system

Abstract

We provide the system. [Solution] A means for generating learning scenarios based on a learner's past learning history and interests using generative artificial intelligence, Means for displaying the generated learning scenario through a user interface, A means of collecting data to record and analyze learners' choices regarding scenarios, Based on the aforementioned collected data, the artificial intelligence evaluates the learner's behavior and generates feedback, Means for displaying the generated feedback through a user interface, A means of providing parents with reports on their child's learning progress and social media usage, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern times, children have increasingly frequent opportunities to access social networking services (SNS), but there are various risks associated with their use. Specifically, these include slander, leakage of personal information, abduction, sexual victimization, etc. With respect to these risks, there is a problem that current guidance by parents and educational institutions is limited and there is a lack of effective educational means. Also, the problem is that customized education tailored to individual children is not sufficiently provided.

Means for Solving the Problems

[0005] This invention provides a learning system utilizing generative artificial intelligence to create an interactive platform where parents and children can learn together how to address the risks of social media. Specifically, the generative AI generates customized learning scenarios based on the learner's past learning history and interests. This allows learners to learn effective ways to deal with risks through realistic scenarios. Furthermore, by recording and analyzing the learner's behavior and providing feedback, the system enhances learning effectiveness and promotes safe social media use. In addition, it provides parents with detailed reports on learning outcomes and social media usage to support monitoring of their children's internet activities. This enables education tailored to each child and efficient measures to address social media-related risks.

[0006] "Generative artificial intelligence" refers to artificial intelligence that has the ability to automatically generate new content and scenarios based on past data and information.

[0007] A "learning scenario" is a specific situation or story in which learners learn how to solve problems in a particular context.

[0008] A "user interface" refers to the screens and operating methods that a user uses to interact with a computer program or system.

[0009] "Evaluating learner behavior and generating feedback" means judging whether a learner's choices and responses are appropriate and providing corresponding advice or additional information.

[0010] A "Report on Learning Outcomes and Social Media Usage" is a report that shows the progress of learners in acquiring knowledge and skills, as well as statistics and behavioral patterns related to their use of social media.

[0011] "Providing reports to parents" means making information about their child's learning progress and social media use available to parents in an easy-to-understand format. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), etc.

[0016] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system implementing this invention utilizes generative artificial intelligence to provide a platform for parents and children to learn together about the risks and safe use of social networking services (SNS).

[0034] This system operates based on the interaction between the server, terminal, and user. First, the server generates learning scenarios tailored to the user's past learning history, interests, and age, and sends these scenarios to the terminal. Specifically, the server has the ability to model potential risk situations that children might face on social media and interactively create scenarios to address them. For example, it might include scenarios such as "How to deal with friend requests from strangers."

[0035] The device displays learning scenarios received from the server to both the child and the parent. Through the user interface, users can choose actions to take in response to the scenarios. As a user, parents and children can work together to discuss and decide how to respond to the presented problems. For example, in response to a request from a stranger, options such as "reject," "consult with parent," and "request additional information" will be presented.

[0036] When a user makes a selection on their device, that information is sent to a server, which evaluates the appropriateness of the action and generates feedback. Based on the evaluation, the server provides the user with appropriate feedback in real time. As a result, users can understand the reasoning behind their chosen actions and enhance their ability to deal with similar situations they encounter on social media.

[0037] Furthermore, the server provides parents with a report summarizing their child's learning outcomes and social media usage. This report includes statistics related to the child's behavioral patterns, acquired skills, and safe social media use, allowing parents to understand their child's development and take appropriate measures.

[0038] The system of this invention provides a means for parents and children to safely learn about the risks of social media and to have a better internet experience.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The server collects data on the learner's past history, interests, and age. After collecting the data, it uses generative artificial intelligence to generate an optimal learning scenario. The generated scenario models the risks associated with the social networking service being studied.

[0042] Step 2:

[0043] The server sends the generated learning scenario to the terminal. The terminal receives it and displays it on the user interface. The displayed scenario is in an interactive format, including actions and choices that the user should consider.

[0044] Step 3:

[0045] Users view scenarios on their devices and select actions in response to the presented questions. For example, if they receive a friend request from a stranger, they may be presented with options such as "reject," "consult a parent," or "verify the information."

[0046] Step 4:

[0047] The device records the user's selections and sends that data to the server. The transmitted data should include the selected action and the process leading up to it.

[0048] Step 5:

[0049] The server analyzes the received selection data and evaluates its appropriateness. During the evaluation process, generative artificial intelligence generates feedback, taking into account the learner's level of understanding and the intentions behind the selections.

[0050] Step 6:

[0051] The server generates feedback based on the evaluation results and sends it to the terminal. The terminal receives the feedback and presents it to the user in an easy-to-understand manner. This allows the user to understand the significance of their choices and deepen their knowledge.

[0052] Step 7:

[0053] The server collects user learning progress and periodically generates reports for parents. These reports are provided via the device, allowing parents to monitor their child's social media usage and learning progress.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] In recent years, as many learners, especially children, utilize social networking services, the risks associated with using them without appropriate knowledge and skills have increased. There is a need for effective educational methods that allow parents and learners to learn safe ways to deal with the risks of social media together, but traditional educational methods have not been able to adequately address this challenge.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes means for generating a learning scenario adjusted by generative artificial intelligence using the learner's age data; means for displaying the generated learning scenario and providing a user interface that allows parents and learners to make choices together; and means for recording the learner's choices and collecting information for analyzing their responses to the learning scenario. This makes it possible for parents and learners to learn safe ways to use social media together.

[0059] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to create new information and content based on past data and patterns.

[0060] A "learning scenario" refers to a set of situations and problems designed to meet specific educational objectives, and is constructed based on the challenges learners may face.

[0061] A "user interface" is an interface through which a user and a system interact, and it plays a role in displaying information and accepting input.

[0062] "Feedback" refers to providing learners with real-time evaluations and comments on their choices and actions, informing them of the appropriateness of their actions and areas for improvement.

[0063] A "report" refers to a document or data that compiles information such as learning outcomes and behavioral patterns and provides it to parents or administrators.

[0064] The system of this invention utilizes generative artificial intelligence to enable parents and children to learn how to use social networking services safely through cooperation. The specific implementation of this system is described below.

[0065] Hardware and software

[0066] server

[0067] The server is equipped with a generative artificial intelligence model and generates learning scenarios. This allows it to create appropriate scenarios based on the learner's age data. The server operates on a cloud-based system and processes scenario and feedback data in real time.

[0068] terminal

[0069] The terminal receives and displays learning scenarios sent from the server. Each user interface is designed to allow parents and children to make interactive selections. The terminal then sends the selection information of the parents and children to the server.

[0070] Data processing and data calculation

[0071] Data processing by the server

[0072] The server analyzes data collected based on the learner's choices and actions. This allows the server to evaluate the learner's behavioral patterns and generate appropriate feedback.

[0073] Specific example

[0074] For example, the server generates a learning scenario themed around "how to respond to a friend request from a stranger." In this scenario, options such as "reject," "consult a parent," and "request additional information" are displayed on the device. Users (parent and child) choose the appropriate option from these choices, and their choices are evaluated by the server.

[0075] Example of a prompt

[0076] An example of a prompt is, "How should you handle social media messages from strangers?" Based on this prompt, the system generates a learning scenario and presents it to the user.

[0077] In this way, this system provides parents and children with the knowledge and skills necessary for safe use of social media.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] The server uses a generative artificial intelligence model to generate appropriate learning scenarios based on the learner's age data and past learning history. In this process, the server models potential risk situations the learner might face on social media and creates scenarios to address them. The output is specific scenario data, which is then used for further processing.

[0081] Step 2:

[0082] The terminal receives scenario data sent from the server as input. Based on the received data, it displays the scenario through a user interface that can be viewed jointly by parent and child. Specifically, choices are displayed on the screen, and parent and child can discuss these choices and select the best answer. As output, response data selected by the user is generated.

[0083] Step 3:

[0084] The user selects the option they deem appropriate from the displayed choices. After making their selection, the selection data is sent from the terminal to the server. The selected response data is used as input and provided to the server as output.

[0085] Step 4:

[0086] The server analyzes the user's selection data that it receives. Based on this data, the server uses artificial intelligence to evaluate the appropriateness of the selected actions and generates feedback messages. The data processing performed here involves the analysis of selection data and the generation of feedback, and the output is the evaluated feedback data.

[0087] Step 5:

[0088] Feedback data generated from the server is sent to the terminal. The terminal receives this as input and presents feedback to the parent and child in real time via the user interface. As output, feedback is displayed to deepen understanding.

[0089] Step 6:

[0090] The server aggregates and analyzes past results data and selected data to generate a report summarizing learning outcomes for parents. All learning history and feedback data are used as input, and a detailed report for parents is created as output. This allows parents to understand their child's growth and learning progress and take necessary actions.

[0091] (Application Example 1)

[0092] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0093] In modern society, while the use of social networking services (SNS) is expanding, the risks faced by minors in particular are increasing. Many parents are also concerned about how to ensure their children use SNS safely. Therefore, there is a need for an effective learning system that allows both parents and children to understand the risks of SNS and acquire the knowledge and skills to use it safely.

[0094] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0095] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for an automated control device equipped with a user interface to provide verbal or visual guidance to the learner, and means for presenting returned feedback in real time and enabling the learner to understand its content visually or audibly. This makes it possible for parents and children to learn interactively and effectively acquire safe usage methods for social networking services.

[0096] "Generative artificial intelligence" is an artificial intelligence that has the ability to generate individual learning scenarios and content based on a learner's past learning history and interests.

[0097] A "user interface" is an interface that allows learners to interact with a system and manipulate the presented information and options visually or audibly.

[0098] An "automatic control device" is a device equipped with the function of automatically providing learners with audio and visual guidance according to a specified program.

[0099] A "server" is a device that centrally manages information within a network, generates learning scenarios, processes feedback, and transmits related data to other terminal devices.

[0100] "Feedback" is a response provided immediately to evaluate the appropriateness of a learner's choices and actions, and to provide information that facilitates improvement and understanding.

[0101] In the system implementing this invention, a server plays a central role. The server utilizes generative artificial intelligence to generate individual learning scenarios based on the learner's past learning history and interests. This allows users to receive learning content tailored to their individual needs.

[0102] The generated learning scenarios are presented to learners through a device equipped with a user interface. The device includes a microphone with voice recognition capabilities and a display that shows visual information, allowing users to interactively progress through the scenarios.

[0103] User choices and actions are transmitted to the server in real time, where the server evaluates this data and generates appropriate feedback. This feedback is immediately presented to the user through the device, helping learners understand the reasoning behind their choices.

[0104] The hardware consists of an automated control system that provides voice and visual guidance. The software utilizes a server communication module implemented in Python, as well as machine learning model libraries such as TENSORFLOW®.

[0105] As a concrete example of implementation, one day a learner is presented with a "scenario for dealing with a suspicious person on social media." The scenario asks in audio, "Your friend has received a message from a stranger. What do you do?" The learner answers, "I would talk to my parents." Feedback based on this choice is generated in real time.

[0106] The AI ​​model is prompted with questions such as, "Generate a scenario for dealing with a suspicious person on social media and consider feedback to guide parents and children to take appropriate action."

[0107] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0108] Step 1:

[0109] The server retrieves the learner's past learning history and interests from a database and inputs them into a generative AI model. Based on this input data, the generative AI model generates a learning scenario suitable for the learner and returns that scenario to the server.

[0110] Step 2:

[0111] The server sends the generated learning scenario to a terminal equipped with a user interface. The terminal presents the received learning scenario to the learner through a graphic display or audio output. For example, it might visually display choices on the screen and explain the scenario aloud.

[0112] Step 3:

[0113] The user interacts with the presented scenario, selecting an action from a set of options using a microphone or touchscreen. This selection is then sent from the device to the server.

[0114] Step 4:

[0115] The server evaluates the received selection results. These selections are then re-inputted into the generating AI model, which generates feedback to determine the appropriateness of the selections. This allows for data processing and logical operations to be performed to obtain evaluation results.

[0116] Step 5:

[0117] The server sends the generated feedback to the device. The device presents the feedback to the learner through a user interface, providing appropriate action guidance via audio or visual means. A concrete example of this might be displaying advice such as, "It is recommended that you talk to your parents."

[0118] Step 6:

[0119] The server generates a report for the parent based on the results of the learning session and other relevant data. The report includes statistics on the learner's behavioral patterns and social media usage, and is sent to the parent's account.

