System

Abstract

A system is provided.SOLUTION: A system comprising: generative artificial intelligence means for generating moral training content according to an action performed by a child; animation generative artificial intelligence means for generating an animation based on the moral training content generated by the generative artificial intelligence means; communication means for transmitting the generated animation to a terminal of a user; and display means for displaying the transmitted animation on the terminal of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Moral education plays an important role in children's development, but it is often difficult to implement at home. Even when parents try to provide moral education, they face challenges such as time and resource constraints and a lack of educational expertise. Furthermore, it is difficult to provide specific educational content that is tailored to children's behavior. For this reason, there is a need for a method to easily and effectively provide moral education at home. [Means for solving the problem]

[0005] To solve this problem, the present invention provides the following means: A system including artificial intelligence means for generating moral education content in response to a child's behavior, and artificial intelligence means for generating animations based on the generated moral education content. The system also includes communication means for transmitting the generated animations to a user's terminal, and display means for displaying the transmitted animations on the user's terminal. This allows parents to easily provide appropriate moral education to their children at home.

[0006] "Generative artificial intelligence means" refers to an artificial intelligence system that analyzes data input by a user and generates appropriate educational content and scenarios based on that data.

[0007] The "video generation artificial intelligence means" refers to an artificial intelligence system for automatically generating animations based on the educational content and scenarios generated by the generation artificial intelligence means.

[0008] "Communication means" refers to a network communication function for transmitting the generated animation data from the server to the user's terminal.

[0009] "Display means" refers to a function for displaying received animation data on the user's terminal.

[0010] "Terminal" refers to a mobile information device such as a smartphone or tablet, which receives input from a user through an application and displays content received from a server.

[0011] "User" refers to a parent or educator who uses the system and is responsible for generating appropriate educational content based on the child's behavior and showing it to the child.

[0012] "Child behavior data" refers to information entered by the user about specific actions and situations that a child has taken. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0017] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0018] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0019] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0026] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0033] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0034] This invention is a system that allows for easy moral education at home. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, enabling effective moral education at home.

[0035] 1. User input operations

[0036] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0037] 2. Processing on the server

[0038] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[0039] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[0040] 3. Display on device

[0041] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on specific behavior. For example, displaying an animation about the "bad effects of lying" can help children understand the wrongness of lying.

[0042] Specific examples

[0043] For example, consider a case where a child bullies a friend. The user (parent) enters "My child bullied his friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0044] In this way, the present invention provides a means for easily and effectively teaching moral education at home. The system automatically generates appropriate content and visually teaches through animation, making it possible to provide children with easy-to-understand, concrete moral education.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] Terminal: Launches the application and presents the user with a login screen.

[0048] Step 2:

[0049] User: Enter login information and press the login button.

[0050] Step 3:

[0051] Terminal: Sends login information to the server.

[0052] Step 4:

[0053] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[0054] Step 5:

[0055] Terminal: Display the dashboard screen.

[0056] Step 6:

[0057] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[0058] Step 7:

[0059] Terminal: Display the action input form.

[0060] Step 8:

[0061] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[0062] Step 9:

[0063] Terminal: Sends input data to the server.

[0064] Step 10:

[0065] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[0066] Step 11:

[0067] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[0068] Step 12:

[0069] Generation AI: Generates a scenario and returns it to the server.

[0070] Step 13:

[0071] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[0072] Step 14:

[0073] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[0074] Step 15:

[0075] Server: Sends the generated animation data to the terminal.

[0076] Step 16:

[0077] Terminal: Receives animation data and converts it into a playable state.

[0078] Step 17:

[0079] User: Presses the animation play button within the application.

[0080] Step 18:

[0081] Terminal: Play the animation.

[0082] Step 19:

[0083] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] A major issue facing modern families is the lack of effective means for moral education. Although parents may warn their children about their behavior in their daily lives, it is difficult to convey concrete and easy-to-understand lessons. For this reason, there is a need to provide simple and effective methods for moral education.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes an AI generating means for generating moral education content in response to the behavior of a child, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal, thereby enabling moral education to be provided at home simply and effectively.

[0089] "Child behavior" refers to a child's specific actions and behaviors in learning, play, and everyday life.

[0090] "Moral education content" refers to scenarios and teaching materials that teach appropriate moral values ​​and ethics based on children's behavior.

[0091] "Generative AI means" refers to an AI algorithm or model that generates appropriate content based on input data.

[0092] "Video generation artificial intelligence means" refers to an artificial intelligence algorithm or model that generates animation based on a provided scenario or content.

[0093] "Communication means" refers to a system or protocol for transmitting or receiving data.

[0094] "Display means" refers to a device or interface for visually displaying data.

[0095] "User's device" refers to an electronic device operated by a user, such as a smartphone, tablet, or computer.

[0096] The present invention relates to a system that allows for easy moral education at home. This system uses a device (such as a smartphone or tablet) to automatically generate moral education animations based on a child's behavior, enabling effective moral education at home.

[0097] It all starts when a user launches an application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, where the user can enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0098] Next, the input behavioral data is sent from the device to the server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI model (e.g., a general generative AI model). As a concrete example, a scenario about the "negative effects of lying" is generated. An example of the prompt sentence used in this case is as follows:

[0099] Example prompt sentence:

[0100] "Generate scenarios of how your child might lie and how it could negatively impact others."

[0101] The server then sends the generated scenario to the video generation AI model, which then requests it to generate animation based on the scenario. The video generation AI model uses a general video generation AI model, generates appropriate animations according to the scenario, and sends them back to the server.

[0102] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on concrete actions. For example, displaying an animation about the "bad effects of lying" can help children understand the evils of lying.

[0103] This system makes it possible to provide moral education at home easily and effectively. The system automatically generates appropriate content and visually educates children through animation, providing them with easy-to-understand, concrete moral education.

[0104] For example, consider the case where a child bullies a friend. The user (parent) enters "my child bullied my friend" into the application. This data is sent to the server, and the generative AI model generates a scenario about the "negative effects of bullying." This scenario is passed to a video generation AI model, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0105] With the above-described configuration, the present invention provides a new means for effectively and simply providing moral education at home.

[0106] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0107] Step 1:

[0108] The user launches the application on their smartphone or tablet. When the application launches, a login screen is displayed. The user enters their login information (username and password) and taps the login button. If the correct login information is entered, the user is taken to the next screen. Input: Username, password. Output: Login completed, dashboard screen displayed.

[0109] Step 2:

[0110] The user enters information about their child's behavior on the dashboard screen. The input is specific behavior (e.g., "My child lied"). When the user enters the information and taps the send button, the data is sent from the device to the server. Input: Specific behavioral data of the child. Output: Behavioral data has been sent to the server.

[0111] Step 3:

[0112] The server receives behavioral data from the user's device. Then, the server analyzes the received behavioral data. Based on the analysis results, it sends a prompt to a generative AI model (e.g., a general generative AI model) to generate an appropriate moral education scenario. Input: Child's behavioral data. Output: Generation of a moral education scenario.

[0113] Step 4:

[0114] The server sends the generated moral education scenario to the video generation AI model and requests it to generate an animation. The video generation AI model generates an animation based on the scenario. Input: Moral education scenario. Output: Generated animation data.

[0115] Step 5:

[0116] The animation data generated by the video generation AI model is sent back to the server. The server then sends the returned animation data to the user's device. Input: Animation data. Output: Animation data has been sent to the user's device.

[0117] Step 6:

[0118] The device receives the animation data sent from the server. Then, the device plays the received animation data within the application and displays it to the user. The user can show the displayed animation to their child to provide moral education based on concrete actions. Input: Animation data. Output: Playback and display of animation.

[0119] Through this series of processes, users can easily generate moral education content for children and provide effective education.

[0120] (Application example 1)

[0121] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] Traditionally, moral education is typically conducted at home or school, with the content and methods largely dependent on teachers and parents. However, systems for effectively and easily implementing moral education at home or in the workplace are not widely available. Furthermore, it is difficult to provide specific moral education content tailored to individual behaviors, and there is a lack of means to realize education tailored to individual situations. Therefore, there is a need for a system that can quickly and effectively provide moral education tailored to the specific behaviors of children and store staff.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0124] In this invention, the server includes an AI generating means for generating moral education content in accordance with the actions of children and store staff, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal. This allows the server to analyze the behavioral data of children and store staff received from the user's terminal, generate appropriate moral education scenarios, and provide visual education in the form of animations.

[0125] "Children and store staff" refers to individuals and employees who need moral education at home or in physical stores.

[0126] "Behavior" refers to the specific actions and movements that children and store staff perform on a daily basis.

[0127] "Moral education content" refers to information, scenarios, or materials created to teach moral values ​​and ethics.

[0128] "Generative artificial intelligence means" refers to artificial intelligence technology for analyzing received behavioral data and generating specific moral education scenarios and content based on it.

[0129] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[0130] "Communication means" refers to the technology and protocols used to transmit the generated animation data from the server to the user's device.

[0131] "Display means" refers to the device or software for visually displaying the animation on the user's terminal.

[0132] "User's device" refers to a mobile electronic device such as a smartphone or tablet that has the function of receiving and displaying moral education content.

[0133] This invention is a system that automatically generates moral education content based on the specific behavior of children and store staff, and provides that content as visual animation. This system uses smartphones and tablets to generate didactic content based on user behavior data, effectively supporting moral education in stores and at home.

[0134] Hardware and software used

[0135] The present invention uses the following hardware and software.

[0136] Hardware:

[0137] Smartphone or tablet (user device)

[0138] software:

[0139] Generative AI models (e.g., OpenAI GPT-4)

[0140] Video generation AI model (e.g. DeepBrain AI)

[0141] System program processing explanation

[0142] 1. User input operations

[0143] First, users launch the application on their smartphone or tablet and enter their login information on the login screen. After logging in, a dashboard will appear where users can enter specific behavioral data of their children and store staff. For example, it can record behaviors such as "children lying" or "staff not properly packaging products."

[0144] 2. Processing on the server

[0145] The input behavioral data is sent from the user's device to a server. The server then uses a generative AI model based on the received behavioral data to generate appropriate moral education scenarios. For example, OpenAI GPT-4 is used to generate scenarios about the "negative effects of lying" and "problems with improper product packaging."

[0146] The generated scenario is then sent to an AI video generation model, which automatically generates animations based on the scenario. This process utilizes technologies such as DeepBrain AI to create visually appealing animations. The server then sends the generated animation data to the user's device.

[0147] 3. Display on device

[0148] The user's device receives the animation data sent from the server and processes it appropriately. As a result, it is possible to provide children and store staff with visual and concrete moral education. The display of this animation helps them understand the problems and impacts of certain behaviors, and encourages appropriate behavior.

[0149] Adding specific examples

[0150] For example, consider a case where a child bullies a friend. The user (parent) enters "my child bullied a friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0151] An example prompt is:

[0152] "Create a moral education scenario about how poor product packaging by staff can affect customers."

[0153] "Create a moral education scenario about the negative consequences of a child lying."

[0154] This allows the present invention to provide a means for effectively conducting moral education based on concrete actions at home and in the workplace.

[0155] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0156] Step 1:

[0157] The user launches the application on their smartphone or tablet and logs in by entering their login information on the login screen. After logging in, a dashboard is displayed on the device, and the user enters specific behavioral data of their child and store staff. This input data may be information such as "the child lied" or "the staff member packaged the product carelessly." The input data is temporarily stored on the device.

[0158] Input: Login information, behavioral data

[0159] Output: Behavioral data stored on the device

[0160] Step 2:

[0161] Behavioral data received from the user's device is sent to a server. The server analyzes the received behavioral data and generates appropriate moral education scenarios using a generative artificial intelligence model (generative AI model). For example, OpenAI GPT-4 is used to generate scenarios about "the negative effects of lying" and "the problems with improper product packaging."

[0162] Input: Behavioral data

[0163] Output: Moral education scenario

[0164] Step 3:

[0165] The server sends the generated moral education scenario to an AI video generation model, asking it to generate an animation based on the scenario. For example, DeepBrain AI is used to generate a visually appealing animation according to the scenario. The AI ​​video generation model generates the animation data and sends it back to the server.

[0166] Input: Moral Education Scenario

[0167] Output: Animation data

[0168] Step 4:

[0169] The server sends the generated animation data to the user's device, which interprets the received animation data and processes it appropriately for display.

[0170] Input: Animation data

[0171] Output: Processed animation data

[0172] Step 5:

[0173] The user's device displays the processed animation, allowing children and store staff to receive moral education based on concrete actions. For example, animations on themes such as "the negative effects of lying" and "the problems with improper product packaging" are visually displayed, enhancing the educational effect.

[0174] Input: Processed animation data

[0175] Output: Rendered animation

[0176] This allows users to provide effective moral education based on specific actions.

[0177] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0178] This invention is a system that allows for easy moral education at home. In particular, it aims to enhance the effectiveness of moral education by combining it with an emotion engine that recognizes the user's emotions. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, and the emotion engine recognizes and appropriately displays the user's emotions, enabling effective moral education at home.

[0179] 1. User input operations

[0180] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0181] 2. Processing on the server

[0182] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[0183] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[0184] 3. Utilizing the Emotion Engine

[0185] The server further recognizes the user's emotions based on the generated moral education content (scenario and animation) using an emotion engine, which analyzes the user's facial expressions and tone of voice to identify the user's current emotions.

[0186] For example, if a user feels stressed or angry when inputting their child's behavior, the emotion engine will recognize that emotion. Based on the emotion engine's results, the video generation AI will customize the animation. For example, an animation themed on the negative effects of lying could be drawn softer or harsher depending on the user's emotion.

[0187] 4. Display on device

[0188] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is displayed to the child.

[0189] This system allows users to easily provide appropriate moral education to their children at home. The emotion engine takes the user's emotions into account and customizes the animation accordingly, enabling more effective education. As a concrete example, consider a case where a child bullies a friend. The user inputs "my child bullied my friend," and the emotion engine recognizes the user's emotions at the time. Based on the results, an animation on the theme of "the negative effects of bullying" is generated, which can be shown to children effectively without the user becoming emotional.

[0190] As described above, the present invention provides a system that recognizes a user's emotions and generates and displays moral education content accordingly, thereby enhancing the effectiveness of moral education at home.

[0191] The processing flow will be explained below.

[0192] Step 1:

[0193] Terminal: Launches the application and presents the user with a login screen.

