System

Abstract

An object of a system according to an embodiment is to prevent net-bullying of a child.SOLUTION: A system includes a pre-installation part of an application, a sentence optimization training part based on generation AI, an analysis part of aggressiveness and flaming elements of a sentence, and a reporting part. The app pre-installation unit pre-installs an app in a device for children sold by a mobile phone carrier. The sentence optimization training unit based on the generated AI performs sentence optimization training based on the generated AI using the application preinstalled by the application preinstallation unit. A sentence aggressiveness and flaming factor analysis part analyzes the aggressiveness and flaming factor of the sentence optimized by the sentence optimization training part based on the generation AI. A notification part notifies parents and schools of the result analyzed by the analysis part of the aggression and flaming elements of the sentence.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not provide sufficient effective means for preventing cyberbullying of children, and there is room for improvement.

[0005] The system according to the embodiment aims to prevent cyberbullying of children before it occurs. [Means for solving the problem]

[0006] The system according to the embodiment includes an app pre-installation unit, a generation AI-based sentence optimization training unit, a sentence aggressiveness and inflammatory elements analysis unit, and a notification unit. The app pre-installation unit pre-installs an app on children's devices sold by mobile phone carriers. The generation AI-based sentence optimization training unit performs sentence optimization training using the app pre-installed by the app pre-installation unit. The sentence aggressiveness and inflammatory elements analysis unit analyzes the sentence aggressiveness and inflammatory elements that have been optimized by the generation AI-based sentence optimization training unit. The notification unit notifies parents and the school of the results of the analysis by the sentence aggressiveness and inflammatory elements analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can prevent cyberbullying of children before it happens. [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) In an embodiment of the present invention, the cyberbullying prevention system is a system in which an app is pre-installed on children's devices sold by mobile phone carriers, and a generation AI optimizes children's writing, analyzes aggression and inflammatory elements, and notifies parents and schools if any changes are detected. This allows the cyberbullying prevention system to prevent children from being cyberbullied and promote healthy communication.

[0029] An anti-cyberbullying system according to an embodiment includes an app pre-installation unit, a generation AI-based text moderation training unit, a text aggressiveness and inflammatory content analysis unit, and a notification unit. The app pre-installation unit pre-installs the app on children's devices sold by mobile phone carriers. For example, when a child purchases a new device, the app is automatically installed as part of the initial setup. The generation AI-based text moderation training unit uses the pre-installed app to train the generation AI to moderate the child's text. For example, if the generation AI detects an offensive word such as "idiot," it provides advice such as, "That word may be hurtful to the other person. Try using a different expression." The generation AI analyzes the child's text using a text generation AI (e.g., LLM) or a multimodal generation AI and generates feedback for moderation. The text aggressiveness and inflammatory content analysis unit analyzes the aggressiveness and inflammatory content of the text moderated by the generation AI-based text moderation training unit. For example, the generation AI analyzes text written by a child on social media or a messaging app to detect offensive language or expressions that may spark outrage. The generation AI scores these elements and issues a warning if a certain standard is exceeded. The notification unit notifies parents and schools of the results of the analysis by the analysis unit of the aggressiveness and outrage elements of the text. For example, if the generation AI detects a change in aggressiveness or outrage elements in a child's text, it notifies parents and schools of this information via email or in-app notification. In this way, the cyberbullying prevention system according to the embodiment can prevent cyberbullying among children and promote healthy communication. For example, educating children not to use offensive language can reduce online troubles. Furthermore, parents and schools can detect problems early and take appropriate measures to support the healthy development of children.

[0030] The pre-installed section of the app can automatically perform customization settings according to the child's age and gender. For example, the pre-installed section of the app automatically performs customization settings based on the child's age and gender when the app is pre-installed. For example, it provides filtering functions according to age and interface designs according to gender. Furthermore, when a parent enters the child's age and gender during initial device setup, the app automatically performs appropriate settings. For example, it sets educational content according to age and theme colors according to gender. Furthermore, when the app is launched for the first time, it provides a wizard for performing customization settings based on the child's age and gender. For example, it selects usage restrictions according to age and characters according to gender. This enables appropriate settings according to the child's age and gender.

[0031] The pre-installed part of the app can provide an interactive tutorial to help parents understand the app's functions. The pre-installed part of the app can provide an interactive tutorial to help parents understand the app's functions, for example, when initially setting up a device. For example, the app's main functions can be explained step by step. Furthermore, during initial setup, a demonstration can be provided to help parents learn how to use the app. For example, the parent can learn by actually operating each function of the app. Furthermore, when the app is launched for the first time, a guided tour can be provided to help parents understand the app's functions. For example, the tutorial can guide parents through each screen of the app in order and explain its functions. This makes it easier for parents to understand the app's functions.

