System

Abstract

An object of a system according to an embodiment is to improve safety in use of a smartphone by a child and to enable a parent to give the smartphone to the child without anxiety.SOLUTION: A system according to an embodiment includes a screen capture unit, an image analysis unit, and a notification generation unit. The screen capture unit periodically and automatically captures the screen of the smartphone. The image analysis unit analyzes the image captured by the screen capture unit. The notification generation unit gives a notification to the protector on the basis of a result analyzed by the image analysis 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] With conventional technology, it is difficult to completely prevent children from encountering problems on harmful websites or social networking sites when using smartphones, and there is room for improvement to alleviate parents' concerns.

[0005] The system according to the embodiment aims to improve the safety of children's smartphone use and enable parents to give smartphones to their children with peace of mind. [Means for solving the problem]

[0006] The system according to the embodiment includes a screen capture unit, an image analysis unit, and a notification generation unit. The screen capture unit automatically captures the smartphone screen periodically. The image analysis unit analyzes the image captured by the screen capture unit. The notification generation unit notifies the guardian based on the results of the analysis by the image analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment improves safety when children use smartphones, allowing parents to give smartphones to their children with peace of mind. [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 monitoring AI system according to an embodiment of the present invention automatically captures smartphone screens on a regular basis, and the generation AI analyzes the images and notifies parents by text. This allows the monitoring AI system to monitor children's smartphone usage and notify parents, thereby ensuring the safety of children.

[0029] A surveillance AI system according to an embodiment includes a screen capture unit, an image analysis unit, and a notification generation unit. The screen capture unit automatically captures the smartphone screen periodically. For example, the screen capture unit captures the smartphone screen every hour and saves the images. The screen capture unit runs in the background, eliminating the need for user interaction. The screen capture unit can also capture images when specific events occur. For example, the screen capture unit captures the smartphone screen every hour and saves the images. Because the screen capture unit runs in the background, it eliminates the need for user interaction. The screen capture unit can also capture images when specific events occur. The image analysis unit analyzes the images captured by the screen capture unit. For example, the generation AI analyzes text within an image using a text generation AI (e.g., LLM). The generation AI can also analyze text and images within an image using a multimodal generation AI. The generation AI can also recognize patterns within an image and detect harmful content using a machine learning algorithm. For example, the generation AI analyzes text within an image using a text generation AI. The multimodal generation AI can also analyze text and images within an image. A machine learning algorithm can also be used to recognize patterns in images and detect harmful content. The notification generation unit notifies parents based on the results of the analysis by the image analysis unit. For example, the generation AI generates a notification such as "Your child is viewing an inappropriate website" based on the analysis results. The generation AI can also send notifications via messaging apps or emails set by parents. The generation AI can also flexibly customize the notification content to suit the parents' preferences. For example, the generation AI generates a notification such as "Your child is viewing an inappropriate website" based on the analysis results. The notification can also be sent via messaging apps or emails set by parents. The notification content can also be flexibly customized to suit the parents' preferences. As a result, the monitoring AI system according to the embodiment can ensure the safety of children by monitoring their smartphone usage and notifying parents.For example, parents can take swift action if their children are browsing harmful websites. Also, problems on social networking sites can be prevented before they occur. Parents can take swift action if their children are browsing harmful websites. Also, problems on social networking sites can be prevented before they occur.

[0030] The screen capture unit can randomly set the timing of capture so that it is not biased towards a specific time period. For example, by randomly setting the timing of capture, the screen capture unit prevents bias towards a specific time period and makes it difficult for children to predict. For example, by capturing at a different time each time, it is possible to prevent children from predicting the timing of capture and avoiding inappropriate behavior. Furthermore, the screen capture unit can introduce an algorithm for capturing at random times and perform capture at random intervals rather than at fixed time intervals. This makes it difficult for children to predict the timing of capture. Furthermore, by randomly setting the timing of capture, it is possible to prevent bias towards a specific time period. For example, by capturing at a different time each time, it is possible to prevent children from predicting the timing of capture and avoiding inappropriate behavior. This makes it difficult to predict the timing of capture, thereby preventing children from engaging in inappropriate behavior.

[0031] The screen capture unit can dynamically adjust the resolution of the captured image to ensure that important information is not missed. For example, the screen capture unit dynamically adjusts the resolution of the captured image to ensure that important information is not missed. For example, if the text is small, the resolution is increased to obtain more detailed information. The screen capture unit also employs an algorithm that automatically adjusts the resolution according to the content of the screen at the time of capture, and captures at a higher resolution if important information is included. The screen capture unit also dynamically adjusts the resolution to ensure that important information is not missed. For example, capturing text and fine details in the image at a higher resolution improves analysis accuracy. This allows important information to be reliably obtained by dynamically adjusting the resolution.

[0032] The screen capture unit can simultaneously perform screen capture and audio capture, and add audio analysis. For example, the screen capture unit can simultaneously perform screen capture and audio capture, and add audio analysis. For example, if a child is uttering inappropriate language, audio analysis can be used to detect this. In addition, by performing audio capture, the screen capture unit analyzes not only the information on the screen but also the audio information. This allows for the early detection of risks that cannot be detected by text alone. In addition, the screen capture unit simultaneously performs screen capture and audio capture, and add audio analysis. For example, if a child is uttering inappropriate language, audio analysis can be used to detect this. In addition, by analyzing audio information, risks that cannot be detected by text alone can be early detected.