[0194] Step 2:

[0195] User: Enter login information and press the login button.

[0196] Step 3:

[0197] Terminal: Sends login information to the server.

[0198] Step 4:

[0199] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[0200] Step 5:

[0201] Terminal: Display the dashboard screen.

[0202] Step 6:

[0203] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[0204] Step 7:

[0205] Terminal: Display the action input form.

[0206] Step 8:

[0207] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[0208] Step 9:

[0209] Terminal: Sends input data to the server.

[0210] Step 10:

[0211] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[0212] Step 11:

[0213] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[0214] Step 12:

[0215] Generation AI: Generates a scenario and returns it to the server.

[0216] Step 13:

[0217] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[0218] Step 14:

[0219] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[0220] Step 15:

[0221] Server: Receives the generated animation data and sends it to the emotion engine.

[0222] Step 16:

[0223] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize emotions.

[0224] Step 17:

[0225] Server: Based on the results of the emotion engine, it sends instructions to the video generation AI to optimize or customize the animation content.

[0226] Step 18:

[0227] Video generation AI: Generates optimized or customized animations and sends them back to the server.

[0228] Step 19:

[0229] Server: Sends the generated final animation data to the terminal.

[0230] Step 20:

[0231] Terminal: Receives animation data and converts it into a playable state.

[0232] Step 20:

[0233] User: Presses the animation play button within the application.

[0234] Step 21:

[0235] Terminal: Play the animation.

[0236] Step 22:

[0237] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[0238] As described above, the present invention can automatically generate and optimally display moral education animations while taking into account the user's emotions, thereby making moral education at home more effective.

[0239] Example 2

[0240] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0241] Moral education at home can be difficult in terms of consistency and effectiveness, and parents in particular often struggle with how they should teach. Furthermore, preparing appropriate educational content tailored to a child's behavior takes a lot of time and effort. For this reason, there is a need for a system that allows for easy and effective moral education at home.

[0242] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0243] In this invention, the server includes input means for a user to input behavioral data of a child using a terminal, artificial intelligence generation means for analyzing the behavioral data of the child received from the input means and generating an appropriate moral education scenario, artificial intelligence video generation means for generating animations based on the moral education scenarios generated by the artificial intelligence generation means, emotion recognition means for customizing the generated animation data in accordance with the emotions of the user, communication means for transmitting the generated animations to the user's terminal, and display means for displaying the transmitted animations on the user's terminal, thereby enabling appropriate moral education to be provided to children at home easily and effectively.

[0244] "User" refers to the individual who operates the system and inputs child behavior data.

[0245] A "terminal" is a device used by a user, and refers to electronic devices such as smartphones, tablets, and personal computers.

[0246] "Behavioral data" refers to information about a child's specific behavior, and includes specific behavioral content entered by the user.

[0247] "Input means" refers to an interface that allows a user to input their child's behavior data into the system using a terminal.

[0248] "Generative artificial intelligence means" refers to the artificial intelligence technology used to analyze input behavioral data and generate appropriate moral education scenarios.

[0249] "Moral education scenarios" refer to plots or storylines of educational content generated in response to children's behavior.

[0250] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[0251] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to identify their emotions.

[0252] "Communication means" refers to the technology for sending and receiving data between the server and the terminal.

[0253] "Display means" refers to an interface for displaying the generated animation on the user's terminal.

[0254] The "system" refers to an integrated solution that links together the above measures to support moral education at home.

[0255] The present invention is a system for improving the effectiveness and efficiency of moral education at home, in which a user inputs data on their child's behavior using a device such as a smartphone or tablet, and generates and displays appropriate moral education content based on that data. The present invention includes the following main components:

[0256] First, the user installs a dedicated application on the device, then launches the application and accesses the login screen. The application can be used by entering login information (username and password) and logging in. After logging in, the user accesses a dashboard screen where they can enter information about their child's behavior. The child's behavior data is specific content, such as "my child lied," and is entered and sent.

[0257] The behavioral data sent from the device is sent to a server, which then analyzes the received data. This analysis uses a generative AI model (e.g., GPT-3) to automatically generate appropriate moral education scenarios based on the input behavioral data. For example, a scenario about the "negative effects of lying" is generated.

[0258] The generated scenario is sent from the server to a video generation AI (e.g., DALL-E), which automatically generates animations based on the generated scenario. The video generation AI visualizes appropriate educational content based on the prompt text.

[0259] The server then uses an emotion engine to analyze the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and customizes the generated animation based on the results. For example, if the user is feeling stressed, the tone of the animation can be softened.

[0260] The server then sends the customized animation data back to the terminal, which then processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is visually displayed to the child.

[0261] As a concrete example, consider the case where a child bullies a friend. The user enters "My child bullied my friend" and submits it. The server analyzes this data and generates a scenario about the "negative effects of bullying." The video generation AI generates an animation based on this scenario, and the emotion engine analyzes the user's emotions. Based on the analysis results of the emotion engine, the video generation AI customizes the animation, which the server then sends back to the device. When the display button on the device is pressed, this customized animation is displayed to the child.

[0262] An example of a prompt is, "Create an animation that teaches children how to respond when they bully their friends. The animation should emphasize the negative effects of bullying friends." Using this prompt, the generative AI model and video generation AI generate a scenario and animation.

[0263] The above is an embodiment of the present invention, which allows users to easily and effectively provide moral education at home.

[0264] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0265] Step 1:

[0266] A user launches an application using a smartphone or tablet. After launching, a login screen appears, and the user enters their login information (username and password) and presses the "Login" button. The application receives the input data (login information) and sends it to the server. The server authenticates the received login information, and if authentication is successful, generates home screen data and sends it back to the device.

[0267] Step 2:

[0268] When the user presses the "Enter new behavioral data" button on the home screen, a behavioral data input form is displayed. The user enters data about their child's behavior (e.g., "My child lied") and presses the "Submit" button. The input data (behavioral data) is sent to the server via the application. The server analyzes the received behavioral data and uses the analysis results to generate appropriate moral education scenarios.

[0269] Step 3:

[0270] The server analyzes the behavioral data and uses a generative AI model (e.g., GPT-3) to generate a specific moral education scenario. Based on the behavioral data received as input (e.g., "the negative effects of lying"), the generative AI model generates a scenario and outputs it to the server in text format. This output data is used in the next step.

[0271] Step 4:

[0272] The server passes the generated scenario text to an animation generation AI model (e.g., DALL-E) and requests it to generate an animation. The video generation AI model generates an animation using a prompt sentence (e.g., "Please generate an animation that shows the negative effects of lying") as input, and sends the generated animation file back to the server. This becomes the output data for the animation generation.

[0273] Step 5:

[0274] The server uses emotion recognition to analyze the user's real-time emotional data. It analyzes input data (facial expressions and tone of voice) provided by the user through the device camera and microphone to identify the user's emotional state (e.g., stress, anger). The results of this analysis are used to customize the next animation.

[0275] Step 6:

[0276] Based on the emotion recognition results, the server customizes the generated animation. For example, if the user is feeling stressed, the tone of the animation generated by the video generation AI model will be softened. The customized animation becomes the final output data and is sent to the device.

[0277] Step 7:

[0278] The server sends customized animation data to the terminal, which encrypts and transmits the animation data via a communication protocol, and the terminal decrypts the received data and prepares for playback.

[0279] Step 8:

[0280] Users can then press the play animation button on their device to view the final customized animation for their child, allowing them to effectively teach morality at home.

[0281] In this way, by clarifying the input data, output data, and specific processing content at each step, the present invention can effectively support moral education at home.

[0282] (Application example 2)

[0283] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0284] Conventional moral education systems have struggled to provide appropriate education about children's behavior at home, and their effectiveness has been limited. In particular, the effectiveness of education is often reduced because parents and educators do not take into account the emotions they feel when explaining children's behavior. Furthermore, educational approaches in brick-and-mortar stores have not been standardized, making it difficult to respond to the diverse behaviors and emotions of children. Therefore, there is a need for a system that can generate customized animations that take into account the user's emotions and provide effective moral education in brick-and-mortar stores.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generation artificial intelligence means for generating moral education content in accordance with the behavior of a child, a video generation artificial intelligence means for generating animation based on the moral education content generated by the generation artificial intelligence means, and a communication means for transmitting the generated animation to a user's terminal. This makes it possible to improve the effectiveness of moral education for diverse behaviors of children at home and in brick-and-mortar stores by using an emotion recognition means for recognizing the user's emotions and customizing the animation based on those emotions.

[0286] "Child behavioral data" refers to information about the actual behavior of children at home, in public places, etc.

[0287] "Moral education content" refers to information and teaching materials used to teach morality and ethics to children.

[0288] "Generative artificial intelligence means" refers to the process of using artificial intelligence to analyze specific data or information and generate some kind of content.

[0289] "Moral education scenarios" refer to stories or scenarios that teach appropriate morals and ethics for specific actions or situations.

[0290] "Video generation artificial intelligence means" refers to the process of generating videos or animations from text or scenario information using artificial intelligence.

[0291] "Communication means" refers to the Internet or network connection methods and systems for sending and receiving data.

[0292] "Display means" refers to a screen, display, or the like for visually showing the generated content to the user.

[0293] "Emotion recognition means" refers to technology or systems that analyze information such as a user's facial expressions and voice to identify the emotions the user is feeling.

[0294] "Animation" refers to visual content that moves and is used to effectively communicate specific content.

[0295] "Customization" refers to the individual adjustment of content or services based on specific conditions or information.

[0296] A "server" refers to a computer system that has the ability to process data and information and communicate with other devices.

[0297] "User" refers to the person who uses the system, especially in the home, this means parents and educators.

[0298] The present invention is a system that automatically generates moral education content in response to children's behavior and displays animations with customized content that takes into account the user's emotions. An outline of how the present invention can be implemented is given below.

[0299] 1. Program Generation

[0300] The system program links the user's terminal, server, and display means. Specifically, it has components such as an interface for inputting children's behavioral data, AI generation means, emotion recognition means, AI video generation means, communication means, and display means.

[0301] 2. System processing overview

[0302] Hardware and software configuration

[0303] Hardware: Devices such as tablets, smartphones, cameras, and microphones.

[0304] Software: Programs written in Python, Java, etc., EmotionRecognizer library, generative AI model, video generation AI model.

[0305] Data processing and calculation flow

[0306] In the system, the user first launches the application on a device such as a tablet or smartphone. The user logs in on the login screen and then enters their child's behavioral data (e.g., "My child lied") on the dashboard screen.

[0307] This input data is sent to the server, where it is processed as follows:

[0308] 1. Analysis and scenario generation:

[0309] A generative artificial intelligence means analyzes the data and generates appropriate moral education scenarios, such as scenarios about the "harmful effects of lying."

[0310] 2. Animation generation:

[0311] The AI ​​animation generating means automatically generates animation based on the generated scenario, and the generated animation is sent back to the server.

[0312] 3. Emotion recognition:

[0313] The emotion recognition unit analyzes the user's facial expressions and voice to identify the current emotion (e.g., stress, anger, etc.) using the tablet's built-in camera and microphone.

[0314] 4. Customizing animation:

[0315] Based on the recognition results of the emotion engine, the animation content is adjusted according to the user's emotions. For example, if the user is very angry, the tone of the animation is adjusted to be softer.

[0316] The data processed and calculated on the server is then sent back to the user's device via a communication means, where the user can play the animation on their device and provide effective moral education to their children.

[0317] 3. Examples of concrete examples and prompts

[0318] As a concrete example, consider the case where a child "lies." The user inputs "my child lied" into the application. The emotion recognition unit recognizes the user's emotion at that time (e.g., very angry), and based on that, generates a softer animation depicting the "negative effects of lying."

[0319] Example prompt sentence:

[0320] "Enter the child lied: Taro lied and pretended to go to school."

[0321] By this process, the present invention enhances the effectiveness of moral education in homes and brick-and-mortar stores.

[0322] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0323] Step 1:

[0324] A user launches an application using a smartphone or tablet device. A login screen is then displayed, and the user enters their login information to log in. If login is successful, the dashboard screen is displayed. At this stage, the input is the user's login information, and the output is the display of the dashboard screen.

[0325] Step 2:

[0326] On the dashboard screen, the user inputs their child's behavioral data. For example, they input information such as "my child lied." This behavioral data is sent from the device to the server. The input is the child's behavioral data, and the output is the data sent to the server.

[0327] Step 3:

[0328] The server analyzes the received behavioral data. During the analysis, it uses a generative artificial intelligence means to generate an appropriate moral education scenario. For example, a scenario on the "harmful effects of lying" is generated. The input is the child's behavioral data, and the output is an educational scenario.

[0329] Step 4:

[0330] The server sends the generated scenario to the video generation AI means and requests it to generate an animation based on the scenario. The video generation AI generates an appropriate animation based on the scenario and sends it back to the server. The input is the moral education scenario, and the output is animation data.

[0331] Step 5:

[0332] The device's camera and microphone are used to capture the user's facial expressions and voice. The data is analyzed by an emotion recognition means to identify the user's emotions. The input is the captured facial and voice data, and the output is the recognized user's emotion data.

[0333] Step 6:

[0334] The server customizes the animation content based on the emotion data. For example, if the user is very angry, it softens the tone of the animation. The input is the user's emotion data and the generated animation, and the output is the customized animation.

[0335] Step 7:

[0336] The server sends customized animation data to the terminal. The input is the customized animation data, and the output is the data sent to the terminal.

[0337] Step 8:

[0338] The terminal displays the received customized animation to the user. The user can show the customized animation to the child by pressing the animation play button. The input is the received animation data, and the output is the display of the animation.

[0339] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0340] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search<url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0341] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0342] [Second embodiment]

[0343] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0344] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0345] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0346] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0347] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0348] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0349] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0350] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0351] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0352] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0353] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0354] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0355] This invention is a system that allows for easy moral education at home. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, enabling effective moral education at home.

[0356] 1. User input operations

[0357] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0358] 2. Processing on the server

[0359] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[0360] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[0361] 3. Display on device

[0362] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on specific behavior. For example, displaying an animation about the "bad effects of lying" can help children understand the wrongness of lying.

[0363] Specific examples

[0364] For example, consider a case where a child bullies a friend. The user (parent) enters "My child bullied his friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0365] In this way, the present invention provides a means for easily and effectively teaching moral education at home. The system automatically generates appropriate content and visually teaches through animation, making it possible to provide children with easy-to-understand, concrete moral education.