[0032] The generative AI text optimization education unit can provide education using specific examples based on a child's interests and concerns. For example, when the generative AI optimizes a child's text, the generative AI uses specific examples based on a child's interests and concerns to provide education. For example, example sentences incorporating characters or themes that the child likes are provided. In addition, a system is constructed in which the generative AI provides text optimization education using specific examples based on a child's interests and concerns. For example, example sentences related to sports or hobbies that the child is interested in are used. In addition, the generative AI provides text optimization education using specific examples based on a child's interests and concerns. For example, stories or scenarios related to topics that the child is interested in are provided. This improves the effectiveness of education by providing education using specific examples based on a child's interests and concerns.

[0033] The sentence optimization education unit using the generation AI can adjust the difficulty level in stages according to the child's level of comprehension. For example, the generation AI generation AI generation AI generation AI builds a system that adjusts the difficulty level of the feedback provided by the generation AI in stages according to the child's level of comprehension. For example, beginner, intermediate, and advanced feedback are prepared and applied according to the child's progress. The generation AI also adjusts the difficulty level of the feedback provided by the generation AI in stages according to the child's level of comprehension. For example, simple advice is provided when the level of comprehension is low, and detailed feedback is provided when the level of comprehension is high. The generation AI also provides feedback that adjusts the difficulty level in stages according to the child's level of comprehension. For example, the content of the feedback is made gradually more complex so that it is easier for the child to understand. This allows the difficulty level to be adjusted in stages according to the child's level of comprehension, making it possible to provide appropriate education.

[0034] A text optimization education unit using generative AI can translate text written by children into other languages ​​and provide optimization education in multiple languages. For example, a text optimization education unit using generative AI uses generative AI to translate text written by children into other languages ​​and build a system to provide optimization education in multiple languages. For example, it translates into English or French and provides optimization education. In addition, text written by children is translated into other languages ​​using generative AI and provides optimization education in multiple languages. For example, it is translated into Spanish or Chinese and provides education in different language environments. In addition, a system is developed that uses generative AI to translate text written by children into multiple languages ​​and provide optimization education. For example, it translates from Japanese to English, French, German, etc. and provides education. In this way, by providing optimization education in multiple languages, appropriate education can be provided even in different language environments.

[0035] The analysis unit for text aggression and inflammatory elements uses past data to learn individual trends, enabling more accurate analysis. For example, when the generation AI analyzes the aggression of text, the analysis unit for text aggression and inflammatory elements uses past data to learn individual trends, building a system that performs more accurate analysis. For example, a model is trained based on past data on aggressive texts. Furthermore, using past data, the generation AI learns individual trends and analyzes the aggression of text with high accuracy. For example, past posts of a specific user are analyzed and those trends are learned. Furthermore, a system is developed in which the generation AI uses past data to learn individual trends and analyze the aggression of text. For example, aggression patterns are identified based on past cases of flaming. This allows for more accurate analysis by learning individual trends using past data.

[0036] The part that analyzes aggressiveness and inflammatory elements in text can suggest specific improvement measures for detected aggressiveness and inflammatory elements. The part that analyzes aggressiveness and inflammatory elements in text will, for example, build a system that suggests specific improvement measures for aggressiveness and inflammatory elements detected by the generation AI. For example, it will suggest replacing aggressive expressions with more appropriate expressions. It will also provide specific improvement measures for aggressiveness and inflammatory elements detected by the generation AI. For example, it will suggest alternative expressions that do not use aggressive language. It will also develop a system that suggests specific improvement measures for aggressiveness and inflammatory elements detected by the generation AI. For example, it will provide advice on how to avoid expressions that may provoke an uproar. This will allow children to learn appropriate expressions by suggesting specific improvement measures.

[0037] The analysis unit for text aggression and inflammatory elements can share the data analyzed by the generative AI with other educational institutions and research institutions to conduct joint research to prevent cyberbullying. The analysis unit for text aggression and inflammatory elements can, for example, share the data analyzed by the generative AI with other educational institutions and research institutions to build a system for joint research to prevent cyberbullying. For example, a data sharing platform can be developed. The data analyzed by the generative AI can also be shared with other educational institutions and research institutions to conduct joint research to prevent cyberbullying. For example, they can conduct joint data analysis and consider effective countermeasures. The data analyzed by the generative AI can also be shared with other educational institutions and research institutions to develop a system for joint research to prevent cyberbullying. For example, an API can be provided for data sharing. This allows data to be shared and joint research can be conducted to consider effective countermeasures to prevent cyberbullying.

[0038] The analysis section for aggressiveness and inflammatory elements in text displays the detected aggressiveness and inflammatory elements in an easy-to-understand visual manner, making it easier for children to recognize the problem themselves. The analysis section for aggressiveness and inflammatory elements in text will, for example, build a system that displays the aggressiveness and inflammatory elements detected by the generation AI in an easy-to-understand visual manner, making it easier for children to recognize the problem themselves. For example, it will highlight aggressive words in red. It will also visually display the aggressiveness and inflammatory elements in text written by children, making it easier for them to recognize the problem. For example, it will indicate parts that may be inflammatory with an icon. We will also develop a system that displays the aggressiveness and inflammatory elements detected by the generation AI in an easy-to-understand visual manner, making it easier for children to recognize the problem themselves. For example, it will display the aggression score in a graph. This makes it easier for children to recognize the problem by displaying it visually.

