data analysis in real time and continuously updates the educational programs. For example, the program content can be adjusted based on new data. This makes it possible to develop educational programs based on the results of data analysis, thereby raising the awareness of those involved.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The child monitoring system can also add a voice recognition function, which can detect children's voices and crying and issue an alert. For example, an alert can be issued if crying is detected. It can also analyze audio data to detect abnormal voice tones and content from children. For example, an alert can be issued if a cry for help is detected. It can also introduce a voice recognition algorithm to analyze children's voices and crying in real time. For example, an alert can be issued if prolonged crying is detected. This allows for the system to detect children's voices and crying and respond quickly.

[0050] Child monitoring systems can also be equipped with sensors that measure heart rate and body temperature to monitor health conditions. For example, a heart rate sensor can be installed to monitor children's heart rates in real time. For example, an alert can be issued if an abnormal heart rate is detected. A body temperature sensor can also be installed to monitor children's body temperatures in real time. For example, an alert can be issued if an abnormal body temperature is detected. It is also possible to build a system that performs an integrated analysis of heart rate and body temperature data to monitor children's health conditions. For example, an alert can be issued if abnormal data is detected. This makes it possible to monitor children's health conditions in real time and quickly detect abnormalities.

[0051] The child monitoring system is also equipped with an emergency button that can be pressed by children when they sense danger, sending an immediate alert. For example, pressing the button sends a notification to parents and other relevant parties. Pressing the emergency button can also automatically send location information to identify the child's current location, allowing parents and other relevant parties to respond quickly, for example. Pressing the emergency button can also automatically generate a voice message to explain the child's situation, providing detailed information about the dangerous situation, for example. This allows for quick alerts to be sent when children sense danger.

[0052] The child monitoring system can also add a camera function, allowing it to take pictures of the situation around the children and send them to their parents. For example, if an abnormal situation is detected, it can automatically take a picture and send it to the parent. The camera function can also be used to monitor the behavior of children in real time. For example, it can allow parents to check the situation of their children on their smartphones. The camera function can also be used to periodically take pictures of the situation around the children and send them to the parents. For example, it can automatically take pictures and send them at regular intervals. This allows the situation around the children to be sent to the parents, allowing for a quick response.

[0053] The child monitoring system can also be equipped with games and educational apps, allowing children to have fun while using it. For example, educational apps can be installed to allow children to learn while having fun. For example, games for learning the alphabet and numbers can be provided. Game apps can also be installed to allow children to enjoy using the system. For example, simple puzzle games or action games can be provided. Educational and game apps can also be updated regularly to prevent children from getting bored. For example, new content can be added. This allows children to enjoy using the Monitoring Kids Mobile Phone.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: AI cameras with motion sensors are installed on buses or in specific areas of the company to constantly monitor human movement and issue alerts if they detect any abnormalities. For example, AI cameras with motion sensors can use infrared sensors, deep learning, and ultrasonic sensors to detect human movement and recognize the presence of people. Step 2: The Mimori Kids Mobile is a device that kindergarteners and toddlers carry that constantly tracks their location. It is equipped with a GPS function and can track children's locations in real time. For example, the Mimori Kids Mobile obtains location information using GPS, Wi-Fi, and cellular communications. Step 3: The alert system uses data from AI cameras with motion sensors and mobile phones to send alerts to relevant parties if an abnormality is detected. For example, the alert system can send alerts via email, SMS, or app notification. This allows for quick alerts to be sent to drivers, childcare workers, parents, police, and fire departments.

[0056] (Example 2) The child monitoring system according to the embodiment of the present invention is a system that prevents kindergarten children from being left behind on buses or infants left behind in the workplace, and in the unlikely event that such an incident occurs, protects children by alerting relevant parties or calling for help from outside the company. This makes it possible for the child monitoring system to prevent accidents from occurring and to respond quickly.

