system

Abstract

An object of a system according to an embodiment is to detect a risk that an adolescent may encounter on an SNS and appropriately deal with the risk.SOLUTION: A system according to an embodiment includes a filtering unit, a risk detection unit, a warning unit, and a reporting unit. The filtering unit analyzes posts or messages on the SNS and filters out dangerous content. The risk detection unit detects a risk that the adolescent may encounter on the SNS on the basis of the content filtered by the filtering unit. The warning unit issues a warning to the adolescent based on the risk detected by the risk detection unit. The reporting unit reports to the protector based on the risk detected by the risk detection unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem of not being able to effectively detect and appropriately address the risks that young people may encounter on social networking sites.

[0005] The system according to the embodiment aims to detect risks that young people may encounter on SNS and deal with them appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a filtering unit, a risk detection unit, a warning unit, and a reporting unit. The filtering unit analyzes posts or messages on the SNS and filters out dangerous content. The risk detection unit detects risks that young people may encounter on the SNS based on the content filtered by the filtering unit. The warning unit issues a warning to young people based on the risks detected by the risk detection unit. The reporting unit reports to parents based on the risks detected by the risk detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect risks that young people may encounter on SNS and deal with them appropriately. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The ProtectU system according to an embodiment of the present invention is a tool for protecting young people from crimes on social media, and provides a safe digital environment while protecting their privacy. As a result, the ProtectU system provides an environment where young people can safely engage in social media activities, and improves online safety awareness while maintaining a trusting relationship between parents and children.

[0029] The ProtectU system according to the embodiment includes a filtering unit, a risk detection unit, a warning unit, and a reporting unit. The filtering unit analyzes posts or messages on social media and filters out dangerous content. For example, the filtering unit detects violent content, inappropriate images, and potentially fraudulent messages, and blocks minors from accessing them. The filtering unit also uses a generation AI to analyze the content of posts and messages on social media and identify dangerous content. The risk detection unit detects risks that minors may encounter on social media based on the content filtered by the filtering unit. For example, the risk detection unit detects risks such as stalking, cyberbullying, and fraud early on and issues a warning to minors. The warning unit issues a warning to minors based on the risks detected by the risk detection unit. For example, the generation AI automatically displays a warning message when a minor attempts to access dangerous content or when a risk is detected. The reporting unit reports to parents based on the risks detected by the risk detection unit. For example, the reporting unit notifies parents of the history of attempts to access dangerous content and details of detected risks. This allows the ProtectU system to provide a safe environment for young people to use social media, improving online safety awareness while maintaining a trusting relationship between parents and children.

[0030] The filtering unit understands the context of content and can determine its risk based on the entire context, rather than on individual words. For example, the filtering unit uses a generation AI to analyze the context of a post or message and determine its risk by understanding the meaning of the entire sentence, rather than on individual words. For example, even if a specific word is included, it will not be filtered if the context is safe. The filtering unit also uses a generation AI to analyze the context and determine its risk by taking into account word combinations and the surrounding context. For example, even if a violent word is included, it will not be filtered if the context is educational. The filtering unit also uses a generation AI to deeply understand the context of a post or message and determine its risk based on the meaning of the entire sentence, rather than on individual words. For example, even if a specific word is included, it will not be filtered if the context is a joke. This improves the accuracy of filtering.

[0031] The filtering unit can refer to the user's past behavioral history and perform individually customized filtering. For example, the generation AI in the filtering unit analyzes the user's past behavioral history and performs individually customized filtering. For example, stricter filtering is applied to users who have a history of accessing dangerous content in the past. The filtering unit also performs individually customized filtering by having the generation AI learn the user's behavioral patterns. For example, for users who tend to access dangerous content during specific times of the day, filtering is strengthened during those times. The filtering unit also performs individually customized filtering by having the generation AI perform based on the user's past behavioral history. For example, for users who tend to access dangerous content of a specific genre, filtering is strengthened for that genre. This improves the accuracy of filtering.

[0032] The filtering unit can be extended to at least one other digital platform, such as an online game or a chat app, in addition to social media. For example, the filtering unit may filter chats and messages in online games as well as social media. For example, the filtering unit may filter violent remarks and inappropriate images in games. The filtering unit may also filter messages in chat apps as well. For example, the filtering unit may filter messages suspected of fraud or inappropriate images in private chats. The filtering unit may also filter other digital platforms (e.g., comments on forums and blogs) as well. For example, the filtering unit may filter offensive comments on forums and inappropriate comments on blogs. This broadens the scope of filtering.

