system

Abstract

An object of a system according to an embodiment is to effectively monitor a child's device usage status and activity on an SNS, detect an abnormality at an early stage, and notify a parent of the abnormality.SOLUTION: A system includes a device monitoring unit, an SNS monitoring unit, and a notification unit. The device monitoring unit monitors a device use state of the child. The SNS monitoring unit monitors an activity on the SNS based on the device usage status monitored by the device monitoring unit. The notification unit notifies the protector of the abnormality detected by the SNS monitoring unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the drawback of making it difficult to effectively monitor children's device usage and social media activity, detect abnormalities early, and notify parents.

[0005] The system according to the embodiment aims to effectively monitor children's device usage and activities on social media, detect abnormalities early, and notify parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a device monitoring unit, an SNS monitoring unit, and a notification unit. The device monitoring unit monitors a child's device usage. The SNS monitoring unit monitors activity on the SNS based on the device usage monitored by the device monitoring unit. The notification unit notifies a parent or guardian of any abnormality detected by the SNS monitoring unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively monitor children's device usage and activities on social media, detect abnormalities early, and notify parents. [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's mobile phone monitoring system according to an embodiment of the present invention is a system that monitors a child's device usage and activity on social media, and notifies parents of any detected abnormalities. This allows parents to keep track of their child's device usage and activity on social media, and to respond quickly if an abnormality is detected.

[0029] A child's mobile phone monitoring system according to an embodiment includes a device monitoring unit, a social networking service (SNS) monitoring unit, and a notification unit. The device monitoring unit monitors a child's device usage. For example, the device monitoring unit records a log of applications used by the child. The device monitoring unit can also notify parents when a child installs a new app. The device monitoring unit can also record the child's frequent use of a particular app. The SNS monitoring unit monitors social networking service (SNS) activity based on the device usage monitored by the device monitoring unit. For example, the SNS monitoring unit accesses the social networking service (SNS) platform used by the child (e.g., LINE, Instagram, etc.) and monitors posts and direct messages. The SNS monitoring unit can also detect keywords and phrases related to bullying and abuse, as well as inappropriate content, using machine learning and natural language processing techniques. The SNS monitoring unit can also notify parents of any abnormalities detected by the SNS monitoring unit. For example, if a child posts inappropriate content on an SNS, the notification unit notifies parents of the information in real time. The notification unit can also limit notifications to items that the parent or guardian wants to detect (e.g., keywords related to bullying). The notification unit can also customize the content of notifications so that parents can respond quickly. As a result, the children's mobile phone monitoring system according to the embodiment allows parents to keep track of their child's device usage and activities on social media, and to respond quickly if an abnormality is detected. For example, if a child installs a new app, parents can check whether the app is safe. Also, if a child is being bullied on social media, parents can quickly understand the information and take appropriate action. Furthermore, the notification system allows parents to receive only the information they need, thereby avoiding excessive monitoring.

[0030] The device monitoring unit can use the generation AI to analyze device usage patterns and detect abnormal usage patterns. For example, the device monitoring unit uses the generation AI to analyze a child's device usage patterns from daily logs and detect abnormal patterns that differ from normal usage patterns. For example, if the child frequently uses the device late at night, the abnormality is detected. The device monitoring unit also uses the generation AI to detect abnormal activity in real time based on the child's device usage history. For example, if the child suddenly starts using an app that is not normally used frequently, the abnormality is detected. The device monitoring unit also uses the generation AI to monitor a child's device usage patterns over a long period of time and detect abnormal usage patterns. For example, if a pattern of using a specific app only during certain hours suddenly changes, the abnormality is detected. This allows the device monitoring unit to detect abnormal device usage patterns and notify parents, thereby ensuring the safety of children.

[0031] The device monitoring unit can track the location information of the device in real time and record usage status in a specific location. For example, the device monitoring unit uses the GPS function of the device to track the location information of the child's device in real time and record usage status in a specific location. For example, it detects use in places other than school or home. The device monitoring unit also records what apps the child uses in a specific location based on the device location information. For example, it can understand usage status in a park or at a friend's house. The device monitoring unit also tracks the location information of the device in real time and analyzes usage patterns in a specific location. For example, it can identify apps that are frequently used in a specific location and record their usage status. This makes it possible to track the location information of the child's device and understand usage status in a specific location.

[0032] The device monitoring unit can be extended to other smart devices in the home to grasp overall usage status. For example, the device monitoring unit can extend the device monitoring function to a smart speaker to record activities when a child uses a voice assistant. For example, the device monitoring unit can analyze the content of voice commands. The device monitoring unit can also extend the device monitoring function to a smart TV to record content that a child is watching. For example, the device monitoring unit can analyze viewing history and viewing time. The device monitoring unit can also extend the device monitoring function to other smart devices in the home (e.g., smart lights and smart thermostats) to grasp overall usage status. For example, the device usage patterns can be analyzed. This makes it possible to grasp the usage status of other smart devices in the home.

[0033] The device monitoring unit can work with a school's learning management system to balance study time and entertainment time. For example, the device monitoring unit can work with a school's learning management system to record a child's device usage and build a system to balance study time and entertainment time. For example, it can record the time spent using study apps and compare it with the time spent using entertainment apps. The device monitoring unit can also work with the school's learning management system to monitor a child's device usage in real time. For example, it can limit device use during class to allow children to focus on their studies. The device monitoring unit can also integrate the device monitoring function with the learning management system to set alerts to balance a child's study time and entertainment time. For example, it can prompt a child to take a break if study time exceeds a certain time. This makes it possible to balance study time and entertainment time.