[0057] A child monitoring system according to an embodiment includes an AI camera with a motion sensor, a mobile phone for monitoring children, and an alert system. The AI ​​camera with a motion sensor is installed on a bus or in a specific area within a company to constantly monitor human movement and issue an alert if an abnormality is detected. For example, the AI ​​camera with a motion sensor detects human movement using an infrared sensor. The AI ​​camera with a motion sensor can also recognize human presence using deep learning. The AI ​​camera with a motion sensor can also detect human movement using an ultrasonic sensor. The mobile phone for monitoring children is a device carried by kindergarteners and toddlers that constantly monitors their location and is equipped with a GPS function, allowing real-time tracking of children's locations. For example, the mobile phone for monitoring children acquires location information using GPS. The mobile phone for monitoring children can also acquire location information using Wi-Fi. The mobile phone for monitoring children can also acquire location information using cellular communication. The alert system uses data from the AI ​​camera with a motion sensor and the mobile phone to send an alert to relevant parties if an abnormality is detected. For example, the alert system sends an alert via email. The alert system can also send alerts using SMS. The alert system can also send alerts using app notifications. As a result, the child monitoring system according to the embodiment can prevent kindergarten children from being left behind on buses or infants left behind in offices and can respond quickly. For example, the alert system can send an alert to drivers or nursery teachers when an abnormality is detected. The alert system can also send an alert to parents when an abnormality is detected. The alert system can also send an alert to the police or fire department when an abnormality is detected.

[0058] AI cameras with motion sensors can learn children's behavior patterns based on video data and predict abnormal behavior. For example, AI cameras with motion sensors can learn children's normal behavior patterns based on video data and predict abnormal behavior. For example, they can issue an alert if a child moves in a way that is different from normal. AI cameras with motion sensors also analyze video data and record children's behavior patterns over time. For example, if abnormal behavior is detected, they can compare it with past behavior patterns to evaluate the degree of abnormality. AI cameras with motion sensors also incorporate algorithms that learn children's behavior patterns in real time and predict abnormal behavior. For example, they can issue an alert if a child does not move for an extended period of time. This makes it possible to predict abnormal behavior and respond quickly.

[0059] AI cameras with motion sensors can monitor the health of children based on video data and detect abnormalities. For example, AI cameras with motion sensors monitor their health by analyzing the complexion and sluggishness of children's movements. For example, they will issue an alert if their complexion turns pale. AI cameras with motion sensors also analyze the speed and frequency of children's movements based on video data to detect abnormalities. For example, they will issue an alert if their movements become extremely slow. AI cameras with motion sensors also incorporate algorithms that monitor children's health in real time and detect abnormalities. For example, they will issue an alert if they detect changes in complexion or sluggish movements. This makes it possible to monitor children's health and quickly detect abnormalities.

[0060] AI cameras with motion sensors can infer emotions from children's facial expressions and detect abnormal emotional states. For example, AI cameras with motion sensors analyze children's facial expressions to infer emotions. For example, they issue an alert if they detect expressions of anxiety or fear. AI cameras with motion sensors also analyze changes in children's facial expressions in real time based on video data to detect abnormal emotional states. For example, they issue an alert if they detect an anxious expression for a long period of time. They also incorporate emotion inference algorithms to infer emotions from children's facial expressions. For example, they issue an alert if they detect expressions of fear or sadness. This makes it possible to monitor children's emotional states and quickly detect abnormalities.

[0061] AI cameras with motion sensors can be installed not only on buses, but also in kindergartens and homes, enabling widespread monitoring. AI cameras with motion sensors can be installed not only on buses, but also in classrooms and playgrounds within kindergartens, enabling widespread monitoring. For example, they can detect abnormal behavior in classrooms. AI cameras with motion sensors can also be installed in homes to monitor the safety of kindergarten children after they return home. For example, they can detect abnormal behavior at home. AI cameras with motion sensors can also be installed in multiple locations both inside and outside kindergartens, creating a system for integrated data analysis. For example, data from inside the kindergarten and at home can be linked to detect abnormalities. This enables widespread monitoring and ensures the safety of kindergarten children.