[0033] The filtering unit can visualize the filtering results and provide a dashboard that allows the user to intuitively understand what content has been filtered. For example, the generation AI in the filtering unit visualizes the filtering results and provides a dashboard that allows the user to intuitively understand what content has been filtered. For example, the type and number of filtered content may be displayed in a graph. The generation AI also visualizes the filtering results and provides a dashboard that allows the user to check details of the filtered content. For example, some of the filtered messages may be displayed and the reasons for the filtering may be explained. The generation AI also visualizes the filtering results and provides a dashboard that allows the user to grasp trends in the filtered content. For example, the time period and frequency of filtered content may be displayed in a graph. This allows the user to intuitively understand the filtering results.

[0034] The risk detection unit tracks a user's behavioral patterns over the long term and can detect abnormal behavior at an early stage. In the risk detection unit, for example, the generation AI tracks a user's behavioral patterns over the long term and detects abnormal behavior at an early stage. For example, it detects behavior such as frequently logging in at unusual times. In addition, the risk detection unit tracks a user's behavioral patterns over the long term and detects abnormal behavior at an early stage. For example, it detects behavior such as frequently interacting with people who are unusual. In addition, the risk detection unit tracks a user's behavioral patterns over the long term and detects abnormal behavior at an early stage. For example, it detects behavior such as frequently sending messages with unusual content. This makes it possible to detect abnormal behavior at an early stage.

[0035] The risk detection unit can refer to the user's geographical location information and identify risks in a specific area. For example, the generating AI can refer to the user's geographical location information and identify risks in a specific area. For example, it can detect risks of stalking or cyberbullying in a specific area. The risk detection unit can also refer to the user's geographical location information and identify risks in a specific area. For example, it can detect risks of fraud or unauthorized access in a specific area. The risk detection unit can also refer to the user's geographical location information and identify risks in a specific area. For example, it can detect risks of criminal activities or dangerous activities in a specific area. This makes it possible to identify risks in a specific area.

[0036] The risk detection unit can be extended to not only social networking sites but also at least one other digital platform, such as an online game or a chat app. For example, the generation AI of the risk detection unit may include chats and messages in online games as well as social networking sites as its risk detection targets. For example, the generation AI may detect risks of stalking and cyberbullying in games. The risk detection unit may also include messages in chat apps as its risk detection targets. For example, the generation AI may detect risks of fraud and unauthorized access in private chats. The risk detection unit may also include other digital platforms (e.g., comments on forums and blogs) as its risk detection targets. For example, the generation AI may detect offensive comments in forums and inappropriate comments in blogs. This broadens the scope of risk detection targets.

[0037] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0038] The risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. In the risk detection unit, for example, the generation AI analyzes a user's social relationships and prioritizes detecting interactions with high-risk parties. For example, it detects interactions with parties who have engaged in problematic behavior in the past. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with anonymous accounts or new accounts. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with parties who have posted inappropriate content in the past. This makes it possible to prioritize detecting interactions with high-risk parties.

[0039] The risk detection unit can refer to the user's risk history and identify the risk of recurrence. In the risk detection unit, for example, the generation AI refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of cyberbullying in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of fraud in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of stalking in the past. This makes it possible to identify the risk of recurrence.

[0040] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0041] The risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. In the risk detection unit, for example, the generation AI analyzes a user's social relationships and prioritizes detecting interactions with high-risk parties. For example, it detects interactions with parties who have engaged in problematic behavior in the past. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with anonymous accounts or new accounts. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with parties who have posted inappropriate content in the past. This makes it possible to prioritize detecting interactions with high-risk parties.

[0042] The risk detection unit can refer to the user's risk history and identify the risk of recurrence. In the risk detection unit, for example, the generation AI refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of cyberbullying in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of fraud in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of stalking in the past. This makes it possible to identify the risk of recurrence.

[0043] The risk detection unit can be extended to not only social networking sites but also at least one other digital platform, such as an online game or a chat app. For example, the generation AI of the risk detection unit may include chats and messages in online games as well as social networking sites as its risk detection targets. For example, the generation AI may detect risks of stalking and cyberbullying in games. The risk detection unit may also include messages in chat apps as its risk detection targets. For example, the generation AI may detect risks of fraud and unauthorized access in private chats. The risk detection unit may also include other digital platforms (e.g., comments on forums and blogs) as its risk detection targets. For example, the generation AI may detect offensive comments in forums and inappropriate comments in blogs. This broadens the scope of risk detection targets.