[0034] The SNS monitoring unit can use the generation AI to analyze friendships on SNS and detect contact with dangerous individuals. For example, the SNS monitoring unit uses the generation AI to analyze a child's friendships on SNS and detect contact with dangerous individuals. For example, it identifies contact with accounts that have caused problems in the past. The SNS monitoring unit also analyzes a child's friend list on SNS and builds a system to detect contact with dangerous individuals. For example, it identifies contact with suspicious accounts or spam accounts. The SNS monitoring unit also uses the generation AI to monitor a child's friendships on SNS in real time and detect contact with dangerous individuals. For example, it analyzes messages containing specific keywords or phrases. This allows it to detect contact with dangerous individuals on SNS and notify parents.

[0035] The SNS monitoring unit can extend its SNS monitoring function to other online communication platforms, such as in-game chats and forums. For example, the SNS monitoring unit extends the SNS monitoring function to in-game chats to monitor what kind of communication a child is having within a game. For example, it identifies bullying or inappropriate comments. The SNS monitoring unit also extends the SNS monitoring function to online communication platforms, such as forums and message boards, to monitor what kind of posts a child is making. For example, it identifies inappropriate content. The SNS monitoring unit also extends the SNS monitoring function to other online communication platforms to monitor what kind of messages a child is sending and receiving. For example, it analyzes messages containing specific keywords or phrases. This allows monitoring of other online communication platforms, such as in-game chats and forums.

[0036] The SNS monitoring unit can link content shared by children on SNS with a system that provides educational feedback. For example, the SNS monitoring unit analyzes content shared by children on SNS and links it with a system that provides educational feedback. For example, it provides appropriate advice or comments on the posted content. The SNS monitoring unit also links content shared on SNS with an educational feedback system and provides feedback on content posted by children. For example, it points out areas for improvement or points to be careful about in the posted content. The SNS monitoring unit also analyzes content shared by children on SNS in real time and builds a system that provides educational feedback. For example, it provides positive feedback on the posted content. This makes it possible to provide educational feedback on content shared on SNS.

[0037] The notification unit uses generation AI to automatically set notification priorities and prioritize sending notifications with high urgency. For example, the notification unit uses generation AI to analyze the content of detected events and build a system that automatically sets notification priorities. For example, messages containing keywords related to bullying or abuse are set as high priority. In addition, to automatically set notification priorities, the notification unit uses generation AI to learn past notification history and prioritize sending notifications with high urgency. For example, the priority is set based on notification patterns when serious problems occurred in the past. In addition, the notification unit uses generation AI to develop a system that evaluates the urgency of events detected in real time and immediately sends high-priority notifications to parents. For example, a notification is sent immediately if contact with a dangerous person is detected. This allows high-urgency notifications to be sent with priority.

[0038] The notification unit can customize the notification content based on the parent's past response history and suggest the optimal response method. The notification unit, for example, builds a system that customizes the notification content based on the parent's past response history and suggests the optimal response method. For example, the notification content is adjusted based on response methods that have been effective in the past. The notification unit also analyzes the parent's past response history and customizes the notification content to suggest the optimal response method. For example, if a specific response method has been successful in the past, that method is recommended. The notification unit also develops a system that customizes the notification content based on the parent's response history and suggests the optimal response method in real time. For example, a specific action plan is suggested based on the past response history. This makes it possible to customize the notification content based on the parent's past response history and suggest the optimal response method.

[0039] The notification unit can share the notification system with other parents and family members in the household and build a mechanism for cooperative response. The notification unit, for example, shares the notification system with other parents and family members in the household and builds a mechanism for cooperative response. For example, it sets up so that all family members can receive notifications. The notification unit also shares the notification system with other parents and family members in the household and adds an alert function for cooperative response. For example, it sends a notification to everyone when a specific event occurs. The notification unit also integrates communication tools for sharing the notification system with all family members and cooperative response. For example, it adds a chat function that allows information to be shared between family members in real time. This allows the notification system to be shared with other parents and family members in the household and cooperative response.

[0040] The notification unit can link the notification system with a school or local support network to enable prompt support to be received. For example, the notification unit builds a system that links the notification system with a school or local support network to enable prompt support to be received. For example, it sends notifications to school counselors or local support organizations. The notification unit also links the notification system with a school or local support network to enable prompt support to be received in an emergency. For example, it immediately requests support when bullying or abuse is detected. The notification unit also integrates the notification system with a school or local support network to enable parents to receive prompt support. For example, it adds a function to automatically send support requests. This allows cooperation with a school or local support network to enable prompt support to be received.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The device monitoring unit monitors a child's device usage. For example, the device monitoring unit records a log of the applications the child uses. The device monitoring unit can also notify parents if the child installs a new app. The device monitoring unit can also record the child's frequent use of a particular app. The social media monitoring unit monitors social media activity based on the device usage monitored by the device monitoring unit. For example, the social media monitoring unit accesses the social media platforms (e.g., LINE and Instagram) used by the child and monitors posts and direct messages. The social media monitoring unit can also use machine learning and natural language processing technologies to detect keywords and phrases related to bullying and abuse, as well as inappropriate content. The social media monitoring unit can also notify parents if a child uses keywords such as "bullying" or "help" on social media. The notification unit notifies parents of any abnormalities detected by the social media monitoring unit. For example, if a child posts inappropriate content on social media, the notification unit notifies parents in real time. The notification unit can also limit notifications to specific items of interest (e.g., keywords related to bullying). The notification unit can also customize the content of notifications so that parents can respond quickly. As a result, the child's mobile phone monitoring system according to the embodiment allows parents to monitor their child's device usage and social networking site activities and respond quickly if an abnormality is detected. For example, if a child installs a new app, parents can check whether the app is safe. Furthermore, if a child is being bullied on social networking sites, parents can quickly understand the information and take appropriate action. Furthermore, the notification system allows parents to receive only the information they need, thereby avoiding excessive monitoring.