[0062] AI cameras with motion sensors can add voice recognition functionality to detect children's voices and crying and issue an alert. AI cameras with motion sensors can add voice recognition functionality to detect children's voices and crying. For example, an alert will be issued if crying is detected. AI cameras with motion sensors can also analyze audio data to detect abnormal voice tones and content from children. For example, an alert will be issued if a cry for help is detected. Furthermore, voice recognition algorithms can be introduced to analyze children's voices and crying in real time. For example, an alert will be issued if prolonged crying is detected. This allows for the detection of children's voices and crying and rapid response.

[0063] The Mimamori Kids Mobile Phone is equipped with sensors that measure heart rate and body temperature, allowing health monitoring. The Mimamori Kids Mobile Phone is equipped with, for example, a heart rate sensor, and monitors the heart rate of the children in real time. For example, an alert is issued if an abnormal heart rate is detected. The Mimamori Kids Mobile Phone is also equipped with a body temperature sensor, and monitors the body temperature of the children in real time. For example, an alert is issued if an abnormal body temperature is detected. The Mimamori Kids Mobile Phone also analyzes heart rate and body temperature data in an integrated manner, creating a system that monitors the health of the children. For example, an alert is issued if abnormal data is detected. This allows the health of the children to be monitored in real time and abnormalities to be detected quickly.

[0064] The Mimamori Kids mobile phone is equipped with an emergency button that can be pressed by a child when they sense danger, sending an immediate alert. The Mimamori Kids mobile phone is equipped with an emergency button that can be pressed by a child when they sense danger, sending an alert. For example, pressing the button sends a notification to parents and other relevant parties. In addition, when the emergency button is pressed, the Mimamori Kids mobile phone automatically sends location information to identify the child's current location. For example, this allows parents and other relevant parties to respond quickly. In addition, when the emergency button is pressed, the Mimamori Kids mobile phone automatically generates a voice message explaining the child's situation. For example, it provides detailed information about the dangerous situation. This allows for a quick alert to be sent when a child senses danger.

[0065] The Mimamori Kids Mobile Phone is equipped with an emotion estimation function, which can infer emotions from the voices and actions of children and detect abnormal emotional states. The Mimamori Kids Mobile Phone, for example, is equipped with an emotion estimation function and can infer emotions from the voices and actions of children. For example, it will issue an alert if it detects emotions such as anxiety or fear. The Mimamori Kids Mobile Phone also analyzes the tone and content of voices to monitor the emotional state of children in real time. For example, it will issue an alert if it detects a cry for help. In addition, an emotion estimation algorithm will be introduced to build a system that infers emotions from children's voices and actions. For example, it will issue an alert if it detects prolonged feelings of anxiety. This makes it possible to monitor the emotional state of children and quickly detect abnormalities.

[0066] The mobile phone for watching over kids can be equipped with a camera function, which allows it to take pictures of the situation around the children and send them to their parents. The mobile phone for watching over kids can be equipped with a camera function, for example, to take pictures of the situation around the children. For example, if an abnormal situation is detected, the mobile phone can automatically take pictures and send them to their parents. The mobile phone for watching over kids can also use the camera function to monitor the behavior of the children in real time. For example, it can allow parents to check the situation of their children on their smartphones. The mobile phone for watching over kids can also use the camera function to periodically take pictures of the situation around the children and send them to their parents. For example, it can automatically take pictures and send them at regular intervals. This allows the situation around the children to be sent to their parents, allowing for a quick response.

[0067] The Mimamori Kids Mobile Phone can be equipped with games and educational apps, allowing kindergarteners to have fun while using it. For example, the Mimamori Kids Mobile Phone can be equipped with educational apps, allowing kindergarteners to learn while having fun. For example, it can provide games for learning the alphabet and numbers. The Mimamori Kids Mobile Phone can also be equipped with game apps, allowing kindergarteners to have fun while using it. For example, it can provide simple puzzle games or action games. The Mimamori Kids Mobile Phone can also regularly update its educational and game apps, allowing kindergarteners to use it without getting bored. For example, new content can be added. This allows kindergarteners to have fun using the Mimamori Kids Mobile Phone.