[0033] The screen capture unit converts the captured image into a 3D model, allowing for more detailed visual analysis. The screen capture unit, for example, converts the captured image into a 3D model, allowing for more detailed visual analysis. For example, the depth and three-dimensional layout of the screen can be reproduced to obtain more detailed information. Furthermore, by converting into a 3D model, the screen capture unit can analyze the information on the screen in three dimensions and obtain more detailed information. For example, when multiple windows overlap, the contents of each window can be analyzed. Furthermore, the screen capture unit converts the captured image into a 3D model, allowing for more detailed visual analysis. For example, the depth and three-dimensional layout of the screen can be reproduced to obtain more detailed information. Thus, by converting into a 3D model, the information on the screen can be analyzed in three dimensions and obtain more detailed information.

[0034] The image analysis unit performs text analysis in addition to image analysis, and is able to perform a detailed analysis of the text information in the image. For example, the image analysis unit performs text analysis in addition to image analysis, and performs a detailed analysis of the text information in the image. For example, text in screenshots is extracted and inappropriate content is detected. The image analysis unit also introduces an algorithm for a detailed analysis of the text information in the image, and detects risks by combining image analysis and text analysis. The image analysis unit also performs text analysis in addition to image analysis, and performs a detailed analysis of the text information in the image. For example, text in screenshots is extracted and inappropriate content is detected. This allows for more accurate detection of risks by performing a detailed analysis of the text information in the image.

[0035] The image analysis unit can learn children's behavioral patterns based on the analysis results and build a predictive model. The image analysis unit, for example, learns children's behavioral patterns based on the analysis results and builds a predictive model. For example, it learns a tendency to use specific apps at specific times of the day. The image analysis unit also introduces an algorithm for learning behavioral patterns and builds a predictive model based on the analysis results. This makes it possible to predict high-risk behavior in advance. The image analysis unit also learns children's behavioral patterns based on the analysis results and builds a predictive model. For example, it learns a tendency to use specific apps at specific times of the day. This makes it possible to predict high-risk behavior in advance by learning behavioral patterns and building a predictive model.

[0036] The image analysis unit can analyze dynamic content by performing video analysis in addition to image analysis. For example, the image analysis unit can analyze dynamic content by performing video analysis in addition to image analysis. For example, it can detect inappropriate scenes in videos. The image analysis unit also introduces an algorithm for video analysis and analyzes dynamic content. This allows it to detect risks not only in images but also in videos. The image analysis unit can analyze video in addition to image analysis and analyze dynamic content. For example, it can detect inappropriate scenes in videos. This allows it to detect risks in dynamic content by performing video analysis.

[0037] The image analysis unit can store the analysis results in the cloud and perform long-term data analysis. The image analysis unit, for example, stores the analysis results in the cloud and performs long-term data analysis. For example, it analyzes changes in behavioral patterns based on past data. The image analysis unit also builds a system that performs long-term data analysis based on the data stored in the cloud. This makes it easier to grasp risk trends. The image analysis unit also stores the analysis results in the cloud and performs long-term data analysis. For example, it analyzes changes in behavioral patterns based on past data. Thus, by storing data in the cloud, long-term data analysis becomes possible.

[0038] The notification generation unit can customize the notification content and provide notifications that match the parent's preferences. The notification generation unit, for example, customizes the notification content and provides notifications that match the parent's preferences. For example, it prioritizes sending notifications that include specific keywords or phrases. The notification generation unit also provides a settings screen for customizing the notification content according to the parent's preferences, allowing the parent to adjust the type and frequency of notifications. The notification generation unit also customizes the notification content and provides notifications that match the parent's preferences. For example, it prioritizes sending notifications that include specific keywords or phrases. In this way, customizing the notification content makes it possible to provide notifications that meet the parent's needs.

[0039] The notification generation unit can dynamically adjust the frequency of notifications and notify only important information. For example, the notification generation unit dynamically adjusts the frequency of notifications and notifies only important information. For example, a notification is sent only when high-risk behavior is detected. The notification generation unit also introduces an algorithm that dynamically adjusts the frequency of notifications and notifies parents of only important information. This reduces unnecessary notifications and eases the burden on parents. The notification generation unit also dynamically adjusts the frequency of notifications and notifies only important information. For example, a notification is sent only when high-risk behavior is detected. This reduces the burden on parents by notifying only important information.

[0040] The notification generation unit can provide voice notifications in addition to text notifications, allowing parents to respond immediately. The notification generation unit, for example, provides voice notifications in addition to text notifications, allowing parents to respond immediately. For example, in the event of an emergency, a voice warning is issued. The notification generation unit can also introduce a system for providing voice notifications, and by using this in combination with text notifications, allows parents to respond immediately. This allows important information to be conveyed quickly. The notification generation unit can also provide voice notifications in addition to text notifications, allowing parents to respond immediately. For example, in the event of an emergency, a voice warning is issued. This allows parents to respond immediately by providing voice notifications.

[0041] The notification generation unit can generate notification content in multiple languages, enabling international use. The notification generation unit, for example, generates notification content in multiple languages, enabling international use. For example, notifications are provided in a language that matches the parent's native language, such as English, Spanish, or Chinese. The notification generation unit also introduces a notification system that supports multiple languages, allowing parents to receive notifications in a language of their choice. This promotes international use. The notification generation unit can also generate notification content in multiple languages, enabling international use. For example, notifications are provided in a language that matches the parent's native language, such as English, Spanish, or Chinese. This allows notifications to be provided in multiple languages, enabling international use.