[0043] The device monitoring unit can use the generation AI to analyze device usage patterns and detect abnormal usage patterns. For example, the generation AI can analyze a child's device usage patterns from daily logs to detect abnormal patterns that differ from normal usage patterns. For example, if the child frequently uses the device late at night, the abnormality can be detected. The device monitoring unit can also use the generation AI to detect abnormal activity in real time based on the child's device usage history. For example, if the child suddenly starts using an app that is not normally used frequently, the abnormality can be detected. The device monitoring unit can also use the generation AI to monitor a child's device usage patterns over a long period of time to detect abnormal usage patterns. For example, if a child suddenly changes from using a specific app only during certain hours, the abnormality can be detected. This allows the system to detect abnormal device usage patterns and notify parents, ensuring the safety of children.

[0044] The device monitoring unit can track the device's location information in real time and record usage in specific locations. For example, it uses the device's GPS function to track the location information of a child's device in real time and record usage in specific locations. For example, it detects use outside of school or home. The device monitoring unit also records what apps a child uses in specific locations based on the device's location information. For example, it can understand usage in a park or at a friend's house. The device monitoring unit also tracks the device's location information in real time and analyzes usage patterns in specific locations. For example, it can identify apps that are frequently used in specific locations and record their usage. This makes it possible to track the location information of a child's device and understand usage in specific locations.

[0045] The device monitoring unit can be extended to other smart devices in the home to understand overall usage. For example, the device monitoring function can be extended to smart speakers to record activities when children use voice assistants. For example, the content of voice commands can be analyzed. The device monitoring unit can also be extended to smart TVs to record content that children are watching. For example, the viewing history and viewing time can be analyzed. The device monitoring unit can also be extended to other smart devices in the home (e.g., smart lights and smart thermostats) to understand overall usage. For example, the device usage patterns can be analyzed. This allows the usage of other smart devices in the home to be understood.

[0046] The device monitoring unit can work with a school's learning management system to balance study time and entertainment time. For example, a child's device usage can be linked with the school's learning management system to build a system that balances study time and entertainment time. For example, the time spent using learning apps can be recorded and compared with the time spent using entertainment apps. The device monitoring unit can also work with the school's learning management system to monitor children's device usage in real time. For example, it can limit device use during class to allow children to focus on their studies. The device monitoring unit can also integrate the learning management system with its device monitoring function to set alerts to balance study time and entertainment time. For example, it can prompt children to take a break if study time exceeds a certain amount. This makes it possible to balance study time and entertainment time.

[0047] The SNS monitoring unit can use generative AI to analyze friendships on SNS and detect contact with dangerous individuals. For example, generative AI can be used to analyze a child's friendships on SNS and detect contact with dangerous individuals. For example, it can identify contact with accounts that have caused problems in the past. The SNS monitoring unit can also analyze a child's friend list on SNS and build a system to detect contact with dangerous individuals. For example, it can identify contact with suspicious accounts or spam accounts. The SNS monitoring unit can also use generative AI to monitor a child's friendships on SNS in real time and detect contact with dangerous individuals. For example, it can analyze messages containing specific keywords or phrases. This can detect contact with dangerous individuals on SNS and notify parents.

[0048] The SNS monitoring unit can extend its SNS monitoring function to other online communication platforms, such as in-game chats and forums. For example, the SNS monitoring function can be extended to in-game chats to monitor what kind of communication a child is having within a game. For example, it can identify bullying or inappropriate comments. The SNS monitoring unit can also extend its SNS monitoring function to online communication platforms, such as forums and message boards, to monitor what kind of posts a child is making. For example, it can identify inappropriate content. The SNS monitoring unit can also extend its SNS monitoring function to other online communication platforms to monitor what kind of messages a child is sending and receiving. For example, it can analyze messages containing specific keywords or phrases. This allows it to monitor other online communication platforms, such as in-game chats and forums.

[0049] The SNS monitoring unit can link content shared by children on SNS with a system that provides educational feedback. For example, the SNS monitoring unit can analyze content shared by children on SNS and link it with a system that provides educational feedback. For example, it can provide appropriate advice or comments on the posted content. The SNS monitoring unit can also link content shared on SNS with an educational feedback system and provide feedback on content posted by children. For example, it can point out areas for improvement or points to be careful about in the posted content. The SNS monitoring unit can also analyze content shared by children on SNS in real time and build a system that provides educational feedback. For example, it can provide positive feedback on the posted content. This makes it possible to provide educational feedback on content shared on SNS.

[0050] The notification unit uses generation AI to automatically set notification priorities and prioritize sending notifications with higher urgency. For example, a system is built using generation AI to analyze the content of detected events and automatically set notification priorities. For example, messages containing keywords related to bullying or abuse are set as high priority. In addition, to automatically set notification priorities, the notification unit uses generation AI to learn past notification history and prioritize sending notifications with higher urgency. For example, the priority is set based on notification patterns when serious problems occurred in the past. The notification unit also uses generation AI to develop a system that evaluates the urgency of events detected in real time and immediately sends high-priority notifications to parents. For example, a notification is sent immediately if contact with a dangerous person is detected. This allows high-urgency notifications to be sent with priority.

[0051] The notification unit can customize the notification content based on the parent's past response history and suggest the optimal response method. For example, a system is constructed that customizes the notification content based on the parent's past response history and suggests the optimal response method. For example, the notification content is adjusted based on response methods that have been effective in the past. The notification unit also analyzes the parent's past response history and customizes the notification content to suggest the optimal response method. For example, if a specific response method has been successful in the past, that method is recommended. The notification unit also develops a system that customizes the notification content based on the parent's response history and suggests the optimal response method in real time. For example, a specific action plan is suggested based on the past response history. This makes it possible to customize the notification content based on the parent's past response history and suggest the optimal response method.