[0039] When issuing a notification, the notification unit can include specific examples and advice to make the notification content easier for parents and teachers to understand. For example, the notification unit will build a system in which, when the generation AI issues a notification, it includes specific examples and advice to make the notification content easier for parents and teachers to understand. For example, it will explain specific examples of offensive language and its impact. In addition, the generation AI will issue notifications that include specific examples and advice to make the notification content easier for parents and teachers to understand. For example, it will specifically indicate problems with a child's writing and how to improve it. In addition, a system will be developed in which, when the generation AI issues a notification, it will include specific examples and advice to make the notification content easier for parents and teachers to understand. For example, it will provide advice based on past cases. In this way, by including specific examples and advice, it will be easier for parents and teachers to understand the notification content.

[0040] The notification unit can provide a function that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, the notification unit will build a system that provides a function that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, it will allow the frequency of notifications to be set on a weekly or monthly basis. In addition, a function will be provided that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, it will be possible to set up a system that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, an interface will be provided that allows the content of notifications to be set in detail. This will allow the frequency and content of notifications to be customized, making it possible to provide information that meets the needs of parents and teachers.

[0041] The notification unit provides notification content as an audio message or a video message, and can transmit information visually or aurally. The notification unit, for example, provides notification content as an audio message or a video message, and builds a system for transmitting information visually or aurally. For example, audio advice or video explanations are provided. Notification content for parents or teachers is also provided as an audio message or a video message. For example, audio warnings or specific video examples are shown. Furthermore, a system is developed that provides notification content as an audio message or a video message, and transmits information visually or aurally. For example, audio instructions or video guidance are provided. As a result, by providing notification content as an audio message or a video message, it becomes possible to transmit information visually or aurally.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The cyberbullying prevention system can also be equipped with a monitoring unit that monitors a child's online activity and issues an alert when specific keywords or phrases are used. For example, if a child uses words like "bully" or "hate" on social media or in chat, the monitoring unit will immediately issue an alert and notify parents or teachers. The monitoring unit can also issue an alert when a child accesses specific websites. For example, it can issue an alert if a site containing violent content is accessed. The monitoring unit can also issue an alert when a child downloads specific apps. For example, it can issue an alert if an app with age restrictions is downloaded. This allows for more strict monitoring of a child's online activity and allows for addressing issues before they occur.

[0044] The cyberbullying prevention system may further include a history storage unit that stores a history of a child's online activities so that parents and teachers can review them later. For example, it may record which websites a child has accessed and which apps a child has used. The history storage unit may also store the contents of messages and posts sent by a child. For example, it may record exchanges on social media or in chats. The history storage unit may also store messages and comments received by a child. For example, it may record messages and comments from other users. This allows parents and teachers to have a detailed understanding of a child's online activities and take appropriate action if a problem occurs.

[0045] The cyberbullying prevention system may further include a reward unit that introduces a reward system for a child's online activities to promote positive behavior. For example, the reward unit may award points when a child communicates without using offensive language. The reward unit may also award points when a child behaves kindly toward other users. For example, points may be awarded when a child sends an encouraging message to a friend. The reward unit may also award points when a child engages in online learning activities. For example, points may be awarded when a child studies using an educational app. This may encourage a child to adopt positive online behavior and promote healthy communication.

[0046] The cyberbullying prevention system may further include a feedback unit that provides feedback on a child's online activities and encourages self-evaluation. For example, positive feedback may be provided for messages or posts sent by the child. The feedback unit may also provide feedback for messages or comments received by the child. For example, feedback expressing gratitude for an encouraging message from a friend may be provided. The feedback unit may also provide tools for the child to self-evaluate their online behavior. For example, a self-evaluation sheet may be used to allow the child to reflect on their own behavior. This allows the child to objectively evaluate and improve their online behavior.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The app pre-installation unit pre-installs the app on child-targeted devices sold by mobile carriers. For example, when a child purchases a new device, the app is automatically installed as part of the initial setup. Step 2: Sentence optimization using generative AI. The education department uses a pre-installed app to train the generative AI to improve children's sentences. For example, the generative AI analyzes sentences entered by children, detects offensive expressions and inflammatory content, and provides feedback for improvement. For example, if the generative AI detects an offensive word such as "idiot," it provides advice such as, "That word may be hurtful to the other person. Try using a different expression." The generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze children's sentences and generate feedback for improvement. Step 3: The analysis unit for aggressiveness and inflammatory elements of text analyzes the aggressiveness and inflammatory elements of texts that have been optimized by the text optimization education unit using the generation AI. For example, the generation AI analyzes texts written by children on social media or messaging apps to detect offensive language or expressions that may cause inflammatory reactions. The generation AI scores these elements and issues a warning if they exceed a certain standard. Step 4: The notification unit notifies parents and schools of the results of the analysis by the analysis unit of aggressiveness and inflammatory elements in the text. For example, if the generation AI detects a change in aggressiveness or inflammatory elements in a child's text, it will notify parents and schools of this information via email or in-app notification.