[0044] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0045] The risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. In the risk detection unit, for example, the generation AI analyzes a user's social relationships and prioritizes detecting interactions with high-risk parties. For example, it detects interactions with parties who have engaged in problematic behavior in the past. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with anonymous accounts or new accounts. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with parties who have posted inappropriate content in the past. This makes it possible to prioritize detecting interactions with high-risk parties.

[0046] The risk detection unit can refer to the user's risk history and identify the risk of recurrence. In the risk detection unit, for example, the generation AI refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of cyberbullying in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of fraud in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of stalking in the past. This makes it possible to identify the risk of recurrence.

[0047] The risk detection unit can be extended to not only social networking sites but also at least one other digital platform, such as an online game or a chat app. For example, the generation AI of the risk detection unit may include chats and messages in online games as well as social networking sites as its risk detection targets. For example, the generation AI may detect risks of stalking and cyberbullying in games. The risk detection unit may also include messages in chat apps as its risk detection targets. For example, the generation AI may detect risks of fraud and unauthorized access in private chats. The risk detection unit may also include other digital platforms (e.g., comments on forums and blogs) as its risk detection targets. For example, the generation AI may detect offensive comments in forums and inappropriate comments in blogs. This broadens the scope of risk detection targets.

[0048] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The ProtectU system can further include a health management unit that monitors the user's health status. The health management unit monitors the user's heart rate and sleep patterns and issues an alert if an abnormality is detected. For example, it issues an alert if the heart rate rises suddenly or if the user continues to be sleep-deprived. The health management unit can also monitor the user's exercise volume and send a notification encouraging exercise if a lack of exercise is detected. Furthermore, the health management unit can manage the user's dietary records and provide advice if the user's nutritional balance is unbalanced. This allows for comprehensive management of the user's health status and reduces health risks.

[0051] The ProtectU system can further include a learning support unit that supports the user's learning activities. The learning support unit monitors the user's learning progress and supports the creation of a learning plan. For example, it visualizes the user's progress toward the learning goals set by the user and evaluates the degree of achievement. The learning support unit can also provide learning content tailored to the user's learning style. For example, it can provide video content to users who are good at visual learning and audio content to users who are good at auditory learning. Furthermore, the learning support unit can provide feedback to maintain the user's motivation for learning. This can improve the user's learning effectiveness.

[0052] The ProtectU system can further include a hobby support unit that makes recommendations based on the user's hobbies and interests. The hobby support unit analyzes the user's past behavioral history and interests and recommends related events and content. For example, if the user is interested in music, it can provide information about nearby concerts. The hobby support unit can also suggest new hobbies based on the user's interests. For example, if the user is interested in outdoor activities, it can suggest new hiking trails and campsites. Furthermore, the hobby support unit can introduce communities related to the user's hobbies and promote interaction with people who share the same hobbies. This can improve the user's quality of life.

[0053] The ProtectU system can also be equipped with a time management unit that supports the user's time management. The time management unit manages the user's schedule and suggests efficient time allocation. For example, it suggests optimal time allocation for tasks set by the user. The time management unit can also analyze how the user uses their time based on their past behavioral history and suggest areas for improvement. For example, it can provide advice on how to reduce wasted time. Furthermore, the time management unit can monitor the user's progress toward achieving their goals and send reminders as necessary. This can improve the user's time management skills.

[0054] The ProtectU system can further include an Internet security unit that supports users' safe Internet use. The Internet security unit monitors users' Internet usage and detects security risks. For example, it detects risks of unauthorized access and phishing scams and issues warnings. The Internet security unit can also suggest security measures for users' devices. For example, it can suggest installing antivirus software or configuring a firewall. Furthermore, the Internet security unit can provide advice on protecting users' privacy. This can improve the safety of users' Internet use.

[0055] The ProtectU system may further include a digital detox unit that supports users' digital detox. The digital detox unit monitors the user's device usage time and encourages them to take a break if excessive usage is detected. For example, if the user continues using the device for a long period of time, the digital detox unit may notify the user to take a break. The digital detox unit may also analyze the user's device usage patterns and propose a specific action plan for digital detox. For example, the digital detox unit may suggest not using the device during certain hours. The digital detox unit may also support the user in setting goals for device usage and monitor the progress of those goals. This effectively supports the user's digital detox.