[0068] The Mimamori Kids' Mobile Phone uses an emotion estimation function to monitor the emotional state of kindergarteners when they use their mobile phones, and can notify parents if there is an abnormality. The Mimamori Kids' Mobile Phone, for example, uses the emotion estimation function to monitor the emotional state of kindergarteners when they use their mobile phones. For example, if emotions of anxiety or fear are detected, it will notify parents. The Mimamori Kids' Mobile Phone also analyzes the emotional changes of kindergarteners in real time while they are using their mobile phones and detects abnormalities. For example, if it detects an extended period of anxious feelings, it will notify parents. In addition, an emotion estimation algorithm is introduced to build a system that monitors the emotional state of kindergarteners when they use their mobile phones. For example, if it detects emotions of fear or sadness, it will notify parents. This makes it possible to monitor the emotional state of kindergarteners when they use their mobile phones and quickly detect abnormalities.

[0069] The alert system uses AI to automatically set the priority of alerts, giving priority to alerts with higher importance. For example, the alert system may introduce AI to automatically set the priority of alerts. For example, it may give priority to alerts with higher urgency. The alert system may also introduce an algorithm in which AI analyzes the content of the alert and evaluates its importance. For example, it may give priority to alerts related to the health status of kindergarten children. The alert system may also adjust the priority of alerts in real time, building a system that quickly notifies high-priority alerts. For example, when multiple alerts occur, it may give priority to the most important alert. This allows high-priority alerts to be notified first, enabling a quick response.

[0070] The alert system uses an emotion estimation function to monitor the emotional state of the parties who receive the alert and encourage them to take an appropriate action. The alert system, for example, uses the emotion estimation function to monitor the emotional state of the parties who receive the alert. For example, if stress or anxiety is detected, the system encourages an appropriate action. The alert system also analyzes the emotional state of the parties in real time when an alert is received and adjusts the priority of the response. For example, it may prioritize calm parties to respond. Furthermore, an emotion estimation algorithm is introduced to build a system that monitors the emotional state of the parties who receive the alert. For example, it may suggest a response method depending on the emotional state. This makes it possible to monitor the emotional state of the parties and encourage an appropriate response.

[0071] The alert system can be linked with a smartwatch or smart speaker, allowing alerts to be received on multiple devices. The alert system can be linked with a smartwatch, for example, to allow alerts to be received. For example, an alert notification can be displayed on the smartwatch. The alert system can also be linked with a smart speaker to notify the alert by voice. For example, in an emergency, the smart speaker can explain the alert content by voice. The alert system can also be built to allow alerts to be received on multiple devices. For example, alerts can be received on a smartphone, smartwatch, and smart speaker simultaneously. This allows alerts to be received on multiple devices, allowing for quick response.

[0072] The alert system can add a chatbot function to enable relevant parties to respond to alerts immediately. For example, the chatbot function can be added to the alert system to enable relevant parties to respond to alerts immediately. For example, the chatbot can suggest a response method. The alert system also uses the chatbot to analyze the content of the alert and instruct relevant parties on an appropriate response. For example, the chatbot can guide relevant parties on the procedures required in an emergency. The alert system also uses the chatbot function to build a system where relevant parties can ask questions or confirm things about alerts. For example, the chatbot can respond in real time. This allows relevant parties to respond to alerts immediately.

[0073] The alert system can use an emotion estimation function to analyze the emotional state of the person receiving the alert and provide advice to reduce stress. The alert system, for example, uses the emotion estimation function to analyze the emotional state of the person receiving the alert. For example, advice is provided if stress or anxiety is detected. The alert system also analyzes the emotional state of the person receiving the alert in real time when the alert is received, and builds a system to provide advice to reduce stress. For example, it suggests relaxation methods. The alert system also introduces an emotion estimation algorithm to build a system to analyze the emotional state of the person receiving the alert. For example, advice to reduce stress is provided depending on the emotional state. This makes it possible to provide advice to reduce stress for the person receiving the alert.