[0120] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0121] The system implementing this invention provides a platform that integrates generative artificial intelligence and an emotion engine to help parents and children learn about the risks and safe use of social media. This system operates through the interaction of a server, a terminal, and a user.

[0122] First, the server uses generative artificial intelligence to generate an appropriate learning scenario based on the learner's past learning history and interests. This scenario anticipates risk situations on social media and helps the user consider specific countermeasures. In addition, an emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions in real time. Based on this information, the server further customizes the learning scenario, adjusting the difficulty level and content to be appropriate according to the user's emotional state.

[0123] The device displays an interactive scenario on the user interface based on the scenario received from the server and the analysis results from the emotion engine. The user considers the various options displayed in the scenario and selects the action they deem most appropriate. This selection takes into account the user's emotional information; for example, if high stress levels are detected, a supportive message may be presented. In particular, it allows the user to learn how to cope when faced with difficult situations on social media.

[0124] When a user makes a selection, the device sends that data to the server. The server analyzes the user's behavioral choice, including emotional data, and uses an AI engine to evaluate the appropriateness of the selection. Furthermore, based on the results of this evaluation, it generates emotionally sensitive feedback to reinforce or improve the user's chosen behavior. For example, positive feedback is returned if the selection was appropriate, and constructive advice is provided if there is room for improvement.

[0125] Subsequently, the server generates a detailed report including the user's learning outcomes and emotional responses, and provides it to the parents. Through this report, parents can gain insights into their child's learning progress and the safety of their social media use. This makes it possible to promote safe social media use while taking into consideration the child's psychological state.

[0126] The system of this invention provides a means to effectively learn about the risks associated with using social media, and in particular, by taking into account the emotional changes of learners, it realizes more personalized educational support.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server collects data to analyze the learner's past history, interests, and behavioral patterns. This prepares the generative artificial intelligence to generate the optimal learning scenario.

[0130] Step 2:

[0131] The emotion engine collects emotional data from the user's facial expressions, tone of voice, and body language to recognize the user's emotions in real time. This emotional data is constantly updated as the user interacts with the device.

[0132] Step 3:

[0133] The server generates learning scenarios based on collected historical and sentiment data. For example, if the stress level is high, the scenario is customized, such as by simplifying the content, to provide the user with the most suitable scenario.

[0134] Step 4:

[0135] The device displays a customized learning scenario sent from the server in its user interface. Based on the presented scenario, the user can choose an action from multiple options.

[0136] Step 5:

[0137] The user makes choices via their device. For example, when asked "How to respond when you receive a message from a stranger," they select the best option from several possible responses. During this process, their emotional response is analyzed again.

[0138] Step 6:

[0139] The device sends user selections and emotional data to the server. The server uses an AI-based algorithm to evaluate the selections and generate feedback that takes the user's emotional state into account.

[0140] Step 7:

[0141] The server sends the generated feedback to the terminal, which then displays it to the user. The feedback is designed not only to deepen the user's understanding but also to take their psychological state into consideration.

[0142] Step 8:

[0143] The server generates a detailed report, including learning progress and emotional responses, and provides it to the parents. Parents use the report as a guide to understand the safety of their child's social media use and their learning progress.

[0144] This series of processes allows the system to provide adaptive learning that incorporates the learner's emotions, effectively improving their skills to cope with social media risks.

[0145] (Example 2)

[0146] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0147] In recent years, with the rapid increase in the use of social networks, there has been a lack of opportunities for minors to learn about the risks they face. Furthermore, personalized learning based on emotions is insufficient, and there are inadequate mechanisms for parents to properly monitor their children's learning progress. This creates challenges that hinder safe and effective network use.

[0148] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0149] In this invention, the server includes means for forming a learning situation based on the learner's past history and interests using generative artificial intelligence, means for analyzing the learner's facial expression and voice information using an emotion analysis engine to identify their emotional state, and means for modifying the generated learning situation according to the learner's emotional state and providing an appropriate difficulty level. This enables the provision of a personalized learning experience while taking the user's emotions into consideration, and allows for appropriate reporting of learning progress to parents.

[0150] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates new information and situations based on past accumulated history and interests.

[0151] An "emotion analysis engine" is a technology that analyzes information such as a user's facial expressions and voice to automatically identify their emotional state.

[0152] "Learning context" refers to educational scenarios and tasks that are generated according to the learner's abilities and needs.

[0153] "Facial expression information" refers to information obtained as digital data of the user's facial movements and expressions.

[0154] "Audio information" refers to information that captures the characteristics of a user's voice, including its tone and speed, as digital data.

[0155] "Emotional state" refers to the mental or emotional response a user exhibits under specific circumstances.

[0156] "Learning progress" indicates the extent to which a learner has progressed in the learning process.

[0157] "Network usage status" refers to the state of how social networks are being used, including risk management and usage patterns.

[0158] The system for carrying out this invention combines generative artificial intelligence and an emotion analysis engine to provide learners with education on the use of social networks. The system consists of a server, terminals, and user interaction.

[0159] The server first uses a database management system to analyze data based on the learner's past history and interests. Based on this analysis, the server utilizes a generative AI model to input prompts. An example prompt might be, "Please provide an appropriate learning scenario regarding risk management on social media." The generated scenario data is then further customized according to the user's characteristics.

[0160] Furthermore, the emotion analysis engine acquires the user's facial expressions and voice information in real time to identify the user's emotional state. Image recognition software and voice analysis tools are used for this analysis. The emotion data obtained in this way is used to adjust the content and difficulty level of the learning scenario.

[0161] The device receives customized scenarios sent from the server and presents them to the user through an interactive user interface. Learning progresses as the user considers the options displayed on the screen and selects actions based on those options. The user's selection information is used to support stress management and the formation of appropriate behaviors.

[0162] When a user makes a selection, that information is sent to the server, where artificial intelligence analyzes the selection. Based on the analysis, the server generates feedback and provides it to the user through the terminal. This feedback is supportive, taking into account the learner's emotional state, and serves as motivation for further learning.

[0163] Ultimately, the server generates and provides parents with a detailed report on the learner's progress and network usage. This report allows parents to accurately understand their child's learning progress and continue using the network with peace of mind.

[0164] This invention enables learners to acquire safer and more effective network usage techniques and provides an emotionally personalized learning experience.

[0165] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0166] Step 1:

[0167] The server retrieves the learner's past history and interest data from the database. This data is input via database operations such as SQL queries. The output is the learner's history information. Based on this history information, the server issues a prompt to the generating AI model: "Please provide an appropriate learning scenario regarding risk management on social media." The generated learning scenario is then output.

[0168] Step 2:

[0169] The server uses an emotion analysis engine to collect user facial and voice information. This information is input through hardware such as cameras and microphones. The emotion analysis engine analyzes the input data and outputs the user's emotional state. For example, if the user is feeling stressed, that emotional state is identified as a numerical value or label.

[0170] Step 3:

[0171] The server customizes the generated learning scenario according to the user's emotional state. The input consists of the generated scenario and emotional state data, and the server modifies the scenario's content and difficulty based on this. For example, if the stress level is high, the high-risk content is simplified. The output is the customized learning scenario.

[0172] Step 4:

[0173] The device displays a customized learning scenario on the user interface. In this step, scenario data received from the server is used as input and presented on the screen using a UI framework. The user views this screen and selects an action from the provided options.

[0174] Step 5:

[0175] When a user makes a selection, that selection information is sent from the terminal to the server. In this process, the user's selection data is used as input and transferred to the server via an HTTP POST request. The server receives this data and prepares it for analysis.

[0176] Step 6:

[0177] The server analyzes the user's selection data and emotional state, and uses an AI engine to evaluate the appropriateness of the selection. In this step, the input selection data is analyzed, and the evaluation results are output. Based on these results, preparations are made to generate and provide emotionally sensitive feedback to the user.

[0178] Step 7:

[0179] The server sends the generated feedback to the device and presents it to the user. The user receives the feedback and can then feel the effectiveness of their learning based on it. Finally, the server compiles the data to provide parents with a report on learning results and social media usage.

[0180] This allows the entire system to work together, resulting in an effective learning process.

[0181] (Application Example 2)

[0182] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0183] In recent years, with the spread of social networking services, the number of cases in which children are exposed to various risks on the internet has increased, but there is a lack of safety education. Furthermore, there are few educational methods that take into account the psychological state of children, and there is a need for more individualized education.

[0184] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0185] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for analyzing the learner's psychological state using an emotion analysis device and adjusting the learning scenario based on the analysis results, and means for providing parents with reports on learning outcomes and social network service usage. This makes it possible to provide personalized and effective safety education regarding the risks children face on the internet, according to their emotional state.

[0186] "Generative artificial intelligence" is an artificial intelligence technology that can generate new information and scenarios based on past data and circumstances, and provide them to learners.

[0187] A "learning scenario" is a set of learning activities or situations designed to help learners acquire specific knowledge or skills.

[0188] A "user interface" is an interface through which learners interact with a system and receive or input information.

[0189] An "emotion analysis device" is a device that analyzes biometric information such as a learner's facial expressions and voice to infer their psychological state.

[0190] "Psychological state" refers to the learner's internal state, including their emotions, mood, and stress level.

[0191] "Means of data collection" refer to methods and devices for recording learners' choices and actions and gathering information necessary for analysis.

[0192] "A means by which artificial intelligence evaluates learner behavior and generates feedback" refers to a function that evaluates the validity of learner choices and actions based on collected data, and provides information for improvement or approval based on the results.

[0193] "Means of providing reports" refers to methods or devices for organizing learners' learning outcomes and SNS usage and reporting them to parents in an easily understandable format.

[0194] This invention's system integrates generative artificial intelligence and emotion analysis functions to provide a platform for parents and children to safely learn about using social media.

[0195] The server uses generative artificial intelligence to generate learning scenarios based on the user's past learning history and interests. These scenarios anticipate potential risk situations in social network services and are designed to teach countermeasures. It also utilizes an emotion analysis system to analyze the user's psychological state and adjust the scenario content and difficulty accordingly. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis.

[0196] The device displays scenarios received from the server and presents them interactively to the user. The user considers the options for the scenario and selects the action they believe to be optimal. The selection results are sent from the device to the server and analyzed by an AI engine. The AI ​​evaluates the validity of the selection and provides the user with emotionally sensitive feedback. In high-stress situations, supportive messages may also be displayed.

[0197] Furthermore, the server generates and provides parents with reports on learning outcomes and social media usage. These reports include recommendations for safe network use that take into account the child's psychological state.

[0198] As a concrete example, a scenario is presented: "What would you do if you received a message from a stranger on social media?" In this scenario, the system analyzes the user's emotional state and provides feedback based on the chosen option. Prompts such as "What would you do in this situation?" and "Please choose from the following options!" are provided.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The server retrieves the learner's past learning history and interests from a database. Using this data as input, it generates learning scenarios using generative artificial intelligence. The output is a scenario tailored to the learner. This process lays the foundation for providing a personalized learning experience.

[0202] Step 2:

[0203] The server acquires the user's facial expressions and voice using an emotion analysis device and analyzes that data. The input is biometric data, and the output is data representing the user's psychological state. TensorFlow is used for the analysis, and the content and difficulty level of the learning scenario are adjusted based on the results.

[0204] Step 3:

[0205] The server sends the generated learning scenario and content adjusted to the user's psychological state to the terminal. The input is the adjusted learning scenario, and the output is an interactive scenario displayed in the user interface. The terminal receives this and presents it to the user.

[0206] Step 4:

[0207] The user considers the options presented in the scenario and makes a selection on the terminal. The input is the scenario options, and the output is data of the user's chosen action. The terminal records the user's selection and sends it to the server.

[0208] Step 5:

[0209] The server receives user selection data and uses an AI engine to evaluate the appropriateness of the user's actions. The input is user selection data, and the output is the evaluation result and feedback message. Based on the evaluation, the server generates emotionally sensitive feedback for the user.

[0210] Step 6:

[0211] The server sends the generated feedback to the terminal and provides it to the user. The input is the feedback message, and the output is the feedback displayed on the user interface. The terminal displays supportive messages and improvement information tailored to the user's emotional state.

[0212] Step 7:

[0213] The server compiles and provides parents with detailed reports on learning outcomes and social media usage. Inputs include all records, including learning history and sentiment data, while output is a report viewable by parents. This allows parents to gain deep insights into their child's learning progress and safe social media use.

[0214] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0215] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0216] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0217] [Second Embodiment]

[0218] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0219] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0220] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0221] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0222] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0223] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0224] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0225] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0226] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0227] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0228] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0229] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0230] The system implementing this invention utilizes generative artificial intelligence to provide a platform for parents and children to learn together about the risks and safe use of social networking services (SNS).