[0042] Monitoring AI apps provide a function that allows the dynamic addition and deletion of apps to be monitored. For example, when a new app is installed, it is automatically added to the list of apps to be monitored. Monitoring AI apps also provide an interface for dynamically managing the apps to be monitored, allowing parents to easily add and delete apps. Monitoring AI apps also provide a function that allows the dynamic addition and deletion of apps to be monitored. For example, when a new app is installed, it is automatically added to the list of apps to be monitored. This allows for flexible monitoring by dynamically managing the apps to be monitored.

[0043] A monitoring AI app can display the monitoring results in real time, allowing parents to respond immediately. A monitoring AI app can, for example, display the monitoring results in real time, allowing parents to respond immediately. For example, if a risk is detected, a notification can be sent immediately. A monitoring AI app can also provide a dashboard for displaying the monitoring results in real time, allowing parents to immediately understand the situation. A monitoring AI app can also display the monitoring results in real time, allowing parents to respond immediately. For example, if a risk is detected, a notification can be sent immediately. This allows parents to respond immediately by displaying the monitoring results in real time.

[0044] Surveillance AI apps expand the scope of monitoring to include not only smartphones but also tablets and PCs. For example, a system can be built to centrally monitor all devices in the home. Surveillance AI apps also provide software to include devices other than smartphones as monitored devices, allowing parents to manage all devices. Surveillance AI apps also expand the scope of monitoring to include not only smartphones but also tablets and PCs. For example, a system can be built to centrally monitor all devices in the home by expanding the scope of monitoring.

[0045] Monitoring AI apps can store monitoring results in the cloud and share them across multiple devices. For example, monitoring AI apps can centrally manage monitoring results from smartphones, tablets, and PCs. Monitoring AI apps can also build a system that shares data across multiple devices based on the monitoring results stored in the cloud. This allows parents to check the monitoring results from any device. Monitoring AI apps can also store monitoring results in the cloud and share them across multiple devices. For example, monitoring results from smartphones, tablets, and PCs can be centrally managed. This allows monitoring results to be shared across multiple devices by storing them in the cloud.

[0046] The analysis results can be encrypted to prevent external access. The analysis results can be encrypted to prevent external access. For example, AES encryption is used to protect the data. In addition, encryption technology is introduced to build a system that safely stores the analysis results. This prevents unauthorized external access. The analysis results can also be encrypted to prevent external access. For example, AES encryption is used to protect the data. In addition, encryption technology is introduced to build a system that safely stores the analysis results. This prevents unauthorized external access.

[0047] The notification generation unit can minimize the content of notifications to parents and respect the privacy of the child. The notification generation unit, for example, minimizes the content of notifications to parents and respects the privacy of the child. For example, it notifies only the minimum amount of information necessary. The notification generation unit also provides a setting screen for minimizing the content of notifications, allowing parents to adjust the level of detail in notifications. The notification generation unit also minimizes the content of notifications to parents and respects the privacy of the child. For example, it notifies only the minimum amount of information necessary. In this way, by minimizing the content of notifications, it is possible to respect the privacy of the child.

[0048] It is possible to provide a function to automatically delete analysis results after a certain period of time. A function to automatically delete analysis results after a certain period of time is provided. For example, data is automatically deleted after one month. In addition, a function to automatically delete analysis results is introduced, allowing parents to set the deletion period. This protects children's privacy. In addition, a function to automatically delete analysis results after a certain period of time is provided. For example, data is automatically deleted after one month. This makes it possible to protect children's privacy by automatically deleting analysis results after a certain period of time.

[0049] The notification generation unit can add a function to notify the guardian after obtaining the child's consent. The notification generation unit adds, for example, a function to notify the guardian after obtaining the child's consent. For example, the notification generation unit may ask the child for confirmation before notifying the guardian. The notification generation unit may also provide an interface for obtaining the child's consent and have the child confirm the notification content. This respects the child's privacy. The notification generation unit also adds a function to notify the guardian after obtaining the child's consent. For example, the notification generation unit may ask the child for confirmation before notifying the guardian. This respects the child's privacy by notifying the guardian after obtaining the child's consent.

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

[0051] The surveillance AI system can also be equipped with an audio analysis unit. This unit uses a smartphone's microphone to capture and analyze surrounding sounds. For example, the audio analysis unit can detect when a child is using inappropriate language or is in a dangerous situation and notify the parent. The audio analysis unit can also convert audio data into text and work with the text analysis unit to detect risks. This allows for more comprehensive monitoring by including audio information in the monitoring target.

[0052] The AI ​​monitoring system can further include a location information acquisition unit. This unit uses the smartphone's GPS function to acquire the child's current location and notify the parent. For example, it can issue a warning if the child enters a specific area or approaches a dangerous location. The location information acquisition unit can also record past location information and analyze behavioral patterns. This further ensures the safety of children.

[0053] The monitoring AI system can also be equipped with a battery monitoring unit. This monitors the smartphone's remaining battery power and notifies parents when it gets low. For example, issuing a warning when the battery drops below 20% reduces the risk of children becoming unreachable. The battery monitoring unit can also record battery usage history and detect abnormal consumption patterns. This allows the system to constantly monitor the battery status and take appropriate action.