[0052] The notification unit can share the notification system with other parents and family members in the household and build a mechanism for cooperative response. For example, the notification system can be shared with other parents and family members in the household to build a mechanism for cooperative response. For example, it can be set up so that all family members can receive notifications. The notification unit can also share the notification system with other parents and family members in the household and add an alert function for cooperative response. For example, it can send a notification to everyone when a specific event occurs. The notification unit can also share the notification system with all family members and integrate communication tools for cooperative response. For example, it can add a chat function that allows family members to share information in real time. This allows the notification system to be shared with other parents and family members in the household and cooperative response.

[0053] The notification unit can link the notification system with schools and local support networks to enable prompt support to be received. For example, a system can be built that links the notification system with schools and local support networks to enable prompt support to be received. For example, notifications can be sent to school counselors and local support organizations. The notification unit can also link the notification system with schools and local support networks to enable prompt support to be received in emergencies. For example, support can be requested immediately if bullying or abuse is detected. The notification unit can also integrate the notification system with schools and local support networks to enable parents to receive prompt support. For example, a function can be added to automatically send requests for support. This allows for linkage with schools and local support networks to enable prompt support to be received.

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

[0055] Step 1: The device monitoring section monitors the child's device usage. For example, it records the application logs the child is using and notifies the parent if a new app is installed. It also records the usage of a particular app if the child uses it frequently. Step 2: The social media monitoring unit monitors social media activity based on the device usage monitored by the device monitoring unit. For example, it accesses the social media platforms used by children and monitors their posts and direct messages. It can also use machine learning and natural language processing techniques to detect keywords and phrases related to bullying and abuse, as well as inappropriate content. Step 3: The notification unit notifies parents of any abnormalities detected by the SNS monitoring unit. For example, if a child posts inappropriate content on a social networking site, the notification unit notifies parents in real time. It is also possible to limit notifications to specific items that you want to detect, and to customize the content of notifications.

[0056] (Example 2) The child's mobile phone monitoring system according to an embodiment of the present invention is a system that monitors a child's device usage and activity on social media, and notifies parents of any detected abnormalities. This allows parents to keep track of their child's device usage and activity on social media, and to respond quickly if an abnormality is detected.

[0057] A child's mobile phone monitoring system according to an embodiment includes a device monitoring unit, a social networking service (SNS) monitoring unit, and a notification unit. The device monitoring unit monitors a child's device usage. For example, the device monitoring unit records a log of applications used by the child. The device monitoring unit can also notify parents when a child installs a new app. The device monitoring unit can also record the child's frequent use of a particular app. The SNS monitoring unit monitors social networking service (SNS) activity based on the device usage monitored by the device monitoring unit. For example, the SNS monitoring unit accesses the social networking service (SNS) platform used by the child (e.g., LINE, Instagram, etc.) and monitors posts and direct messages. The SNS monitoring unit can also detect keywords and phrases related to bullying and abuse, as well as inappropriate content, using machine learning and natural language processing techniques. The SNS monitoring unit can also notify parents of any abnormalities detected by the SNS monitoring unit. For example, if a child posts inappropriate content on an SNS, the notification unit notifies parents of the information in real time. The notification unit can also limit notifications to items that the parent or guardian wants to detect (e.g., keywords related to bullying). The notification unit can also customize the content of notifications so that parents can respond quickly. As a result, the children's mobile phone monitoring system according to the embodiment allows parents to keep track of their child's device usage and activities on social media, and to respond quickly if an abnormality is detected. For example, if a child installs a new app, parents can check whether the app is safe. Also, if a child is being bullied on social media, parents can quickly understand the information and take appropriate action. Furthermore, the notification system allows parents to receive only the information they need, thereby avoiding excessive monitoring.

[0058] The device monitoring unit can use the generation AI to analyze device usage patterns and detect abnormal usage patterns. For example, the device monitoring unit uses the generation AI to analyze a child's device usage patterns from daily logs and detect abnormal patterns that differ from normal usage patterns. For example, if the child frequently uses the device late at night, the abnormality is detected. The device monitoring unit also uses the generation AI to detect abnormal activity in real time based on the child's device usage history. For example, if the child suddenly starts using an app that is not normally used frequently, the abnormality is detected. The device monitoring unit also uses the generation AI to monitor a child's device usage patterns over a long period of time and detect abnormal usage patterns. For example, if a pattern of using a specific app only during certain hours suddenly changes, the abnormality is detected. This allows the device monitoring unit to detect abnormal device usage patterns and notify parents, thereby ensuring the safety of children.

[0059] The device monitoring unit can track the location information of the device in real time and record usage status in a specific location. For example, the device monitoring unit uses the GPS function of the device to track the location information of the child's device in real time and record usage status in a specific location. For example, it detects use in places other than school or home. The device monitoring unit also records what apps the child uses in a specific location based on the device location information. For example, it can understand usage status in a park or at a friend's house. The device monitoring unit also tracks the location information of the device in real time and analyzes usage patterns in a specific location. For example, it can identify apps that are frequently used in a specific location and record their usage status. This makes it possible to track the location information of the child's device and understand usage status in a specific location.

[0060] The device monitoring unit can use the emotion estimation function to estimate the child's emotion while using the device and notify the parent if the child is feeling stressed or anxious. For example, the device monitoring unit uses the emotion estimation function to analyze the child's facial expression and voice while using the device and notify the parent if the child is feeling stressed or anxious. For example, facial expression is analyzed using facial recognition technology. The device monitoring unit also uses the device's camera and microphone to estimate the child's emotional state in real time and notify the parent if the child is feeling stressed or anxious. For example, the device monitoring unit analyzes the tone of voice and speaking style. The device monitoring unit also uses the emotion estimation function to estimate the child's emotion while using a specific app and notify the parent if the child is feeling stressed or anxious. For example, the device monitoring unit analyzes the child's emotion while using a game or social networking site. This allows the device monitoring unit to understand the child's emotion while using the device and notify the parent if the child is feeling stressed or anxious.