[0366] The processing flow will be explained below.

[0367] Step 1:

[0368] Terminal: Launches the application and presents the user with a login screen.

[0369] Step 2:

[0370] User: Enter login information and press the login button.

[0371] Step 3:

[0372] Terminal: Sends login information to the server.

[0373] Step 4:

[0374] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[0375] Step 5:

[0376] Terminal: Display the dashboard screen.

[0377] Step 6:

[0378] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[0379] Step 7:

[0380] Terminal: Display the action input form.

[0381] Step 8:

[0382] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[0383] Step 9:

[0384] Terminal: Sends input data to the server.

[0385] Step 10:

[0386] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[0387] Step 11:

[0388] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[0389] Step 12:

[0390] Generation AI: Generates a scenario and returns it to the server.

[0391] Step 13:

[0392] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[0393] Step 14:

[0394] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[0395] Step 15:

[0396] Server: Sends the generated animation data to the terminal.

[0397] Step 16:

[0398] Terminal: Receives animation data and converts it into a playable state.

[0399] Step 17:

[0400] User: Presses the animation play button within the application.

[0401] Step 18:

[0402] Terminal: Play the animation.

[0403] Step 19:

[0404] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[0405] Example 1

[0406] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0407] A major issue facing modern families is the lack of effective means for moral education. Although parents may warn their children about their behavior in their daily lives, it is difficult to convey concrete and easy-to-understand lessons. For this reason, there is a need to provide simple and effective methods for moral education.

[0408] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0409] In this invention, the server includes an AI generating means for generating moral education content in response to the behavior of a child, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal, thereby enabling moral education to be provided at home simply and effectively.

[0410] "Child behavior" refers to a child's specific actions and behaviors in learning, play, and everyday life.

[0411] "Moral education content" refers to scenarios and teaching materials that teach appropriate moral values ​​and ethics based on children's behavior.

[0412] "Generative AI means" refers to an AI algorithm or model that generates appropriate content based on input data.

[0413] "Video generation artificial intelligence means" refers to an artificial intelligence algorithm or model that generates animation based on a provided scenario or content.

[0414] "Communication means" refers to a system or protocol for transmitting or receiving data.

[0415] "Display means" refers to a device or interface for visually displaying data.

[0416] "User's device" refers to an electronic device operated by a user, such as a smartphone, tablet, or computer.

[0417] The present invention relates to a system that allows for easy moral education at home. This system uses a device (such as a smartphone or tablet) to automatically generate moral education animations based on a child's behavior, enabling effective moral education at home.

[0418] It all starts when a user launches an application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, where the user can enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0419] Next, the input behavioral data is sent from the device to the server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI model (e.g., a general generative AI model). As a concrete example, a scenario about the "negative effects of lying" is generated. An example of the prompt sentence used in this case is as follows:

[0420] Example prompt sentence:

[0421] "Generate scenarios of how your child might lie and how it could negatively impact others."

[0422] The server then sends the generated scenario to the video generation AI model, which then requests it to generate animation based on the scenario. The video generation AI model uses a general video generation AI model, generates appropriate animations according to the scenario, and sends them back to the server.

[0423] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on concrete actions. For example, displaying an animation about the "bad effects of lying" can help children understand the evils of lying.

[0424] This system makes it possible to provide moral education at home easily and effectively. The system automatically generates appropriate content and visually educates children through animation, providing them with easy-to-understand, concrete moral education.

[0425] For example, consider the case where a child bullies a friend. The user (parent) enters "my child bullied my friend" into the application. This data is sent to the server, and the generative AI model generates a scenario about the "negative effects of bullying." This scenario is passed to a video generation AI model, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0426] With the above-described configuration, the present invention provides a new means for effectively and simply providing moral education at home.

[0427] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0428] Step 1:

[0429] The user launches the application on their smartphone or tablet. When the application launches, a login screen is displayed. The user enters their login information (username and password) and taps the login button. If the correct login information is entered, the user is taken to the next screen. Input: Username, password. Output: Login completed, dashboard screen displayed.

[0430] Step 2:

[0431] The user enters information about their child's behavior on the dashboard screen. The input is specific behavior (e.g., "My child lied"). When the user enters the information and taps the send button, the data is sent from the device to the server. Input: Specific behavioral data of the child. Output: Behavioral data has been sent to the server.

[0432] Step 3:

[0433] The server receives behavioral data from the user's device. Then, the server analyzes the received behavioral data. Based on the analysis results, it sends a prompt to a generative AI model (e.g., a general generative AI model) to generate an appropriate moral education scenario. Input: Child's behavioral data. Output: Generation of a moral education scenario.

[0434] Step 4:

[0435] The server sends the generated moral education scenario to the video generation AI model and requests it to generate an animation. The video generation AI model generates an animation based on the scenario. Input: Moral education scenario. Output: Generated animation data.

[0436] Step 5:

[0437] The animation data generated by the video generation AI model is sent back to the server. The server then sends the returned animation data to the user's device. Input: Animation data. Output: Animation data has been sent to the user's device.

[0438] Step 6:

[0439] The device receives the animation data sent from the server. Then, the device plays the received animation data within the application and displays it to the user. The user can show the displayed animation to their child to provide moral education based on concrete actions. Input: Animation data. Output: Playback and display of animation.

[0440] Through this series of processes, users can easily generate moral education content for children and provide effective education.

[0441] (Application example 1)

[0442] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0443] Traditionally, moral education is typically conducted at home or school, with the content and methods largely dependent on teachers and parents. However, systems for effectively and easily implementing moral education at home or in the workplace are not widely available. Furthermore, it is difficult to provide specific moral education content tailored to individual behaviors, and there is a lack of means to realize education tailored to individual situations. Therefore, there is a need for a system that can quickly and effectively provide moral education tailored to the specific behaviors of children and store staff.

[0444] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0445] In this invention, the server includes an AI generating means for generating moral education content in accordance with the actions of children and store staff, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal. This allows the server to analyze the behavioral data of children and store staff received from the user's terminal, generate appropriate moral education scenarios, and provide visual education in the form of animations.

[0446] "Children and store staff" refers to individuals and employees who need moral education at home or in physical stores.

[0447] "Behavior" refers to the specific actions and movements that children and store staff perform on a daily basis.

[0448] "Moral education content" refers to information, scenarios, or materials created to teach moral values ​​and ethics.

[0449] "Generative artificial intelligence means" refers to artificial intelligence technology for analyzing received behavioral data and generating specific moral education scenarios and content based on it.

[0450] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[0451] "Communication means" refers to the technology and protocols used to transmit the generated animation data from the server to the user's device.

[0452] "Display means" refers to the device or software for visually displaying the animation on the user's terminal.

[0453] "User's device" refers to a mobile electronic device such as a smartphone or tablet that has the function of receiving and displaying moral education content.

[0454] This invention is a system that automatically generates moral education content based on the specific behavior of children and store staff, and provides that content as visual animation. This system uses smartphones and tablets to generate didactic content based on user behavior data, effectively supporting moral education in stores and at home.

[0455] Hardware and software used

[0456] The present invention uses the following hardware and software.

[0457] Hardware:

[0458] Smartphone or tablet (user device)

[0459] software:

[0460] Generative AI models (e.g., OpenAI GPT-4)

[0461] Video generation AI model (e.g. DeepBrain AI)

[0462] System program processing explanation

[0463] 1. User input operations

[0464] First, users launch the application on their smartphone or tablet and enter their login information on the login screen. After logging in, a dashboard will appear where users can enter specific behavioral data of their children and store staff. For example, it can record behaviors such as "children lying" or "staff not properly packaging products."

[0465] 2. Processing on the server

[0466] The input behavioral data is sent from the user's device to a server. The server then uses a generative AI model based on the received behavioral data to generate appropriate moral education scenarios. For example, OpenAI GPT-4 is used to generate scenarios about the "negative effects of lying" and "problems with improper product packaging."

[0467] The generated scenario is then sent to an AI video generation model, which automatically generates animations based on the scenario. This process utilizes technologies such as DeepBrain AI to create visually appealing animations. The server then sends the generated animation data to the user's device.

[0468] 3. Display on device

[0469] The user's device receives the animation data sent from the server and processes it appropriately. As a result, it is possible to provide children and store staff with visual and concrete moral education. The display of this animation helps them understand the problems and impacts of certain behaviors, and encourages appropriate behavior.

[0470] Adding specific examples

[0471] For example, consider a case where a child bullies a friend. The user (parent) enters "my child bullied a friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0472] An example prompt is:

[0473] "Create a moral education scenario about how poor product packaging by staff can affect customers."

[0474] "Create a moral education scenario about the negative consequences of a child lying."

[0475] This allows the present invention to provide a means for effectively conducting moral education based on concrete actions at home and in the workplace.

[0476] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0477] Step 1:

[0478] The user launches the application on their smartphone or tablet and logs in by entering their login information on the login screen. After logging in, a dashboard is displayed on the device, and the user enters specific behavioral data of their child and store staff. This input data may be information such as "the child lied" or "the staff member packaged the product carelessly." The input data is temporarily stored on the device.

[0479] Input: Login information, behavioral data

[0480] Output: Behavioral data stored on the device

[0481] Step 2:

[0482] Behavioral data received from the user's device is sent to a server. The server analyzes the received behavioral data and generates appropriate moral education scenarios using a generative artificial intelligence model (generative AI model). For example, OpenAI GPT-4 is used to generate scenarios about "the negative effects of lying" and "the problems with improper product packaging."

[0483] Input: Behavioral data

[0484] Output: Moral education scenario

[0485] Step 3:

[0486] The server sends the generated moral education scenario to an AI video generation model, asking it to generate an animation based on the scenario. For example, DeepBrain AI is used to generate a visually appealing animation according to the scenario. The AI ​​video generation model generates the animation data and sends it back to the server.

[0487] Input: Moral Education Scenario

[0488] Output: Animation data

[0489] Step 4:

[0490] The server sends the generated animation data to the user's device, which interprets the received animation data and processes it appropriately for display.

[0491] Input: Animation data

[0492] Output: Processed animation data

[0493] Step 5:

[0494] The user's device displays the processed animation, allowing children and store staff to receive moral education based on concrete actions. For example, animations on themes such as "the negative effects of lying" and "the problems with improper product packaging" are visually displayed, enhancing the educational effect.

[0495] Input: Processed animation data

[0496] Output: Rendered animation

[0497] This allows users to provide effective moral education based on specific actions.

[0498] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0499] This invention is a system that allows for easy moral education at home. In particular, it aims to enhance the effectiveness of moral education by combining it with an emotion engine that recognizes the user's emotions. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, and the emotion engine recognizes and appropriately displays the user's emotions, enabling effective moral education at home.

[0500] 1. User input operations

[0501] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0502] 2. Processing on the server

[0503] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[0504] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[0505] 3. Utilizing the Emotion Engine

[0506] The server further recognizes the user's emotions based on the generated moral education content (scenario and animation) using an emotion engine, which analyzes the user's facial expressions and tone of voice to identify the user's current emotions.

[0507] For example, if a user feels stressed or angry when inputting their child's behavior, the emotion engine will recognize that emotion. Based on the emotion engine's results, the video generation AI will customize the animation. For example, an animation themed on the negative effects of lying could be drawn softer or harsher depending on the user's emotion.

[0508] 4. Display on device

[0509] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is displayed to the child.

[0510] This system allows users to easily provide appropriate moral education to their children at home. The emotion engine takes the user's emotions into account and customizes the animation accordingly, enabling more effective education. As a concrete example, consider a case where a child bullies a friend. The user inputs "my child bullied my friend," and the emotion engine recognizes the user's emotions at the time. Based on the results, an animation on the theme of "the negative effects of bullying" is generated, which can be shown to children effectively without the user becoming emotional.

[0511] As described above, the present invention provides a system that recognizes a user's emotions and generates and displays moral education content accordingly, thereby enhancing the effectiveness of moral education at home.

[0512] The processing flow will be explained below.

[0513] Step 1:

[0514] Terminal: Launches the application and presents the user with a login screen.

[0515] Step 2:

[0516] User: Enter login information and press the login button.

[0517] Step 3:

[0518] Terminal: Sends login information to the server.

[0519] Step 4:

[0520] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[0521] Step 5:

[0522] Terminal: Display the dashboard screen.

[0523] Step 6:

[0524] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[0525] Step 7:

[0526] Terminal: Display the action input form.

[0527] Step 8:

[0528] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[0529] Step 9:

[0530] Terminal: Sends input data to the server.

[0531] Step 10:

[0532] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[0533] Step 11:

[0534] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[0535] Step 12:

[0536] Generation AI: Generates a scenario and returns it to the server.

[0537] Step 13:

[0538] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[0539] Step 14:

[0540] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[0541] Step 15:

[0542] Server: Receives the generated animation data and sends it to the emotion engine.

[0543] Step 16:

[0544] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize emotions.

[0545] Step 17:

[0546] Server: Based on the results of the emotion engine, it sends instructions to the video generation AI to optimize or customize the animation content.

[0547] Step 18:

[0548] Video generation AI: Generates optimized or customized animations and sends them back to the server.

[0549] Step 19:

[0550] Server: Sends the generated final animation data to the terminal.

[0551] Step 20:

[0552] Terminal: Receives animation data and converts it into a playable state.

[0553] Step 20:

[0554] User: Presses the animation play button within the application.

[0555] Step 21:

[0556] Terminal: Play the animation.

[0557] Step 22:

[0558] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[0559] As described above, the present invention can automatically generate and optimally display moral education animations while taking into account the user's emotions, thereby making moral education at home more effective.

[0560] Example 2

[0561] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0562] Moral education at home can be difficult in terms of consistency and effectiveness, and parents in particular often struggle with how they should teach. Furthermore, preparing appropriate educational content tailored to a child's behavior takes a lot of time and effort. For this reason, there is a need for a system that allows for easy and effective moral education at home.

[0563] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0564] In this invention, the server includes input means for a user to input behavioral data of a child using a terminal, artificial intelligence generation means for analyzing the behavioral data of the child received from the input means and generating an appropriate moral education scenario, artificial intelligence video generation means for generating animations based on the moral education scenarios generated by the artificial intelligence generation means, emotion recognition means for customizing the generated animation data in accordance with the emotions of the user, communication means for transmitting the generated animations to the user's terminal, and display means for displaying the transmitted animations on the user's terminal, thereby enabling appropriate moral education to be provided to children at home easily and effectively.