[0074] The function for requesting external help uses AI to automatically prioritize emergency contacts, allowing for quick contact with the most appropriate contact. The function for requesting external help, for example, uses AI to automatically prioritize emergency contacts. For example, it prioritizes the contact who can respond most quickly. The function for requesting external help also employs an algorithm that analyzes emergency contact data and automatically selects the most appropriate contact. For example, it selects a contact based on past response history. The function for requesting external help also adjusts the priority of emergency contacts in real time, building a system that quickly contacts the most appropriate contact. For example, it selects the most appropriate contact when there are multiple contacts. This allows for quick contact with the most appropriate emergency contact.

[0075] The function for requesting help from outside the company can generate an automated voice message to explain the situation in detail. The function for requesting help from outside the company can, for example, generate an automated voice message to explain the situation in detail when contacting an outside party. For example, a voice message can be automatically generated to contact an outside party in an emergency. The function for requesting help from outside the company can also introduce an automated voice message generation algorithm to provide detailed explanations according to the situation. For example, the current situation of the children and the necessary response can be explained in voice. The function for requesting help from outside the company can also generate automated voice messages in real time, building a system to be used immediately when contacting an outside party. For example, this allows for a quick response in an emergency. This makes it possible to generate an automated voice message to explain the situation in detail in an emergency.

[0076] The function for requesting help from outside the company can use an emotion estimation function to monitor the emotional state of emergency contacts and encourage an appropriate response. The function for requesting help from outside the company can, for example, use an emotion estimation function to monitor the emotional state of emergency contacts. For example, if stress or anxiety is detected, an appropriate response can be encouraged. The function for requesting help from outside the company can also analyze the emotional state of contacts in real time when making an emergency call and adjust the priority of the response. For example, it can prioritize responding to calm contacts. Furthermore, an emotion estimation algorithm can be introduced to build a system that monitors the emotional state of emergency contacts. For example, it can suggest a response method based on the emotional state. This makes it possible to monitor the emotional state of emergency contacts and encourage an appropriate response.

[0077] The function for requesting help from outside the company can be linked with a smart home system, allowing emergency calls to be made from devices within the home. The function for requesting help from outside the company can be linked with a smart home system, for example, allowing emergency calls to be made from devices within the home. For example, an emergency call can be made from a smart speaker. The function for requesting help from outside the company can also be linked with smart home devices to create a system that allows emergency calls to be made from multiple devices within the home. For example, calls can be made from smartphones and tablets. The function for requesting help from outside the company can also generate an automated voice message that explains the situation in detail when an emergency call is made from a device within the home through the smart home system. For example, this allows for a quick response in the event of an emergency. This allows emergency calls to be made from devices within the home.

[0078] The function for requesting help from outside the company can use an emotion estimation function to analyze the emotional state of an emergency contact and provide advice to reduce stress. The function for requesting help from outside the company can, for example, use the emotion estimation function to analyze the emotional state of an emergency contact. For example, advice can be provided if stress or anxiety is detected. Furthermore, the function for requesting help from outside the company can analyze the emotional state of a contact in real time when making an emergency call and build a system to provide advice to reduce stress. For example, relaxation methods can be suggested. Furthermore, an emotion estimation algorithm can be introduced to build a system that analyzes the emotional state of an emergency contact. For example, advice to reduce stress can be provided depending on the emotional state. This makes it possible to analyze the emotional state of an emergency contact and provide advice to reduce stress.