[0061] The device monitoring unit can be extended to other smart devices in the home to grasp overall usage status. For example, the device monitoring unit can extend the device monitoring function to a smart speaker to record activities when a child uses a voice assistant. For example, the device monitoring unit can analyze the content of voice commands. The device monitoring unit can also extend the device monitoring function to a smart TV to record content that a child is watching. For example, the device monitoring unit can analyze viewing history and viewing time. The device monitoring unit can also extend the device monitoring function to other smart devices in the home (e.g., smart lights and smart thermostats) to grasp overall usage status. For example, the device usage patterns can be analyzed. This makes it possible to grasp the usage status of other smart devices in the home.

[0062] The device monitoring unit can work with a school's learning management system to balance study time and entertainment time. For example, the device monitoring unit can work with a school's learning management system to record a child's device usage and build a system to balance study time and entertainment time. For example, it can record the time spent using study apps and compare it with the time spent using entertainment apps. The device monitoring unit can also work with the school's learning management system to monitor a child's device usage in real time. For example, it can limit device use during class to allow children to focus on their studies. The device monitoring unit can also integrate the device monitoring function with the learning management system to set alerts to balance a child's study time and entertainment time. For example, it can prompt a child to take a break if study time exceeds a certain time. This makes it possible to balance study time and entertainment time.

[0063] The device monitoring unit can use the emotion estimation function to analyze the emotions a child feels when using a specific app and recommend the use of apps that elicit positive emotions. For example, the device monitoring unit can use the emotion estimation function to analyze the emotions a child feels when using a specific app in real time and recommend the use of apps that elicit positive emotions. For example, it can recommend educational apps or relaxation apps. The device monitoring unit can also analyze the child's emotional state and build a system that recommends the use of apps that elicit positive emotions. For example, it can preferentially display apps with high emotion scores. The device monitoring unit can also identify apps that make a child feel positive emotions based on the emotion estimation data and recommend the use of those apps. For example, it can restrict the use of games and entertainment apps and recommend educational apps. This makes it possible to recommend the use of apps that elicit positive emotions.

[0064] The SNS monitoring unit can use the generation AI to analyze friendships on SNS and detect contact with dangerous individuals. For example, the SNS monitoring unit uses the generation AI to analyze a child's friendships on SNS and detect contact with dangerous individuals. For example, it identifies contact with accounts that have caused problems in the past. The SNS monitoring unit also analyzes a child's friend list on SNS and builds a system to detect contact with dangerous individuals. For example, it identifies contact with suspicious accounts or spam accounts. The SNS monitoring unit also uses the generation AI to monitor a child's friendships on SNS in real time and detect contact with dangerous individuals. For example, it analyzes messages containing specific keywords or phrases. This allows it to detect contact with dangerous individuals on SNS and notify parents.

[0065] The SNS monitoring unit can use the emotion estimation function to evaluate the emotional impact of messages received by a child on SNS and notify parents if the message has a negative impact. For example, the SNS monitoring unit uses the emotion estimation function to evaluate the emotional impact of messages received by a child on SNS in real time and notify parents if the message has a negative impact. For example, it identifies messages that are bullying or harassing. The SNS monitoring unit also analyzes the emotional impact of messages received by a child on SNS and builds a system that notifies parents if the message has a negative impact. For example, it identifies messages with a low emotion score. The SNS monitoring unit also evaluates the emotional impact of messages received by a child on SNS based on the emotion estimation data and notifies parents if the message has a negative impact. For example, it identifies messages that cause stress or anxiety. This makes it possible to detect the negative impact of messages received on SNS and notify parents.

[0066] The SNS monitoring unit can extend its SNS monitoring function to other online communication platforms, such as in-game chats and forums. For example, the SNS monitoring unit extends the SNS monitoring function to in-game chats to monitor what kind of communication a child is having within a game. For example, it identifies bullying or inappropriate comments. The SNS monitoring unit also extends the SNS monitoring function to online communication platforms, such as forums and message boards, to monitor what kind of posts a child is making. For example, it identifies inappropriate content. The SNS monitoring unit also extends the SNS monitoring function to other online communication platforms to monitor what kind of messages a child is sending and receiving. For example, it analyzes messages containing specific keywords or phrases. This allows monitoring of other online communication platforms, such as in-game chats and forums.

[0067] The SNS monitoring unit can link content shared by children on SNS with a system that provides educational feedback. For example, the SNS monitoring unit analyzes content shared by children on SNS and links it with a system that provides educational feedback. For example, it provides appropriate advice or comments on the posted content. The SNS monitoring unit also links content shared on SNS with an educational feedback system and provides feedback on content posted by children. For example, it points out areas for improvement or points to be careful about in the posted content. The SNS monitoring unit also analyzes content shared by children on SNS in real time and builds a system that provides educational feedback. For example, it provides positive feedback on the posted content. This makes it possible to provide educational feedback on content shared on SNS.

[0068] The SNS monitoring unit can use the emotion estimation function to identify content on SNS that evokes positive emotions in children and promote the sharing of such content. For example, the SNS monitoring unit uses the emotion estimation function to identify content on SNS that evokes positive emotions in children and build a system that promotes the sharing of such content. For example, posts with high emotion scores are preferentially displayed. The SNS monitoring unit also analyzes content on SNS that evokes positive emotions in children and promotes the sharing of such content. For example, posts that have received positive feedback are identified. The SNS monitoring unit also develops a system that identifies content on SNS that evokes positive emotions in children based on the emotion estimation data and promotes the sharing of that content. For example, posts that evoke positive emotions are recommended. This promotes the sharing of content that evokes positive emotions.