[0565] "User" refers to the individual who operates the system and inputs child behavior data.

[0566] A "terminal" is a device used by a user, and refers to electronic devices such as smartphones, tablets, and personal computers.

[0567] "Behavioral data" refers to information about a child's specific behavior, and includes specific behavioral content entered by the user.

[0568] "Input means" refers to an interface that allows a user to input their child's behavior data into the system using a terminal.

[0569] "Generative artificial intelligence means" refers to the artificial intelligence technology used to analyze input behavioral data and generate appropriate moral education scenarios.

[0570] "Moral education scenarios" refer to plots or storylines of educational content generated in response to children's behavior.

[0571] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[0572] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to identify their emotions.

[0573] "Communication means" refers to the technology for sending and receiving data between the server and the terminal.

[0574] "Display means" refers to an interface for displaying the generated animation on the user's terminal.

[0575] The "system" refers to an integrated solution that links together the above measures to support moral education at home.

[0576] The present invention is a system for improving the effectiveness and efficiency of moral education at home, in which a user inputs data on their child's behavior using a device such as a smartphone or tablet, and generates and displays appropriate moral education content based on that data. The present invention includes the following main components:

[0577] First, the user installs a dedicated application on the device, then launches the application and accesses the login screen. The application can be used by entering login information (username and password) and logging in. After logging in, the user accesses a dashboard screen where they can enter information about their child's behavior. The child's behavior data is specific content, such as "my child lied," and is entered and sent.

[0578] The behavioral data sent from the device is sent to a server, which then analyzes the received data. This analysis uses a generative AI model (e.g., GPT-3) to automatically generate appropriate moral education scenarios based on the input behavioral data. For example, a scenario about the "negative effects of lying" is generated.

[0579] The generated scenario is sent from the server to a video generation AI (e.g., DALL-E), which automatically generates animations based on the generated scenario. The video generation AI visualizes appropriate educational content based on the prompt text.

[0580] The server then uses an emotion engine to analyze the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and customizes the generated animation based on the results. For example, if the user is feeling stressed, the tone of the animation can be softened.

[0581] The server then sends the customized animation data back to the terminal, which then processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is visually displayed to the child.

[0582] As a concrete example, consider the case where a child bullies a friend. The user enters "My child bullied my friend" and submits it. The server analyzes this data and generates a scenario about the "negative effects of bullying." The video generation AI generates an animation based on this scenario, and the emotion engine analyzes the user's emotions. Based on the analysis results of the emotion engine, the video generation AI customizes the animation, which the server then sends back to the device. When the display button on the device is pressed, this customized animation is displayed to the child.

[0583] An example of a prompt is, "Create an animation that teaches children how to respond when they bully their friends. The animation should emphasize the negative effects of bullying friends." Using this prompt, the generative AI model and video generation AI generate a scenario and animation.

[0584] The above is an embodiment of the present invention, which allows users to easily and effectively provide moral education at home.

[0585] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0586] Step 1:

[0587] A user launches an application using a smartphone or tablet. After launching, a login screen appears, and the user enters their login information (username and password) and presses the "Login" button. The application receives the input data (login information) and sends it to the server. The server authenticates the received login information, and if authentication is successful, generates home screen data and sends it back to the device.

[0588] Step 2:

[0589] When the user presses the "Enter new behavioral data" button on the home screen, a behavioral data input form is displayed. The user enters data about their child's behavior (e.g., "My child lied") and presses the "Submit" button. The input data (behavioral data) is sent to the server via the application. The server analyzes the received behavioral data and uses the analysis results to generate appropriate moral education scenarios.

[0590] Step 3:

[0591] The server analyzes the behavioral data and uses a generative AI model (e.g., GPT-3) to generate a specific moral education scenario. Based on the behavioral data received as input (e.g., "the negative effects of lying"), the generative AI model generates a scenario and outputs it to the server in text format. This output data is used in the next step.

[0592] Step 4:

[0593] The server passes the generated scenario text to an animation generation AI model (e.g., DALL-E) and requests it to generate an animation. The video generation AI model generates an animation using a prompt sentence (e.g., "Please generate an animation that shows the negative effects of lying") as input, and sends the generated animation file back to the server. This becomes the output data for the animation generation.

[0594] Step 5:

[0595] The server uses emotion recognition to analyze the user's real-time emotional data. It analyzes input data (facial expressions and tone of voice) provided by the user through the device camera and microphone to identify the user's emotional state (e.g., stress, anger). The results of this analysis are used to customize the next animation.

[0596] Step 6:

[0597] Based on the emotion recognition results, the server customizes the generated animation. For example, if the user is feeling stressed, the tone of the animation generated by the video generation AI model will be softened. The customized animation becomes the final output data and is sent to the device.

[0598] Step 7:

[0599] The server sends customized animation data to the terminal, which encrypts and transmits the animation data via a communication protocol, and the terminal decrypts the received data and prepares for playback.

[0600] Step 8:

[0601] Users can then press the play animation button on their device to view the final customized animation for their child, allowing them to effectively teach morality at home.

[0602] In this way, by clarifying the input data, output data, and specific processing content at each step, the present invention can effectively support moral education at home.

[0603] (Application example 2)

[0604] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0605] Conventional moral education systems have struggled to provide appropriate education about children's behavior at home, and their effectiveness has been limited. In particular, the effectiveness of education is often reduced because parents and educators do not take into account the emotions they feel when explaining children's behavior. Furthermore, educational approaches in brick-and-mortar stores have not been standardized, making it difficult to respond to the diverse behaviors and emotions of children. Therefore, there is a need for a system that can generate customized animations that take into account the user's emotions and provide effective moral education in brick-and-mortar stores.

[0606] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generation artificial intelligence means for generating moral education content in accordance with the behavior of a child, a video generation artificial intelligence means for generating animation based on the moral education content generated by the generation artificial intelligence means, and a communication means for transmitting the generated animation to a user's terminal. This makes it possible to improve the effectiveness of moral education for diverse behaviors of children at home and in brick-and-mortar stores by using an emotion recognition means for recognizing the user's emotions and customizing the animation based on those emotions.

[0607] "Child behavioral data" refers to information about the actual behavior of children at home, in public places, etc.

[0608] "Moral education content" refers to information and teaching materials used to teach morality and ethics to children.

[0609] "Generative artificial intelligence means" refers to the process of using artificial intelligence to analyze specific data or information and generate some kind of content.

[0610] "Moral education scenarios" refer to stories or scenarios that teach appropriate morals and ethics for specific actions or situations.

[0611] "Video generation artificial intelligence means" refers to the process of generating videos or animations from text or scenario information using artificial intelligence.

[0612] "Communication means" refers to the Internet or network connection methods and systems for sending and receiving data.

[0613] "Display means" refers to a screen, display, or the like for visually showing the generated content to the user.

[0614] "Emotion recognition means" refers to technology or systems that analyze information such as a user's facial expressions and voice to identify the emotions the user is feeling.

[0615] "Animation" refers to visual content that moves and is used to effectively communicate specific content.

[0616] "Customization" refers to the individual adjustment of content or services based on specific conditions or information.

[0617] A "server" refers to a computer system that has the ability to process data and information and communicate with other devices.

[0618] "User" refers to the person who uses the system, especially in the home, this means parents and educators.

[0619] The present invention is a system that automatically generates moral education content in response to children's behavior and displays animations with customized content that takes into account the user's emotions. An outline of how the present invention can be implemented is given below.

[0620] 1. Program Generation

[0621] The system program links the user's terminal, server, and display means. Specifically, it has components such as an interface for inputting children's behavioral data, AI generation means, emotion recognition means, AI video generation means, communication means, and display means.

[0622] 2. System processing overview

[0623] Hardware and software configuration

[0624] Hardware: Devices such as tablets, smartphones, cameras, and microphones.

[0625] Software: Programs written in Python, Java, etc., EmotionRecognizer library, generative AI model, video generation AI model.

[0626] Data processing and calculation flow

[0627] In the system, the user first launches the application on a device such as a tablet or smartphone. The user logs in on the login screen and then enters their child's behavioral data (e.g., "My child lied") on the dashboard screen.

[0628] This input data is sent to the server, where it is processed as follows:

[0629] 1. Analysis and scenario generation:

[0630] A generative artificial intelligence means analyzes the data and generates appropriate moral education scenarios, such as scenarios about the "harmful effects of lying."

[0631] 2. Animation generation:

[0632] The AI ​​animation generating means automatically generates animation based on the generated scenario, and the generated animation is sent back to the server.

[0633] 3. Emotion recognition:

[0634] The emotion recognition unit analyzes the user's facial expressions and voice to identify the current emotion (e.g., stress, anger, etc.) using the tablet's built-in camera and microphone.

[0635] 4. Customizing animation:

[0636] Based on the recognition results of the emotion engine, the animation content is adjusted according to the user's emotions. For example, if the user is very angry, the tone of the animation is adjusted to be softer.

[0637] The data processed and calculated on the server is then sent back to the user's device via a communication means, where the user can play the animation on their device and provide effective moral education to their children.

[0638] 3. Examples of concrete examples and prompts

[0639] As a concrete example, consider the case where a child "lies." The user inputs "my child lied" into the application. The emotion recognition unit recognizes the user's emotion at that time (e.g., very angry), and based on that, generates a softer animation depicting the "negative effects of lying."

[0640] Example prompt sentence:

[0641] "Enter the child lied: Taro lied and pretended to go to school."

[0642] By this process, the present invention enhances the effectiveness of moral education in homes and brick-and-mortar stores.

[0643] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0644] Step 1:

[0645] A user launches an application using a smartphone or tablet device. A login screen is then displayed, and the user enters their login information to log in. If login is successful, the dashboard screen is displayed. At this stage, the input is the user's login information, and the output is the display of the dashboard screen.

[0646] Step 2:

[0647] On the dashboard screen, the user inputs their child's behavioral data. For example, they input information such as "my child lied." This behavioral data is sent from the device to the server. The input is the child's behavioral data, and the output is the data sent to the server.

[0648] Step 3:

[0649] The server analyzes the received behavioral data. During the analysis, it uses a generative artificial intelligence means to generate an appropriate moral education scenario. For example, a scenario on the "harmful effects of lying" is generated. The input is the child's behavioral data, and the output is an educational scenario.

[0650] Step 4:

[0651] The server sends the generated scenario to the video generation AI means and requests it to generate an animation based on the scenario. The video generation AI generates an appropriate animation based on the scenario and sends it back to the server. The input is the moral education scenario, and the output is animation data.

[0652] Step 5:

[0653] The device's camera and microphone are used to capture the user's facial expressions and voice. The data is analyzed by an emotion recognition means to identify the user's emotions. The input is the captured facial and voice data, and the output is the recognized user's emotion data.

[0654] Step 6:

[0655] The server customizes the animation content based on the emotion data. For example, if the user is very angry, it softens the tone of the animation. The input is the user's emotion data and the generated animation, and the output is the customized animation.

[0656] Step 7:

[0657] The server sends customized animation data to the terminal. The input is the customized animation data, and the output is the data sent to the terminal.

[0658] Step 8:

[0659] The terminal displays the received customized animation to the user. The user can show the customized animation to the child by pressing the animation play button. The input is the received animation data, and the output is the display of the animation.

[0660] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0661] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0662] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0663] [Third embodiment]

[0664] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0665] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0666] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0667] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0668] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0669] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0670] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0671] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0672] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0673] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0674] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0675] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0676] This invention is a system that allows for easy moral education at home. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, enabling effective moral education at home.

[0677] 1. User input operations

[0678] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0679] 2. Processing on the server

[0680] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[0681] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[0682] 3. Display on device

[0683] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on specific behavior. For example, displaying an animation about the "bad effects of lying" can help children understand the wrongness of lying.

[0684] Specific examples

[0685] For example, consider a case where a child bullies a friend. The user (parent) enters "My child bullied his friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0686] In this way, the present invention provides a means for easily and effectively teaching moral education at home. The system automatically generates appropriate content and visually teaches through animation, making it possible to provide children with easy-to-understand, concrete moral education.

[0687] The processing flow will be explained below.

[0688] Step 1:

[0689] Terminal: Launches the application and presents the user with a login screen.

[0690] Step 2:

[0691] User: Enter login information and press the login button.

[0692] Step 3:

[0693] Terminal: Sends login information to the server.

[0694] Step 4:

[0695] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[0696] Step 5:

[0697] Terminal: Display the dashboard screen.

[0698] Step 6:

[0699] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[0700] Step 7:

[0701] Terminal: Display the action input form.

[0702] Step 8:

[0703] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[0704] Step 9:

[0705] Terminal: Sends input data to the server.

[0706] Step 10:

[0707] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[0708] Step 11:

[0709] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[0710] Step 12:

[0711] Generation AI: Generates a scenario and returns it to the server.

[0712] Step 13:

[0713] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[0714] Step 14:

[0715] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[0716] Step 15:

[0717] Server: Sends the generated animation data to the terminal.

[0718] Step 16:

[0719] Terminal: Receives animation data and converts it into a playable state.

[0720] Step 17:

[0721] User: Presses the animation play button within the application.

[0722] Step 18:

[0723] Terminal: Play the animation.

[0724] Step 19:

[0725] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[0726] Example 1

[0727] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0728] A major issue facing modern families is the lack of effective means for moral education. Although parents may warn their children about their behavior in their daily lives, it is difficult to convey concrete and easy-to-understand lessons. For this reason, there is a need to provide simple and effective methods for moral education.

[0729] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0730] In this invention, the server includes an AI generating means for generating moral education content in response to the behavior of a child, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal, thereby enabling moral education to be provided at home simply and effectively.

[0731] "Child behavior" refers to a child's specific actions and behaviors in learning, play, and everyday life.

[0732] "Moral education content" refers to scenarios and teaching materials that teach appropriate moral values ​​and ethics based on children's behavior.

[0733] "Generative AI means" refers to an AI algorithm or model that generates appropriate content based on input data.

[0734] "Video generation artificial intelligence means" refers to an artificial intelligence algorithm or model that generates animation based on a provided scenario or content.

[0735] "Communication means" refers to a system or protocol for transmitting or receiving data.

[0736] "Display means" refers to a device or interface for visually displaying data.

[0737] "User's device" refers to an electronic device operated by a user, such as a smartphone, tablet, or computer.