[0231] This system operates based on the interaction between the server, terminal, and user. First, the server generates learning scenarios tailored to the user's past learning history, interests, and age, and sends these scenarios to the terminal. Specifically, the server has the ability to model potential risk situations that children might face on social media and interactively create scenarios to address them. For example, it might include scenarios such as "How to deal with friend requests from strangers."

[0232] The device displays learning scenarios received from the server to both the child and the parent. Through the user interface, users can choose actions to take in response to the scenarios. As a user, parents and children can work together to discuss and decide how to respond to the presented problems. For example, in response to a request from a stranger, options such as "reject," "consult with parent," and "request additional information" will be presented.

[0233] When a user makes a selection on their device, that information is sent to a server, which evaluates the appropriateness of the action and generates feedback. Based on the evaluation, the server provides the user with appropriate feedback in real time. As a result, users can understand the reasoning behind their chosen actions and enhance their ability to deal with similar situations they encounter on social media.

[0234] Furthermore, the server provides parents with a report summarizing their child's learning outcomes and social media usage. This report includes statistics related to the child's behavioral patterns, acquired skills, and safe social media use, allowing parents to understand their child's development and take appropriate measures.

[0235] The system of this invention provides a means for parents and children to safely learn about the risks of social media and to have a better internet experience.

[0236] The following describes the processing flow.

[0237] Step 1:

[0238] The server collects data on the learner's past history, interests, and age. After collecting the data, it uses generative artificial intelligence to generate an optimal learning scenario. The generated scenario models the risks associated with the social networking service being studied.

[0239] Step 2:

[0240] The server sends the generated learning scenario to the terminal. The terminal receives it and displays it on the user interface. The displayed scenario is in an interactive format, including actions and choices that the user should consider.

[0241] Step 3:

[0242] Users view scenarios on their devices and select actions in response to the presented questions. For example, if they receive a friend request from a stranger, they may be presented with options such as "reject," "consult a parent," or "verify the information."

[0243] Step 4:

[0244] The device records the user's selections and sends that data to the server. The transmitted data should include the selected action and the process leading up to it.

[0245] Step 5:

[0246] The server analyzes the received selection data and evaluates its appropriateness. During the evaluation process, generative artificial intelligence generates feedback, taking into account the learner's level of understanding and the intentions behind the selections.

[0247] Step 6:

[0248] The server generates feedback based on the evaluation results and sends it to the terminal. The terminal receives the feedback and presents it to the user in an easy-to-understand manner. This allows the user to understand the significance of their choices and deepen their knowledge.

[0249] Step 7:

[0250] The server collects user learning progress and periodically generates reports for parents. These reports are provided via the device, allowing parents to monitor their child's social media usage and learning progress.

[0251] (Example 1)

[0252] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0253] In recent years, as many learners, especially children, utilize social networking services, the risks associated with using them without appropriate knowledge and skills have increased. There is a need for effective educational methods that allow parents and learners to learn safe ways to deal with the risks of social media together, but traditional educational methods have not been able to adequately address this challenge.

[0254] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0255] In this invention, the server includes means for generating a learning scenario adjusted by generative artificial intelligence using the learner's age data; means for displaying the generated learning scenario and providing a user interface that allows parents and learners to make choices together; and means for recording the learner's choices and collecting information for analyzing their responses to the learning scenario. This makes it possible for parents and learners to learn safe ways to use social media together.

[0256] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to create new information and content based on past data and patterns.

[0257] A "learning scenario" refers to a set of situations and problems designed to meet specific educational objectives, and is constructed based on the challenges learners may face.

[0258] A "user interface" is an interface through which a user and a system interact, and it plays a role in displaying information and accepting input.

[0259] "Feedback" refers to providing learners with real-time evaluations and comments on their choices and actions, informing them of the appropriateness of their actions and areas for improvement.

[0260] A "report" refers to a document or data that compiles information such as learning outcomes and behavioral patterns and provides it to parents or administrators.

[0261] The system of this invention utilizes generative artificial intelligence to enable parents and children to learn how to use social networking services safely through cooperation. The specific implementation of this system is described below.

[0262] Hardware and software

[0263] server

[0264] The server is equipped with a generative artificial intelligence model and generates learning scenarios. This allows it to create appropriate scenarios based on the learner's age data. The server operates on a cloud-based system and processes scenario and feedback data in real time.

[0265] terminal

[0266] The terminal receives and displays learning scenarios sent from the server. Each user interface is designed to allow parents and children to make interactive selections. The terminal then sends the selection information of the parents and children to the server.

[0267] Data processing and data calculation

[0268] Data processing by the server

[0269] The server analyzes data collected based on the learner's choices and actions. This allows the server to evaluate the learner's behavioral patterns and generate appropriate feedback.

[0270] Specific example

[0271] For example, the server generates a learning scenario themed around "how to respond to a friend request from a stranger." In this scenario, options such as "reject," "consult a parent," and "request additional information" are displayed on the device. Users (parent and child) choose the appropriate option from these choices, and their choices are evaluated by the server.

[0272] Example of a prompt

[0273] An example of a prompt is, "How should you handle social media messages from strangers?" Based on this prompt, the system generates a learning scenario and presents it to the user.

[0274] In this way, this system provides parents and children with the knowledge and skills necessary for safe use of social media.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] The server uses a generative artificial intelligence model to take the learner's age data and past learning history as input and generate an appropriate learning scenario. At this time, the server models the risk situations that the learner may face on SNS and creates scenarios to address them. As output, specific scenario data is obtained, and this data is used for subsequent processing.

[0278] Step 2:

[0279] The terminal receives the scenario data sent from the server as input. Based on the received data, the scenario is displayed via a user interface that can be jointly viewed by parents and children. Specifically, options are displayed on the screen, and parents and children can discuss these options and select the optimal answer. As output, response data selected by the user is generated.

[0280] Step 3:

[0281] The user selects what they think is appropriate from the displayed options. After the selection is completed, the selected data is sent from the terminal to the server. The selected response data is used as input and provided to the server as output.

[0282] Step 4:

[0283] The server analyzes the received user selection data. Based on this data, the server uses artificial intelligence to evaluate the appropriateness of the selected action and generates a feedback message. The data processing performed here is the analysis of the selection data and the generation of feedback, and the evaluated feedback data is obtained as output.

[0284] Step 5:

[0285] The feedback data generated by the server is transmitted to the terminal. The terminal receives this as input and presents feedback to the parent and child in real time via the user interface. As output, a feedback display for enhancing understanding is performed.

[0286] Step 6:

[0287] The server aggregates and analyzes the previous result data and selection data in order to generate a report summarizing the learning results for the parent. All learning histories and feedback data are used as input, and a detailed report for the parent is created as output. This enables the parent to grasp the growth and learning progress of the child and take necessary measures.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In modern society, while the use of social network services (SNS) is increasingly expanding, the risks faced particularly by minors are increasing. Many parents are also worried about the safe use of SNS for their children. Therefore, an effective learning system is needed that enables parents and children to understand the risks of SNS together and acquire the knowledge and technology for safe use.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for an automated control device equipped with a user interface to provide verbal or visual guidance to the learner, and means for presenting returned feedback in real time and enabling the learner to understand its content visually or audibly. This makes it possible for parents and children to learn interactively and effectively acquire safe usage methods for social networking services.

[0293] "Generative artificial intelligence" is an artificial intelligence that has the ability to generate individual learning scenarios and content based on a learner's past learning history and interests.

[0294] A "user interface" is an interface that allows learners to interact with a system and manipulate the presented information and options visually or audibly.

[0295] An "automatic control device" is a device equipped with the function of automatically providing learners with audio and visual guidance according to a specified program.

[0296] A "server" is a device that centrally manages information within a network, generates learning scenarios, processes feedback, and transmits related data to other terminal devices.

[0297] "Feedback" is a response provided immediately to evaluate the appropriateness of a learner's choices and actions, and to provide information that facilitates improvement and understanding.

[0298] In the system implementing this invention, a server plays a central role. The server utilizes generative artificial intelligence to generate individual learning scenarios based on the learner's past learning history and interests. This allows users to receive learning content tailored to their individual needs.

[0299] The generated learning scenarios are presented to learners through a device equipped with a user interface. The device includes a microphone with voice recognition capabilities and a display that shows visual information, allowing users to interactively progress through the scenarios.

[0300] User choices and actions are transmitted to the server in real time, where the server evaluates this data and generates appropriate feedback. This feedback is immediately presented to the user through the device, helping learners understand the reasoning behind their choices.

[0301] The hardware will consist of an automated control system that provides voice and visual guidance. The software will utilize a server communication module implemented in Python, as well as machine learning model libraries such as TensorFlow.

[0302] As a concrete example of implementation, one day a learner is presented with a "scenario for dealing with a suspicious person on social media." The scenario asks in audio, "Your friend has received a message from a stranger. What do you do?" The learner answers, "I would talk to my parents." Feedback based on this choice is generated in real time.

[0303] The AI ​​model is prompted with questions such as, "Generate a scenario for dealing with a suspicious person on social media and consider feedback to guide parents and children to take appropriate action."

[0304] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0305] Step 1:

[0306] The server retrieves the learner's past learning history and interests from a database and inputs them into a generative AI model. Based on this input data, the generative AI model generates a learning scenario suitable for the learner and returns that scenario to the server.

[0307] Step 2:

[0308] The server sends the generated learning scenario to a terminal equipped with a user interface. The terminal presents the received learning scenario to the learner through a graphic display or audio output. For example, visually display options on the screen and explain the scenario verbally.

[0309] Step 3:

[0310] The user operates on the presented scenario. Using a microphone or touch screen, the user selects an action from the options. This selection result is sent from the terminal to the server.

[0311] Step 4:

[0312] The server evaluates the received selection result. This option is re - input into the generating AI model to generate feedback for judging the appropriateness of the selection. Thereby, it becomes possible to obtain an evaluation result by performing data processing and logical operations.

[0313] Step 5:

[0314] The server sends the generated feedback to the terminal. The terminal presents the feedback to the learner through the user interface, indicating appropriate action guidelines either audibly or visually. As a specific example, it is to display advice such as "It is recommended to consult with parents".

[0315] Step 6:

[0316] The server creates a report for the parent based on the results of that learning session and other related data. The report includes statistics on the learner's behavior patterns and SNS usage status, and sends it to the parent's account.

[0317] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0318] The system implementing this invention provides a platform that integrates generative artificial intelligence and an emotion engine to help parents and children learn about the risks and safe use of social media. This system operates through the interaction of a server, a terminal, and a user.

[0319] First, the server uses generative artificial intelligence to generate an appropriate learning scenario based on the learner's past learning history and interests. This scenario anticipates risk situations on social media and helps the user consider specific countermeasures. In addition, an emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions in real time. Based on this information, the server further customizes the learning scenario, adjusting the difficulty level and content to be appropriate according to the user's emotional state.

[0320] The device displays an interactive scenario on the user interface based on the scenario received from the server and the analysis results from the emotion engine. The user considers the various options displayed in the scenario and selects the action they deem most appropriate. This selection takes into account the user's emotional information; for example, if high stress levels are detected, a supportive message may be presented. In particular, it allows the user to learn how to cope when faced with difficult situations on social media.

[0321] When a user makes a selection, the device sends that data to the server. The server analyzes the user's behavioral choice, including emotional data, and uses an AI engine to evaluate the appropriateness of the selection. Furthermore, based on the results of this evaluation, it generates emotionally sensitive feedback to reinforce or improve the user's chosen behavior. For example, positive feedback is returned if the selection was appropriate, and constructive advice is provided if there is room for improvement.

[0322] Subsequently, the server generates a detailed report including the user's learning outcomes and emotional responses, and provides it to the parents. Through this report, parents can gain insights into their child's learning progress and the safety of their social media use. This makes it possible to promote safe social media use while taking into consideration the child's psychological state.

[0323] The system of this invention provides a means to effectively learn about the risks associated with using social media, and in particular, by taking into account the emotional changes of learners, it realizes more personalized educational support.

[0324] The following describes the processing flow.

[0325] Step 1:

[0326] The server collects data to analyze the learner's past history, interests, and behavioral patterns. This prepares the generative artificial intelligence to generate the optimal learning scenario.

[0327] Step 2:

[0328] The emotion engine collects emotional data from the user's facial expressions, tone of voice, and body language to recognize the user's emotions in real time. This emotional data is constantly updated as the user interacts with the device.