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

[0057] Step 1: The filtering unit analyzes posts and messages on social media and filters out dangerous content. For example, it detects violent content, inappropriate images, and messages suspected of fraud, preventing minors from accessing them. Generative AI is also used to analyze the content of posts and messages on social media and identify dangerous content. Step 2: The risk detection unit detects risks that young people may encounter on social media based on the content filtered by the filtering unit, such as stalking, cyberbullying, and fraud, at an early stage. Step 3: The warning unit issues a warning to the minor based on the risks detected by the risk detection unit. For example, if a minor attempts to access dangerous content or if a risk is detected, the generation AI automatically displays a warning message. Step 4: The reporting unit reports to the parent based on the risks detected by the risk detection unit, for example, notifying the parent of the history of attempts to access dangerous content and details of any risks detected.

[0058] (Example 2) The ProtectU system according to an embodiment of the present invention is a tool for protecting young people from crimes on social media, and provides a safe digital environment while protecting their privacy. As a result, the ProtectU system provides an environment where young people can safely engage in social media activities, and improves online safety awareness while maintaining a trusting relationship between parents and children.

[0059] The ProtectU system according to the embodiment includes a filtering unit, a risk detection unit, a warning unit, and a reporting unit. The filtering unit analyzes posts or messages on social media and filters out dangerous content. For example, the filtering unit detects violent content, inappropriate images, and potentially fraudulent messages, and blocks minors from accessing them. The filtering unit also uses a generation AI to analyze the content of posts and messages on social media and identify dangerous content. The risk detection unit detects risks that minors may encounter on social media based on the content filtered by the filtering unit. For example, the risk detection unit detects risks such as stalking, cyberbullying, and fraud early on and issues a warning to minors. The warning unit issues a warning to minors based on the risks detected by the risk detection unit. For example, the generation AI automatically displays a warning message when a minor attempts to access dangerous content or when a risk is detected. The reporting unit reports to parents based on the risks detected by the risk detection unit. For example, the reporting unit notifies parents of the history of attempts to access dangerous content and details of detected risks. This allows the ProtectU system to provide a safe environment for young people to use social media, improving online safety awareness while maintaining a trusting relationship between parents and children.

[0060] The filtering unit understands the context of content and can determine its risk based on the entire context, rather than on individual words. For example, the filtering unit uses a generation AI to analyze the context of a post or message and determine its risk by understanding the meaning of the entire sentence, rather than on individual words. For example, even if a specific word is included, it will not be filtered if the context is safe. The filtering unit also uses a generation AI to analyze the context and determine its risk by taking into account word combinations and the surrounding context. For example, even if a violent word is included, it will not be filtered if the context is educational. The filtering unit also uses a generation AI to deeply understand the context of a post or message and determine its risk based on the meaning of the entire sentence, rather than on individual words. For example, even if a specific word is included, it will not be filtered if the context is a joke. This improves the accuracy of filtering.

[0061] The filtering unit can refer to the user's past behavioral history and perform individually customized filtering. For example, the generation AI in the filtering unit analyzes the user's past behavioral history and performs individually customized filtering. For example, stricter filtering is applied to users who have a history of accessing dangerous content in the past. The filtering unit also performs individually customized filtering by having the generation AI learn the user's behavioral patterns. For example, for users who tend to access dangerous content during specific times of the day, filtering is strengthened during those times. The filtering unit also performs individually customized filtering by having the generation AI perform based on the user's past behavioral history. For example, for users who tend to access dangerous content of a specific genre, filtering is strengthened for that genre. This improves the accuracy of filtering.

[0062] The filtering unit can use the emotion estimation function to analyze the emotional tone of posts or messages and preferentially filter content that contains negative emotions. For example, the generation AI in the filtering unit uses the emotion estimation function to analyze the emotional tone of posts or messages and preferentially filter content that contains negative emotions. For example, messages with strong emotions of anger or sadness are filtered out. The generation AI in the filtering unit also uses the emotion estimation function to analyze the emotional tone of posts or messages and preferentially filter content that contains aggressive emotions. For example, messages with strong emotions of insult or threat are filtered out. The generation AI in the filtering unit also uses the emotion estimation function to analyze the emotional tone of posts or messages and preferentially filter content that contains negative emotions. For example, messages with strong emotions of despair or hatred are filtered out. This improves the accuracy of filtering content that contains negative emotions.