[0069] The notification unit uses generation AI to automatically set notification priorities and prioritize sending notifications with high urgency. For example, the notification unit uses generation AI to analyze the content of detected events and build a system that automatically sets notification priorities. For example, messages containing keywords related to bullying or abuse are set as high priority. In addition, to automatically set notification priorities, the notification unit uses generation AI to learn past notification history and prioritize sending notifications with high urgency. For example, the priority is set based on notification patterns when serious problems occurred in the past. In addition, the notification unit uses generation AI to develop a system that evaluates the urgency of events detected in real time and immediately sends high-priority notifications to parents. For example, a notification is sent immediately if contact with a dangerous person is detected. This allows high-urgency notifications to be sent with priority.

[0070] The notification unit can customize the notification content based on the parent's past response history and suggest the optimal response method. The notification unit, for example, builds a system that customizes the notification content based on the parent's past response history and suggests the optimal response method. For example, the notification content is adjusted based on response methods that have been effective in the past. The notification unit also analyzes the parent's past response history and customizes the notification content to suggest the optimal response method. For example, if a specific response method has been successful in the past, that method is recommended. The notification unit also develops a system that customizes the notification content based on the parent's response history and suggests the optimal response method in real time. For example, a specific action plan is suggested based on the past response history. This makes it possible to customize the notification content based on the parent's past response history and suggest the optimal response method.

[0071] The notification unit can use the emotion estimation function to analyze the emotions felt by the guardian when receiving the notification and provide advice to reduce stress. The notification unit, for example, uses the emotion estimation function to build a system that analyzes the emotions felt by the guardian when receiving the notification in real time and provides advice to reduce stress. For example, it suggests relaxation methods or counseling. The notification unit also analyzes the emotions felt by the guardian when receiving the notification and provides specific advice to reduce stress. For example, it recommends relaxing music if the emotion score is high. The notification unit also analyzes the emotions felt by the guardian when receiving the notification based on the emotion estimation data and develops a system that provides advice to reduce stress in real time. For example, it suggests stress management techniques. This makes it possible to provide advice to reduce stress for the guardian.

[0072] The notification unit can share the notification system with other parents and family members in the household and build a mechanism for cooperative response. The notification unit, for example, shares the notification system with other parents and family members in the household and builds a mechanism for cooperative response. For example, it sets up so that all family members can receive notifications. The notification unit also shares the notification system with other parents and family members in the household and adds an alert function for cooperative response. For example, it sends a notification to everyone when a specific event occurs. The notification unit also integrates communication tools for sharing the notification system with all family members and cooperative response. For example, it adds a chat function that allows information to be shared between family members in real time. This allows the notification system to be shared with other parents and family members in the household and cooperative response.

[0073] The notification unit can link the notification system with a school or local support network to enable prompt support to be received. For example, the notification unit builds a system that links the notification system with a school or local support network to enable prompt support to be received. For example, it sends notifications to school counselors or local support organizations. The notification unit also links the notification system with a school or local support network to enable prompt support to be received in an emergency. For example, it immediately requests support when bullying or abuse is detected. The notification unit also integrates the notification system with a school or local support network to enable parents to receive prompt support. For example, it adds a function to automatically send support requests. This allows cooperation with a school or local support network to enable prompt support to be received.

[0074] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0075] The device monitoring unit monitors a child's device usage. For example, the device monitoring unit records a log of the applications the child uses. The device monitoring unit can also notify parents if the child installs a new app. The device monitoring unit can also record the child's frequent use of a particular app. The social media monitoring unit monitors social media activity based on the device usage monitored by the device monitoring unit. For example, the social media monitoring unit accesses the social media platforms (e.g., LINE and Instagram) used by the child and monitors posts and direct messages. The social media monitoring unit can also use machine learning and natural language processing technologies to detect keywords and phrases related to bullying and abuse, as well as inappropriate content. The social media monitoring unit can also notify parents if a child uses keywords such as "bullying" or "help" on social media. The notification unit notifies parents of any abnormalities detected by the social media monitoring unit. For example, if a child posts inappropriate content on social media, the notification unit notifies parents in real time. The notification unit can also limit notifications to specific items of interest (e.g., keywords related to bullying). The notification unit can also customize the content of notifications so that parents can respond quickly. As a result, the child's mobile phone monitoring system according to the embodiment allows parents to monitor their child's device usage and social networking site activities and respond quickly if an abnormality is detected. For example, if a child installs a new app, parents can check whether the app is safe. Furthermore, if a child is being bullied on social networking sites, parents can quickly understand the information and take appropriate action. Furthermore, the notification system allows parents to receive only the information they need, thereby avoiding excessive monitoring.

[0076] The device monitoring unit can use the generation AI to analyze device usage patterns and detect abnormal usage patterns. For example, the generation AI can analyze a child's device usage patterns from daily logs to detect abnormal patterns that differ from normal usage patterns. For example, if the child frequently uses the device late at night, the abnormality can be detected. The device monitoring unit can also use the generation AI to detect abnormal activity in real time based on the child's device usage history. For example, if the child suddenly starts using an app that is not normally used frequently, the abnormality can be detected. The device monitoring unit can also use the generation AI to monitor a child's device usage patterns over a long period of time to detect abnormal usage patterns. For example, if a child suddenly changes from using a specific app only during certain hours, the abnormality can be detected. This allows the system to detect abnormal device usage patterns and notify parents, ensuring the safety of children.