[0329] Step 3:

[0330] The server generates learning scenarios based on collected historical and sentiment data. For example, if the stress level is high, the scenario is customized, such as by simplifying the content, to provide the user with the most suitable scenario.

[0331] Step 4:

[0332] The device displays a customized learning scenario sent from the server in its user interface. Based on the presented scenario, the user can choose an action from multiple options.

[0333] Step 5:

[0334] The user makes choices via their device. For example, when asked "How to respond when you receive a message from a stranger," they select the best option from several possible responses. During this process, their emotional response is analyzed again.

[0335] Step 6:

[0336] The device sends user selections and emotional data to the server. The server uses an AI-based algorithm to evaluate the selections and generate feedback that takes the user's emotional state into account.

[0337] Step 7:

[0338] The server sends the generated feedback to the terminal, which then displays it to the user. The feedback is designed not only to deepen the user's understanding but also to take their psychological state into consideration.

[0339] Step 8:

[0340] The server generates a detailed report, including learning progress and emotional responses, and provides it to the parents. Parents use the report as a guide to understand the safety of their child's social media use and their learning progress.

[0341] This series of processes allows the system to provide adaptive learning that incorporates the learner's emotions, effectively improving their skills to cope with social media risks.

[0342] (Example 2)

[0343] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0344] In recent years, with the rapid increase in the use of social networks, there has been a lack of opportunities for minors to learn about the risks they face. Furthermore, personalized learning based on emotions is insufficient, and there are inadequate mechanisms for parents to properly monitor their children's learning progress. This creates challenges that hinder safe and effective network use.

[0345] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0346] In this invention, the server includes means for forming a learning situation based on the learner's past history and interests using generative artificial intelligence, means for analyzing the learner's facial expression and voice information using an emotion analysis engine to identify their emotional state, and means for modifying the generated learning situation according to the learner's emotional state and providing an appropriate difficulty level. This enables the provision of a personalized learning experience while taking the user's emotions into consideration, and allows for appropriate reporting of learning progress to parents.

[0347] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates new information and situations based on past accumulated history and interests.

[0348] An "emotion analysis engine" is a technology that analyzes information such as a user's facial expressions and voice to automatically identify their emotional state.

[0349] "Learning context" refers to educational scenarios and tasks that are generated according to the learner's abilities and needs.

[0350] "Facial expression information" refers to information obtained as digital data of the user's facial movements and expressions.

[0351] "Audio information" refers to information that captures the characteristics of a user's voice, including its tone and speed, as digital data.

[0352] "Emotional state" refers to the mental or emotional response a user exhibits under specific circumstances.

[0353] "Learning progress" indicates the extent to which a learner has progressed in the learning process.

[0354] "Network usage status" refers to the state of how social networks are being used, including risk management and usage patterns.

[0355] The system for carrying out this invention combines generative artificial intelligence and an emotion analysis engine to provide learners with education on the use of social networks. The system consists of a server, terminals, and user interaction.

[0356] The server first uses a database management system to analyze data based on the learner's past history and interests. Based on this analysis, the server utilizes a generative AI model to input prompts. An example prompt might be, "Please provide an appropriate learning scenario regarding risk management on social media." The generated scenario data is then further customized according to the user's characteristics.

[0357] Furthermore, the emotion analysis engine acquires the user's facial expressions and voice information in real time to identify the user's emotional state. Image recognition software and voice analysis tools are used for this analysis. The emotion data obtained in this way is used to adjust the content and difficulty level of the learning scenario.

[0358] The device receives customized scenarios sent from the server and presents them to the user through an interactive user interface. Learning progresses as the user considers the options displayed on the screen and selects actions based on those options. The user's selection information is used to support stress management and the formation of appropriate behaviors.

[0359] When a user makes a selection, that information is sent to the server, where artificial intelligence analyzes the selection. Based on the analysis, the server generates feedback and provides it to the user through the terminal. This feedback is supportive, taking into account the learner's emotional state, and serves as motivation for further learning.

[0360] Ultimately, the server generates and provides parents with a detailed report on the learner's progress and network usage. This report allows parents to accurately understand their child's learning progress and continue using the network with peace of mind.

[0361] This invention enables learners to acquire safer and more effective network usage techniques and provides an emotionally personalized learning experience.

[0362] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0363] Step 1:

[0364] The server retrieves the learner's past history and interest data from the database. This data is input via database operations such as SQL queries. The output is the learner's history information. Based on this history information, the server issues a prompt to the generating AI model: "Please provide an appropriate learning scenario regarding risk management on social media." The generated learning scenario is then output.

[0365] Step 2:

[0366] The server uses an emotion analysis engine to collect user facial and voice information. This information is input through hardware such as cameras and microphones. The emotion analysis engine analyzes the input data and outputs the user's emotional state. For example, if the user is feeling stressed, that emotional state is identified as a numerical value or label.

[0367] Step 3:

[0368] The server customizes the generated learning scenario according to the user's emotional state. The input consists of the generated scenario and emotional state data, and the server modifies the scenario's content and difficulty based on this. For example, if the stress level is high, the high-risk content is simplified. The output is the customized learning scenario.

[0369] Step 4:

[0370] The device displays a customized learning scenario on the user interface. In this step, scenario data received from the server is used as input and presented on the screen using a UI framework. The user views this screen and selects an action from the provided options.

[0371] Step 5:

[0372] When a user makes a selection, that selection information is sent from the terminal to the server. In this process, the user's selection data is used as input and transferred to the server via an HTTP POST request. The server receives this data and prepares it for analysis.

[0373] Step 6:

[0374] The server analyzes the user's selection data and emotional state, and uses an AI engine to evaluate the appropriateness of the selection. In this step, the input selection data is analyzed, and the evaluation results are output. Based on these results, preparations are made to generate and provide emotionally sensitive feedback to the user.

[0375] Step 7:

[0376] The server sends the generated feedback to the device and presents it to the user. The user receives the feedback and can then feel the effectiveness of their learning based on it. Finally, the server compiles the data to provide parents with a report on learning results and social media usage.

[0377] This allows the entire system to work together, resulting in an effective learning process.

[0378] (Application Example 2)

[0379] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0380] In recent years, with the spread of social networking services, the number of cases in which children are exposed to various risks on the internet has increased, but there is a lack of safety education. Furthermore, there are few educational methods that take into account the psychological state of children, and there is a need for more individualized education.

[0381] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0382] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for analyzing the learner's psychological state using an emotion analysis device and adjusting the learning scenario based on the analysis results, and means for providing parents with reports on learning outcomes and social network service usage. This makes it possible to provide personalized and effective safety education regarding the risks children face on the internet, according to their emotional state.

[0383] "Generative artificial intelligence" is an artificial intelligence technology that can generate new information and scenarios based on past data and circumstances, and provide them to learners.

[0384] A "learning scenario" is a set of learning activities or situations designed to help learners acquire specific knowledge or skills.

[0385] A "user interface" is an interface through which learners interact with a system and receive or input information.

[0386] An "emotion analysis device" is a device that analyzes biometric information such as a learner's facial expressions and voice to infer their psychological state.

[0387] "Psychological state" refers to the learner's internal state, including their emotions, mood, and stress level.

[0388] "Means of data collection" refer to methods and devices for recording learners' choices and actions and gathering information necessary for analysis.

[0389] "A means by which artificial intelligence evaluates learner behavior and generates feedback" refers to a function that evaluates the validity of learner choices and actions based on collected data, and provides information for improvement or approval based on the results.

[0390] "Means of providing reports" refers to methods or devices for organizing learners' learning outcomes and SNS usage and reporting them to parents in an easily understandable format.

[0391] This invention's system integrates generative artificial intelligence and emotion analysis functions to provide a platform for parents and children to safely learn about using social media.

[0392] The server uses generative artificial intelligence to generate learning scenarios based on the user's past learning history and interests. These scenarios anticipate potential risk situations in social network services and are designed to teach countermeasures. It also utilizes an emotion analysis system to analyze the user's psychological state and adjust the scenario content and difficulty accordingly. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis.

[0393] The device displays scenarios received from the server and presents them interactively to the user. The user considers the options for the scenario and selects the action they believe to be optimal. The selection results are sent from the device to the server and analyzed by an AI engine. The AI ​​evaluates the validity of the selection and provides the user with emotionally sensitive feedback. In high-stress situations, supportive messages may also be displayed.

[0394] Furthermore, the server generates and provides parents with reports on learning outcomes and social media usage. These reports include recommendations for safe network use that take into account the child's psychological state.

[0395] As a concrete example, a scenario is presented: "What would you do if you received a message from a stranger on social media?" In this scenario, the system analyzes the user's emotional state and provides feedback based on the chosen option. Prompts such as "What would you do in this situation?" and "Please choose from the following options!" are provided.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The server retrieves the learner's past learning history and interests from a database. Using this data as input, it generates learning scenarios using generative artificial intelligence. The output is a scenario tailored to the learner. This process lays the foundation for providing a personalized learning experience.

[0399] Step 2:

[0400] The server acquires the user's facial expressions and voice using an emotion analysis device and analyzes that data. The input is biometric data, and the output is data representing the user's psychological state. TensorFlow is used for the analysis, and the content and difficulty level of the learning scenario are adjusted based on the results.

[0401] Step 3:

[0402] The server sends the generated learning scenario and content adjusted to the user's psychological state to the terminal. The input is the adjusted learning scenario, and the output is an interactive scenario displayed in the user interface. The terminal receives this and presents it to the user.

[0403] Step 4:

[0404] The user considers the options presented in the scenario and makes a selection on the terminal. The input is the scenario options, and the output is data of the user's chosen action. The terminal records the user's selection and sends it to the server.

[0405] Step 5:

[0406] The server receives user selection data and uses an AI engine to evaluate the appropriateness of the user's actions. The input is user selection data, and the output is the evaluation result and feedback message. Based on the evaluation, the server generates emotionally sensitive feedback for the user.

[0407] Step 6:

[0408] The server sends the generated feedback to the terminal and provides it to the user. The input is the feedback message, and the output is the feedback displayed on the user interface. The terminal displays supportive messages and improvement information tailored to the user's emotional state.

[0409] Step 7:

[0410] The server compiles and provides parents with detailed reports on learning outcomes and social media usage. Inputs include all records, including learning history and sentiment data, while output is a report viewable by parents. This allows parents to gain deep insights into their child's learning progress and safe social media use.

[0411] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0412] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0413] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0414] [Third Embodiment]

[0415] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0416] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0417] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0418] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0419] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0420] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0421] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0422] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0423] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0424] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0425] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0426] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0427] The system implementing this invention utilizes generative artificial intelligence to provide a platform for parents and children to learn together about the risks and safe use of social networking services (SNS).

[0428] This system operates based on the interaction between the server, terminal, and user. First, the server generates learning scenarios tailored to the user's past learning history, interests, and age, and sends these scenarios to the terminal. Specifically, the server has the ability to model potential risk situations that children might face on social media and interactively create scenarios to address them. For example, it might include scenarios such as "How to deal with friend requests from strangers."

[0429] The device displays learning scenarios received from the server to both the child and the parent. Through the user interface, users can choose actions to take in response to the scenarios. As a user, parents and children can work together to discuss and decide how to respond to the presented problems. For example, in response to a request from a stranger, options such as "reject," "consult with parent," and "request additional information" will be presented.

[0430] When a user makes a selection on their device, that information is sent to a server, which evaluates the appropriateness of the action and generates feedback. Based on the evaluation, the server provides the user with appropriate feedback in real time. As a result, users can understand the reasoning behind their chosen actions and enhance their ability to deal with similar situations they encounter on social media.

[0431] Furthermore, the server provides parents with a report summarizing their child's learning outcomes and social media usage. This report includes statistics related to the child's behavioral patterns, acquired skills, and safe social media use, allowing parents to understand their child's development and take appropriate measures.

[0432] The system of this invention provides a means for parents and children to safely learn about the risks of social media and to have a better internet experience.

[0433] The following describes the processing flow.

[0434] Step 1:

[0435] The server collects data on the learner's past history, interests, and age. After collecting the data, it uses generative artificial intelligence to generate an optimal learning scenario. The generated scenario models the risks associated with the social networking service being studied.

[0436] Step 2:

[0437] The server sends the generated learning scenario to the terminal. The terminal receives it and displays it on the user interface. The displayed scenario is in an interactive format, including actions and choices that the user should consider.

[0438] Step 3:

[0439] Users view scenarios on their devices and select actions in response to the presented questions. For example, if they receive a friend request from a stranger, they may be presented with options such as "reject," "consult a parent," or "verify the information."