[0049] (Example 2) In an embodiment of the present invention, the cyberbullying prevention system is a system in which an app is pre-installed on children's devices sold by mobile phone carriers, and a generation AI optimizes children's writing, analyzes aggression and inflammatory elements, and notifies parents and schools if any changes are detected. This allows the cyberbullying prevention system to prevent children from being cyberbullied and promote healthy communication.

[0050] An anti-cyberbullying system according to an embodiment includes an app pre-installation unit, a generation AI-based text moderation training unit, a text aggressiveness and inflammatory content analysis unit, and a notification unit. The app pre-installation unit pre-installs the app on children's devices sold by mobile phone carriers. For example, when a child purchases a new device, the app is automatically installed as part of the initial setup. The generation AI-based text moderation training unit uses the pre-installed app to train the generation AI to moderate the child's text. For example, if the generation AI detects an offensive word such as "idiot," it provides advice such as, "That word may be hurtful to the other person. Try using a different expression." The generation AI analyzes the child's text using a text generation AI (e.g., LLM) or a multimodal generation AI and generates feedback for moderation. The text aggressiveness and inflammatory content analysis unit analyzes the aggressiveness and inflammatory content of the text moderated by the generation AI-based text moderation training unit. For example, the generation AI analyzes text written by a child on social media or a messaging app to detect offensive language or expressions that may spark outrage. The generation AI scores these elements and issues a warning if a certain standard is exceeded. The notification unit notifies parents and schools of the results of the analysis by the analysis unit of the aggressiveness and outrage elements of the text. For example, if the generation AI detects a change in aggressiveness or outrage elements in a child's text, it notifies parents and schools of this information via email or in-app notification. In this way, the cyberbullying prevention system according to the embodiment can prevent cyberbullying among children and promote healthy communication. For example, educating children not to use offensive language can reduce online troubles. Furthermore, parents and schools can detect problems early and take appropriate measures to support the healthy development of children.

[0051] The pre-installed section of the app can automatically perform customization settings according to the child's age and gender. For example, the pre-installed section of the app automatically performs customization settings based on the child's age and gender when the app is pre-installed. For example, it provides filtering functions according to age and interface designs according to gender. Furthermore, when a parent enters the child's age and gender during initial device setup, the app automatically performs appropriate settings. For example, it sets educational content according to age and theme colors according to gender. Furthermore, when the app is launched for the first time, it provides a wizard for performing customization settings based on the child's age and gender. For example, it selects usage restrictions according to age and characters according to gender. This enables appropriate settings according to the child's age and gender.

[0052] The pre-installed part of the app can provide an interactive tutorial to help parents understand the app's functions. The pre-installed part of the app can provide an interactive tutorial to help parents understand the app's functions, for example, when initially setting up a device. For example, the app's main functions can be explained step by step. Furthermore, during initial setup, a demonstration can be provided to help parents learn how to use the app. For example, the parent can learn by actually operating each function of the app. Furthermore, when the app is launched for the first time, a guided tour can be provided to help parents understand the app's functions. For example, the tutorial can guide parents through each screen of the app in order and explain its functions. This makes it easier for parents to understand the app's functions.

[0053] The pre-installed unit of the app can use an emotion estimation function to analyze a child's emotions when using the device for the first time and provide guidance to provide a positive experience. For example, when the device is used for the first time, the pre-installed unit of the app can use the emotion estimation function to analyze a child's emotions and provide guidance to provide a positive experience. For example, if the child is nervous, the pre-installed unit can provide advice to relax. Furthermore, when the device is used for the first time, the pre-installed unit of the app can use the emotion estimation function to monitor the child's emotions in real time and provide interactive guidance to provide a positive experience. For example, if the child is having fun, the pre-installed unit can display praise. Furthermore, when the device is used for the first time, the pre-installed unit of the app can use the emotion estimation function to analyze the child's emotions and provide customized guidance to provide a positive experience. For example, the pre-installed unit can provide animations or voice messages that correspond to the child's emotions. This can provide a positive experience when the child uses the device for the first time.

[0054] The generative AI text optimization education unit can provide education using specific examples based on a child's interests and concerns. For example, when the generative AI optimizes a child's text, the generative AI uses specific examples based on a child's interests and concerns to provide education. For example, example sentences incorporating characters or themes that the child likes are provided. In addition, a system is constructed in which the generative AI provides text optimization education using specific examples based on a child's interests and concerns. For example, example sentences related to sports or hobbies that the child is interested in are used. In addition, the generative AI provides text optimization education using specific examples based on a child's interests and concerns. For example, stories or scenarios related to topics that the child is interested in are provided. This improves the effectiveness of education by providing education using specific examples based on a child's interests and concerns.