[0077] The device monitoring unit can track the device's location information in real time and record usage in specific locations. For example, it uses the device's GPS function to track the location information of a child's device in real time and record usage in specific locations. For example, it detects use outside of school or home. The device monitoring unit also records what apps a child uses in specific locations based on the device's location information. For example, it can understand usage in a park or at a friend's house. The device monitoring unit also tracks the device's location information in real time and analyzes usage patterns in specific locations. For example, it can identify apps that are frequently used in specific locations and record their usage. This makes it possible to track the location information of a child's device and understand usage in specific locations.

[0078] The device monitoring unit can use the emotion estimation function to estimate the child's emotion while using the device and notify the parent if the child is feeling stressed or anxious. For example, the emotion estimation function can be used to analyze the child's facial expressions and voice while using the device, and notify the parent if the child is feeling stressed or anxious. For example, facial expressions can be analyzed using facial recognition technology. The device monitoring unit can also estimate the child's emotional state in real time using the device's camera and microphone, and notify the parent if the child is feeling stressed or anxious. For example, the device monitoring unit can analyze the tone of voice and speaking style. The device monitoring unit can also use the emotion estimation function to estimate the child's emotion while using a specific app, and notify the parent if the child is feeling stressed or anxious. For example, the device monitoring unit can analyze the child's emotion while using a game or social networking site. This allows the device to understand the child's emotion while using the device and notify the parent if the child is feeling stressed or anxious.

[0079] The device monitoring unit can be extended to other smart devices in the home to understand overall usage. For example, the device monitoring function can be extended to smart speakers to record activities when children use voice assistants. For example, the content of voice commands can be analyzed. The device monitoring unit can also be extended to smart TVs to record content that children are watching. For example, the viewing history and viewing time can be analyzed. The device monitoring unit can also be extended to other smart devices in the home (e.g., smart lights and smart thermostats) to understand overall usage. For example, the device usage patterns can be analyzed. This allows the usage of other smart devices in the home to be understood.

[0080] The device monitoring unit can work with a school's learning management system to balance study time and entertainment time. For example, a child's device usage can be linked with the school's learning management system to build a system that balances study time and entertainment time. For example, the time spent using learning apps can be recorded and compared with the time spent using entertainment apps. The device monitoring unit can also work with the school's learning management system to monitor children's device usage in real time. For example, it can limit device use during class to allow children to focus on their studies. The device monitoring unit can also integrate the learning management system with its device monitoring function to set alerts to balance study time and entertainment time. For example, it can prompt children to take a break if study time exceeds a certain amount. This makes it possible to balance study time and entertainment time.

[0081] The device monitoring unit can use the emotion estimation function to analyze the emotions a child feels when using a specific app and recommend the use of apps that elicit positive emotions. For example, the emotion estimation function can be used to analyze the emotions a child feels when using a specific app in real time and recommend the use of apps that elicit positive emotions. For example, educational apps and relaxation apps can be recommended. The device monitoring unit can also build a system that analyzes the child's emotional state and recommends the use of apps that elicit positive emotions. For example, apps with high emotion scores can be preferentially displayed. The device monitoring unit can also identify apps that make a child feel positive emotions based on the emotion estimation data and recommend the use of those apps. For example, the device monitoring unit can limit the use of games and entertainment apps and recommend educational apps. This makes it possible to recommend the use of apps that elicit positive emotions.

[0082] The SNS monitoring unit can use generative AI to analyze friendships on SNS and detect contact with dangerous individuals. For example, generative AI can be used to analyze a child's friendships on SNS and detect contact with dangerous individuals. For example, it can identify contact with accounts that have caused problems in the past. The SNS monitoring unit can also analyze a child's friend list on SNS and build a system to detect contact with dangerous individuals. For example, it can identify contact with suspicious accounts or spam accounts. The SNS monitoring unit can also use generative AI to monitor a child's friendships on SNS in real time and detect contact with dangerous individuals. For example, it can analyze messages containing specific keywords or phrases. This can detect contact with dangerous individuals on SNS and notify parents.

[0083] The SNS monitoring unit can use the emotion estimation function to evaluate the emotional impact of messages received by a child on SNS and notify parents if the message has a negative impact. For example, the emotion estimation function can be used to evaluate the emotional impact of messages received by a child on SNS in real time and notify parents if the message has a negative impact. For example, it can identify messages that bully or harass. The SNS monitoring unit can also analyze the emotional impact of messages received by a child on SNS and build a system that notifies parents if the message has a negative impact. For example, it can identify messages with low emotion scores. The SNS monitoring unit can also evaluate the emotional impact of messages received by a child on SNS based on the emotion estimation data and notify parents if the message has a negative impact. For example, it can identify messages that cause stress or anxiety. This makes it possible to detect the negative impact of messages received on SNS and notify parents.

[0084] The SNS monitoring unit can extend its SNS monitoring function to other online communication platforms, such as in-game chats and forums. For example, the SNS monitoring function can be extended to in-game chats to monitor what kind of communication a child is having within a game. For example, it can identify bullying or inappropriate comments. The SNS monitoring unit can also extend its SNS monitoring function to online communication platforms, such as forums and message boards, to monitor what kind of posts a child is making. For example, it can identify inappropriate content. The SNS monitoring unit can also extend its SNS monitoring function to other online communication platforms to monitor what kind of messages a child is sending and receiving. For example, it can analyze messages containing specific keywords or phrases. This allows it to monitor other online communication platforms, such as in-game chats and forums.