[0440] Step 4:

[0441] The device records the user's selections and sends that data to the server. The transmitted data should include the selected action and the process leading up to it.

[0442] Step 5:

[0443] The server analyzes the received selection data and evaluates its appropriateness. During the evaluation process, generative artificial intelligence generates feedback, taking into account the learner's level of understanding and the intentions behind the selections.

[0444] Step 6:

[0445] The server generates feedback based on the evaluation results and sends it to the terminal. The terminal receives the feedback and presents it to the user in an easy-to-understand manner. This allows the user to understand the significance of their choices and deepen their knowledge.

[0446] Step 7:

[0447] The server collects user learning progress and periodically generates reports for parents. These reports are provided via the device, allowing parents to monitor their child's social media usage and learning progress.

[0448] (Example 1)

[0449] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0450] In recent years, as many learners, especially children, utilize social networking services, the risks associated with using them without appropriate knowledge and skills have increased. There is a need for effective educational methods that allow parents and learners to learn safe ways to deal with the risks of social media together, but traditional educational methods have not been able to adequately address this challenge.

[0451] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0452] In this invention, the server includes means for generating a learning scenario adjusted by generative artificial intelligence using the learner's age data; means for displaying the generated learning scenario and providing a user interface that allows parents and learners to make choices together; and means for recording the learner's choices and collecting information for analyzing their responses to the learning scenario. This makes it possible for parents and learners to learn safe ways to use social media together.

[0453] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to create new information and content based on past data and patterns.

[0454] A "learning scenario" refers to a set of situations and problems designed to meet specific educational objectives, and is constructed based on the challenges learners may face.

[0455] A "user interface" is an interface through which a user and a system interact, and it plays a role in displaying information and accepting input.

[0456] "Feedback" refers to providing learners with real-time evaluations and comments on their choices and actions, informing them of the appropriateness of their actions and areas for improvement.

[0457] A "report" refers to a document or data that compiles information such as learning outcomes and behavioral patterns and provides it to parents or administrators.

[0458] The system of this invention utilizes generative artificial intelligence to enable parents and children to learn how to use social networking services safely through cooperation. The specific implementation of this system is described below.

[0459] Hardware and software

[0460] server

[0461] The server is equipped with a generative artificial intelligence model and generates learning scenarios. This allows it to create appropriate scenarios based on the learner's age data. The server operates on a cloud-based system and processes scenario and feedback data in real time.

[0462] terminal

[0463] The terminal receives and displays learning scenarios sent from the server. Each user interface is designed to allow parents and children to make interactive selections. The terminal then sends the selection information of the parents and children to the server.

[0464] Data processing and data calculation

[0465] Data processing by the server

[0466] The server analyzes data collected based on the learner's choices and actions. This allows the server to evaluate the learner's behavioral patterns and generate appropriate feedback.

[0467] Specific example

[0468] For example, the server generates a learning scenario themed around "how to respond to a friend request from a stranger." In this scenario, options such as "reject," "consult a parent," and "request additional information" are displayed on the device. Users (parent and child) choose the appropriate option from these choices, and their choices are evaluated by the server.

[0469] Example of a prompt

[0470] An example of a prompt is, "How should you handle social media messages from strangers?" Based on this prompt, the system generates a learning scenario and presents it to the user.

[0471] In this way, this system provides parents and children with the knowledge and skills necessary for safe use of social media.

[0472] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0473] Step 1:

[0474] The server uses a generative artificial intelligence model to generate appropriate learning scenarios based on the learner's age data and past learning history. In this process, the server models potential risk situations the learner might face on social media and creates scenarios to address them. The output is specific scenario data, which is then used for further processing.

[0475] Step 2:

[0476] The terminal receives scenario data sent from the server as input. Based on the received data, it displays the scenario through a user interface that can be viewed jointly by parent and child. Specifically, choices are displayed on the screen, and parent and child can discuss these choices and select the best answer. As output, response data selected by the user is generated.

[0477] Step 3:

[0478] The user selects the option they deem appropriate from the displayed choices. After making their selection, the selection data is sent from the terminal to the server. The selected response data is used as input and provided to the server as output.

[0479] Step 4:

[0480] The server analyzes the user's selection data that it receives. Based on this data, the server uses artificial intelligence to evaluate the appropriateness of the selected actions and generates feedback messages. The data processing performed here involves the analysis of selection data and the generation of feedback, and the output is the evaluated feedback data.

[0481] Step 5:

[0482] Feedback data generated from the server is sent to the terminal. The terminal receives this as input and presents feedback to the parent and child in real time via the user interface. As output, feedback is displayed to deepen understanding.

[0483] Step 6:

[0484] The server aggregates and analyzes past results data and selected data to generate a report summarizing learning outcomes for parents. All learning history and feedback data are used as input, and a detailed report for parents is created as output. This allows parents to understand their child's growth and learning progress and take necessary actions.

[0485] (Application Example 1)

[0486] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] In modern society, while the use of social networking services (SNS) is expanding, the risks faced by minors in particular are increasing. Many parents are also concerned about how to ensure their children use SNS safely. Therefore, there is a need for an effective learning system that allows both parents and children to understand the risks of SNS and acquire the knowledge and skills to use it safely.

[0488] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0489] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for an automated control device equipped with a user interface to provide verbal or visual guidance to the learner, and means for presenting returned feedback in real time and enabling the learner to understand its content visually or audibly. This makes it possible for parents and children to learn interactively and effectively acquire safe usage methods for social networking services.

[0490] "Generative artificial intelligence" is an artificial intelligence that has the ability to generate individual learning scenarios and content based on a learner's past learning history and interests.

[0491] A "user interface" is an interface that allows learners to interact with a system and manipulate the presented information and options visually or audibly.

[0492] An "automatic control device" is a device equipped with the function of automatically providing learners with audio and visual guidance according to a specified program.

[0493] A "server" is a device that centrally manages information within a network, generates learning scenarios, processes feedback, and transmits related data to other terminal devices.

[0494] "Feedback" is a response provided immediately to evaluate the appropriateness of a learner's choices and actions, and to provide information that facilitates improvement and understanding.

[0495] In the system implementing this invention, a server plays a central role. The server utilizes generative artificial intelligence to generate individual learning scenarios based on the learner's past learning history and interests. This allows users to receive learning content tailored to their individual needs.

[0496] The generated learning scenarios are presented to learners through a device equipped with a user interface. The device includes a microphone with voice recognition capabilities and a display that shows visual information, allowing users to interactively progress through the scenarios.

[0497] User choices and actions are transmitted to the server in real time, where the server evaluates this data and generates appropriate feedback. This feedback is immediately presented to the user through the device, helping learners understand the reasoning behind their choices.

[0498] The hardware will consist of an automated control system that provides voice and visual guidance. The software will utilize a server communication module implemented in Python, as well as machine learning model libraries such as TensorFlow.

[0499] As a concrete example of implementation, one day a learner is presented with a "scenario for dealing with a suspicious person on social media." The scenario asks in audio, "Your friend has received a message from a stranger. What do you do?" The learner answers, "I would talk to my parents." Feedback based on this choice is generated in real time.

[0500] The AI ​​model is prompted with questions such as, "Generate a scenario for dealing with a suspicious person on social media and consider feedback to guide parents and children to take appropriate action."

[0501] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0502] Step 1:

[0503] The server retrieves the learner's past learning history and interests from a database and inputs them into a generative AI model. Based on this input data, the generative AI model generates a learning scenario suitable for the learner and returns that scenario to the server.

[0504] Step 2:

[0505] The server sends the generated learning scenario to a terminal equipped with a user interface. The terminal presents the received learning scenario to the learner through a graphic display or audio output. For example, it might visually display choices on the screen and explain the scenario aloud.

[0506] Step 3:

[0507] The user interacts with the presented scenario, selecting an action from a set of options using a microphone or touchscreen. This selection is then sent from the device to the server.

[0508] Step 4:

[0509] The server evaluates the received selection results. These selections are then re-inputted into the generating AI model, which generates feedback to determine the appropriateness of the selections. This allows for data processing and logical operations to be performed to obtain evaluation results.

[0510] Step 5:

[0511] The server sends the generated feedback to the device. The device presents the feedback to the learner through a user interface, providing appropriate action guidance via audio or visual means. A concrete example of this might be displaying advice such as, "It is recommended that you talk to your parents."

[0512] Step 6:

[0513] The server generates a report for the parent based on the results of the learning session and other relevant data. The report includes statistics on the learner's behavioral patterns and social media usage, and is sent to the parent's account.

[0514] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0515] The system implementing this invention provides a platform that integrates generative artificial intelligence and an emotion engine to help parents and children learn about the risks and safe use of social media. This system operates through the interaction of a server, a terminal, and a user.

[0516] First, the server uses generative artificial intelligence to generate an appropriate learning scenario based on the learner's past learning history and interests. This scenario anticipates risk situations on social media and helps the user consider specific countermeasures. In addition, an emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions in real time. Based on this information, the server further customizes the learning scenario, adjusting the difficulty level and content to be appropriate according to the user's emotional state.

[0517] The device displays an interactive scenario on the user interface based on the scenario received from the server and the analysis results from the emotion engine. The user considers the various options displayed in the scenario and selects the action they deem most appropriate. This selection takes into account the user's emotional information; for example, if high stress levels are detected, a supportive message may be presented. In particular, it allows the user to learn how to cope when faced with difficult situations on social media.

[0518] When a user makes a selection, the device sends that data to the server. The server analyzes the user's behavioral choice, including emotional data, and uses an AI engine to evaluate the appropriateness of the selection. Furthermore, based on the results of this evaluation, it generates emotionally sensitive feedback to reinforce or improve the user's chosen behavior. For example, positive feedback is returned if the selection was appropriate, and constructive advice is provided if there is room for improvement.

[0519] Subsequently, the server generates a detailed report including the user's learning outcomes and emotional responses, and provides it to the parents. Through this report, parents can gain insights into their child's learning progress and the safety of their social media use. This makes it possible to promote safe social media use while taking into consideration the child's psychological state.

[0520] The system of this invention provides a means to effectively learn about the risks associated with using social media, and in particular, by taking into account the emotional changes of learners, it realizes more personalized educational support.

[0521] The following describes the processing flow.

[0522] Step 1:

[0523] The server collects data to analyze the learner's past history, interests, and behavioral patterns. This prepares the generative artificial intelligence to generate the optimal learning scenario.

[0524] Step 2:

[0525] The emotion engine collects emotional data from the user's facial expressions, tone of voice, and body language to recognize the user's emotions in real time. This emotional data is constantly updated as the user interacts with the device.

[0526] Step 3:

[0527] The server generates learning scenarios based on collected historical and sentiment data. For example, if the stress level is high, the scenario is customized, such as by simplifying the content, to provide the user with the most suitable scenario.

[0528] Step 4:

[0529] The device displays a customized learning scenario sent from the server in its user interface. Based on the presented scenario, the user can choose an action from multiple options.

[0530] Step 5:

[0531] The user makes choices via their device. For example, when asked "How to respond when you receive a message from a stranger," they select the best option from several possible responses. During this process, their emotional response is analyzed again.

[0532] Step 6:

[0533] The device sends user selections and emotional data to the server. The server uses an AI-based algorithm to evaluate the selections and generate feedback that takes the user's emotional state into account.

[0534] Step 7:

[0535] The server sends the generated feedback to the terminal, which then displays it to the user. The feedback is designed not only to deepen the user's understanding but also to take their psychological state into consideration.

[0536] Step 8:

[0537] The server generates a detailed report, including learning progress and emotional responses, and provides it to the parents. Parents use the report as a guide to understand the safety of their child's social media use and their learning progress.

[0538] This series of processes allows the system to provide adaptive learning that incorporates the learner's emotions, effectively improving their skills to cope with social media risks.

[0539] (Example 2)

[0540] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0541] In recent years, with the rapid increase in the use of social networks, there has been a lack of opportunities for minors to learn about the risks they face. Furthermore, personalized learning based on emotions is insufficient, and there are inadequate mechanisms for parents to properly monitor their children's learning progress. This creates challenges that hinder safe and effective network use.

[0542] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0543] In this invention, the server includes means for forming a learning situation based on the learner's past history and interests using generative artificial intelligence, means for analyzing the learner's facial expression and voice information using an emotion analysis engine to identify their emotional state, and means for modifying the generated learning situation according to the learner's emotional state and providing an appropriate difficulty level. This enables the provision of a personalized learning experience while taking the user's emotions into consideration, and allows for appropriate reporting of learning progress to parents.