[0738] The present invention relates to a system that allows for easy moral education at home. This system uses a device (such as a smartphone or tablet) to automatically generate moral education animations based on a child's behavior, enabling effective moral education at home.

[0739] It all starts when a user launches an application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, where the user can enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0740] Next, the input behavioral data is sent from the device to the server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI model (e.g., a general generative AI model). As a concrete example, a scenario about the "negative effects of lying" is generated. An example of the prompt sentence used in this case is as follows:

[0741] Example prompt sentence:

[0742] "Generate scenarios of how your child might lie and how it could negatively impact others."

[0743] The server then sends the generated scenario to the video generation AI model, which then requests it to generate animation based on the scenario. The video generation AI model uses a general video generation AI model, generates appropriate animations according to the scenario, and sends them back to the server.

[0744] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on concrete actions. For example, displaying an animation about the "bad effects of lying" can help children understand the evils of lying.

[0745] This system makes it possible to provide moral education at home easily and effectively. The system automatically generates appropriate content and visually educates children through animation, providing them with easy-to-understand, concrete moral education.

[0746] For example, consider the case where a child bullies a friend. The user (parent) enters "my child bullied my friend" into the application. This data is sent to the server, and the generative AI model generates a scenario about the "negative effects of bullying." This scenario is passed to a video generation AI model, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0747] With the above-described configuration, the present invention provides a new means for effectively and simply providing moral education at home.

[0748] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0749] Step 1:

[0750] The user launches the application on their smartphone or tablet. When the application launches, a login screen is displayed. The user enters their login information (username and password) and taps the login button. If the correct login information is entered, the user is taken to the next screen. Input: Username, password. Output: Login completed, dashboard screen displayed.

[0751] Step 2:

[0752] The user enters information about their child's behavior on the dashboard screen. The input is specific behavior (e.g., "My child lied"). When the user enters the information and taps the send button, the data is sent from the device to the server. Input: Specific behavioral data of the child. Output: Behavioral data has been sent to the server.

[0753] Step 3:

[0754] The server receives behavioral data from the user's device. Then, the server analyzes the received behavioral data. Based on the analysis results, it sends a prompt to a generative AI model (e.g., a general generative AI model) to generate an appropriate moral education scenario. Input: Child's behavioral data. Output: Generation of a moral education scenario.

[0755] Step 4:

[0756] The server sends the generated moral education scenario to the video generation AI model and requests it to generate an animation. The video generation AI model generates an animation based on the scenario. Input: Moral education scenario. Output: Generated animation data.

[0757] Step 5:

[0758] The animation data generated by the video generation AI model is sent back to the server. The server then sends the returned animation data to the user's device. Input: Animation data. Output: Animation data has been sent to the user's device.

[0759] Step 6:

[0760] The device receives the animation data sent from the server. Then, the device plays the received animation data within the application and displays it to the user. The user can show the displayed animation to their child to provide moral education based on concrete actions. Input: Animation data. Output: Playback and display of animation.

[0761] Through this series of processes, users can easily generate moral education content for children and provide effective education.

[0762] (Application example 1)

[0763] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0764] Traditionally, moral education is typically conducted at home or school, with the content and methods largely dependent on teachers and parents. However, systems for effectively and easily implementing moral education at home or in the workplace are not widely available. Furthermore, it is difficult to provide specific moral education content tailored to individual behaviors, and there is a lack of means to realize education tailored to individual situations. Therefore, there is a need for a system that can quickly and effectively provide moral education tailored to the specific behaviors of children and store staff.

[0765] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0766] In this invention, the server includes an AI generating means for generating moral education content in accordance with the actions of children and store staff, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal. This allows the server to analyze the behavioral data of children and store staff received from the user's terminal, generate appropriate moral education scenarios, and provide visual education in the form of animations.

[0767] "Children and store staff" refers to individuals and employees who need moral education at home or in physical stores.

[0768] "Behavior" refers to the specific actions and movements that children and store staff perform on a daily basis.

[0769] "Moral education content" refers to information, scenarios, or materials created to teach moral values ​​and ethics.

[0770] "Generative artificial intelligence means" refers to artificial intelligence technology for analyzing received behavioral data and generating specific moral education scenarios and content based on it.

[0771] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[0772] "Communication means" refers to the technology and protocols used to transmit the generated animation data from the server to the user's device.

[0773] "Display means" refers to the device or software for visually displaying the animation on the user's terminal.

[0774] "User's device" refers to a mobile electronic device such as a smartphone or tablet that has the function of receiving and displaying moral education content.

[0775] This invention is a system that automatically generates moral education content based on the specific behavior of children and store staff, and provides that content as visual animation. This system uses smartphones and tablets to generate didactic content based on user behavior data, effectively supporting moral education in stores and at home.

[0776] Hardware and software used

[0777] The present invention uses the following hardware and software.

[0778] Hardware:

[0779] Smartphone or tablet (user device)

[0780] software:

[0781] Generative AI models (e.g., OpenAI GPT-4)

[0782] Video generation AI model (e.g. DeepBrain AI)

[0783] System program processing explanation

[0784] 1. User input operations

[0785] First, users launch the application on their smartphone or tablet and enter their login information on the login screen. After logging in, a dashboard will appear where users can enter specific behavioral data of their children and store staff. For example, it can record behaviors such as "children lying" or "staff not properly packaging products."

[0786] 2. Processing on the server

[0787] The input behavioral data is sent from the user's device to a server. The server then uses a generative AI model based on the received behavioral data to generate appropriate moral education scenarios. For example, OpenAI GPT-4 is used to generate scenarios about the "negative effects of lying" and "problems with improper product packaging."

[0788] The generated scenario is then sent to an AI video generation model, which automatically generates animations based on the scenario. This process utilizes technologies such as DeepBrain AI to create visually appealing animations. The server then sends the generated animation data to the user's device.

[0789] 3. Display on device

[0790] The user's device receives the animation data sent from the server and processes it appropriately. As a result, it is possible to provide children and store staff with visual and concrete moral education. The display of this animation helps them understand the problems and impacts of certain behaviors, and encourages appropriate behavior.

[0791] Adding specific examples

[0792] For example, consider a case where a child bullies a friend. The user (parent) enters "my child bullied a friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[0793] An example prompt is:

[0794] "Create a moral education scenario about how poor product packaging by staff can affect customers."

[0795] "Create a moral education scenario about the negative consequences of a child lying."

[0796] This allows the present invention to provide a means for effectively conducting moral education based on concrete actions at home and in the workplace.

[0797] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0798] Step 1:

[0799] The user launches the application on their smartphone or tablet and logs in by entering their login information on the login screen. After logging in, a dashboard is displayed on the device, and the user enters specific behavioral data of their child and store staff. This input data may be information such as "the child lied" or "the staff member packaged the product carelessly." The input data is temporarily stored on the device.

[0800] Input: Login information, behavioral data

[0801] Output: Behavioral data stored on the device

[0802] Step 2:

[0803] Behavioral data received from the user's device is sent to a server. The server analyzes the received behavioral data and generates appropriate moral education scenarios using a generative artificial intelligence model (generative AI model). For example, OpenAI GPT-4 is used to generate scenarios about "the negative effects of lying" and "the problems with improper product packaging."

[0804] Input: Behavioral data

[0805] Output: Moral education scenario

[0806] Step 3:

[0807] The server sends the generated moral education scenario to an AI video generation model, asking it to generate an animation based on the scenario. For example, DeepBrain AI is used to generate a visually appealing animation according to the scenario. The AI ​​video generation model generates the animation data and sends it back to the server.

[0808] Input: Moral Education Scenario

[0809] Output: Animation data

[0810] Step 4:

[0811] The server sends the generated animation data to the user's device, which interprets the received animation data and processes it appropriately for display.

[0812] Input: Animation data

[0813] Output: Processed animation data

[0814] Step 5:

[0815] The user's device displays the processed animation, allowing children and store staff to receive moral education based on concrete actions. For example, animations on themes such as "the negative effects of lying" and "the problems with improper product packaging" are visually displayed, enhancing the educational effect.

[0816] Input: Processed animation data

[0817] Output: Rendered animation

[0818] This allows users to provide effective moral education based on specific actions.

[0819] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0820] This invention is a system that allows for easy moral education at home. In particular, it aims to enhance the effectiveness of moral education by combining it with an emotion engine that recognizes the user's emotions. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, and the emotion engine recognizes and appropriately displays the user's emotions, enabling effective moral education at home.

[0821] 1. User input operations

[0822] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[0823] 2. Processing on the server

[0824] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[0825] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[0826] 3. Utilizing the Emotion Engine

[0827] The server further recognizes the user's emotions based on the generated moral education content (scenario and animation) using an emotion engine, which analyzes the user's facial expressions and tone of voice to identify the user's current emotions.

[0828] For example, if a user feels stressed or angry when inputting their child's behavior, the emotion engine will recognize that emotion. Based on the emotion engine's results, the video generation AI will customize the animation. For example, an animation themed on the negative effects of lying could be drawn softer or harsher depending on the user's emotion.

[0829] 4. Display on device

[0830] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is displayed to the child.

[0831] This system allows users to easily provide appropriate moral education to their children at home. The emotion engine takes the user's emotions into account and customizes the animation accordingly, enabling more effective education. As a concrete example, consider a case where a child bullies a friend. The user inputs "my child bullied my friend," and the emotion engine recognizes the user's emotions at the time. Based on the results, an animation on the theme of "the negative effects of bullying" is generated, which can be shown to children effectively without the user becoming emotional.

[0832] As described above, the present invention provides a system that recognizes a user's emotions and generates and displays moral education content accordingly, thereby enhancing the effectiveness of moral education at home.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] Terminal: Launches the application and presents the user with a login screen.

[0836] Step 2:

[0837] User: Enter login information and press the login button.

[0838] Step 3:

[0839] Terminal: Sends login information to the server.

[0840] Step 4:

[0841] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[0842] Step 5:

[0843] Terminal: Display the dashboard screen.

[0844] Step 6:

[0845] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[0846] Step 7:

[0847] Terminal: Display the action input form.

[0848] Step 8:

[0849] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[0850] Step 9:

[0851] Terminal: Sends input data to the server.

[0852] Step 10:

[0853] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[0854] Step 11:

[0855] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[0856] Step 12:

[0857] Generation AI: Generates a scenario and returns it to the server.

[0858] Step 13:

[0859] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[0860] Step 14:

[0861] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[0862] Step 15:

[0863] Server: Receives the generated animation data and sends it to the emotion engine.

[0864] Step 16:

[0865] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize emotions.

[0866] Step 17:

[0867] Server: Based on the results of the emotion engine, it sends instructions to the video generation AI to optimize or customize the animation content.

[0868] Step 18:

[0869] Video generation AI: Generates optimized or customized animations and sends them back to the server.

[0870] Step 19:

[0871] Server: Sends the generated final animation data to the terminal.

[0872] Step 20:

[0873] Terminal: Receives animation data and converts it into a playable state.

[0874] Step 20:

[0875] User: Presses the animation play button within the application.

[0876] Step 21:

[0877] Terminal: Play the animation.

[0878] Step 22:

[0879] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[0880] As described above, the present invention can automatically generate and optimally display moral education animations while taking into account the user's emotions, thereby making moral education at home more effective.

[0881] Example 2

[0882] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0883] Moral education at home can be difficult in terms of consistency and effectiveness, and parents in particular often struggle with how they should teach. Furthermore, preparing appropriate educational content tailored to a child's behavior takes a lot of time and effort. For this reason, there is a need for a system that allows for easy and effective moral education at home.

[0884] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0885] In this invention, the server includes input means for a user to input behavioral data of a child using a terminal, artificial intelligence generation means for analyzing the behavioral data of the child received from the input means and generating an appropriate moral education scenario, artificial intelligence video generation means for generating animations based on the moral education scenarios generated by the artificial intelligence generation means, emotion recognition means for customizing the generated animation data in accordance with the emotions of the user, communication means for transmitting the generated animations to the user's terminal, and display means for displaying the transmitted animations on the user's terminal, thereby enabling appropriate moral education to be provided to children at home easily and effectively.

[0886] "User" refers to the individual who operates the system and inputs child behavior data.

[0887] A "terminal" is a device used by a user, and refers to electronic devices such as smartphones, tablets, and personal computers.

[0888] "Behavioral data" refers to information about a child's specific behavior, and includes specific behavioral content entered by the user.

[0889] "Input means" refers to an interface that allows a user to input their child's behavior data into the system using a terminal.

[0890] "Generative artificial intelligence means" refers to the artificial intelligence technology used to analyze input behavioral data and generate appropriate moral education scenarios.

[0891] "Moral education scenarios" refer to plots or storylines of educational content generated in response to children's behavior.

[0892] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[0893] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to identify their emotions.

[0894] "Communication means" refers to the technology for sending and receiving data between the server and the terminal.

[0895] "Display means" refers to an interface for displaying the generated animation on the user's terminal.

[0896] The "system" refers to an integrated solution that links together the above measures to support moral education at home.

[0897] The present invention is a system for improving the effectiveness and efficiency of moral education at home, in which a user inputs data on their child's behavior using a device such as a smartphone or tablet, and generates and displays appropriate moral education content based on that data. The present invention includes the following main components:

[0898] First, the user installs a dedicated application on the device, then launches the application and accesses the login screen. The application can be used by entering login information (username and password) and logging in. After logging in, the user accesses a dashboard screen where they can enter information about their child's behavior. The child's behavior data is specific content, such as "my child lied," and is entered and sent.

[0899] The behavioral data sent from the device is sent to a server, which then analyzes the received data. This analysis uses a generative AI model (e.g., GPT-3) to automatically generate appropriate moral education scenarios based on the input behavioral data. For example, a scenario about the "negative effects of lying" is generated.

[0900] The generated scenario is sent from the server to a video generation AI (e.g., DALL-E), which automatically generates animations based on the generated scenario. The video generation AI visualizes appropriate educational content based on the prompt text.

[0901] The server then uses an emotion engine to analyze the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and customizes the generated animation based on the results. For example, if the user is feeling stressed, the tone of the animation can be softened.

[0902] The server then sends the customized animation data back to the terminal, which then processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is visually displayed to the child.