[0063] The filtering unit can be extended to at least one other digital platform, such as an online game or a chat app, in addition to social media. For example, the filtering unit may filter chats and messages in online games as well as social media. For example, the filtering unit may filter violent remarks and inappropriate images in games. The filtering unit may also filter messages in chat apps as well. For example, the filtering unit may filter messages suspected of fraud or inappropriate images in private chats. The filtering unit may also filter other digital platforms (e.g., comments on forums and blogs) as well. For example, the filtering unit may filter offensive comments on forums and inappropriate comments on blogs. This broadens the scope of filtering.

[0064] The filtering unit can visualize the filtering results and provide a dashboard that allows the user to intuitively understand what content has been filtered. For example, the generation AI in the filtering unit visualizes the filtering results and provides a dashboard that allows the user to intuitively understand what content has been filtered. For example, the type and number of filtered content may be displayed in a graph. The generation AI also visualizes the filtering results and provides a dashboard that allows the user to check details of the filtered content. For example, some of the filtered messages may be displayed and the reasons for the filtering may be explained. The generation AI also visualizes the filtering results and provides a dashboard that allows the user to grasp trends in the filtered content. For example, the time period and frequency of filtered content may be displayed in a graph. This allows the user to intuitively understand the filtering results.

[0065] The filtering unit can use the emotion estimation function to collect users' emotional reactions to filtered content and use the collected information to improve the filtering algorithm. For example, the generation AI in the filtering unit uses the emotion estimation function to collect users' emotional reactions to filtered content and use the collected information to improve the filtering algorithm. For example, if a user is dissatisfied with the filtering results, the algorithm is adjusted based on that feedback. The filtering unit also uses the emotion estimation function to collect users' emotional reactions to filtered content and use the collected information to improve the filtering algorithm. For example, if a user is satisfied with the filtering results, the algorithm is strengthened based on that feedback. The filtering unit also uses the emotion estimation function to collect users' emotional reactions to filtered content and use the collected information to improve the filtering algorithm. For example, if a user has neutral feelings about the filtering results, the algorithm is fine-tuned based on that feedback. This improves the accuracy of the filtering algorithm.

[0066] The risk detection unit tracks a user's behavioral patterns over the long term and can detect abnormal behavior at an early stage. In the risk detection unit, for example, the generation AI tracks a user's behavioral patterns over the long term and detects abnormal behavior at an early stage. For example, it detects behavior such as frequently logging in at unusual times. In addition, the risk detection unit tracks a user's behavioral patterns over the long term and detects abnormal behavior at an early stage. For example, it detects behavior such as frequently interacting with people who are unusual. In addition, the risk detection unit tracks a user's behavioral patterns over the long term and detects abnormal behavior at an early stage. For example, it detects behavior such as frequently sending messages with unusual content. This makes it possible to detect abnormal behavior at an early stage.

[0067] The risk detection unit can refer to the user's geographical location information and identify risks in a specific area. For example, the generating AI can refer to the user's geographical location information and identify risks in a specific area. For example, it can detect risks of stalking or cyberbullying in a specific area. The risk detection unit can also refer to the user's geographical location information and identify risks in a specific area. For example, it can detect risks of fraud or unauthorized access in a specific area. The risk detection unit can also refer to the user's geographical location information and identify risks in a specific area. For example, it can detect risks of criminal activities or dangerous activities in a specific area. This makes it possible to identify risks in a specific area.

[0068] The risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. In the risk detection unit, for example, the generation AI uses the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects sudden changes in emotions. In addition, the risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects the persistence of negative emotions over a long period of time. In addition, the risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects sudden fluctuations in emotions. This makes it possible to detect an emotionally unstable state as a risk.

[0069] The risk detection unit can be extended to not only social networking sites but also at least one other digital platform, such as an online game or a chat app. For example, the generation AI of the risk detection unit may include chats and messages in online games as well as social networking sites as its risk detection targets. For example, the generation AI may detect risks of stalking and cyberbullying in games. The risk detection unit may also include messages in chat apps as its risk detection targets. For example, the generation AI may detect risks of fraud and unauthorized access in private chats. The risk detection unit may also include other digital platforms (e.g., comments on forums and blogs) as its risk detection targets. For example, the generation AI may detect offensive comments in forums and inappropriate comments in blogs. This broadens the scope of risk detection targets.