[0544] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates new information and situations based on past accumulated history and interests.

[0545] An "emotion analysis engine" is a technology that analyzes information such as a user's facial expressions and voice to automatically identify their emotional state.

[0546] "Learning context" refers to educational scenarios and tasks that are generated according to the learner's abilities and needs.

[0547] "Facial expression information" refers to information obtained as digital data of the user's facial movements and expressions.

[0548] "Audio information" refers to information that captures the characteristics of a user's voice, including its tone and speed, as digital data.

[0549] "Emotional state" refers to the mental or emotional response a user exhibits under specific circumstances.

[0550] "Learning progress" indicates the extent to which a learner has progressed in the learning process.

[0551] "Network usage status" refers to the state of how social networks are being used, including risk management and usage patterns.

[0552] The system for carrying out this invention combines generative artificial intelligence and an emotion analysis engine to provide learners with education on the use of social networks. The system consists of a server, terminals, and user interaction.

[0553] The server first uses a database management system to analyze data based on the learner's past history and interests. Based on this analysis, the server utilizes a generative AI model to input prompts. An example prompt might be, "Please provide an appropriate learning scenario regarding risk management on social media." The generated scenario data is then further customized according to the user's characteristics.

[0554] Furthermore, the emotion analysis engine acquires the user's facial expressions and voice information in real time to identify the user's emotional state. Image recognition software and voice analysis tools are used for this analysis. The emotion data obtained in this way is used to adjust the content and difficulty level of the learning scenario.

[0555] The device receives customized scenarios sent from the server and presents them to the user through an interactive user interface. Learning progresses as the user considers the options displayed on the screen and selects actions based on those options. The user's selection information is used to support stress management and the formation of appropriate behaviors.

[0556] When a user makes a selection, that information is sent to the server, where artificial intelligence analyzes the selection. Based on the analysis, the server generates feedback and provides it to the user through the terminal. This feedback is supportive, taking into account the learner's emotional state, and serves as motivation for further learning.

[0557] Ultimately, the server generates and provides parents with a detailed report on the learner's progress and network usage. This report allows parents to accurately understand their child's learning progress and continue using the network with peace of mind.

[0558] This invention enables learners to acquire safer and more effective network usage techniques and provides an emotionally personalized learning experience.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] The server retrieves the learner's past history and interest data from the database. This data is input via database operations such as SQL queries. The output is the learner's history information. Based on this history information, the server issues a prompt to the generating AI model: "Please provide an appropriate learning scenario regarding risk management on social media." The generated learning scenario is then output.

[0562] Step 2:

[0563] The server uses an emotion analysis engine to collect user facial and voice information. This information is input through hardware such as cameras and microphones. The emotion analysis engine analyzes the input data and outputs the user's emotional state. For example, if the user is feeling stressed, that emotional state is identified as a numerical value or label.

[0564] Step 3:

[0565] The server customizes the generated learning scenario according to the user's emotional state. The input consists of the generated scenario and emotional state data, and the server modifies the scenario's content and difficulty based on this. For example, if the stress level is high, the high-risk content is simplified. The output is the customized learning scenario.

[0566] Step 4:

[0567] The device displays a customized learning scenario on the user interface. In this step, scenario data received from the server is used as input and presented on the screen using a UI framework. The user views this screen and selects an action from the provided options.

[0568] Step 5:

[0569] When a user makes a selection, that selection information is sent from the terminal to the server. In this process, the user's selection data is used as input and transferred to the server via an HTTP POST request. The server receives this data and prepares it for analysis.

[0570] Step 6:

[0571] The server analyzes the user's selection data and emotional state, and uses an AI engine to evaluate the appropriateness of the selection. In this step, the input selection data is analyzed, and the evaluation results are output. Based on these results, preparations are made to generate and provide emotionally sensitive feedback to the user.

[0572] Step 7:

[0573] The server sends the generated feedback to the device and presents it to the user. The user receives the feedback and can then feel the effectiveness of their learning based on it. Finally, the server compiles the data to provide parents with a report on learning results and social media usage.

[0574] This allows the entire system to work together, resulting in an effective learning process.

[0575] (Application Example 2)

[0576] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0577] In recent years, with the spread of social networking services, the number of cases in which children are exposed to various risks on the internet has increased, but there is a lack of safety education. Furthermore, there are few educational methods that take into account the psychological state of children, and there is a need for more individualized education.

[0578] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0579] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for analyzing the learner's psychological state using an emotion analysis device and adjusting the learning scenario based on the analysis results, and means for providing parents with reports on learning outcomes and social network service usage. This makes it possible to provide personalized and effective safety education regarding the risks children face on the internet, according to their emotional state.

[0580] "Generative artificial intelligence" is an artificial intelligence technology that can generate new information and scenarios based on past data and circumstances, and provide them to learners.

[0581] A "learning scenario" is a set of learning activities or situations designed to help learners acquire specific knowledge or skills.

[0582] A "user interface" is an interface through which learners interact with a system and receive or input information.

[0583] An "emotion analysis device" is a device that analyzes biometric information such as a learner's facial expressions and voice to infer their psychological state.

[0584] "Psychological state" refers to the learner's internal state, including their emotions, mood, and stress level.

[0585] "Means of data collection" refer to methods and devices for recording learners' choices and actions and gathering information necessary for analysis.

[0586] "A means by which artificial intelligence evaluates learner behavior and generates feedback" refers to a function that evaluates the validity of learner choices and actions based on collected data, and provides information for improvement or approval based on the results.

[0587] "Means of providing reports" refers to methods or devices for organizing learners' learning outcomes and SNS usage and reporting them to parents in an easily understandable format.

[0588] This invention's system integrates generative artificial intelligence and emotion analysis functions to provide a platform for parents and children to safely learn about using social media.

[0589] The server uses generative artificial intelligence to generate learning scenarios based on the user's past learning history and interests. These scenarios anticipate potential risk situations in social network services and are designed to teach countermeasures. It also utilizes an emotion analysis system to analyze the user's psychological state and adjust the scenario content and difficulty accordingly. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis.

[0590] The device displays scenarios received from the server and presents them interactively to the user. The user considers the options for the scenario and selects the action they believe to be optimal. The selection results are sent from the device to the server and analyzed by an AI engine. The AI ​​evaluates the validity of the selection and provides the user with emotionally sensitive feedback. In high-stress situations, supportive messages may also be displayed.

[0591] Furthermore, the server generates and provides parents with reports on learning outcomes and social media usage. These reports include recommendations for safe network use that take into account the child's psychological state.

[0592] As a concrete example, a scenario is presented: "What would you do if you received a message from a stranger on social media?" In this scenario, the system analyzes the user's emotional state and provides feedback based on the chosen option. Prompts such as "What would you do in this situation?" and "Please choose from the following options!" are provided.

[0593] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0594] Step 1:

[0595] The server retrieves the learner's past learning history and interests from a database. Using this data as input, it generates learning scenarios using generative artificial intelligence. The output is a scenario tailored to the learner. This process lays the foundation for providing a personalized learning experience.

[0596] Step 2:

[0597] The server acquires the user's facial expressions and voice using an emotion analysis device and analyzes that data. The input is biometric data, and the output is data representing the user's psychological state. TensorFlow is used for the analysis, and the content and difficulty level of the learning scenario are adjusted based on the results.

[0598] Step 3:

[0599] The server sends the generated learning scenario and content adjusted to the user's psychological state to the terminal. The input is the adjusted learning scenario, and the output is an interactive scenario displayed in the user interface. The terminal receives this and presents it to the user.

[0600] Step 4:

[0601] The user considers the options presented in the scenario and makes a selection on the terminal. The input is the scenario options, and the output is data of the user's chosen action. The terminal records the user's selection and sends it to the server.

[0602] Step 5:

[0603] The server receives user selection data and uses an AI engine to evaluate the appropriateness of the user's actions. The input is user selection data, and the output is the evaluation result and feedback message. Based on the evaluation, the server generates emotionally sensitive feedback for the user.

[0604] Step 6:

[0605] The server sends the generated feedback to the terminal and provides it to the user. The input is the feedback message, and the output is the feedback displayed on the user interface. The terminal displays supportive messages and improvement information tailored to the user's emotional state.

[0606] Step 7:

[0607] The server compiles and provides parents with detailed reports on learning outcomes and social media usage. Inputs include all records, including learning history and sentiment data, while output is a report viewable by parents. This allows parents to gain deep insights into their child's learning progress and safe social media use.

[0608] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0609] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0610] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0611] [Fourth Embodiment]

[0612] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0613] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0614] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0615] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0616] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0617] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0618] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0619] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0620] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0621] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0622] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0623] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0624] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0625] The system implementing this invention utilizes generative artificial intelligence to provide a platform for parents and children to learn together about the risks and safe use of social networking services (SNS).

[0626] This system operates based on the interaction between the server, terminal, and user. First, the server generates learning scenarios tailored to the user's past learning history, interests, and age, and sends these scenarios to the terminal. Specifically, the server has the ability to model potential risk situations that children might face on social media and interactively create scenarios to address them. For example, it might include scenarios such as "How to deal with friend requests from strangers."

[0627] The device displays learning scenarios received from the server to both the child and the parent. Through the user interface, users can choose actions to take in response to the scenarios. As a user, parents and children can work together to discuss and decide how to respond to the presented problems. For example, in response to a request from a stranger, options such as "reject," "consult with parent," and "request additional information" will be presented.

[0628] When a user makes a selection on their device, that information is sent to a server, which evaluates the appropriateness of the action and generates feedback. Based on the evaluation, the server provides the user with appropriate feedback in real time. As a result, users can understand the reasoning behind their chosen actions and enhance their ability to deal with similar situations they encounter on social media.

[0629] Furthermore, the server provides parents with a report summarizing their child's learning outcomes and social media usage. This report includes statistics related to the child's behavioral patterns, acquired skills, and safe social media use, allowing parents to understand their child's development and take appropriate measures.

[0630] The system of this invention provides a means for parents and children to safely learn about the risks of social media and to have a better internet experience.

[0631] The following describes the processing flow.

[0632] Step 1:

[0633] The server collects data on the learner's past history, interests, and age. After collecting the data, it uses generative artificial intelligence to generate an optimal learning scenario. The generated scenario models the risks associated with the social networking service being studied.

[0634] Step 2:

[0635] The server sends the generated learning scenario to the terminal. The terminal receives it and displays it on the user interface. The displayed scenario is in an interactive format, including actions and choices that the user should consider.

[0636] Step 3:

[0637] Users view scenarios on their devices and select actions in response to the presented questions. For example, if they receive a friend request from a stranger, they may be presented with options such as "reject," "consult a parent," or "verify the information."

[0638] Step 4:

[0639] The device records the user's selections and sends that data to the server. The transmitted data should include the selected action and the process leading up to it.

[0640] Step 5:

[0641] The server analyzes the received selection data and evaluates its appropriateness. During the evaluation process, generative artificial intelligence generates feedback, taking into account the learner's level of understanding and the intentions behind the selections.

[0642] Step 6:

[0643] The server generates feedback based on the evaluation results and sends it to the terminal. The terminal receives the feedback and presents it to the user in an easy-to-understand manner. This allows the user to understand the significance of their choices and deepen their knowledge.

[0644] Step 7:

[0645] The server collects user learning progress and periodically generates reports for parents. These reports are provided via the device, allowing parents to monitor their child's social media usage and learning progress.

[0646] (Example 1)

[0647] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0648] In recent years, as many learners, especially children, utilize social networking services, the risks associated with using them without appropriate knowledge and skills have increased. There is a need for effective educational methods that allow parents and learners to learn safe ways to deal with the risks of social media together, but traditional educational methods have not been able to adequately address this challenge.

[0649] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0650] In this invention, the server includes means for generating a learning scenario adjusted by generative artificial intelligence using the learner's age data; means for displaying the generated learning scenario and providing a user interface that allows parents and learners to make choices together; and means for recording the learner's choices and collecting information for analyzing their responses to the learning scenario. This makes it possible for parents and learners to learn safe ways to use social media together.

[0651] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to create new information and content based on past data and patterns.

[0652] A "learning scenario" refers to a set of situations and problems designed to meet specific educational objectives, and is constructed based on the challenges learners may face.

[0653] A "user interface" is an interface through which a user and a system interact, and it plays a role in displaying information and accepting input.

[0654] "Feedback" refers to providing learners with real-time evaluations and comments on their choices and actions, informing them of the appropriateness of their actions and areas for improvement.

[0655] A "report" refers to a document or data that compiles information such as learning outcomes and behavioral patterns and provides it to parents or administrators.