[0054] The monitoring AI system can also be equipped with an app usage time limiter. The app usage time limiter limits the amount of time a specific app can be used to prevent excessive use. For example, it can limit the amount of time a game app can be used to one hour per day. The app usage time limiter can also automatically lock an app and notify parents if the usage time exceeds the limit. This can prevent children from becoming addicted to smartphones and promote healthy smartphone use.

[0055] The monitoring AI system can further include a web filtering unit. The web filtering unit monitors websites accessed by children and blocks inappropriate content. For example, it can restrict access to adult sites or sites containing violent content. The web filtering unit can also record access history and send periodic reports to parents. This helps ensure that children are able to use the Internet in a safe environment.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The screen capture module automatically captures the smartphone screen periodically. For example, it captures the smartphone screen every hour and saves the image. The screen capture module runs in the background and does not require user operation. It can also capture when a specific event occurs. Step 2: The image analysis unit analyzes the image captured by the screen capture unit. For example, the generative AI may use a text generation AI (e.g., LLM) to analyze the text within the image. Alternatively, a multimodal generative AI may be used to analyze the text and images within the image. Furthermore, machine learning algorithms may be used to recognize patterns within the image and detect harmful content. Step 3: The notification generation unit notifies parents based on the results of the analysis by the image analysis unit. For example, the generation AI generates a notification such as "Your child is viewing an inappropriate website" based on the analysis results. Notifications can also be sent via messaging apps or emails set by parents. The content of notifications can be flexibly customized to suit parents' preferences.

[0058] (Example 2) The monitoring AI system according to an embodiment of the present invention automatically captures smartphone screens on a regular basis, and the generation AI analyzes the images and notifies parents by text. This allows the monitoring AI system to monitor children's smartphone usage and notify parents, thereby ensuring the safety of children.

[0059] A surveillance AI system according to an embodiment includes a screen capture unit, an image analysis unit, and a notification generation unit. The screen capture unit automatically captures the smartphone screen periodically. For example, the screen capture unit captures the smartphone screen every hour and saves the images. The screen capture unit runs in the background, eliminating the need for user interaction. The screen capture unit can also capture images when specific events occur. For example, the screen capture unit captures the smartphone screen every hour and saves the images. Because the screen capture unit runs in the background, it eliminates the need for user interaction. The screen capture unit can also capture images when specific events occur. The image analysis unit analyzes the images captured by the screen capture unit. For example, the generation AI analyzes text within an image using a text generation AI (e.g., LLM). The generation AI can also analyze text and images within an image using a multimodal generation AI. The generation AI can also recognize patterns within an image and detect harmful content using a machine learning algorithm. For example, the generation AI analyzes text within an image using a text generation AI. The multimodal generation AI can also analyze text and images within an image. A machine learning algorithm can also be used to recognize patterns in images and detect harmful content. The notification generation unit notifies parents based on the results of the analysis by the image analysis unit. For example, the generation AI generates a notification such as "Your child is viewing an inappropriate website" based on the analysis results. The generation AI can also send notifications via messaging apps or emails set by parents. The generation AI can also flexibly customize the notification content to suit the parents' preferences. For example, the generation AI generates a notification such as "Your child is viewing an inappropriate website" based on the analysis results. The notification can also be sent via messaging apps or emails set by parents. The notification content can also be flexibly customized to suit the parents' preferences. As a result, the monitoring AI system according to the embodiment can ensure the safety of children by monitoring their smartphone usage and notifying parents.For example, parents can take swift action if their children are browsing harmful websites. Also, problems on social networking sites can be prevented before they occur. Parents can take swift action if their children are browsing harmful websites. Also, problems on social networking sites can be prevented before they occur.

[0060] The screen capture unit can randomly set the timing of capture so that it is not biased towards a specific time period. For example, by randomly setting the timing of capture, the screen capture unit prevents bias towards a specific time period and makes it difficult for children to predict. For example, by capturing at a different time each time, it is possible to prevent children from predicting the timing of capture and avoiding inappropriate behavior. Furthermore, the screen capture unit can introduce an algorithm for capturing at random times and perform capture at random intervals rather than at fixed time intervals. This makes it difficult for children to predict the timing of capture. Furthermore, by randomly setting the timing of capture, it is possible to prevent bias towards a specific time period. For example, by capturing at a different time each time, it is possible to prevent children from predicting the timing of capture and avoiding inappropriate behavior. This makes it difficult to predict the timing of capture, thereby preventing children from engaging in inappropriate behavior.

[0061] The screen capture unit can dynamically adjust the resolution of the captured image to ensure that important information is not missed. For example, the screen capture unit dynamically adjusts the resolution of the captured image to ensure that important information is not missed. For example, if the text is small, the resolution is increased to obtain more detailed information. The screen capture unit also employs an algorithm that automatically adjusts the resolution according to the content of the screen at the time of capture, and captures at a higher resolution if important information is included. The screen capture unit also dynamically adjusts the resolution to ensure that important information is not missed. For example, capturing text and fine details in the image at a higher resolution improves analysis accuracy. This allows important information to be reliably obtained by dynamically adjusting the resolution.

[0062] The screen capture unit can use the emotion estimation function to capture at a time when it is estimated that the child's emotions are heightened. For example, the screen capture unit uses the emotion estimation function to capture at a time when it is estimated that the child's emotions are heightened. For example, capturing when the child is excited detects high-risk behavior. The screen capture unit also introduces an algorithm that analyzes the child's emotional state in real time and captures when the child's emotions are heightened. This allows for early detection of high-risk behavior. The screen capture unit also uses the emotion estimation function to capture at a time when it is estimated that the child's emotions are heightened. For example, capturing when the child is excited detects high-risk behavior. This allows for early detection of high-risk behavior by capturing at a time when the child's emotions are heightened.