[0079] Data analysis and feedback uses AI to perform a detailed analysis of the causes of accidents based on collected data and propose specific preventive measures. Data analysis and feedback, for example, uses AI to perform a detailed analysis of the causes of accidents based on collected data. For example, past data is analyzed to identify common causes. Data analysis and feedback also introduces algorithms that analyze the causes of accidents and propose specific preventive measures. For example, preventive measures are proposed when specific actions or situations cause accidents. Data analysis and feedback also builds a system that analyzes collected data in real time and identifies the causes of accidents. For example, preventive measures are proposed when abnormal data is detected. This makes it possible to perform a detailed analysis of the causes of accidents and propose specific preventive measures.

[0080] Data analysis and feedback can improve the accuracy of a system by automatically providing feedback on improvements to the system based on the results of data analysis. For example, data analysis and feedback can automatically provide feedback on improvements to the system based on the results of data analysis. For example, system settings can be adjusted based on the analysis results. Data analysis and feedback can also be used to build a system that provides feedback on data analysis results in real time to improve the accuracy of the system. For example, the system can be adjusted when abnormal data is detected. Data analysis and feedback can also be used to introduce an algorithm that identifies improvements to the system based on the results of data analysis and provides automatic feedback. For example, system parameters can be adjusted based on the analysis results. This allows for automatic feedback to improve the accuracy of the system.

[0081] Data analysis and feedback can use an emotion estimation function to analyze the emotional state of those involved and evaluate the impact of emotional factors on accidents. Data analysis and feedback, for example, uses the emotion estimation function to analyze the emotional state of those involved. For example, it evaluates the impact of stress and anxiety on accidents. Data analysis and feedback can also analyze emotional states in real time and build a system to evaluate the impact of emotional factors on accidents. For example, it evaluates that the risk of an accident increases when the emotional state worsens. Data analysis and feedback can also introduce an emotion estimation algorithm to build a system to analyze the emotional state of those involved. For example, it evaluates the risk of an accident based on the emotional state and suggests preventive measures. This makes it possible to evaluate the impact of emotional factors on accidents and suggest preventive measures.

[0082] Data analysis and feedback can share the results of data analysis with other educational institutions and childcare facilities, making it possible to propose widespread prevention measures. Data analysis and feedback, for example, can build a system for sharing data analysis results with other educational institutions and childcare facilities and proposing widespread prevention measures. For example, common problems can be identified and countermeasures can be shared. Data analysis and feedback can also be used to work with other facilities to propose prevention measures based on the analysis results. For example, data can be shared and effective countermeasures can be jointly developed. Data analysis and feedback can also be used to share data analysis results in real time, making it possible to build a system for working with other facilities to implement prevention measures. For example, countermeasures can be shared immediately if abnormal data is detected. This makes it possible to share data analysis results and propose widespread prevention measures.

[0083] Data analysis and feedback can develop educational programs based on the results of data analysis, thereby raising the awareness of those involved. Data analysis and feedback can, for example, develop educational programs based on the results of data analysis. For example, a program can be provided to teach the causes of accidents and preventative measures. Data analysis and feedback can also develop training programs based on the analysis results to raise the awareness of those involved. For example, practical training can be provided using simulations. Data analysis and feedback can also build a system that reflects the results of data analysis in real time and continuously updates the educational programs. For example, the program content can be adjusted based on new data. This makes it possible to develop educational programs based on the results of data analysis, thereby raising the awareness of those involved.

[0084] Data analysis and feedback can use an emotion estimation function to analyze the emotional state of those involved and evaluate the impact of emotional factors on accidents. Data analysis and feedback, for example, uses the emotion estimation function to analyze the emotional state of those involved. For example, it evaluates the impact of stress and anxiety on accidents. Data analysis and feedback can also analyze emotional states in real time and build a system to evaluate the impact of emotional factors on accidents. For example, it can evaluate that the risk of an accident increases when the emotional state worsens. Data analysis and feedback can also introduce an emotion estimation algorithm to build a system to analyze the emotional state of those involved. For example, it can evaluate the risk of an accident according to the emotional state and suggest preventive measures. This makes it possible to evaluate the impact of emotional factors on accidents and suggest preventive measures.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The child monitoring system can also add a voice recognition function, which can detect children's voices and crying and issue an alert. For example, an alert can be issued if crying is detected. It can also analyze audio data to detect abnormal voice tones and content from children. For example, an alert can be issued if a cry for help is detected. It can also introduce a voice recognition algorithm to analyze children's voices and crying in real time. For example, an alert can be issued if prolonged crying is detected. This allows for the system to detect children's voices and crying and respond quickly.