[0070] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0071] The risk detection unit can use the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, the generation AI in the risk detection unit uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is dissatisfied with the risk detection results, the algorithm is adjusted based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is satisfied with the risk detection results, the algorithm is strengthened based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user has a neutral feeling about the risk detection results, the algorithm is fine-tuned based on that feedback. This improves the accuracy of the risk detection algorithm.

[0072] The risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. In the risk detection unit, for example, the generation AI analyzes a user's social relationships and prioritizes detecting interactions with high-risk parties. For example, it detects interactions with parties who have engaged in problematic behavior in the past. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with anonymous accounts or new accounts. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with parties who have posted inappropriate content in the past. This makes it possible to prioritize detecting interactions with high-risk parties.

[0073] The risk detection unit can refer to the user's risk history and identify the risk of recurrence. In the risk detection unit, for example, the generation AI refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of cyberbullying in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of fraud in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of stalking in the past. This makes it possible to identify the risk of recurrence.

[0074] The risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. In the risk detection unit, for example, the generation AI uses the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects sudden changes in emotions. In addition, the risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects the persistence of negative emotions over a long period of time. In addition, the risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects sudden fluctuations in emotions. This makes it possible to detect an emotionally unstable state as a risk.

[0075] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0076] The risk detection unit can use the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, the generation AI in the risk detection unit uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is dissatisfied with the risk detection results, the algorithm is adjusted based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is satisfied with the risk detection results, the algorithm is strengthened based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user has a neutral feeling about the risk detection results, the algorithm is fine-tuned based on that feedback. This improves the accuracy of the risk detection algorithm.

[0077] The risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. In the risk detection unit, for example, the generation AI analyzes a user's social relationships and prioritizes detecting interactions with high-risk parties. For example, it detects interactions with parties who have engaged in problematic behavior in the past. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with anonymous accounts or new accounts. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with parties who have posted inappropriate content in the past. This makes it possible to prioritize detecting interactions with high-risk parties.

[0078] The risk detection unit can refer to the user's risk history and identify the risk of recurrence. In the risk detection unit, for example, the generation AI refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of cyberbullying in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of fraud in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of stalking in the past. This makes it possible to identify the risk of recurrence.

[0079] The risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. In the risk detection unit, for example, the generation AI uses the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects sudden changes in emotions. In addition, the risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects the persistence of negative emotions over a long period of time. In addition, the risk detection unit can use the emotion estimation function to analyze the user's emotional changes and detect an emotionally unstable state as a risk. For example, it detects sudden fluctuations in emotions. This makes it possible to detect an emotionally unstable state as a risk.

[0080] The risk detection unit can be extended to not only social networking sites but also at least one other digital platform, such as an online game or a chat app. For example, the generation AI of the risk detection unit may include chats and messages in online games as well as social networking sites as its risk detection targets. For example, the generation AI may detect risks of stalking and cyberbullying in games. The risk detection unit may also include messages in chat apps as its risk detection targets. For example, the generation AI may detect risks of fraud and unauthorized access in private chats. The risk detection unit may also include other digital platforms (e.g., comments on forums and blogs) as its risk detection targets. For example, the generation AI may detect offensive comments in forums and inappropriate comments in blogs. This broadens the scope of risk detection targets.

[0081] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0082] The risk detection unit can use the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, the generation AI in the risk detection unit uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is dissatisfied with the risk detection results, the algorithm is adjusted based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is satisfied with the risk detection results, the algorithm is strengthened based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user has a neutral feeling about the risk detection results, the algorithm is fine-tuned based on that feedback. This improves the accuracy of the risk detection algorithm.

[0083] The risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. In the risk detection unit, for example, the generation AI analyzes a user's social relationships and prioritizes detecting interactions with high-risk parties. For example, it detects interactions with parties who have engaged in problematic behavior in the past. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with anonymous accounts or new accounts. In addition, the risk detection unit can analyze a user's social relationships and prioritize detecting interactions with high-risk parties. For example, it detects interactions with parties who have posted inappropriate content in the past. This makes it possible to prioritize detecting interactions with high-risk parties.

[0084] The risk detection unit can refer to the user's risk history and identify the risk of recurrence. In the risk detection unit, for example, the generation AI refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of cyberbullying in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of fraud in the past. In addition, the risk detection unit refers to the user's past risk history and identifies the risk of recurrence. For example, it detects the risk of recurrence for a user who has been a victim of stalking in the past. This makes it possible to identify the risk of recurrence.