[0085] The SNS monitoring unit can link content shared by children on SNS with a system that provides educational feedback. For example, the SNS monitoring unit can analyze content shared by children on SNS and link it with a system that provides educational feedback. For example, it can provide appropriate advice or comments on the posted content. The SNS monitoring unit can also link content shared on SNS with an educational feedback system and provide feedback on content posted by children. For example, it can point out areas for improvement or points to be careful about in the posted content. The SNS monitoring unit can also analyze content shared by children on SNS in real time and build a system that provides educational feedback. For example, it can provide positive feedback on the posted content. This makes it possible to provide educational feedback on content shared on SNS.

[0086] The SNS monitoring unit can use the emotion estimation function to identify content on SNS that evokes positive emotions among children and promote the sharing of such content. For example, a system can be constructed that uses the emotion estimation function to identify content on SNS that evokes positive emotions among children and promote the sharing of such content. For example, posts with high emotion scores can be preferentially displayed. The SNS monitoring unit can also analyze content on SNS that evokes positive emotions among children and promote the sharing of such content. For example, posts that have received positive feedback can be identified. The SNS monitoring unit can also use the emotion estimation data to identify content on SNS that evokes positive emotions among children and promote the sharing of such content. For example, posts that evoke positive emotions can be recommended. This can promote the sharing of content that evokes positive emotions.

[0087] The notification unit uses generation AI to automatically set notification priorities and prioritize sending notifications with higher urgency. For example, a system is built using generation AI to analyze the content of detected events and automatically set notification priorities. For example, messages containing keywords related to bullying or abuse are set as high priority. In addition, to automatically set notification priorities, the notification unit uses generation AI to learn past notification history and prioritize sending notifications with higher urgency. For example, the priority is set based on notification patterns when serious problems occurred in the past. The notification unit also uses generation AI to develop a system that evaluates the urgency of events detected in real time and immediately sends high-priority notifications to parents. For example, a notification is sent immediately if contact with a dangerous person is detected. This allows high-urgency notifications to be sent with priority.

[0088] The notification unit can customize the notification content based on the parent's past response history and suggest the optimal response method. For example, a system is constructed that customizes the notification content based on the parent's past response history and suggests the optimal response method. For example, the notification content is adjusted based on response methods that have been effective in the past. The notification unit also analyzes the parent's past response history and customizes the notification content to suggest the optimal response method. For example, if a specific response method has been successful in the past, that method is recommended. The notification unit also develops a system that customizes the notification content based on the parent's response history and suggests the optimal response method in real time. For example, a specific action plan is suggested based on the past response history. This makes it possible to customize the notification content based on the parent's past response history and suggest the optimal response method.

[0089] The notification unit can use the emotion estimation function to analyze the emotions felt by parents when they receive a notification and provide advice to reduce stress. For example, a system can be constructed that uses the emotion estimation function to analyze the emotions felt by parents when they receive a notification in real time and provide advice to reduce stress. For example, the system can suggest relaxation methods or counseling. The notification unit can also analyze the emotions felt by parents when they receive a notification and provide specific advice to reduce stress. For example, it can recommend relaxing music if the emotion score is high. The notification unit can also analyze the emotions felt by parents when they receive a notification based on the emotion estimation data and develop a system that provides advice to reduce stress in real time. For example, it can suggest stress management techniques. This can provide advice to reduce stress for parents.

[0090] The notification unit can share the notification system with other parents and family members in the household and build a mechanism for cooperative response. For example, the notification system can be shared with other parents and family members in the household to build a mechanism for cooperative response. For example, it can be set up so that all family members can receive notifications. The notification unit can also share the notification system with other parents and family members in the household and add an alert function for cooperative response. For example, it can send a notification to everyone when a specific event occurs. The notification unit can also share the notification system with all family members and integrate communication tools for cooperative response. For example, it can add a chat function that allows family members to share information in real time. This allows the notification system to be shared with other parents and family members in the household and cooperative response.

[0091] The notification unit can link the notification system with schools and local support networks to enable prompt support to be received. For example, a system can be built that links the notification system with schools and local support networks to enable prompt support to be received. For example, notifications can be sent to school counselors and local support organizations. The notification unit can also link the notification system with schools and local support networks to enable prompt support to be received in emergencies. For example, support can be requested immediately if bullying or abuse is detected. The notification unit can also integrate the notification system with schools and local support networks to enable parents to receive prompt support. For example, a function can be added to automatically send requests for support. This allows for linkage with schools and local support networks to enable prompt support to be received.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The device monitoring section monitors the child's device usage. For example, it records the application logs the child is using and notifies the parent if a new app is installed. It also records the usage of a particular app if the child uses it frequently. Step 2: The social media monitoring unit monitors social media activity based on the device usage monitored by the device monitoring unit. For example, it accesses the social media platforms used by children and monitors their posts and direct messages. It can also use machine learning and natural language processing techniques to detect keywords and phrases related to bullying and abuse, as well as inappropriate content. Step 3: The notification unit notifies parents of any abnormalities detected by the SNS monitoring unit. For example, if a child posts inappropriate content on a social networking site, the notification unit notifies parents in real time. It is also possible to limit notifications to specific items that you want to detect, and to customize the content of notifications.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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).

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a device monitoring unit that monitors children's device usage; an SNS monitoring unit that monitors activities on an SNS based on the device usage status monitored by the device monitoring unit; a notification unit that notifies a guardian of an abnormality detected by the SNS monitoring unit. A system characterized by:

2. The device monitoring unit Uses generative AI to analyze device usage patterns and detect anomalous usage patterns 2. The system of claim 1.

3. The device monitoring unit Track device location in real time and record usage in specific locations 2. The system of claim 1.

4. The device monitoring unit Estimates a child's emotions while using the device and notifies the parent if the child is experiencing stress or anxiety 2. The system of claim 1.

5. The device monitoring unit Expand to other smart devices in your home to understand overall usage 2. The system of claim 1.

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