[0055] The sentence optimization education unit using the generation AI can adjust the difficulty level in stages according to the child's level of comprehension. For example, the generation AI generation AI generation AI generation AI builds a system that adjusts the difficulty level of the feedback provided by the generation AI in stages according to the child's level of comprehension. For example, beginner, intermediate, and advanced feedback are prepared and applied according to the child's progress. The generation AI also adjusts the difficulty level of the feedback provided by the generation AI in stages according to the child's level of comprehension. For example, simple advice is provided when the level of comprehension is low, and detailed feedback is provided when the level of comprehension is high. The generation AI also provides feedback that adjusts the difficulty level in stages according to the child's level of comprehension. For example, the content of the feedback is made gradually more complex so that it is easier for the child to understand. This allows the difficulty level to be adjusted in stages according to the child's level of comprehension, making it possible to provide appropriate education.

[0056] The generative AI writing optimization education unit uses an emotion estimation function to analyze the emotions a child feels when writing and provide advice to change negative emotions to positive ones. For example, the generative AI writing optimization education unit uses the emotion estimation function to build a system that analyzes the emotions a child feels when writing and provides advice to change negative emotions to positive ones. For example, if a child is angry, advice to relax is provided. The emotion estimation function also analyzes the emotions a child feels when writing and provides specific advice to change negative emotions to positive ones. For example, if a child is sad, an encouraging message is displayed. The emotion estimation function also analyzes the emotions a child feels when writing in real time and provides advice to change negative emotions to positive ones. For example, if a child is feeling anxious, advice to reassure is provided. This promotes healthy communication by changing negative emotions when a child writes to positive ones.

[0057] A text optimization education unit using generative AI can translate text written by children into other languages ​​and provide optimization education in multiple languages. For example, a text optimization education unit using generative AI uses generative AI to translate text written by children into other languages ​​and build a system to provide optimization education in multiple languages. For example, it translates into English or French and provides optimization education. In addition, text written by children is translated into other languages ​​using generative AI and provides optimization education in multiple languages. For example, it is translated into Spanish or Chinese and provides education in different language environments. In addition, a system is developed that uses generative AI to translate text written by children into multiple languages ​​and provide optimization education. For example, it translates from Japanese to English, French, German, etc. and provides education. In this way, by providing optimization education in multiple languages, appropriate education can be provided even in different language environments.

[0058] The generative AI text optimization education unit can use the emotion estimation function to collect other children's emotional reactions to text written by a child and provide education to promote empathy. The generative AI text optimization education unit, for example, uses the emotion estimation function to collect other children's emotional reactions to text written by a child and builds a system to provide education to promote empathy. For example, it provides other children's emotional reactions as feedback. It also collects other children's emotional reactions to text written by a child using the emotion estimation function and provides specific education to promote empathy. For example, it provides workshops to help children understand the emotions of other children. It also uses the emotion estimation function to collect other children's emotional reactions to text written by a child in real time and provides education to promote empathy. For example, it provides simulations based on the emotional reactions of other children. This allows the collection of other children's emotional reactions and promotion of empathy, thereby improving children's social skills.

[0059] The analysis unit for text aggression and inflammatory elements uses past data to learn individual trends, enabling more accurate analysis. For example, when the generation AI analyzes the aggression of text, the analysis unit for text aggression and inflammatory elements uses past data to learn individual trends, building a system that performs more accurate analysis. For example, a model is trained based on past data on aggressive texts. Furthermore, using past data, the generation AI learns individual trends and analyzes the aggression of text with high accuracy. For example, past posts of a specific user are analyzed and those trends are learned. Furthermore, a system is developed in which the generation AI uses past data to learn individual trends and analyze the aggression of text. For example, aggression patterns are identified based on past cases of flaming. This allows for more accurate analysis by learning individual trends using past data.

[0060] The part that analyzes aggressiveness and inflammatory elements in text can suggest specific improvement measures for detected aggressiveness and inflammatory elements. The part that analyzes aggressiveness and inflammatory elements in text will, for example, build a system that suggests specific improvement measures for aggressiveness and inflammatory elements detected by the generation AI. For example, it will suggest replacing aggressive expressions with more appropriate expressions. It will also provide specific improvement measures for aggressiveness and inflammatory elements detected by the generation AI. For example, it will suggest alternative expressions that do not use aggressive language. It will also develop a system that suggests specific improvement measures for aggressiveness and inflammatory elements detected by the generation AI. For example, it will provide advice on how to avoid expressions that may provoke an uproar. This will allow children to learn appropriate expressions by suggesting specific improvement measures.