[0656] The system of this invention utilizes generative artificial intelligence to enable parents and children to learn how to use social networking services safely through cooperation. The specific implementation of this system is described below.

[0657] Hardware and software

[0658] server

[0659] The server is equipped with a generative artificial intelligence model and generates learning scenarios. This allows it to create appropriate scenarios based on the learner's age data. The server operates on a cloud-based system and processes scenario and feedback data in real time.

[0660] terminal

[0661] The terminal receives and displays learning scenarios sent from the server. Each user interface is designed to allow parents and children to make interactive selections. The terminal then sends the selection information of the parents and children to the server.

[0662] Data processing and data calculation

[0663] Data processing by the server

[0664] The server analyzes data collected based on the learner's choices and actions. This allows the server to evaluate the learner's behavioral patterns and generate appropriate feedback.

[0665] Specific example

[0666] For example, the server generates a learning scenario themed around "how to respond to a friend request from a stranger." In this scenario, options such as "reject," "consult a parent," and "request additional information" are displayed on the device. Users (parent and child) choose the appropriate option from these choices, and their choices are evaluated by the server.

[0667] Example of a prompt

[0668] An example of a prompt is, "How should you handle social media messages from strangers?" Based on this prompt, the system generates a learning scenario and presents it to the user.

[0669] In this way, this system provides parents and children with the knowledge and skills necessary for safe use of social media.

[0670] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0671] Step 1:

[0672] The server uses a generative artificial intelligence model to generate appropriate learning scenarios based on the learner's age data and past learning history. In this process, the server models potential risk situations the learner might face on social media and creates scenarios to address them. The output is specific scenario data, which is then used for further processing.

[0673] Step 2:

[0674] The terminal receives scenario data sent from the server as input. Based on the received data, it displays the scenario through a user interface that can be viewed jointly by parent and child. Specifically, choices are displayed on the screen, and parent and child can discuss these choices and select the best answer. As output, response data selected by the user is generated.

[0675] Step 3:

[0676] The user selects the option they deem appropriate from the displayed choices. After making their selection, the selection data is sent from the terminal to the server. The selected response data is used as input and provided to the server as output.

[0677] Step 4:

[0678] The server analyzes the user's selection data that it receives. Based on this data, the server uses artificial intelligence to evaluate the appropriateness of the selected actions and generates feedback messages. The data processing performed here involves the analysis of selection data and the generation of feedback, and the output is the evaluated feedback data.

[0679] Step 5:

[0680] Feedback data generated from the server is sent to the terminal. The terminal receives this as input and presents feedback to the parent and child in real time via the user interface. As output, feedback is displayed to deepen understanding.

[0681] Step 6:

[0682] The server aggregates and analyzes past results data and selected data to generate a report summarizing learning outcomes for parents. All learning history and feedback data are used as input, and a detailed report for parents is created as output. This allows parents to understand their child's growth and learning progress and take necessary actions.

[0683] (Application Example 1)

[0684] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0685] In modern society, while the use of social networking services (SNS) is expanding, the risks faced by minors in particular are increasing. Many parents are also concerned about how to ensure their children use SNS safely. Therefore, there is a need for an effective learning system that allows both parents and children to understand the risks of SNS and acquire the knowledge and skills to use it safely.

[0686] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0687] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for an automated control device equipped with a user interface to provide verbal or visual guidance to the learner, and means for presenting returned feedback in real time and enabling the learner to understand its content visually or audibly. This makes it possible for parents and children to learn interactively and effectively acquire safe usage methods for social networking services.

[0688] "Generative artificial intelligence" is an artificial intelligence that has the ability to generate individual learning scenarios and content based on a learner's past learning history and interests.

[0689] A "user interface" is an interface that allows learners to interact with a system and manipulate the presented information and options visually or audibly.

[0690] An "automatic control device" is a device equipped with the function of automatically providing learners with audio and visual guidance according to a specified program.

[0691] A "server" is a device that centrally manages information within a network, generates learning scenarios, processes feedback, and transmits related data to other terminal devices.

[0692] "Feedback" is a response provided immediately to evaluate the appropriateness of a learner's choices and actions, and to provide information that facilitates improvement and understanding.

[0693] In the system implementing this invention, a server plays a central role. The server utilizes generative artificial intelligence to generate individual learning scenarios based on the learner's past learning history and interests. This allows users to receive learning content tailored to their individual needs.

[0694] The generated learning scenarios are presented to learners through a device equipped with a user interface. The device includes a microphone with voice recognition capabilities and a display that shows visual information, allowing users to interactively progress through the scenarios.

[0695] User choices and actions are transmitted to the server in real time, where the server evaluates this data and generates appropriate feedback. This feedback is immediately presented to the user through the device, helping learners understand the reasoning behind their choices.

[0696] The hardware will consist of an automated control system that provides voice and visual guidance. The software will utilize a server communication module implemented in Python, as well as machine learning model libraries such as TensorFlow.

[0697] As a concrete example of implementation, one day a learner is presented with a "scenario for dealing with a suspicious person on social media." The scenario asks in audio, "Your friend has received a message from a stranger. What do you do?" The learner answers, "I would talk to my parents." Feedback based on this choice is generated in real time.

[0698] The AI ​​model is prompted with questions such as, "Generate a scenario for dealing with a suspicious person on social media and consider feedback to guide parents and children to take appropriate action."

[0699] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0700] Step 1:

[0701] The server retrieves the learner's past learning history and interests from a database and inputs them into a generative AI model. Based on this input data, the generative AI model generates a learning scenario suitable for the learner and returns that scenario to the server.

[0702] Step 2:

[0703] The server sends the generated learning scenario to a terminal equipped with a user interface. The terminal presents the received learning scenario to the learner through a graphic display or audio output. For example, it might visually display choices on the screen and explain the scenario aloud.

[0704] Step 3:

[0705] The user interacts with the presented scenario, selecting an action from a set of options using a microphone or touchscreen. This selection is then sent from the device to the server.

[0706] Step 4:

[0707] The server evaluates the received selection results. These selections are then re-inputted into the generating AI model, which generates feedback to determine the appropriateness of the selections. This allows for data processing and logical operations to be performed to obtain evaluation results.

[0708] Step 5:

[0709] The server sends the generated feedback to the device. The device presents the feedback to the learner through a user interface, providing appropriate action guidance via audio or visual means. A concrete example of this might be displaying advice such as, "It is recommended that you talk to your parents."

[0710] Step 6:

[0711] The server generates a report for the parent based on the results of the learning session and other relevant data. The report includes statistics on the learner's behavioral patterns and social media usage, and is sent to the parent's account.

[0712] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0713] The system implementing this invention provides a platform that integrates generative artificial intelligence and an emotion engine to help parents and children learn about the risks and safe use of social media. This system operates through the interaction of a server, a terminal, and a user.

[0714] First, the server uses generative artificial intelligence to generate an appropriate learning scenario based on the learner's past learning history and interests. This scenario anticipates risk situations on social media and helps the user consider specific countermeasures. In addition, an emotion engine analyzes the user's facial expressions and tone of voice to recognize emotions in real time. Based on this information, the server further customizes the learning scenario, adjusting the difficulty level and content to be appropriate according to the user's emotional state.

[0715] The device displays an interactive scenario on the user interface based on the scenario received from the server and the analysis results from the emotion engine. The user considers the various options displayed in the scenario and selects the action they deem most appropriate. This selection takes into account the user's emotional information; for example, if high stress levels are detected, a supportive message may be presented. In particular, it allows the user to learn how to cope when faced with difficult situations on social media.

[0716] When a user makes a selection, the device sends that data to the server. The server analyzes the user's behavioral choice, including emotional data, and uses an AI engine to evaluate the appropriateness of the selection. Furthermore, based on the results of this evaluation, it generates emotionally sensitive feedback to reinforce or improve the user's chosen behavior. For example, positive feedback is returned if the selection was appropriate, and constructive advice is provided if there is room for improvement.

[0717] Subsequently, the server generates a detailed report including the user's learning outcomes and emotional responses, and provides it to the parents. Through this report, parents can gain insights into their child's learning progress and the safety of their social media use. This makes it possible to promote safe social media use while taking into consideration the child's psychological state.

[0718] The system of this invention provides a means to effectively learn about the risks associated with using social media, and in particular, by taking into account the emotional changes of learners, it realizes more personalized educational support.

[0719] The following describes the processing flow.

[0720] Step 1:

[0721] The server collects data to analyze the learner's past history, interests, and behavioral patterns. This prepares the generative artificial intelligence to generate the optimal learning scenario.

[0722] Step 2:

[0723] The emotion engine collects emotional data from the user's facial expressions, tone of voice, and body language to recognize the user's emotions in real time. This emotional data is constantly updated as the user interacts with the device.

[0724] Step 3:

[0725] The server generates learning scenarios based on collected historical and sentiment data. For example, if the stress level is high, the scenario is customized, such as by simplifying the content, to provide the user with the most suitable scenario.

[0726] Step 4:

[0727] The device displays a customized learning scenario sent from the server in its user interface. Based on the presented scenario, the user can choose an action from multiple options.

[0728] Step 5:

[0729] The user makes choices via their device. For example, when asked "How to respond when you receive a message from a stranger," they select the best option from several possible responses. During this process, their emotional response is analyzed again.

[0730] Step 6:

[0731] The device sends user selections and emotional data to the server. The server uses an AI-based algorithm to evaluate the selections and generate feedback that takes the user's emotional state into account.

[0732] Step 7:

[0733] The server sends the generated feedback to the terminal, which then displays it to the user. The feedback is designed not only to deepen the user's understanding but also to take their psychological state into consideration.

[0734] Step 8:

[0735] The server generates a detailed report, including learning progress and emotional responses, and provides it to the parents. Parents use the report as a guide to understand the safety of their child's social media use and their learning progress.

[0736] This series of processes allows the system to provide adaptive learning that incorporates the learner's emotions, effectively improving their skills to cope with social media risks.

[0737] (Example 2)

[0738] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0739] In recent years, with the rapid increase in the use of social networks, there has been a lack of opportunities for minors to learn about the risks they face. Furthermore, personalized learning based on emotions is insufficient, and there are inadequate mechanisms for parents to properly monitor their children's learning progress. This creates challenges that hinder safe and effective network use.

[0740] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0741] In this invention, the server includes means for forming a learning situation based on the learner's past history and interests using generative artificial intelligence, means for analyzing the learner's facial expression and voice information using an emotion analysis engine to identify their emotional state, and means for modifying the generated learning situation according to the learner's emotional state and providing an appropriate difficulty level. This enables the provision of a personalized learning experience while taking the user's emotions into consideration, and allows for appropriate reporting of learning progress to parents.

[0742] "Generative artificial intelligence" is an artificial intelligence technology that automatically generates new information and situations based on past accumulated history and interests.

[0743] An "emotion analysis engine" is a technology that analyzes information such as a user's facial expressions and voice to automatically identify their emotional state.

[0744] "Learning context" refers to educational scenarios and tasks that are generated according to the learner's abilities and needs.

[0745] "Facial expression information" refers to information obtained as digital data of the user's facial movements and expressions.

[0746] "Audio information" refers to information that captures the characteristics of a user's voice, including its tone and speed, as digital data.

[0747] "Emotional state" refers to the mental or emotional response a user exhibits under specific circumstances.

[0748] "Learning progress" indicates the extent to which a learner has progressed in the learning process.

[0749] "Network usage status" refers to the state of how social networks are being used, including risk management and usage patterns.

[0750] The system for carrying out this invention combines generative artificial intelligence and an emotion analysis engine to provide learners with education on the use of social networks. The system consists of a server, terminals, and user interaction.

[0751] The server first uses a database management system to analyze data based on the learner's past history and interests. Based on this analysis, the server utilizes a generative AI model to input prompts. An example prompt might be, "Please provide an appropriate learning scenario regarding risk management on social media." The generated scenario data is then further customized according to the user's characteristics.

[0752] Furthermore, the emotion analysis engine acquires the user's facial expressions and voice information in real time to identify the user's emotional state. Image recognition software and voice analysis tools are used for this analysis. The emotion data obtained in this way is used to adjust the content and difficulty level of the learning scenario.

[0753] The device receives customized scenarios sent from the server and presents them to the user through an interactive user interface. Learning progresses as the user considers the options displayed on the screen and selects actions based on those options. The user's selection information is used to support stress management and the formation of appropriate behaviors.