[0087] Child monitoring systems can also be equipped with sensors that measure heart rate and body temperature to monitor health conditions. For example, a heart rate sensor can be installed to monitor children's heart rates in real time. For example, an alert can be issued if an abnormal heart rate is detected. A body temperature sensor can also be installed to monitor children's body temperatures in real time. For example, an alert can be issued if an abnormal body temperature is detected. It is also possible to build a system that performs an integrated analysis of heart rate and body temperature data to monitor children's health conditions. For example, an alert can be issued if abnormal data is detected. This makes it possible to monitor children's health conditions in real time and quickly detect abnormalities.

[0088] The child monitoring system is also equipped with an emergency button that can be pressed by children when they sense danger, sending an immediate alert. For example, pressing the button sends a notification to parents and other relevant parties. Pressing the emergency button can also automatically send location information to identify the child's current location, allowing parents and other relevant parties to respond quickly, for example. Pressing the emergency button can also automatically generate a voice message to explain the child's situation, providing detailed information about the dangerous situation, for example. This allows for quick alerts to be sent when children sense danger.

[0089] The child monitoring system can also add a camera function, allowing it to take pictures of the situation around the children and send them to their parents. For example, if an abnormal situation is detected, it can automatically take a picture and send it to the parent. The camera function can also be used to monitor the behavior of children in real time. For example, it can allow parents to check the situation of their children on their smartphones. The camera function can also be used to periodically take pictures of the situation around the children and send them to the parents. For example, it can automatically take pictures and send them at regular intervals. This allows the situation around the children to be sent to the parents, allowing for a quick response.

[0090] The child monitoring system can also be equipped with games and educational apps, allowing children to have fun while using it. For example, educational apps can be installed to allow children to learn while having fun. For example, games for learning the alphabet and numbers can be provided. Game apps can also be installed to allow children to enjoy using the system. For example, simple puzzle games or action games can be provided. Educational and game apps can also be updated regularly to prevent children from getting bored. For example, new content can be added. This allows children to enjoy using the Monitoring Kids Mobile Phone.

[0091] The child monitoring system can also use an emotion estimation function to monitor the emotional state of children when they use their mobile phones and notify parents if any abnormalities are detected. For example, the emotion estimation function can be used to monitor the emotional state of children when they use their mobile phones. For example, if emotions such as anxiety or fear are detected, the parent will be notified. It can also analyze children's emotional changes in real time while they are using their mobile phones and detect abnormalities. For example, if prolonged feelings of anxiety are detected, the parent will be notified. It is also possible to introduce an emotion estimation algorithm and build a system that monitors the emotional state of children when they use their mobile phones. For example, if emotions such as fear or sadness are detected, the parent will be notified. This makes it possible to monitor children's emotional state when they use their mobile phones and quickly detect abnormalities.

[0092] The child monitoring system can further use an emotion estimation function to monitor the emotional state of those involved who receive the alert and encourage them to take an appropriate action. For example, the emotion estimation function can be used to monitor the emotional state of those involved who receive the alert. For example, if stress or anxiety is detected, an appropriate response can be encouraged. The emotional state of those involved can also be analyzed in real time when an alert is received and the priority of the response can be adjusted. For example, calm people can be given priority in responding. It is also possible to introduce an emotion estimation algorithm and build a system that monitors the emotional state of those involved who receive the alert. For example, a response method can be suggested depending on the emotional state. This makes it possible to monitor the emotional state of those involved and encourage them to take an appropriate action.