[0903] As a concrete example, consider the case where a child bullies a friend. The user enters "My child bullied my friend" and submits it. The server analyzes this data and generates a scenario about the "negative effects of bullying." The video generation AI generates an animation based on this scenario, and the emotion engine analyzes the user's emotions. Based on the analysis results of the emotion engine, the video generation AI customizes the animation, which the server then sends back to the device. When the display button on the device is pressed, this customized animation is displayed to the child.

[0904] An example of a prompt is, "Create an animation that teaches children how to respond when they bully their friends. The animation should emphasize the negative effects of bullying friends." Using this prompt, the generative AI model and video generation AI generate a scenario and animation.

[0905] The above is an embodiment of the present invention, which allows users to easily and effectively provide moral education at home.

[0906] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0907] Step 1:

[0908] A user launches an application using a smartphone or tablet. After launching, a login screen appears, and the user enters their login information (username and password) and presses the "Login" button. The application receives the input data (login information) and sends it to the server. The server authenticates the received login information, and if authentication is successful, generates home screen data and sends it back to the device.

[0909] Step 2:

[0910] When the user presses the "Enter new behavioral data" button on the home screen, a behavioral data input form is displayed. The user enters data about their child's behavior (e.g., "My child lied") and presses the "Submit" button. The input data (behavioral data) is sent to the server via the application. The server analyzes the received behavioral data and uses the analysis results to generate appropriate moral education scenarios.

[0911] Step 3:

[0912] The server analyzes the behavioral data and uses a generative AI model (e.g., GPT-3) to generate a specific moral education scenario. Based on the behavioral data received as input (e.g., "the negative effects of lying"), the generative AI model generates a scenario and outputs it to the server in text format. This output data is used in the next step.

[0913] Step 4:

[0914] The server passes the generated scenario text to an animation generation AI model (e.g., DALL-E) and requests it to generate an animation. The video generation AI model generates an animation using a prompt sentence (e.g., "Please generate an animation that shows the negative effects of lying") as input, and sends the generated animation file back to the server. This becomes the output data for the animation generation.

[0915] Step 5:

[0916] The server uses emotion recognition to analyze the user's real-time emotional data. It analyzes input data (facial expressions and tone of voice) provided by the user through the device camera and microphone to identify the user's emotional state (e.g., stress, anger). The results of this analysis are used to customize the next animation.

[0917] Step 6:

[0918] Based on the emotion recognition results, the server customizes the generated animation. For example, if the user is feeling stressed, the tone of the animation generated by the video generation AI model will be softened. The customized animation becomes the final output data and is sent to the device.

[0919] Step 7:

[0920] The server sends customized animation data to the terminal, which encrypts and transmits the animation data via a communication protocol, and the terminal decrypts the received data and prepares for playback.

[0921] Step 8:

[0922] Users can then press the play animation button on their device to view the final customized animation for their child, allowing them to effectively teach morality at home.

[0923] In this way, by clarifying the input data, output data, and specific processing content at each step, the present invention can effectively support moral education at home.

[0924] (Application example 2)

[0925] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0926] Conventional moral education systems have struggled to provide appropriate education about children's behavior at home, and their effectiveness has been limited. In particular, the effectiveness of education is often reduced because parents and educators do not take into account the emotions they feel when explaining children's behavior. Furthermore, educational approaches in brick-and-mortar stores have not been standardized, making it difficult to respond to the diverse behaviors and emotions of children. Therefore, there is a need for a system that can generate customized animations that take into account the user's emotions and provide effective moral education in brick-and-mortar stores.

[0927] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generation artificial intelligence means for generating moral education content in accordance with the behavior of a child, a video generation artificial intelligence means for generating animation based on the moral education content generated by the generation artificial intelligence means, and a communication means for transmitting the generated animation to a user's terminal. This makes it possible to improve the effectiveness of moral education for diverse behaviors of children at home and in brick-and-mortar stores by using an emotion recognition means for recognizing the user's emotions and customizing the animation based on those emotions.

[0928] "Child behavioral data" refers to information about the actual behavior of children at home, in public places, etc.

[0929] "Moral education content" refers to information and teaching materials used to teach morality and ethics to children.

[0930] "Generative artificial intelligence means" refers to the process of using artificial intelligence to analyze specific data or information and generate some kind of content.

[0931] "Moral education scenarios" refer to stories or scenarios that teach appropriate morals and ethics for specific actions or situations.

[0932] "Video generation artificial intelligence means" refers to the process of generating videos or animations from text or scenario information using artificial intelligence.

[0933] "Communication means" refers to the Internet or network connection methods and systems for sending and receiving data.

[0934] "Display means" refers to a screen, display, or the like for visually showing the generated content to the user.

[0935] "Emotion recognition means" refers to technology or systems that analyze information such as a user's facial expressions and voice to identify the emotions the user is feeling.

[0936] "Animation" refers to visual content that moves and is used to effectively communicate specific content.

[0937] "Customization" refers to the individual adjustment of content or services based on specific conditions or information.

[0938] A "server" refers to a computer system that has the ability to process data and information and communicate with other devices.

[0939] "User" refers to the person who uses the system, especially in the home, this means parents and educators.

[0940] The present invention is a system that automatically generates moral education content in response to children's behavior and displays animations with customized content that takes into account the user's emotions. An outline of how the present invention can be implemented is given below.

[0941] 1. Program Generation

[0942] The system program links the user's terminal, server, and display means. Specifically, it has components such as an interface for inputting children's behavioral data, AI generation means, emotion recognition means, AI video generation means, communication means, and display means.

[0943] 2. System processing overview

[0944] Hardware and software configuration

[0945] Hardware: Devices such as tablets, smartphones, cameras, and microphones.

[0946] Software: Programs written in Python, Java, etc., EmotionRecognizer library, generative AI model, video generation AI model.

[0947] Data processing and calculation flow

[0948] In the system, the user first launches the application on a device such as a tablet or smartphone. The user logs in on the login screen and then enters their child's behavioral data (e.g., "My child lied") on the dashboard screen.

[0949] This input data is sent to the server, where it is processed as follows:

[0950] 1. Analysis and scenario generation:

[0951] A generative artificial intelligence means analyzes the data and generates appropriate moral education scenarios, such as scenarios about the "harmful effects of lying."

[0952] 2. Animation generation:

[0953] The AI ​​animation generating means automatically generates animation based on the generated scenario, and the generated animation is sent back to the server.

[0954] 3. Emotion recognition:

[0955] The emotion recognition unit analyzes the user's facial expressions and voice to identify the current emotion (e.g., stress, anger, etc.) using the tablet's built-in camera and microphone.

[0956] 4. Customizing animation:

[0957] Based on the recognition results of the emotion engine, the animation content is adjusted according to the user's emotions. For example, if the user is very angry, the tone of the animation is adjusted to be softer.

[0958] The data processed and calculated on the server is then sent back to the user's device via a communication means, where the user can play the animation on their device and provide effective moral education to their children.

[0959] 3. Examples of concrete examples and prompts

[0960] As a concrete example, consider the case where a child "lies." The user inputs "my child lied" into the application. The emotion recognition unit recognizes the user's emotion at that time (e.g., very angry), and based on that, generates a softer animation depicting the "negative effects of lying."

[0961] Example prompt sentence:

[0962] "Enter the child lied: Taro lied and pretended to go to school."

[0963] By this process, the present invention enhances the effectiveness of moral education in homes and brick-and-mortar stores.

[0964] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0965] Step 1:

[0966] A user launches an application using a smartphone or tablet device. A login screen is then displayed, and the user enters their login information to log in. If login is successful, the dashboard screen is displayed. At this stage, the input is the user's login information, and the output is the display of the dashboard screen.

[0967] Step 2:

[0968] On the dashboard screen, the user inputs their child's behavioral data. For example, they input information such as "my child lied." This behavioral data is sent from the device to the server. The input is the child's behavioral data, and the output is the data sent to the server.

[0969] Step 3:

[0970] The server analyzes the received behavioral data. During the analysis, it uses a generative artificial intelligence means to generate an appropriate moral education scenario. For example, a scenario on the "harmful effects of lying" is generated. The input is the child's behavioral data, and the output is an educational scenario.

[0971] Step 4:

[0972] The server sends the generated scenario to the video generation AI means and requests it to generate an animation based on the scenario. The video generation AI generates an appropriate animation based on the scenario and sends it back to the server. The input is the moral education scenario, and the output is animation data.

[0973] Step 5:

[0974] The device's camera and microphone are used to capture the user's facial expressions and voice. The data is analyzed by an emotion recognition means to identify the user's emotions. The input is the captured facial and voice data, and the output is the recognized user's emotion data.

[0975] Step 6:

[0976] The server customizes the animation content based on the emotion data. For example, if the user is very angry, it softens the tone of the animation. The input is the user's emotion data and the generated animation, and the output is the customized animation.

[0977] Step 7:

[0978] The server sends customized animation data to the terminal. The input is the customized animation data, and the output is the data sent to the terminal.

[0979] Step 8:

[0980] The terminal displays the received customized animation to the user. The user can show the customized animation to the child by pressing the animation play button. The input is the received animation data, and the output is the display of the animation.

[0981] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0982] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0983] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0984] [Fourth embodiment]

[0985] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0986] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0987] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0988] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0989] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0990] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0991] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0992] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0993] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0994] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0995] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0996] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0997] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0998] This invention is a system that allows for easy moral education at home. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, enabling effective moral education at home.

[0999] 1. User input operations

[1000] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[1001] 2. Processing on the server

[1002] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[1003] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[1004] 3. Display on device

[1005] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on specific behavior. For example, displaying an animation about the "bad effects of lying" can help children understand the wrongness of lying.

[1006] Specific examples

[1007] For example, consider a case where a child bullies a friend. The user (parent) enters "My child bullied his friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[1008] In this way, the present invention provides a means for easily and effectively teaching moral education at home. The system automatically generates appropriate content and visually teaches through animation, making it possible to provide children with easy-to-understand, concrete moral education.

[1009] The processing flow will be explained below.

[1010] Step 1:

[1011] Terminal: Launches the application and presents the user with a login screen.

[1012] Step 2:

[1013] User: Enter login information and press the login button.

[1014] Step 3:

[1015] Terminal: Sends login information to the server.

[1016] Step 4:

[1017] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[1018] Step 5:

[1019] Terminal: Display the dashboard screen.

[1020] Step 6:

[1021] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[1022] Step 7:

[1023] Terminal: Display the action input form.

[1024] Step 8:

[1025] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[1026] Step 9:

[1027] Terminal: Sends input data to the server.

[1028] Step 10:

[1029] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[1030] Step 11:

[1031] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[1032] Step 12:

[1033] Generation AI: Generates a scenario and returns it to the server.

[1034] Step 13:

[1035] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[1036] Step 14:

[1037] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[1038] Step 15:

[1039] Server: Sends the generated animation data to the terminal.

[1040] Step 16:

[1041] Terminal: Receives animation data and converts it into a playable state.

[1042] Step 17:

[1043] User: Presses the animation play button within the application.

[1044] Step 18:

[1045] Terminal: Play the animation.

[1046] Step 19:

[1047] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[1048] Example 1

[1049] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1050] A major issue facing modern families is the lack of effective means for moral education. Although parents may warn their children about their behavior in their daily lives, it is difficult to convey concrete and easy-to-understand lessons. For this reason, there is a need to provide simple and effective methods for moral education.

[1051] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1052] In this invention, the server includes an AI generating means for generating moral education content in response to the behavior of a child, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal, thereby enabling moral education to be provided at home simply and effectively.

[1053] "Child behavior" refers to a child's specific actions and behaviors in learning, play, and everyday life.

[1054] "Moral education content" refers to scenarios and teaching materials that teach appropriate moral values ​​and ethics based on children's behavior.

[1055] "Generative AI means" refers to an AI algorithm or model that generates appropriate content based on input data.

[1056] "Video generation artificial intelligence means" refers to an artificial intelligence algorithm or model that generates animation based on a provided scenario or content.

[1057] "Communication means" refers to a system or protocol for transmitting or receiving data.

[1058] "Display means" refers to a device or interface for visually displaying data.

[1059] "User's device" refers to an electronic device operated by a user, such as a smartphone, tablet, or computer.

[1060] The present invention relates to a system that allows for easy moral education at home. This system uses a device (such as a smartphone or tablet) to automatically generate moral education animations based on a child's behavior, enabling effective moral education at home.

[1061] It all starts when a user launches an application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, where the user can enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[1062] Next, the input behavioral data is sent from the device to the server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI model (e.g., a general generative AI model). As a concrete example, a scenario about the "negative effects of lying" is generated. An example of the prompt sentence used in this case is as follows:

[1063] Example prompt sentence:

[1064] "Generate scenarios of how your child might lie and how it could negatively impact others."

[1065] The server then sends the generated scenario to the video generation AI model, which then requests it to generate animation based on the scenario. The video generation AI model uses a general video generation AI model, generates appropriate animations according to the scenario, and sends them back to the server.

[1066] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. By showing the generated animation to children, users can provide moral education based on concrete actions. For example, displaying an animation about the "bad effects of lying" can help children understand the evils of lying.

[1067] This system makes it possible to provide moral education at home easily and effectively. The system automatically generates appropriate content and visually educates children through animation, providing them with easy-to-understand, concrete moral education.

[1068] For example, consider the case where a child bullies a friend. The user (parent) enters "my child bullied my friend" into the application. This data is sent to the server, and the generative AI model generates a scenario about the "negative effects of bullying." This scenario is passed to a video generation AI model, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[1069] With the above-described configuration, the present invention provides a new means for effectively and simply providing moral education at home.

[1070] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1071] Step 1:

[1072] The user launches the application on their smartphone or tablet. When the application launches, a login screen is displayed. The user enters their login information (username and password) and taps the login button. If the correct login information is entered, the user is taken to the next screen. Input: Username, password. Output: Login completed, dashboard screen displayed.

[1073] Step 2:

[1074] The user enters information about their child's behavior on the dashboard screen. The input is specific behavior (e.g., "My child lied"). When the user enters the information and taps the send button, the data is sent from the device to the server. Input: Specific behavioral data of the child. Output: Behavioral data has been sent to the server.

[1075] Step 3:

[1076] The server receives behavioral data from the user's device. Then, the server analyzes the received behavioral data. Based on the analysis results, it sends a prompt to a generative AI model (e.g., a general generative AI model) to generate an appropriate moral education scenario. Input: Child's behavioral data. Output: Generation of a moral education scenario.