[0754] When a user makes a selection, that information is sent to the server, where artificial intelligence analyzes the selection. Based on the analysis, the server generates feedback and provides it to the user through the terminal. This feedback is supportive, taking into account the learner's emotional state, and serves as motivation for further learning.

[0755] Ultimately, the server generates and provides parents with a detailed report on the learner's progress and network usage. This report allows parents to accurately understand their child's learning progress and continue using the network with peace of mind.

[0756] This invention enables learners to acquire safer and more effective network usage techniques and provides an emotionally personalized learning experience.

[0757] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0758] Step 1:

[0759] The server retrieves the learner's past history and interest data from the database. This data is input via database operations such as SQL queries. The output is the learner's history information. Based on this history information, the server issues a prompt to the generating AI model: "Please provide an appropriate learning scenario regarding risk management on social media." The generated learning scenario is then output.

[0760] Step 2:

[0761] The server uses an emotion analysis engine to collect user facial and voice information. This information is input through hardware such as cameras and microphones. The emotion analysis engine analyzes the input data and outputs the user's emotional state. For example, if the user is feeling stressed, that emotional state is identified as a numerical value or label.

[0762] Step 3:

[0763] The server customizes the generated learning scenario according to the user's emotional state. The input consists of the generated scenario and emotional state data, and the server modifies the scenario's content and difficulty based on this. For example, if the stress level is high, the high-risk content is simplified. The output is the customized learning scenario.

[0764] Step 4:

[0765] The device displays a customized learning scenario on the user interface. In this step, scenario data received from the server is used as input and presented on the screen using a UI framework. The user views this screen and selects an action from the provided options.

[0766] Step 5:

[0767] When a user makes a selection, that selection information is sent from the terminal to the server. In this process, the user's selection data is used as input and transferred to the server via an HTTP POST request. The server receives this data and prepares it for analysis.

[0768] Step 6:

[0769] The server analyzes the user's selection data and emotional state, and uses an AI engine to evaluate the appropriateness of the selection. In this step, the input selection data is analyzed, and the evaluation results are output. Based on these results, preparations are made to generate and provide emotionally sensitive feedback to the user.

[0770] Step 7:

[0771] The server sends the generated feedback to the device and presents it to the user. The user receives the feedback and can then feel the effectiveness of their learning based on it. Finally, the server compiles the data to provide parents with a report on learning results and social media usage.

[0772] This allows the entire system to work together, resulting in an effective learning process.

[0773] (Application Example 2)

[0774] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0775] In recent years, with the spread of social networking services, the number of cases in which children are exposed to various risks on the internet has increased, but there is a lack of safety education. Furthermore, there are few educational methods that take into account the psychological state of children, and there is a need for more individualized education.

[0776] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0777] In this invention, the server includes means for generating learning scenarios based on the learner's past learning history and interests using generative artificial intelligence, means for analyzing the learner's psychological state using an emotion analysis device and adjusting the learning scenario based on the analysis results, and means for providing parents with reports on learning outcomes and social network service usage. This makes it possible to provide personalized and effective safety education regarding the risks children face on the internet, according to their emotional state.

[0778] "Generative artificial intelligence" is an artificial intelligence technology that can generate new information and scenarios based on past data and circumstances, and provide them to learners.

[0779] A "learning scenario" is a set of learning activities or situations designed to help learners acquire specific knowledge or skills.

[0780] A "user interface" is an interface through which learners interact with a system and receive or input information.

[0781] An "emotion analysis device" is a device that analyzes biometric information such as a learner's facial expressions and voice to infer their psychological state.

[0782] "Psychological state" refers to the learner's internal state, including their emotions, mood, and stress level.

[0783] "Means of data collection" refer to methods and devices for recording learners' choices and actions and gathering information necessary for analysis.

[0784] "A means by which artificial intelligence evaluates learner behavior and generates feedback" refers to a function that evaluates the validity of learner choices and actions based on collected data, and provides information for improvement or approval based on the results.

[0785] "Means of providing reports" refers to methods or devices for organizing learners' learning outcomes and SNS usage and reporting them to parents in an easily understandable format.

[0786] This invention's system integrates generative artificial intelligence and emotion analysis functions to provide a platform for parents and children to safely learn about using social media.

[0787] The server uses generative artificial intelligence to generate learning scenarios based on the user's past learning history and interests. These scenarios anticipate potential risk situations in social network services and are designed to teach countermeasures. It also utilizes an emotion analysis system to analyze the user's psychological state and adjust the scenario content and difficulty accordingly. Machine learning frameworks such as TensorFlow and PyTorch are used for this analysis.

[0788] The device displays scenarios received from the server and presents them interactively to the user. The user considers the options for the scenario and selects the action they believe to be optimal. The selection results are sent from the device to the server and analyzed by an AI engine. The AI ​​evaluates the validity of the selection and provides the user with emotionally sensitive feedback. In high-stress situations, supportive messages may also be displayed.

[0789] Furthermore, the server generates and provides parents with reports on learning outcomes and social media usage. These reports include recommendations for safe network use that take into account the child's psychological state.

[0790] As a concrete example, a scenario is presented: "What would you do if you received a message from a stranger on social media?" In this scenario, the system analyzes the user's emotional state and provides feedback based on the chosen option. Prompts such as "What would you do in this situation?" and "Please choose from the following options!" are provided.

[0791] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0792] Step 1:

[0793] The server retrieves the learner's past learning history and interests from a database. Using this data as input, it generates learning scenarios using generative artificial intelligence. The output is a scenario tailored to the learner. This process lays the foundation for providing a personalized learning experience.

[0794] Step 2:

[0795] The server acquires the user's facial expressions and voice using an emotion analysis device and analyzes that data. The input is biometric data, and the output is data representing the user's psychological state. TensorFlow is used for the analysis, and the content and difficulty level of the learning scenario are adjusted based on the results.

[0796] Step 3:

[0797] The server sends the generated learning scenario and content adjusted to the user's psychological state to the terminal. The input is the adjusted learning scenario, and the output is an interactive scenario displayed in the user interface. The terminal receives this and presents it to the user.

[0798] Step 4:

[0799] The user considers the options presented in the scenario and makes a selection on the terminal. The input is the scenario options, and the output is data of the user's chosen action. The terminal records the user's selection and sends it to the server.

[0800] Step 5:

[0801] The server receives user selection data and uses an AI engine to evaluate the appropriateness of the user's actions. The input is user selection data, and the output is the evaluation result and feedback message. Based on the evaluation, the server generates emotionally sensitive feedback for the user.

[0802] Step 6:

[0803] The server sends the generated feedback to the terminal and provides it to the user. The input is the feedback message, and the output is the feedback displayed on the user interface. The terminal displays supportive messages and improvement information tailored to the user's emotional state.

[0804] Step 7:

[0805] The server compiles and provides parents with detailed reports on learning outcomes and social media usage. Inputs include all records, including learning history and sentiment data, while output is a report viewable by parents. This allows parents to gain deep insights into their child's learning progress and safe social media use.

[0806] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0807] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0808] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0809] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0810] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0811] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0812] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0813] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0814] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0815] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0816] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0817] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0818] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0819] 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.

[0820] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0821] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0822] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0823] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0824] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0825] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0826] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0827] The following is further disclosed regarding the embodiments described above.

[0828] (Claim 1)

[0829] A means for generating learning scenarios based on a learner's past learning history and interests using generative artificial intelligence,

[0830] Means for displaying the generated learning scenario through a user interface,

[0831] A means of collecting data to record and analyze learners' choices regarding scenarios,

[0832] Based on the aforementioned collected data, the artificial intelligence evaluates the learner's behavior and generates feedback,

[0833] Means for displaying the generated feedback through a user interface,

[0834] A means of providing parents with reports on their child's learning progress and social media usage,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, which provides a scenario for learners to learn how to effectively cope with risks in social network services.

[0838] (Claim 3)

[0839] The system according to claim 1, which provides means for parents and learners to participate in scenario-based learning together and to enhance the technology for secure network use.

[0840] "Example 1"

[0841] (Claim 1)

[0842] A means of generating a learning scenario adjusted by generative artificial intelligence using learner age data,

[0843] Means for displaying the generated learning scenario and providing a user interface that allows parents and learners to make joint choices,

[0844] A means of recording learner selection information and collecting information for analyzing responses to learning scenarios,

[0845] Based on the information collected, the artificial intelligence evaluates the appropriateness of the learner's response and generates immediate feedback.

[0846] The generated feedback is presented through a user interface, providing a means to facilitate the learner's understanding of their actions.

[0847] A means of creating and providing parents with reports on the learner's ability improvement and network usage,

[0848] A system that includes this.

[0849] (Claim 2)

[0850] The system according to claim 1, comprising scenarios for developing learners' ability to appropriately respond to potential dangers in social network services.

[0851] (Claim 3)

[0852] The system according to claim 1, which provides a means for parents and learners to collaborate in learning scenarios and enhance their technical skills in a safe online environment.

[0853] "Application Example 1"

[0854] (Claim 1)

[0855] A means for generating learning scenarios based on a learner's past learning history and interests using generative artificial intelligence,

[0856] Means for displaying the generated learning scenario through a user interface,

[0857] A means of collecting data to record and analyze learners' choices regarding scenarios,

[0858] Based on the aforementioned collected data, the artificial intelligence evaluates the learner's behavior and generates feedback,

[0859] Means for displaying the generated feedback through a user interface,

[0860] A means of providing parents with reports on their child's learning progress and social media usage,

[0861] An automated control device equipped with a user interface provides means for giving verbal or visual guidance to learners,

[0862] A means of presenting returned feedback in real time and enabling learners to understand its content visually or audibly,

[0863] A system that includes this.

[0864] (Claim 2)

[0865] The system according to claim 1, which provides a scenario for learners to learn how to effectively cope with risks in social network services.

[0866] (Claim 3)

[0867] The system according to claim 1, which provides means for parents and learners to participate in scenario-based learning together and to enhance the technology for secure network use.

[0868] "Example 2 of combining an emotion engine"

[0869] (Claim 1)

[0870] A means of shaping the learning situation based on the learner's past history and interests using generative artificial intelligence,

[0871] A means for analyzing a learner's facial expression and voice information using an emotion analysis engine to identify their emotional state,

[0872] The generated learning situation is modified according to the learner's emotional state, and means are provided to provide an appropriate difficulty level.

[0873] Means for presenting the corrected learning status through a display device,

[0874] A means of recording learner selection information and accumulating behavioral data using communication means,

[0875] Based on the aforementioned accumulated behavioral data, the artificial intelligence analyzes the learner's choices and generates a response;

[0876] A means for presenting the generated response through a display device and for presenting the learner with an opinion that takes emotions into consideration,

[0877] A means of providing parents with reports on their child's learning progress and network usage,

[0878] A system that includes this.

[0879] (Claim 2)

[0880] The system according to claim 1, which provides a situation for learners to learn how to effectively respond to dangers in social networks.

[0881] (Claim 3)

[0882] The system according to claim 1, which provides means for parents and learners to jointly participate in situational learning and to enhance the technology for safe use of information networks.

[0883] "Application example 2 when combining with an emotional engine"

[0884] (Claim 1)

[0885] A means for generating learning scenarios based on a learner's past learning history and interests using generative artificial intelligence,

[0886] Means for displaying the generated learning scenario through a user interface,

[0887] A means of analyzing the learner's psychological state using an emotion analysis device and adjusting the learning scenario based on the analysis results,

[0888] A means of collecting data to record and analyze learners' choices regarding scenarios,

[0889] Based on the collected data and analyzed sentiment data, the artificial intelligence evaluates the learner's behavior and generates feedback.

[0890] Means for displaying the generated feedback through a user interface,

[0891] A means of providing parents with reports on learning outcomes and use of social networking services,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, which provides a scenario for learners to learn how to effectively cope with risks in social network services.

[0895] (Claim 3)

[0896] The system according to claim 1, which provides means for parents and learners to participate in scenario-based learning together and to enhance the technology for secure network use. [Explanation of symbols]

[0897] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for generating learning scenarios based on a learner's past learning history and interests using generative artificial intelligence, Means for displaying the generated learning scenario through a user interface, A means of collecting data to record and analyze learners' choices regarding scenarios, Based on the aforementioned collected data, the artificial intelligence evaluates the learner's behavior and generates feedback, Means for displaying the generated feedback through a user interface, A means of providing parents with reports on their child's learning progress and social media usage, A system that includes this.

2. The system according to claim 1, which provides a scenario for learners to learn how to effectively cope with risks in social network services.

3. The system according to claim 1, which provides means for parents and learners to participate in scenario-based learning together and to enhance the technology for secure network use.

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