[0085] The risk detection unit can be extended to not only social networking sites but also at least one other digital platform, such as an online game or a chat app. For example, the generation AI of the risk detection unit may include chats and messages in online games as well as social networking sites as its risk detection targets. For example, the generation AI may detect risks of stalking and cyberbullying in games. The risk detection unit may also include messages in chat apps as its risk detection targets. For example, the generation AI may detect risks of fraud and unauthorized access in private chats. The risk detection unit may also include other digital platforms (e.g., comments on forums and blogs) as its risk detection targets. For example, the generation AI may detect offensive comments in forums and inappropriate comments in blogs. This broadens the scope of risk detection targets.

[0086] The risk detection unit can visualize the risk detection results and provide a dashboard that allows the user to intuitively understand what risks have been detected. For example, the risk detection unit visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to intuitively understand what risks have been detected. For example, the type and number of detected risks are displayed in a graph. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to check the details of the detected risks. For example, the specific content of the detected risks and the time of occurrence are displayed. The risk detection unit also visualizes the risk detection results using the generation AI and provides a dashboard that allows the user to grasp the trends of the detected risks. For example, the frequency of risk occurrence and time period are displayed in a graph. This allows the user to intuitively understand the risk detection results.

[0087] The risk detection unit can use the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, the generation AI in the risk detection unit uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is dissatisfied with the risk detection results, the algorithm is adjusted based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user is satisfied with the risk detection results, the algorithm is strengthened based on that feedback. The risk detection unit also uses the emotion estimation function to collect the user's emotional reactions to the risk detection results and use the collected information to improve the risk detection algorithm. For example, if the user has a neutral feeling about the risk detection results, the algorithm is fine-tuned based on that feedback. This improves the accuracy of the risk detection algorithm.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The ProtectU system can further include a health management unit that monitors the user's health status. The health management unit monitors the user's heart rate and sleep patterns and issues an alert if an abnormality is detected. For example, it issues an alert if the heart rate rises suddenly or if the user continues to be sleep-deprived. The health management unit can also monitor the user's exercise volume and send a notification encouraging exercise if a lack of exercise is detected. Furthermore, the health management unit can manage the user's dietary records and provide advice if the user's nutritional balance is unbalanced. This allows for comprehensive management of the user's health status and reduces health risks.

[0090] The ProtectU system can further include a learning support unit that supports the user's learning activities. The learning support unit monitors the user's learning progress and supports the creation of a learning plan. For example, it visualizes the user's progress toward the learning goals set by the user and evaluates the degree of achievement. The learning support unit can also provide learning content tailored to the user's learning style. For example, it can provide video content to users who are good at visual learning and audio content to users who are good at auditory learning. Furthermore, the learning support unit can provide feedback to maintain the user's motivation for learning. This can improve the user's learning effectiveness.

[0091] The ProtectU system can further include a hobby support unit that makes recommendations based on the user's hobbies and interests. The hobby support unit analyzes the user's past behavioral history and interests and recommends related events and content. For example, if the user is interested in music, it can provide information about nearby concerts. The hobby support unit can also suggest new hobbies based on the user's interests. For example, if the user is interested in outdoor activities, it can suggest new hiking trails and campsites. Furthermore, the hobby support unit can introduce communities related to the user's hobbies and promote interaction with people who share the same hobbies. This can improve the user's quality of life.

[0092] The ProtectU system may further include a relaxation support unit that estimates the user's emotions and provides relaxation content based on the emotions. The relaxation support unit estimates the user's emotions and provides relaxation music or meditation guides when stress levels are high. For example, if the user is feeling stressed, it plays relaxing music. The relaxation support unit may also suggest relaxation exercises based on the user's emotions. For example, if the user is feeling anxious, it may suggest deep breathing or yoga exercises. The relaxation support unit may also suggest relaxation aromas based on the user's emotions. This helps reduce the user's stress and supports their physical and mental health.

[0093] The ProtectU system can further include an emotional learning support unit that estimates the user's emotions and provides learning support based on those emotions. The emotional learning support unit estimates the user's emotions and sends encouraging messages when the user's motivation to learn is low. For example, if the user feels anxious about learning, it can provide encouraging words or share successful experiences. The emotional learning support unit can also adjust learning content based on the user's emotions. For example, if the user is tired, it can provide content that allows learning in a short amount of time. Furthermore, the emotional learning support unit can make suggestions to improve the learning environment based on the user's emotions. This can improve the user's learning effectiveness.