[1077] Step 4:

[1078] The server sends the generated moral education scenario to the video generation AI model and requests it to generate an animation. The video generation AI model generates an animation based on the scenario. Input: Moral education scenario. Output: Generated animation data.

[1079] Step 5:

[1080] The animation data generated by the video generation AI model is sent back to the server. The server then sends the returned animation data to the user's device. Input: Animation data. Output: Animation data has been sent to the user's device.

[1081] Step 6:

[1082] The device receives the animation data sent from the server. Then, the device plays the received animation data within the application and displays it to the user. The user can show the displayed animation to their child to provide moral education based on concrete actions. Input: Animation data. Output: Playback and display of animation.

[1083] Through this series of processes, users can easily generate moral education content for children and provide effective education.

[1084] (Application example 1)

[1085] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1086] Traditionally, moral education is typically conducted at home or school, with the content and methods largely dependent on teachers and parents. However, systems for effectively and easily implementing moral education at home or in the workplace are not widely available. Furthermore, it is difficult to provide specific moral education content tailored to individual behaviors, and there is a lack of means to realize education tailored to individual situations. Therefore, there is a need for a system that can quickly and effectively provide moral education tailored to the specific behaviors of children and store staff.

[1087] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1088] In this invention, the server includes an AI generating means for generating moral education content in accordance with the actions of children and store staff, an AI video generating means for generating animations based on the moral education content generated by the AI ​​generating means, and a communication means for transmitting the generated animations to a user's terminal. This allows the server to analyze the behavioral data of children and store staff received from the user's terminal, generate appropriate moral education scenarios, and provide visual education in the form of animations.

[1089] "Children and store staff" refers to individuals and employees who need moral education at home or in physical stores.

[1090] "Behavior" refers to the specific actions and movements that children and store staff perform on a daily basis.

[1091] "Moral education content" refers to information, scenarios, or materials created to teach moral values ​​and ethics.

[1092] "Generative artificial intelligence means" refers to artificial intelligence technology for analyzing received behavioral data and generating specific moral education scenarios and content based on it.

[1093] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[1094] "Communication means" refers to the technology and protocols used to transmit the generated animation data from the server to the user's device.

[1095] "Display means" refers to the device or software for visually displaying the animation on the user's terminal.

[1096] "User's device" refers to a mobile electronic device such as a smartphone or tablet that has the function of receiving and displaying moral education content.

[1097] This invention is a system that automatically generates moral education content based on the specific behavior of children and store staff, and provides that content as visual animation. This system uses smartphones and tablets to generate didactic content based on user behavior data, effectively supporting moral education in stores and at home.

[1098] Hardware and software used

[1099] The present invention uses the following hardware and software.

[1100] Hardware:

[1101] Smartphone or tablet (user device)

[1102] software:

[1103] Generative AI models (e.g., OpenAI GPT-4)

[1104] Video generation AI model (e.g. DeepBrain AI)

[1105] System program processing explanation

[1106] 1. User input operations

[1107] First, users launch the application on their smartphone or tablet and enter their login information on the login screen. After logging in, a dashboard will appear where users can enter specific behavioral data of their children and store staff. For example, it can record behaviors such as "children lying" or "staff not properly packaging products."

[1108] 2. Processing on the server

[1109] The input behavioral data is sent from the user's device to a server. The server then uses a generative AI model based on the received behavioral data to generate appropriate moral education scenarios. For example, OpenAI GPT-4 is used to generate scenarios about the "negative effects of lying" and "problems with improper product packaging."

[1110] The generated scenario is then sent to an AI video generation model, which automatically generates animations based on the scenario. This process utilizes technologies such as DeepBrain AI to create visually appealing animations. The server then sends the generated animation data to the user's device.

[1111] 3. Display on device

[1112] The user's device receives the animation data sent from the server and processes it appropriately. As a result, it is possible to provide children and store staff with visual and concrete moral education. The display of this animation helps them understand the problems and impacts of certain behaviors, and encourages appropriate behavior.

[1113] Adding specific examples

[1114] For example, consider a case where a child bullies a friend. The user (parent) enters "my child bullied a friend" into the application. This data is sent to the server, and the generation AI generates a scenario about the "negative effects of bullying." This scenario is passed to the video generation AI, which generates an animation on the theme of "how much impact bullying a friend has on others." This animation is sent to the device, and parents can show it to their child to help them understand the negative effects bullying has on others.

[1115] An example prompt is:

[1116] "Create a moral education scenario about how poor product packaging by staff can affect customers."

[1117] "Create a moral education scenario about the negative consequences of a child lying."

[1118] This allows the present invention to provide a means for effectively conducting moral education based on concrete actions at home and in the workplace.

[1119] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1120] Step 1:

[1121] The user launches the application on their smartphone or tablet and logs in by entering their login information on the login screen. After logging in, a dashboard is displayed on the device, and the user enters specific behavioral data of their child and store staff. This input data may be information such as "the child lied" or "the staff member packaged the product carelessly." The input data is temporarily stored on the device.

[1122] Input: Login information, behavioral data

[1123] Output: Behavioral data stored on the device

[1124] Step 2:

[1125] Behavioral data received from the user's device is sent to a server. The server analyzes the received behavioral data and generates appropriate moral education scenarios using a generative artificial intelligence model (generative AI model). For example, OpenAI GPT-4 is used to generate scenarios about "the negative effects of lying" and "the problems with improper product packaging."

[1126] Input: Behavioral data

[1127] Output: Moral education scenario

[1128] Step 3:

[1129] The server sends the generated moral education scenario to an AI video generation model, asking it to generate an animation based on the scenario. For example, DeepBrain AI is used to generate a visually appealing animation according to the scenario. The AI ​​video generation model generates the animation data and sends it back to the server.

[1130] Input: Moral Education Scenario

[1131] Output: Animation data

[1132] Step 4:

[1133] The server sends the generated animation data to the user's device, which interprets the received animation data and processes it appropriately for display.

[1134] Input: Animation data

[1135] Output: Processed animation data

[1136] Step 5:

[1137] The user's device displays the processed animation, allowing children and store staff to receive moral education based on concrete actions. For example, animations on themes such as "the negative effects of lying" and "the problems with improper product packaging" are visually displayed, enhancing the educational effect.

[1138] Input: Processed animation data

[1139] Output: Rendered animation

[1140] This allows users to provide effective moral education based on specific actions.

[1141] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1142] This invention is a system that allows for easy moral education at home. In particular, it aims to enhance the effectiveness of moral education by combining it with an emotion engine that recognizes the user's emotions. This system uses devices such as smartphones and tablets to automatically generate moral education animations based on children's behavior, and the emotion engine recognizes and appropriately displays the user's emotions, enabling effective moral education at home.

[1143] 1. User input operations

[1144] First, the user launches the application on their smartphone or tablet. When the application launches, a login screen appears, and the user logs in by entering their login information. After logging in, a dashboard screen appears, allowing the user to enter information about their child's behavior. For example, they can enter specific behaviors such as "my child lied."

[1145] 2. Processing on the server

[1146] The input behavioral data is sent from the device to a server. The server analyzes the received data and generates an appropriate moral education scenario based on it. This scenario is generated using a generative AI (e.g., a generative AI model). As a specific example, a scenario about the "negative effects of lying" is generated.

[1147] Next, the server sends the generated scenario to an animation generation AI (e.g., an animation generation AI model) and requests it to generate animation based on the scenario. The animation generation AI generates appropriate animation according to the scenario and sends it back to the server.

[1148] 3. Utilizing the Emotion Engine

[1149] The server further recognizes the user's emotions based on the generated moral education content (scenario and animation) using an emotion engine, which analyzes the user's facial expressions and tone of voice to identify the user's current emotions.

[1150] For example, if a user feels stressed or angry when inputting their child's behavior, the emotion engine will recognize that emotion. Based on the emotion engine's results, the video generation AI will customize the animation. For example, an animation themed on the negative effects of lying could be drawn softer or harsher depending on the user's emotion.

[1151] 4. Display on device

[1152] The animation data returned from the server is sent back to the device. The device processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is displayed to the child.

[1153] This system allows users to easily provide appropriate moral education to their children at home. The emotion engine takes the user's emotions into account and customizes the animation accordingly, enabling more effective education. As a concrete example, consider a case where a child bullies a friend. The user inputs "my child bullied my friend," and the emotion engine recognizes the user's emotions at the time. Based on the results, an animation on the theme of "the negative effects of bullying" is generated, which can be shown to children effectively without the user becoming emotional.

[1154] As described above, the present invention provides a system that recognizes a user's emotions and generates and displays moral education content accordingly, thereby enhancing the effectiveness of moral education at home.

[1155] The processing flow will be explained below.

[1156] Step 1:

[1157] Terminal: Launches the application and presents the user with a login screen.

[1158] Step 2:

[1159] User: Enter login information and press the login button.

[1160] Step 3:

[1161] Terminal: Sends login information to the server.

[1162] Step 4:

[1163] Server: Checks login information, and if authentication is successful, sends the dashboard screen data to the terminal.

[1164] Step 5:

[1165] Terminal: Display the dashboard screen.

[1166] Step 6:

[1167] User: Press the "Enter Bad Behavior" button on the dashboard screen.

[1168] Step 7:

[1169] Terminal: Display the action input form.

[1170] Step 8:

[1171] User: Select or enter the child's behavior in a text field (e.g., "Lie").

[1172] Step 9:

[1173] Terminal: Sends input data to the server.

[1174] Step 10:

[1175] Server: Analyzes the received input data (if the input data is "I lied", generates a corresponding moral education scenario).

[1176] Step 11:

[1177] Server: Based on the analysis results, the data is sent to a generation AI, which generates a moral education scenario (example scenario: "About the negative effects of lying").

[1178] Step 12:

[1179] Generation AI: Generates a scenario and returns it to the server.

[1180] Step 13:

[1181] Server: Sends the generated scenario to the video generation AI and requests it to generate an animation.

[1182] Step 14:

[1183] Video generation AI: Generates animation based on the scenario and sends it back to the server.

[1184] Step 15:

[1185] Server: Receives the generated animation data and sends it to the emotion engine.

[1186] Step 16:

[1187] Emotion engine: Analyzes the user's facial expressions and tone of voice to recognize emotions.

[1188] Step 17:

[1189] Server: Based on the results of the emotion engine, it sends instructions to the video generation AI to optimize or customize the animation content.

[1190] Step 18:

[1191] Video generation AI: Generates optimized or customized animations and sends them back to the server.

[1192] Step 19:

[1193] Server: Sends the generated final animation data to the terminal.

[1194] Step 20:

[1195] Terminal: Receives animation data and converts it into a playable state.

[1196] Step 20:

[1197] User: Presses the animation play button within the application.

[1198] Step 21:

[1199] Terminal: Play the animation.

[1200] Step 22:

[1201] User: By showing the animation to children, they can learn concrete moral lessons (e.g., about the negative effects of lying).

[1202] As described above, the present invention can automatically generate and optimally display moral education animations while taking into account the user's emotions, thereby making moral education at home more effective.

[1203] Example 2

[1204] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1205] Moral education at home can be difficult in terms of consistency and effectiveness, and parents in particular often struggle with how they should teach. Furthermore, preparing appropriate educational content tailored to a child's behavior takes a lot of time and effort. For this reason, there is a need for a system that allows for easy and effective moral education at home.

[1206] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1207] In this invention, the server includes input means for a user to input behavioral data of a child using a terminal, artificial intelligence generation means for analyzing the behavioral data of the child received from the input means and generating an appropriate moral education scenario, artificial intelligence video generation means for generating animations based on the moral education scenarios generated by the artificial intelligence generation means, emotion recognition means for customizing the generated animation data in accordance with the emotions of the user, communication means for transmitting the generated animations to the user's terminal, and display means for displaying the transmitted animations on the user's terminal, thereby enabling appropriate moral education to be provided to children at home easily and effectively.

[1208] "User" refers to the individual who operates the system and inputs child behavior data.

[1209] A "terminal" is a device used by a user, and refers to electronic devices such as smartphones, tablets, and personal computers.

[1210] "Behavioral data" refers to information about a child's specific behavior, and includes specific behavioral content entered by the user.

[1211] "Input means" refers to an interface that allows a user to input their child's behavior data into the system using a terminal.

[1212] "Generative artificial intelligence means" refers to the artificial intelligence technology used to analyze input behavioral data and generate appropriate moral education scenarios.

[1213] "Moral education scenarios" refer to plots or storylines of educational content generated in response to children's behavior.

[1214] "Video generation artificial intelligence means" refers to artificial intelligence technology for automatically generating animations based on the generated moral education scenario.

[1215] "Emotion recognition means" refers to technology that analyzes a user's facial expressions and tone of voice to identify their emotions.

[1216] "Communication means" refers to the technology for sending and receiving data between the server and the terminal.

[1217] "Display means" refers to an interface for displaying the generated animation on the user's terminal.

[1218] The "system" refers to an integrated solution that links together the above measures to support moral education at home.

[1219] The present invention is a system for improving the effectiveness and efficiency of moral education at home, in which a user inputs data on their child's behavior using a device such as a smartphone or tablet, and generates and displays appropriate moral education content based on that data. The present invention includes the following main components:

[1220] First, the user installs a dedicated application on the device, then launches the application and accesses the login screen. The application can be used by entering login information (username and password) and logging in. After logging in, the user accesses a dashboard screen where they can enter information about their child's behavior. The child's behavior data is specific content, such as "my child lied," and is entered and sent.

[1221] The behavioral data sent from the device is sent to a server, which then analyzes the received data. This analysis uses a generative AI model (e.g., GPT-3) to automatically generate appropriate moral education scenarios based on the input behavioral data. For example, a scenario about the "negative effects of lying" is generated.

[1222] The generated scenario is sent from the server to a video generation AI (e.g., DALL-E), which automatically generates animations based on the generated scenario. The video generation AI visualizes appropriate educational content based on the prompt text.

[1223] The server then uses an emotion engine to analyze the user's emotions in real time. The emotion engine analyzes the user's facial expressions and tone of voice and customizes the generated animation based on the results. For example, if the user is feeling stressed, the tone of the animation can be softened.

[1224] The server then sends the customized animation data back to the terminal, which then processes the received animation data appropriately and displays it to the user. When the user presses the animation play button, the generated animation is visually displayed to the child.