[0061] The analysis unit for text aggression and inflammatory elements uses an emotion estimation function to monitor changes in emotions when a child writes in real time, making it possible to detect problems early. The analysis unit for text aggression and inflammatory elements, for example, uses the emotion estimation function to monitor changes in emotions when a child writes in real time, building a system that detects problems early. For example, a warning is issued if a child is feeling angry. The emotion estimation function also monitors changes in emotions when a child writes in real time, making it possible to detect problems early. For example, a notification is sent to warn a child if the child is sad. The emotion estimation function is also used to monitor changes in emotions when a child writes in real time, making it possible to detect problems early. For example, support is provided if a child is feeling anxious. In this way, by monitoring changes in emotions in real time, problems can be detected early and appropriate measures can be taken.

[0062] The analysis unit for text aggression and inflammatory elements can share the data analyzed by the generative AI with other educational institutions and research institutions to conduct joint research to prevent cyberbullying. The analysis unit for text aggression and inflammatory elements can, for example, share the data analyzed by the generative AI with other educational institutions and research institutions to build a system for joint research to prevent cyberbullying. For example, a data sharing platform can be developed. The data analyzed by the generative AI can also be shared with other educational institutions and research institutions to conduct joint research to prevent cyberbullying. For example, they can conduct joint data analysis and consider effective countermeasures. The data analyzed by the generative AI can also be shared with other educational institutions and research institutions to develop a system for joint research to prevent cyberbullying. For example, an API can be provided for data sharing. This allows data to be shared and joint research can be conducted to consider effective countermeasures to prevent cyberbullying.

[0063] The analysis section for aggressiveness and inflammatory elements in text displays the detected aggressiveness and inflammatory elements in an easy-to-understand visual manner, making it easier for children to recognize the problem themselves. The analysis section for aggressiveness and inflammatory elements in text will, for example, build a system that displays the aggressiveness and inflammatory elements detected by the generation AI in an easy-to-understand visual manner, making it easier for children to recognize the problem themselves. For example, it will highlight aggressive words in red. It will also visually display the aggressiveness and inflammatory elements in text written by children, making it easier for them to recognize the problem. For example, it will indicate parts that may be inflammatory with an icon. We will also develop a system that displays the aggressiveness and inflammatory elements detected by the generation AI in an easy-to-understand visual manner, making it easier for children to recognize the problem themselves. For example, it will display the aggression score in a graph. This makes it easier for children to recognize the problem by displaying it visually.

[0064] The analysis unit for text aggression and inflammatory elements can use an emotion estimation function to analyze the emotional reactions of other users to text written by children and evaluate the social impact. The analysis unit for text aggression and inflammatory elements can, for example, use the emotion estimation function to analyze the emotional reactions of other users to text written by children and build a system to evaluate the social impact. For example, the ratio of positive and negative reactions is displayed. The emotion estimation function also analyzes the emotional reactions of other users to text written by children and evaluates the social impact. For example, the degree to which the text evokes empathy is scored. The emotion estimation function also analyzes the emotional reactions of other users to text written by children in real time and develops a system to evaluate the social impact. For example, changes in emotional reactions are displayed over time. This allows the emotional reactions of other users to be analyzed and the social impact of a child's text to be understood.

[0065] When issuing a notification, the notification unit can include specific examples and advice to make the notification content easier for parents and teachers to understand. For example, the notification unit will build a system in which, when the generation AI issues a notification, it includes specific examples and advice to make the notification content easier for parents and teachers to understand. For example, it will explain specific examples of offensive language and its impact. In addition, the generation AI will issue notifications that include specific examples and advice to make the notification content easier for parents and teachers to understand. For example, it will specifically indicate problems with a child's writing and how to improve it. In addition, a system will be developed in which, when the generation AI issues a notification, it will include specific examples and advice to make the notification content easier for parents and teachers to understand. For example, it will provide advice based on past cases. In this way, by including specific examples and advice, it will be easier for parents and teachers to understand the notification content.

[0066] The notification unit can provide a function that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, the notification unit will build a system that provides a function that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, it will allow the frequency of notifications to be set on a weekly or monthly basis. In addition, a function will be provided that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, it will be possible to set up a system that allows the frequency and content of notifications to be customized according to the needs of parents and teachers. For example, an interface will be provided that allows the content of notifications to be set in detail. This will allow the frequency and content of notifications to be customized, making it possible to provide information that meets the needs of parents and teachers.

[0067] The notification unit can use the emotion estimation function to analyze the emotional reactions of parents and teachers who receive the notification and provide appropriate support. The notification unit, for example, uses the emotion estimation function to analyze the emotional reactions of parents and teachers who receive the notification and build a system that provides appropriate support. For example, if a parent is feeling anxious, the notification unit provides advice to reassure the parent. The emotion estimation function also analyzes the emotional reactions of parents and teachers who receive the notification and provides appropriate support. For example, if a teacher is feeling stressed, the notification unit makes suggestions to help them relax. The emotion estimation function is also used to develop a system that analyzes the emotional reactions of parents and teachers who receive the notification in real time and provides appropriate support. For example, customized support is provided according to the emotional reaction. This enables more effective responses by analyzing the emotional reactions of parents and teachers who receive the notification and providing appropriate support.