[0063] The screen capture unit can simultaneously perform screen capture and audio capture, and add audio analysis. For example, the screen capture unit can simultaneously perform screen capture and audio capture, and add audio analysis. For example, if a child is uttering inappropriate language, audio analysis can be used to detect this. In addition, by performing audio capture, the screen capture unit analyzes not only the information on the screen but also the audio information. This allows for the early detection of risks that cannot be detected by text alone. In addition, the screen capture unit simultaneously performs screen capture and audio capture, and add audio analysis. For example, if a child is uttering inappropriate language, audio analysis can be used to detect this. In addition, by analyzing audio information, risks that cannot be detected by text alone can be early detected.

[0064] The screen capture unit converts the captured image into a 3D model, allowing for more detailed visual analysis. The screen capture unit, for example, converts the captured image into a 3D model, allowing for more detailed visual analysis. For example, the depth and three-dimensional layout of the screen can be reproduced to obtain more detailed information. Furthermore, by converting into a 3D model, the screen capture unit can analyze the information on the screen in three dimensions and obtain more detailed information. For example, when multiple windows overlap, the contents of each window can be analyzed. Furthermore, the screen capture unit converts the captured image into a 3D model, allowing for more detailed visual analysis. For example, the depth and three-dimensional layout of the screen can be reproduced to obtain more detailed information. Thus, by converting into a 3D model, the information on the screen can be analyzed in three dimensions and obtain more detailed information.

[0065] The screen capture unit uses the emotion estimation function to capture when the child is relaxed and understand their normal behavioral patterns. The screen capture unit, for example, uses the emotion estimation function to capture when the child is relaxed and understand their normal behavioral patterns. For example, the screen usage status when the child is relaxed is recorded. The screen capture unit also introduces an algorithm that analyzes the child's emotional state in real time and captures when the child is relaxed. This makes it possible to understand normal behavioral patterns and easily detect abnormal behavior. The screen capture unit also uses the emotion estimation function to capture when the child is relaxed and understand their normal behavioral patterns. For example, the screen usage status when the child is relaxed is recorded. This makes it possible to understand behavioral patterns when the child is relaxed and easily detect abnormal behavior.

[0066] The image analysis unit performs text analysis in addition to image analysis, and is able to perform a detailed analysis of the text information in the image. For example, the image analysis unit performs text analysis in addition to image analysis, and performs a detailed analysis of the text information in the image. For example, text in screenshots is extracted and inappropriate content is detected. The image analysis unit also introduces an algorithm for a detailed analysis of the text information in the image, and detects risks by combining image analysis and text analysis. The image analysis unit also performs text analysis in addition to image analysis, and performs a detailed analysis of the text information in the image. For example, text in screenshots is extracted and inappropriate content is detected. This allows for more accurate detection of risks by performing a detailed analysis of the text information in the image.

[0067] The image analysis unit can learn children's behavioral patterns based on the analysis results and build a predictive model. The image analysis unit, for example, learns children's behavioral patterns based on the analysis results and builds a predictive model. For example, it learns a tendency to use specific apps at specific times of the day. The image analysis unit also introduces an algorithm for learning behavioral patterns and builds a predictive model based on the analysis results. This makes it possible to predict high-risk behavior in advance. The image analysis unit also learns children's behavioral patterns based on the analysis results and builds a predictive model. For example, it learns a tendency to use specific apps at specific times of the day. This makes it possible to predict high-risk behavior in advance by learning behavioral patterns and building a predictive model.

[0068] The image analysis unit uses the emotion estimation function to analyze the emotions of people in an image and detect dangerous situations early. The image analysis unit, for example, uses the emotion estimation function to analyze the emotions of people in an image and detect dangerous situations early. For example, a warning is issued if a child is feeling anxious or scared. The image analysis unit also introduces an algorithm for analyzing the emotions of people in an image and detects dangerous situations early. This allows measures to be taken before a child falls into a dangerous situation. The image analysis unit also uses the emotion estimation function to analyze the emotions of people in an image and detect dangerous situations early. For example, a warning is issued if a child is feeling anxious or scared. This allows dangerous situations to be detected early by analyzing emotions.

[0069] The image analysis unit can analyze dynamic content by performing video analysis in addition to image analysis. For example, the image analysis unit can analyze dynamic content by performing video analysis in addition to image analysis. For example, it can detect inappropriate scenes in videos. The image analysis unit also introduces an algorithm for video analysis and analyzes dynamic content. This allows it to detect risks not only in images but also in videos. The image analysis unit can analyze video in addition to image analysis and analyze dynamic content. For example, it can detect inappropriate scenes in videos. This allows it to detect risks in dynamic content by performing video analysis.

[0070] The image analysis unit can store the analysis results in the cloud and perform long-term data analysis. The image analysis unit, for example, stores the analysis results in the cloud and performs long-term data analysis. For example, it analyzes changes in behavioral patterns based on past data. The image analysis unit also builds a system that performs long-term data analysis based on the data stored in the cloud. This makes it easier to grasp risk trends. The image analysis unit also stores the analysis results in the cloud and performs long-term data analysis. For example, it analyzes changes in behavioral patterns based on past data. Thus, by storing data in the cloud, long-term data analysis becomes possible.