[1225] As a concrete example, consider the case where a child bullies a friend. The user enters "My child bullied my friend" and submits it. The server analyzes this data and generates a scenario about the "negative effects of bullying." The video generation AI generates an animation based on this scenario, and the emotion engine analyzes the user's emotions. Based on the analysis results of the emotion engine, the video generation AI customizes the animation, which the server then sends back to the device. When the display button on the device is pressed, this customized animation is displayed to the child.

[1226] An example of a prompt is, "Create an animation that teaches children how to respond when they bully their friends. The animation should emphasize the negative effects of bullying friends." Using this prompt, the generative AI model and video generation AI generate a scenario and animation.

[1227] The above is an embodiment of the present invention, which allows users to easily and effectively provide moral education at home.

[1228] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1229] Step 1:

[1230] A user launches an application using a smartphone or tablet. After launching, a login screen appears, and the user enters their login information (username and password) and presses the "Login" button. The application receives the input data (login information) and sends it to the server. The server authenticates the received login information, and if authentication is successful, generates home screen data and sends it back to the device.

[1231] Step 2:

[1232] When the user presses the "Enter new behavioral data" button on the home screen, a behavioral data input form is displayed. The user enters data about their child's behavior (e.g., "My child lied") and presses the "Submit" button. The input data (behavioral data) is sent to the server via the application. The server analyzes the received behavioral data and uses the analysis results to generate appropriate moral education scenarios.

[1233] Step 3:

[1234] The server analyzes the behavioral data and uses a generative AI model (e.g., GPT-3) to generate a specific moral education scenario. Based on the behavioral data received as input (e.g., "the negative effects of lying"), the generative AI model generates a scenario and outputs it to the server in text format. This output data is used in the next step.

[1235] Step 4:

[1236] The server passes the generated scenario text to an animation generation AI model (e.g., DALL-E) and requests it to generate an animation. The video generation AI model generates an animation using a prompt sentence (e.g., "Please generate an animation that shows the negative effects of lying") as input, and sends the generated animation file back to the server. This becomes the output data for the animation generation.

[1237] Step 5:

[1238] The server uses emotion recognition to analyze the user's real-time emotional data. It analyzes input data (facial expressions and tone of voice) provided by the user through the device camera and microphone to identify the user's emotional state (e.g., stress, anger). The results of this analysis are used to customize the next animation.

[1239] Step 6:

[1240] Based on the emotion recognition results, the server customizes the generated animation. For example, if the user is feeling stressed, the tone of the animation generated by the video generation AI model will be softened. The customized animation becomes the final output data and is sent to the device.

[1241] Step 7:

[1242] The server sends customized animation data to the terminal, which encrypts and transmits the animation data via a communication protocol, and the terminal decrypts the received data and prepares for playback.

[1243] Step 8:

[1244] Users can then press the play animation button on their device to view the final customized animation for their child, allowing them to effectively teach morality at home.

[1245] In this way, by clarifying the input data, output data, and specific processing content at each step, the present invention can effectively support moral education at home.

[1246] (Application example 2)

[1247] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1248] Conventional moral education systems have struggled to provide appropriate education about children's behavior at home, and their effectiveness has been limited. In particular, the effectiveness of education is often reduced because parents and educators do not take into account the emotions they feel when explaining children's behavior. Furthermore, educational approaches in brick-and-mortar stores have not been standardized, making it difficult to respond to the diverse behaviors and emotions of children. Therefore, there is a need for a system that can generate customized animations that take into account the user's emotions and provide effective moral education in brick-and-mortar stores.

[1249] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes a generation artificial intelligence means for generating moral education content in accordance with the behavior of a child, a video generation artificial intelligence means for generating animation based on the moral education content generated by the generation artificial intelligence means, and a communication means for transmitting the generated animation to a user's terminal. This makes it possible to improve the effectiveness of moral education for diverse behaviors of children at home and in brick-and-mortar stores by using an emotion recognition means for recognizing the user's emotions and customizing the animation based on those emotions.

[1250] "Child behavioral data" refers to information about the actual behavior of children at home, in public places, etc.

[1251] "Moral education content" refers to information and teaching materials used to teach morality and ethics to children.

[1252] "Generative artificial intelligence means" refers to the process of using artificial intelligence to analyze specific data or information and generate some kind of content.

[1253] "Moral education scenarios" refer to stories or scenarios that teach appropriate morals and ethics for specific actions or situations.

[1254] "Video generation artificial intelligence means" refers to the process of generating videos or animations from text or scenario information using artificial intelligence.

[1255] "Communication means" refers to the Internet or network connection methods and systems for sending and receiving data.

[1256] "Display means" refers to a screen, display, or the like for visually showing the generated content to the user.

[1257] "Emotion recognition means" refers to technology or systems that analyze information such as a user's facial expressions and voice to identify the emotions the user is feeling.

[1258] "Animation" refers to visual content that moves and is used to effectively communicate specific content.

[1259] "Customization" refers to the individual adjustment of content or services based on specific conditions or information.

[1260] A "server" refers to a computer system that has the ability to process data and information and communicate with other devices.

[1261] "User" refers to the person who uses the system, especially in the home, this means parents and educators.

[1262] The present invention is a system that automatically generates moral education content in response to children's behavior and displays animations with customized content that takes into account the user's emotions. An outline of how the present invention can be implemented is given below.

[1263] 1. Program Generation

[1264] The system program links the user's terminal, server, and display means. Specifically, it has components such as an interface for inputting children's behavioral data, AI generation means, emotion recognition means, AI video generation means, communication means, and display means.

[1265] 2. System processing overview

[1266] Hardware and software configuration

[1267] Hardware: Devices such as tablets, smartphones, cameras, and microphones.

[1268] Software: Programs written in Python, Java, etc., EmotionRecognizer library, generative AI model, video generation AI model.

[1269] Data processing and calculation flow

[1270] In the system, the user first launches the application on a device such as a tablet or smartphone. The user logs in on the login screen and then enters their child's behavioral data (e.g., "My child lied") on the dashboard screen.

[1271] This input data is sent to the server, where it is processed as follows:

[1272] 1. Analysis and scenario generation:

[1273] A generative artificial intelligence means analyzes the data and generates appropriate moral education scenarios, such as scenarios about the "harmful effects of lying."

[1274] 2. Animation generation:

[1275] The AI ​​animation generating means automatically generates animation based on the generated scenario, and the generated animation is sent back to the server.

[1276] 3. Emotion recognition:

[1277] The emotion recognition unit analyzes the user's facial expressions and voice to identify the current emotion (e.g., stress, anger, etc.) using the tablet's built-in camera and microphone.

[1278] 4. Customizing animation:

[1279] Based on the recognition results of the emotion engine, the animation content is adjusted according to the user's emotions. For example, if the user is very angry, the tone of the animation is adjusted to be softer.

[1280] The data processed and calculated on the server is then sent back to the user's device via a communication means, where the user can play the animation on their device and provide effective moral education to their children.

[1281] 3. Examples of concrete examples and prompts

[1282] As a concrete example, consider the case where a child "lies." The user inputs "my child lied" into the application. The emotion recognition unit recognizes the user's emotion at that time (e.g., very angry), and based on that, generates a softer animation depicting the "negative effects of lying."

[1283] Example prompt sentence:

[1284] "Enter the child lied: Taro lied and pretended to go to school."

[1285] By this process, the present invention enhances the effectiveness of moral education in homes and brick-and-mortar stores.

[1286] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1287] Step 1:

[1288] A user launches an application using a smartphone or tablet device. A login screen is then displayed, and the user enters their login information to log in. If login is successful, the dashboard screen is displayed. At this stage, the input is the user's login information, and the output is the display of the dashboard screen.

[1289] Step 2:

[1290] On the dashboard screen, the user inputs their child's behavioral data. For example, they input information such as "my child lied." This behavioral data is sent from the device to the server. The input is the child's behavioral data, and the output is the data sent to the server.

[1291] Step 3:

[1292] The server analyzes the received behavioral data. During the analysis, it uses a generative artificial intelligence means to generate an appropriate moral education scenario. For example, a scenario on the "harmful effects of lying" is generated. The input is the child's behavioral data, and the output is an educational scenario.

[1293] Step 4:

[1294] The server sends the generated scenario to the video generation AI means and requests it to generate an animation based on the scenario. The video generation AI generates an appropriate animation based on the scenario and sends it back to the server. The input is the moral education scenario, and the output is animation data.

[1295] Step 5:

[1296] The device's camera and microphone are used to capture the user's facial expressions and voice. The data is analyzed by an emotion recognition means to identify the user's emotions. The input is the captured facial and voice data, and the output is the recognized user's emotion data.

[1297] Step 6:

[1298] The server customizes the animation content based on the emotion data. For example, if the user is very angry, it softens the tone of the animation. The input is the user's emotion data and the generated animation, and the output is the customized animation.

[1299] Step 7:

[1300] The server sends customized animation data to the terminal. The input is the customized animation data, and the output is the data sent to the terminal.

[1301] Step 8:

[1302] The terminal displays the received customized animation to the user. The user can show the customized animation to the child by pressing the animation play button. The input is the received animation data, and the output is the display of the animation.

[1303] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1304] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1305] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1306] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1307] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1308] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1309] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1310] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1311] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1312] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1313] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1314] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1315] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1316] 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.

[1317] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1318] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1319] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1320] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1321] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1322] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1323] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1324] The following is further disclosed regarding the above embodiment.

[1325] (Claim 1)

[1326] A generating artificial intelligence means for generating moral education content according to the behavior of a child;

[1327] a moving image generating artificial intelligence means for generating animations based on the moral education content generated by the generating artificial intelligence means;

[1328] a communication means for transmitting the generated animation to a user's terminal;

[1329] a display means for displaying the transmitted animation on a user's terminal;

[1330] A system including:

[1331] (Claim 2)

[1332] 2. The system according to claim 1, wherein the artificial intelligence generating means analyzes the behavioral data of the child received from the user's terminal and generates an appropriate moral education scenario.

[1333] (Claim 3)

[1334] 2. The system according to claim 1, wherein the video generation artificial intelligence means automatically generates animations based on the moral education scenarios.

[1335] "Example 1"

[1336] (Claim 1)

[1337] A generating artificial intelligence means for generating moral education content according to the behavior of a child;

[1338] a moving image generating artificial intelligence means for generating animations based on the moral education content generated by the generating artificial intelligence means;

[1339] a communication means for transmitting the generated animation to a user's terminal;

[1340] a display means for displaying the transmitted animation on a user's terminal;

[1341] A system including:

[1342] (Claim 2)

[1343] 2. The system according to claim 1, wherein the artificial intelligence generating means analyzes the behavioral data of the child received from the user's terminal and generates an appropriate moral education scenario.

[1344] (Claim 3)

[1345] 2. The system according to claim 1, wherein the video generation artificial intelligence means automatically generates animations based on the moral education scenarios.

[1346] "Application Example 1"

[1347] (Claim 1)

[1348] A generating artificial intelligence means for generating moral education content according to the actions of children and store staff;

[1349] a moving image generating artificial intelligence means for generating animations based on the moral education content generated by the generating artificial intelligence means;

[1350] a communication means for transmitting the generated animation to a user's terminal;

[1351] a display means for displaying the transmitted animation on a user's terminal;

[1352] A system including:

[1353] (Claim 2)

[1354] 2. The system according to claim 1, wherein the artificial intelligence generating means analyzes behavioral data of children and store staff received from a user's terminal and generates an appropriate moral education scenario.

[1355] (Claim 3)

[1356] 2. The system according to claim 1, wherein the video generation artificial intelligence means automatically generates animations based on the moral education scenarios.

[1357] "Example 2: Combining Emotion Engines"

[1358] (Claim 1)

[1359] an input means for a user to input behavior data of a child using a terminal;

[1360] a generation artificial intelligence means for analyzing the behavioral data of the child received from the input means and generating an appropriate moral education scenario;

[1361] a moving image generating artificial intelligence means for generating animations based on the moral education scenarios generated by the generating artificial intelligence means;

[1362] emotion recognition means for customizing the generated animation data according to the emotion of a user;

[1363] a communication means for transmitting the generated animation to a user's terminal;

[1364] a display means for displaying the transmitted animation on a user's terminal;

[1365] A system including:

[1366] (Claim 2)

[1367] 2. The system according to claim 1, wherein the artificial intelligence generating means analyzes the behavioral data of the child received from the user's terminal and generates an appropriate moral education scenario.

[1368] (Claim 3)

[1369] 2. The system according to claim 1, wherein the video generation artificial intelligence means automatically generates animations based on the moral education scenarios.

[1370] (Claim 4)

[1371] 2. The system according to claim 1, wherein the emotion recognition means analyzes the user's facial expression and tone of voice, and customizes the animation based on the analysis results.

[1372] "Application example 2 when combining emotion engines"

[1373] (Claim 1)

[1374] A generating artificial intelligence means for generating moral education content according to the behavior of a child;

[1375] a moving image generating artificial intelligence means for generating animations based on the moral education content generated by the generating artificial intelligence means;

[1376] a communication means for transmitting the generated animation to a user's terminal;

[1377] a display means for displaying the transmitted animation on a user's terminal;

[1378] emotion recognition means for recognizing an emotion of a user and customizing an animation based on the emotion;

[1379] A system including:

[1380] (Claim 2)

[1381] The artificial intelligence generating means analyzes the behavioral data of the child received from the user's terminal and generates an appropriate moral education scenario;

[1382] 2. The system according to claim 1, wherein the emotion recognition means analyzes the user's facial expressions and voice to identify emotions, and the animation generation artificial intelligence means customizes animation based on the emotions.

[1383] (Claim 3)

[1384] The system according to claim 1, wherein the video generation artificial intelligence means automatically generates animation based on the moral education scenario, and displays customized animation based on emotion data obtained by the emotion recognition means on a display means. [Explanation of symbols]

[1385] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A generating artificial intelligence means for generating moral education content according to the behavior of a child; a moving image generating artificial intelligence means for generating animations based on the moral education content generated by the generating artificial intelligence means; a communication means for transmitting the generated animation to a user's terminal; a display means for displaying the transmitted animation on a user's terminal; A system including:

2. 2. The system according to claim 1, wherein the artificial intelligence generating means analyzes the behavioral data of the child received from the user's terminal and generates an appropriate moral education scenario.

3. 2. The system according to claim 1, wherein said artificial intelligence means for generating animation automatically generates animation based on said moral education scenario.

Images