[0068] The notification unit provides notification content as an audio message or a video message, and can transmit information visually or aurally. The notification unit, for example, provides notification content as an audio message or a video message, and builds a system for transmitting information visually or aurally. For example, audio advice or video explanations are provided. Notification content for parents or teachers is also provided as an audio message or a video message. For example, audio warnings or specific video examples are shown. Furthermore, a system is developed that provides notification content as an audio message or a video message, and transmits information visually or aurally. For example, audio instructions or video guidance are provided. As a result, by providing notification content as an audio message or a video message, it becomes possible to transmit information visually or aurally.

[0069] The notification unit can use the emotion estimation function to monitor the emotional reactions of parents and teachers who receive notifications in real time and promote an appropriate response. The notification unit, for example, uses the emotion estimation function to monitor the emotional reactions of parents and teachers who receive notifications in real time and build a system to promote an appropriate response. For example, if a parent is feeling anxious, the notification unit provides advice to reassure the parent. The emotion estimation function also monitors the emotional reactions of parents and teachers who receive notifications in real time and promotes an appropriate response. For example, if a teacher is feeling stressed, the notification unit makes suggestions to help them relax. The emotion estimation function also monitors the emotional reactions of parents and teachers who receive notifications in real time and develops a system to promote an appropriate response. For example, customized support is provided according to the emotional response. This enables more effective support by monitoring the emotional reactions of parents and teachers who receive notifications in real time and promoting an appropriate response.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The cyberbullying prevention system can also be equipped with a monitoring unit that monitors a child's online activity and issues an alert when specific keywords or phrases are used. For example, if a child uses words like "bully" or "hate" on social media or in chat, the monitoring unit will immediately issue an alert and notify parents or teachers. The monitoring unit can also issue an alert when a child accesses specific websites. For example, it can issue an alert if a site containing violent content is accessed. The monitoring unit can also issue an alert when a child downloads specific apps. For example, it can issue an alert if an app with age restrictions is downloaded. This allows for more strict monitoring of a child's online activity and allows for addressing issues before they occur.

[0072] The cyberbullying prevention system may further include a history storage unit that stores a history of a child's online activities so that parents and teachers can review them later. For example, it may record which websites a child has accessed and which apps a child has used. The history storage unit may also store the contents of messages and posts sent by a child. For example, it may record exchanges on social media or in chats. The history storage unit may also store messages and comments received by a child. For example, it may record messages and comments from other users. This allows parents and teachers to have a detailed understanding of a child's online activities and take appropriate action if a problem occurs.

[0073] The cyberbullying prevention system may further include a reward unit that introduces a reward system for a child's online activities to promote positive behavior. For example, the reward unit may award points when a child communicates without using offensive language. The reward unit may also award points when a child behaves kindly toward other users. For example, points may be awarded when a child sends an encouraging message to a friend. The reward unit may also award points when a child engages in online learning activities. For example, points may be awarded when a child studies using an educational app. This may encourage a child to adopt positive online behavior and promote healthy communication.

[0074] The cyberbullying prevention system may further include a feedback unit that provides feedback on a child's online activities and encourages self-evaluation. For example, positive feedback may be provided for messages or posts sent by the child. The feedback unit may also provide feedback for messages or comments received by the child. For example, feedback expressing gratitude for an encouraging message from a friend may be provided. The feedback unit may also provide tools for the child to self-evaluate their online behavior. For example, a self-evaluation sheet may be used to allow the child to reflect on their own behavior. This allows the child to objectively evaluate and improve their online behavior.

[0075] The cyberbullying prevention system may further include an emotion monitoring unit that uses an emotion estimation function for a child's online activities to monitor a child's emotions in real time and promote appropriate responses. For example, if a child is feeling angry or sad, it may provide advice to relax. The emotion monitoring unit may also provide positive feedback if a child is having fun, such as by displaying words of praise or encouraging messages. Furthermore, if a child is feeling anxious, it may provide support to reassure the child, such as by providing relaxing music or videos. In this way, the system can support the healthy development of children by monitoring a child's emotions in real time and promoting appropriate responses.

[0076] The cyberbullying prevention system may further include a sentiment analysis unit that uses the emotion estimation function for the child's online activities to analyze other users' emotional reactions to writings by the child and evaluate the social impact. For example, it analyzes how other users felt about writings by the child. The sentiment analysis unit can also analyze emotional reactions to messages and comments received by the child. For example, it analyzes how the child felt about messages from friends. Furthermore, the sentiment analysis unit can evaluate the social impact based on the emotional reactions received from other users about the child's online behavior. For example, it evaluates the positive or negative impact that the child's behavior has had on other users. This makes it possible to understand the impact of the child's online behavior on society and take appropriate measures.