[0071] The image analysis unit can use the emotion estimation function to analyze the emotions of people in an image and recommend content that elicits positive emotions. The image analysis unit, for example, uses the emotion estimation function to analyze the emotions of people in an image and recommend content that elicits positive emotions. For example, it can suggest content that helps children relax. The image analysis unit also introduces an algorithm for analyzing the emotions of people in an image and recommends content that elicits positive emotions. This reduces stress in children. The image analysis unit also uses the emotion estimation function to analyze the emotions of people in an image and recommends content that elicits positive emotions. For example, it can suggest content that helps children relax. This reduces stress in children by recommending content that elicits positive emotions.

[0072] The notification generation unit can customize the notification content and provide notifications that match the parent's preferences. The notification generation unit, for example, customizes the notification content and provides notifications that match the parent's preferences. For example, it prioritizes sending notifications that include specific keywords or phrases. The notification generation unit also provides a settings screen for customizing the notification content according to the parent's preferences, allowing the parent to adjust the type and frequency of notifications. The notification generation unit also customizes the notification content and provides notifications that match the parent's preferences. For example, it prioritizes sending notifications that include specific keywords or phrases. In this way, customizing the notification content makes it possible to provide notifications that meet the parent's needs.

[0073] The notification generation unit can dynamically adjust the frequency of notifications and notify only important information. For example, the notification generation unit dynamically adjusts the frequency of notifications and notifies only important information. For example, a notification is sent only when high-risk behavior is detected. The notification generation unit also introduces an algorithm that dynamically adjusts the frequency of notifications and notifies parents of only important information. This reduces unnecessary notifications and eases the burden on parents. The notification generation unit also dynamically adjusts the frequency of notifications and notifies only important information. For example, a notification is sent only when high-risk behavior is detected. This reduces the burden on parents by notifying only important information.

[0074] The notification generation unit can use the emotion estimation function to generate notification content according to the emotion of the guardian. The notification generation unit, for example, uses the emotion estimation function to generate notification content according to the emotion of the guardian. For example, if the guardian is feeling stressed, the notification is provided in gentle words. The notification generation unit also introduces an algorithm that analyzes the emotional state of the guardian in real time and customizes the notification content based on the results. This provides a notification that takes the emotion of the guardian into consideration. The notification generation unit also uses the emotion estimation function to generate notification content according to the emotion of the guardian. For example, if the guardian is feeling stressed, the notification is provided in gentle words. This allows the notification to take the emotion of the guardian into consideration, thereby reducing stress.

[0075] The notification generation unit can provide voice notifications in addition to text notifications, allowing parents to respond immediately. The notification generation unit, for example, provides voice notifications in addition to text notifications, allowing parents to respond immediately. For example, in the event of an emergency, a voice warning is issued. The notification generation unit can also introduce a system for providing voice notifications, and by using this in combination with text notifications, allows parents to respond immediately. This allows important information to be conveyed quickly. The notification generation unit can also provide voice notifications in addition to text notifications, allowing parents to respond immediately. For example, in the event of an emergency, a voice warning is issued. This allows parents to respond immediately by providing voice notifications.

[0076] The notification generation unit can generate notification content in multiple languages, enabling international use. The notification generation unit, for example, generates notification content in multiple languages, enabling international use. For example, notifications are provided in a language that matches the parent's native language, such as English, Spanish, or Chinese. The notification generation unit also introduces a notification system that supports multiple languages, allowing parents to receive notifications in a language of their choice. This promotes international use. The notification generation unit can also generate notification content in multiple languages, enabling international use. For example, notifications are provided in a language that matches the parent's native language, such as English, Spanish, or Chinese. This allows notifications to be provided in multiple languages, enabling international use.

[0077] The notification generation unit uses the emotion estimation function to notify the guardian when he / she is relaxed, thereby reducing stress. The notification generation unit, for example, uses the emotion estimation function to notify the guardian when he / she is relaxed, thereby reducing stress. For example, the notification is sent during a time period when the guardian is relaxed. The notification generation unit also introduces an algorithm that analyzes the emotional state of the guardian in real time and sends a notification when he / she is relaxed, thereby reducing stress caused by the notification. The notification generation unit also uses the emotion estimation function to notify the guardian when he / she is relaxed, thereby reducing stress. For example, the notification is sent during a time period when the guardian is relaxed. As a result, by sending a notification when he / she is relaxed, stress can be reduced for the guardian.

[0078] Monitoring AI apps provide a function that allows the dynamic addition and deletion of apps to be monitored. For example, when a new app is installed, it is automatically added to the list of apps to be monitored. Monitoring AI apps also provide an interface for dynamically managing the apps to be monitored, allowing parents to easily add and delete apps. Monitoring AI apps also provide a function that allows the dynamic addition and deletion of apps to be monitored. For example, when a new app is installed, it is automatically added to the list of apps to be monitored. This allows for flexible monitoring by dynamically managing the apps to be monitored.

[0079] A monitoring AI app can display the monitoring results in real time, allowing parents to respond immediately. A monitoring AI app can, for example, display the monitoring results in real time, allowing parents to respond immediately. For example, if a risk is detected, a notification can be sent immediately. A monitoring AI app can also provide a dashboard for displaying the monitoring results in real time, allowing parents to immediately understand the situation. A monitoring AI app can also display the monitoring results in real time, allowing parents to respond immediately. For example, if a risk is detected, a notification can be sent immediately. This allows parents to respond immediately by displaying the monitoring results in real time.