[0093] The child monitoring system can further use an emotion estimation function to analyze the emotional state of the person receiving the alert and provide advice to reduce stress. For example, the emotion estimation function can be used to analyze the emotional state of the person receiving the alert. For example, advice can be provided if stress or anxiety is detected. It is also possible to build a system that analyzes the emotional state of the person receiving the alert in real time and provides advice to reduce stress. For example, it can suggest relaxation methods. It is also possible to build a system that introduces an emotion estimation algorithm to analyze the emotional state of the person receiving the alert. For example, it can provide advice to reduce stress depending on the emotional state. This makes it possible to provide advice to reduce stress for the person receiving the alert.

[0094] The child monitoring system can further use an emotion estimation function to analyze the emotional state of those involved and evaluate the impact of emotional factors on accidents. For example, the emotion estimation function can be used to analyze the emotional state of those involved. For example, the impact of stress and anxiety on accidents can be evaluated. It is also possible to build a system that analyzes emotional states in real time and evaluates the impact of emotional factors on accidents. For example, it can evaluate whether the risk of an accident increases when the emotional state worsens. It is also possible to build a system that analyzes the emotional state of those involved by introducing an emotion estimation algorithm. For example, it can evaluate the risk of an accident based on the emotional state and suggest preventive measures. This makes it possible to evaluate the impact of emotional factors on accidents and suggest preventive measures.

[0095] The child monitoring system can further use emotion estimation functions to monitor the emotional state of emergency contacts and encourage appropriate responses. For example, the emotion estimation function can be used to monitor the emotional state of emergency contacts. For example, if stress or anxiety is detected, an appropriate response can be encouraged. The system can also analyze the emotional state of contacts in real time during an emergency and adjust the priority of responses. For example, calm contacts can be given priority in responding. It is also possible to introduce emotion estimation algorithms and build a system that monitors the emotional state of emergency contacts. For example, it can suggest a response method based on the emotional state. This makes it possible to monitor the emotional state of emergency contacts and encourage appropriate responses.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: AI cameras with motion sensors are installed on buses or in specific areas of the company to constantly monitor human movement and issue alerts if they detect any abnormalities. For example, AI cameras with motion sensors can use infrared sensors, deep learning, and ultrasonic sensors to detect human movement and recognize the presence of people. Step 2: The Mimori Kids Mobile is a device that kindergarteners and toddlers carry that constantly tracks their location. It is equipped with a GPS function and can track children's locations in real time. For example, the Mimori Kids Mobile obtains location information using GPS, Wi-Fi, and cellular communications. Step 3: The alert system uses data from AI cameras with motion sensors and mobile phones to send alerts to relevant parties if an abnormality is detected. For example, the alert system can send alerts via email, SMS, or app notification. This allows for quick alerts to be sent to drivers, childcare workers, parents, police, and fire departments.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0111] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0126] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

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

[0142] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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. AI camera with human sensor, A mobile phone for watching over kids, an alert system; The AI ​​camera with a human sensor is They are installed on buses and in specific areas within companies, constantly monitoring people's movements and issuing an alert if they detect any abnormalities. The aforementioned Mimamori Kids Mobile is This device is carried by kindergarteners and young children and allows them to keep track of their location at all times.It is equipped with a GPS function and can track children's locations in real time. The alert system comprises: If an abnormality is detected based on data from the AI ​​camera with motion sensor and the mobile phone for watching over kids, an alert will be sent to the relevant parties. A system characterized by:

2. The AI ​​camera with a human sensor is Based on video data, the system learns children's behavior patterns and predicts abnormal behavior.

2. The system of claim 1.

3. The AI ​​camera with a human sensor is Based on the video data, the health condition of the children is monitored and the abnormalities are detected.

2. The system of claim 1.

4. The AI ​​camera with a human sensor is Estimate emotions from the children's facial expressions and detect abnormal emotional states.

2. The system of claim 1.

5. The AI ​​camera with a human sensor is The system will be installed not only on the bus but also in kindergartens and homes, enabling widespread monitoring.

2. The system of claim 1.

Images