[0094] The ProtectU system may further include an emotion feedback unit that estimates the user's emotion and provides feedback based on the emotion. The emotion feedback unit estimates the user's emotion and sends a message of praise if the user has a positive emotion. For example, if the user feels a sense of accomplishment, the emotion feedback unit sends a message praising the user's efforts. The emotion feedback unit can also suggest areas for improvement based on the user's emotion. For example, if the user is depressed after failing, the emotion feedback unit can specifically suggest areas for improvement next time. Furthermore, the emotion feedback unit can adjust the timing of feedback based on the user's emotion. This helps maintain the user's motivation and support their growth.

[0095] The ProtectU system can further include an emotional communication support unit that estimates the user's emotions and provides communication support based on the emotions. The emotional communication support unit estimates the user's emotions and suggests a communication method according to the emotions. For example, if the user is feeling angry, it provides advice on how to stay calm. The emotional communication support unit can also suggest appropriate communication timing based on the user's emotions. For example, if the user is tired, it can suggest that the user communicate after taking a rest. Furthermore, the emotional communication support unit can adjust the content of communication based on the user's emotions. This can improve the user's communication skills.

[0096] The ProtectU system can also be equipped with a time management unit that supports the user's time management. The time management unit manages the user's schedule and suggests efficient time allocation. For example, it suggests optimal time allocation for tasks set by the user. The time management unit can also analyze how the user uses their time based on their past behavioral history and suggest areas for improvement. For example, it can provide advice on how to reduce wasted time. Furthermore, the time management unit can monitor the user's progress toward achieving their goals and send reminders as necessary. This can improve the user's time management skills.

[0097] The ProtectU system can further include an Internet security unit that supports users' safe Internet use. The Internet security unit monitors users' Internet usage and detects security risks. For example, it detects risks of unauthorized access and phishing scams and issues warnings. The Internet security unit can also suggest security measures for users' devices. For example, it can suggest installing antivirus software or configuring a firewall. Furthermore, the Internet security unit can provide advice on protecting users' privacy. This can improve the safety of users' Internet use.

[0098] The ProtectU system may further include a digital detox unit that supports users' digital detox. The digital detox unit monitors the user's device usage time and encourages them to take a break if excessive usage is detected. For example, if the user continues using the device for a long period of time, the digital detox unit may notify the user to take a break. The digital detox unit may also analyze the user's device usage patterns and propose a specific action plan for digital detox. For example, the digital detox unit may suggest not using the device during certain hours. The digital detox unit may also support the user in setting goals for device usage and monitor the progress of those goals. This effectively supports the user's digital detox.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The filtering unit analyzes posts and messages on social media and filters out dangerous content. For example, it detects violent content, inappropriate images, and messages suspected of fraud, preventing minors from accessing them. Generative AI is also used to analyze the content of posts and messages on social media and identify dangerous content. Step 2: The risk detection unit detects risks that young people may encounter on social media based on the content filtered by the filtering unit, such as stalking, cyberbullying, and fraud, at an early stage. Step 3: The warning unit issues a warning to the minor based on the risks detected by the risk detection unit. For example, if a minor attempts to access dangerous content or if a risk is detected, the generation AI automatically displays a warning message. Step 4: The reporting unit reports to the parent based on the risks detected by the risk detection unit, for example, notifying the parent of the history of attempts to access dangerous content and details of any risks detected.

[0101] 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.

[0102] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0103] 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.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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 AI 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.

[0118] 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.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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 AI 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.

[0133] 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.

[0134] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0135] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0146] 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.

[0147] 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.

[0148] 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 AI 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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]

[0168] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A filtering unit that analyzes posts or messages on SNS and filters out dangerous content; a risk detection unit that detects risks that young people may encounter on SNS based on the content filtered by the filtering unit; a warning unit that issues a warning to a minor based on the risk detected by the risk detection unit; a reporting unit that reports to a guardian based on the risk detected by the risk detection unit. A system characterized by:

2. The filtering unit Understand the context of the content and determine risk across the entire context, not just individual words 2. The system of claim 1.

3. The filtering unit Refer to the user's past behavior history and perform individually customized filtering 2. The system of claim 1.

4. The filtering unit Analyzing the emotional tone of the post or message and preferentially filtering content containing negative sentiment.

2. The system of claim 1.

5. The filtering unit Extending beyond the aforementioned social networking site to at least one other digital platform, such as an online game or chat app 2. The system of claim 1.

Images