[0077] The cyberbullying prevention system may further include an emotion conversion unit that uses the emotion estimation function for the child's online activities to analyze the emotion the child expresses when writing and provides advice to change negative emotions into positive ones. For example, if the child is angry, the emotion conversion unit may provide advice to relax. Also, if the child is sad, the emotion conversion unit may display an encouraging message. Furthermore, if the child is feeling anxious, the emotion conversion unit may provide advice to reassure the child. For example, the emotion conversion unit may teach breathing techniques or stretching techniques to help the child relax. In this way, healthy communication can be promoted by changing the negative emotions the child expresses when writing into positive ones.

[0078] The cyberbullying prevention system may further include an emotional empathy unit that uses the emotion estimation function for the child's online activities to collect other children's emotional reactions to the child's writings and provides education to promote empathy. For example, it provides feedback on the emotional reactions of other children. The emotional empathy unit may also collect other children's emotional reactions to the child's writings in real time and provide specific education to promote empathy. For example, it may provide a workshop to help children understand the emotions of other children. Furthermore, the emotional empathy unit may provide simulations based on the emotional reactions of other children. For example, it may use scenarios that reproduce the emotional reactions of other children to provide training to learn empathy. In this way, it is possible to improve the child's social skills by collecting other children's emotional reactions and promoting empathy.

[0079] The cyberbullying prevention system may further include an emotion monitoring unit that uses an emotion estimation function for a child's online activities to monitor emotional changes in real time when a child writes and detect problems early. For example, a warning may be issued if a child is feeling angry. The emotion monitoring unit may also send a notification to warn a child if they are sad. Furthermore, the emotion monitoring unit may provide support if a child is feeling anxious. For example, it may provide advice on how to relax or a message to reassure the child. In this way, by monitoring emotional changes in real time, problems can be detected early and appropriate measures can be taken.

[0080] The cyberbullying prevention system may further include a sentiment analysis unit that uses the emotion estimation function for the child's online activities to analyze other users' emotional reactions to writings by the child and evaluate the social impact. For example, it analyzes how other users felt about writings by the child. The sentiment analysis unit can also analyze emotional reactions to messages and comments received by the child. For example, it analyzes how the child felt about messages from friends. Furthermore, the sentiment analysis unit can evaluate the social impact based on the emotional reactions received from other users about the child's online behavior. For example, it evaluates the positive or negative impact that the child's behavior has had on other users. This makes it possible to understand the impact of the child's online behavior on society and take appropriate measures.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The app pre-installation unit pre-installs the app on child-targeted devices sold by mobile carriers. For example, when a child purchases a new device, the app is automatically installed as part of the initial setup. Step 2: Sentence optimization using generative AI. The education department uses a pre-installed app to train the generative AI to improve children's sentences. For example, the generative AI analyzes sentences entered by children, detects offensive expressions and inflammatory content, and provides feedback for improvement. For example, if the generative AI detects an offensive word such as "idiot," it provides advice such as, "That word may be hurtful to the other person. Try using a different expression." The generative AI uses text generation AI (e.g., LLM) or multimodal generation AI to analyze children's sentences and generate feedback for improvement. Step 3: The analysis unit for aggressiveness and inflammatory elements of text analyzes the aggressiveness and inflammatory elements of texts that have been optimized by the text optimization education unit using the generation AI. For example, the generation AI analyzes texts written by children on social media or messaging apps to detect offensive language or expressions that may cause inflammatory reactions. The generation AI scores these elements and issues a warning if they exceed a certain standard. Step 4: The notification unit notifies parents and schools of the results of the analysis by the analysis unit of aggressiveness and inflammatory elements in the text. For example, if the generation AI detects a change in aggressiveness or inflammatory elements in a child's text, it will notify parents and schools of this information via email or in-app notification.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The 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.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 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.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

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

[0111] In the 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.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The 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.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 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. The app pre-installation department pre-installs apps on children's devices sold by mobile phone carriers, a generation AI sentence optimization training unit that performs sentence optimization training using a generation AI using the app pre-installed by the app pre-installation unit; An analysis unit for analyzing offensive and inflammatory elements of text that has been optimized by the text optimization education unit using the generation AI; a notification unit that notifies parents and a school of the results of the analysis by the analysis unit of the offensiveness and inflammatory elements of the text. A system characterized by:

2. The pre-installed part of the application Automatically customize settings based on your child's age and gender 2. The system of claim 1.

3. The pre-installed part of the application Providing an interactive tutorial to help the parent understand the app's features 2. The system of claim 1.

4. The pre-installed part of the application Analyzes emotions when a child first uses the device and provides guidance to ensure a positive experience 2. The system of claim 1.

5. The text optimization education department using the generation AI is Teaching using concrete examples based on children's interests 2. The system of claim 1.

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