[0080] The monitoring AI app can use an emotion estimation function to analyze a child's emotions when using a specific app. For example, the monitoring AI app uses the emotion estimation function to analyze a child's emotions when using a specific app. For example, it can analyze emotions when using a game app to detect stress or excitement. The monitoring AI app can also employ an algorithm that analyzes a child's emotions in real time when using a specific app to detect high-risk emotional states. The monitoring AI app can also use the emotion estimation function to analyze a child's emotions when using a specific app. For example, it can analyze emotions when using a game app to detect stress or excitement. In this way, it can detect high-risk emotional states by analyzing emotions when using a specific app.

[0081] Surveillance AI apps expand the scope of monitoring to include not only smartphones but also tablets and PCs. For example, a system can be built to centrally monitor all devices in the home. Surveillance AI apps also provide software to include devices other than smartphones as monitored devices, allowing parents to manage all devices. Surveillance AI apps also expand the scope of monitoring to include not only smartphones but also tablets and PCs. For example, a system can be built to centrally monitor all devices in the home by expanding the scope of monitoring.

[0082] Monitoring AI apps can store monitoring results in the cloud and share them across multiple devices. For example, monitoring AI apps can centrally manage monitoring results from smartphones, tablets, and PCs. Monitoring AI apps can also build a system that shares data across multiple devices based on the monitoring results stored in the cloud. This allows parents to check the monitoring results from any device. Monitoring AI apps can also store monitoring results in the cloud and share them across multiple devices. For example, monitoring results from smartphones, tablets, and PCs can be centrally managed. This allows monitoring results to be shared across multiple devices by storing them in the cloud.

[0083] The monitoring AI app can use an emotion estimation function to analyze a child's emotions when using a specific app and recommend apps that elicit positive emotions. For example, the monitoring AI app can use the emotion estimation function to analyze a child's emotions when using a specific app and recommend apps that elicit positive emotions. For example, it can suggest apps that help them relax. The monitoring AI app can also introduce an algorithm that analyzes a child's emotions in real time when using a specific app and recommends apps that elicit positive emotions. The monitoring AI app can also use the emotion estimation function to analyze a child's emotions when using a specific app and recommend apps that elicit positive emotions. For example, it can suggest apps that help them relax. This can reduce a child's stress by recommending apps that elicit positive emotions.

[0084] The analysis results can be encrypted to prevent external access. The analysis results can be encrypted to prevent external access. For example, AES encryption is used to protect the data. In addition, encryption technology is introduced to build a system that safely stores the analysis results. This prevents unauthorized external access. The analysis results can also be encrypted to prevent external access. For example, AES encryption is used to protect the data. In addition, encryption technology is introduced to build a system that safely stores the analysis results. This prevents unauthorized external access.

[0085] The notification generation unit can minimize the content of notifications to parents and respect the privacy of the child. The notification generation unit, for example, minimizes the content of notifications to parents and respects the privacy of the child. For example, it notifies only the minimum amount of information necessary. The notification generation unit also provides a setting screen for minimizing the content of notifications, allowing parents to adjust the level of detail in notifications. The notification generation unit also minimizes the content of notifications to parents and respects the privacy of the child. For example, it notifies only the minimum amount of information necessary. In this way, by minimizing the content of notifications, it is possible to respect the privacy of the child.

[0086] The notification generation unit can use the emotion estimation function to generate an analysis result that takes into consideration the child's emotions. The notification generation unit, for example, uses the emotion estimation function to generate an analysis result that takes into consideration the child's emotions. For example, if the child is feeling stressed, the analysis result is notified in a gentle manner. The notification generation unit also introduces an algorithm that analyzes the child's emotional state in real time and generates an analysis result based on the result. This allows for a notification that takes into consideration the child's emotions. The notification generation unit also uses the emotion estimation function to generate an analysis result that takes into consideration the child's emotions. For example, if the child is feeling stressed, the analysis result is notified in a gentle manner. This allows for the child's stress to be reduced by generating an analysis result that takes into consideration the child's emotions.

[0087] It is possible to provide a function to automatically delete analysis results after a certain period of time. A function to automatically delete analysis results after a certain period of time is provided. For example, data is automatically deleted after one month. In addition, a function to automatically delete analysis results is introduced, allowing parents to set the deletion period. This protects children's privacy. In addition, a function to automatically delete analysis results after a certain period of time is provided. For example, data is automatically deleted after one month. This makes it possible to protect children's privacy by automatically deleting analysis results after a certain period of time.

[0088] The notification generation unit can add a function to notify the guardian after obtaining the child's consent. The notification generation unit adds, for example, a function to notify the guardian after obtaining the child's consent. For example, the notification generation unit may ask the child for confirmation before notifying the guardian. The notification generation unit may also provide an interface for obtaining the child's consent and have the child confirm the notification content. This respects the child's privacy. The notification generation unit also adds a function to notify the guardian after obtaining the child's consent. For example, the notification generation unit may ask the child for confirmation before notifying the guardian. This respects the child's privacy by notifying the guardian after obtaining the child's consent.

[0089] The analysis unit uses the emotion estimation function to perform an analysis when the child is relaxed, thereby reducing stress. The analysis unit, for example, uses the emotion estimation function to perform an analysis when the child is relaxed, thereby reducing stress. For example, the analysis is performed during a time period when the child is relaxed. The analysis unit also introduces an algorithm that analyzes the child's emotional state in real time and performs the analysis when the child is relaxed. This reduces stress caused by the analysis. The analysis unit also uses the emotion estimation function to perform an analysis when the child is relaxed, thereby reducing stress. For example, the analysis is performed during a time period when the child is relaxed. This reduces stress in the child by performing the analysis when the child is relaxed.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The surveillance AI system can also be equipped with an audio analysis unit. This unit uses a smartphone's microphone to capture and analyze surrounding sounds. For example, the audio analysis unit can detect when a child is using inappropriate language or is in a dangerous situation and notify the parent. The audio analysis unit can also convert audio data into text and work with the text analysis unit to detect risks. This allows for more comprehensive monitoring by including audio information in the monitoring target.

[0092] The AI ​​monitoring system can further include a location information acquisition unit. This unit uses the smartphone's GPS function to acquire the child's current location and notify the parent. For example, it can issue a warning if the child enters a specific area or approaches a dangerous location. The location information acquisition unit can also record past location information and analyze behavioral patterns. This further ensures the safety of children.

[0093] The monitoring AI system can also be equipped with a battery monitoring unit. This monitors the smartphone's remaining battery power and notifies parents when it gets low. For example, issuing a warning when the battery drops below 20% reduces the risk of children becoming unreachable. The battery monitoring unit can also record battery usage history and detect abnormal consumption patterns. This allows the system to constantly monitor the battery status and take appropriate action.

[0094] The monitoring AI system can also be equipped with an app usage time limiter. The app usage time limiter limits the amount of time a specific app can be used to prevent excessive use. For example, it can limit the amount of time a game app can be used to one hour per day. The app usage time limiter can also automatically lock an app and notify parents if the usage time exceeds the limit. This can prevent children from becoming addicted to smartphones and promote healthy smartphone use.

[0095] The monitoring AI system can further include a web filtering unit. The web filtering unit monitors websites accessed by children and blocks inappropriate content. For example, it can restrict access to adult sites or sites containing violent content. The web filtering unit can also record access history and send periodic reports to parents. This helps ensure that children are able to use the Internet in a safe environment.

[0096] The monitoring AI system can also use emotion estimation to take action based on a child's emotions. For example, if it estimates that a child is feeling stressed, it can play relaxing music. It can also use emotion estimation to send a notification to parents when a child is feeling anxious, encouraging them to take appropriate action. This allows for support tailored to a child's emotional state and maintaining their mental health.

[0097] The monitoring AI system can further use emotion estimation functions to capture when a child is relaxed and understand normal behavioral patterns. For example, by recording screen usage when a child is relaxed, it becomes easier to detect abnormal behavior. Also, by using emotion estimation functions to capture when a child is relaxed, it becomes easier to understand normal behavioral patterns and detect abnormal behavior. By understanding behavioral patterns when a child is relaxed, it becomes easier to detect abnormal behavior.

[0098] The monitoring AI system can further use its emotion estimation function to analyze the emotions a child is feeling when using a specific app and recommend apps that elicit positive emotions. For example, it can suggest apps that help them relax. The emotion estimation function can also be used to analyze the emotions a child is feeling in real time when using a specific app and recommend apps that elicit positive emotions. This can help reduce stress in children by recommending apps that elicit positive emotions.

[0099] The monitoring AI system can also use its emotion estimation function to generate notification content that reflects the parent's emotions. For example, if the parent is feeling stressed, the system can send a notification in gentle language. The emotion estimation function can also be used to analyze the parent's emotional state in real time and customize the notification content based on the results. This allows for notifications that take the parent's emotions into consideration, thereby reducing stress.

[0100] The monitoring AI system can also use an emotion estimation function to generate analysis results that take into account a child's emotions. For example, if a child is feeling stressed, the analysis results will be communicated in a gentler manner. The emotion estimation function can also be used to analyze a child's emotional state in real time and generate analysis results based on the results. This allows for notifications that take into account a child's emotions, thereby reducing stress in children.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The screen capture module automatically captures the smartphone screen periodically. For example, it captures the smartphone screen every hour and saves the image. The screen capture module runs in the background and does not require user operation. It can also capture when a specific event occurs. Step 2: The image analysis unit analyzes the image captured by the screen capture unit. For example, the generative AI may use a text generation AI (e.g., LLM) to analyze the text within the image. Alternatively, a multimodal generative AI may be used to analyze the text and images within the image. Furthermore, machine learning algorithms may be used to recognize patterns within the image and detect harmful content. Step 3: The notification generation unit notifies parents based on the results of the analysis by the image analysis unit. For example, the generation AI generates a notification such as "Your child is viewing an inappropriate website" based on the analysis results. Notifications can also be sent via messaging apps or emails set by parents. The content of notifications can be flexibly customized to suit parents' preferences.

[0103] 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.

[0104] 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.

[0105] 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.

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

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0119] 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.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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]

[0170] 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 screen capture section that automatically captures the smartphone screen periodically, an image analysis unit that analyzes the image captured by the screen capture unit; a notification generating unit that notifies the guardian based on the result of the analysis by the image analyzing unit. A system characterized by:

2. The screen capture unit Set capture timing randomly to avoid bias towards specific time periods 2. The system of claim 1.

3. The screen capture unit Dynamically adjusts capture image resolution to ensure you don't miss any important information 2. The system of claim 1.

4. The screen capture unit Capture when you think your child is emotionally charged 2. The system of claim 1.

5. The screen capture unit Capture not only the screen but also audio at the same time and add audio analysis 2. The system of claim 1.

Images