system

Abstract

We provide the system. [Solution] A means of collecting information about children obtained from users, A means of generating customized educational content using generative AI based on collected information, A means for sending and displaying the generated content on the user's terminal, A device equipped with conversational AI that enables interaction with children, A means of collecting and analyzing user feedback, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Providing personalized childcare advice and educational content according to the individual situations of each child and family has been time-consuming and laborious with conventional methods and difficult to perform efficiently. In particular, there is an increasing need for a system that can obtain appropriate information without much effort while individual responses according to the age, interests, and development status of children are required. In addition, there is also a shortage of educational opportunities to improve parents' childcare skills.

Means for Solving the Problems

[0005] This invention provides a system that generates customized educational content using a generative AI based on information about children obtained from users, and displays it on the user's device. This system incorporates a conversational AI that enables interaction, supporting two-way learning with children. Furthermore, by collecting and analyzing user feedback, more accurate personalization becomes possible. In addition, personalized online courses and training programs are provided to improve parents' parenting skills. This allows for efficient parenting support tailored to individual needs.

[0006] A "user" refers to someone who uses the system to receive parenting advice and educational content.

[0007] "Child information" refers to attribute information such as the child's age, interests, developmental stage, and family background.

[0008] "Generative AI" refers to artificial intelligence technology that automatically generates educational content and advice based on input data.

[0009] "Educational content" refers to teaching materials, information, and activities provided to support children's learning and development.

[0010] "User terminal" refers to a device (e.g., smartphone or tablet) used by a user to receive and interact with educational content.

[0011] "Conversational AI" refers to voice or text-based dialogue systems that enable natural language interaction with children.

[0012] "Feedback" refers to the evaluations and opinions that users give regarding the content and advice they receive.

[0013] An "online course" refers to an educational program delivered via the internet that aims to improve parents' childcare skills.

[0014] A "training program" refers to a series of learning activities and exercises designed to improve parents' specific parenting skills. [Brief explanation of the drawing]

[0015] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiment for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system that begins with the user inputting information about their child, and then provides content and advice generated based on that information.

[0037] First, the user enters basic information about their child via their device, namely their age, interests, developmental stage, and details about specific childcare needs. This information is immediately sent to the server.

[0038] The server analyzes the received information and generates optimal educational content by referring to a database. Generative AI is used here to create individualized learning plans tailored to age and interests. For example, a 3-year-old child interested in animals would be provided with an interactive game where they can learn animal names and characteristics. The server then sends this generated content back to the device.

[0039] The device receives instructions from the server, displays generated content on the screen, and allows children to interact with it. In particular, a conversational AI handles the interaction, asking children questions and receiving answers via voice. For example, the device might display a quiz such as, "What sounds do lions make?" and provide feedback based on the child's answer.

[0040] Furthermore, with the aim of providing ongoing education for parents, the server offers users online courses and training programs to improve their parenting skills. These are customized based on parents' needs, focusing on specific aspects. Users can access these educational resources through their devices and learn at home.

[0041] Finally, users input feedback on their experience and the content they received through the system, which is then sent back to the server. The server analyzes this feedback and incorporates it into future content generation, providing a more personalized service. This creates a system where optimal parenting support is continuously provided to the user.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses their device to enter information about their child's age, interests, developmental stage, and specific parenting needs. This information is sent to the server via a secure protocol.

[0045] Step 2:

[0046] The server stores the received information in a database and simultaneously begins analysis. During the analysis, it prepares to generate optimal educational content using AI based on the input information.

[0047] Step 3:

[0048] The server consults a database to determine appropriate educational content. For example, it selects videos, games, or quizzes suitable for a specified age and interests.

[0049] Step 4:

[0050] The server uses generative AI to customize selected educational content, providing children with a more personalized learning experience.

[0051] Step 5:

[0052] The server sends customized content to the terminal, along with the necessary interface configuration information.

[0053] Step 6:

[0054] The device displays the received educational content on the user screen. The interface is optimized for ease of use by children.

[0055] Step 7:

[0056] The device enables conversational AI and initiates two-way interaction with the child. For example, the device uses voice recognition to ask quizzes, understand the child's answers, and provide follow-up support.

[0057] Step 8:

[0058] Users submit feedback on the provided content and learning experience through their devices. This feedback is sent to the server and used to improve future content.

[0059] Step 9:

[0060] The server analyzes the feedback and adjusts the next generation process based on the results. This enables continuous improvement to provide a better user experience.

[0061] (Example 1)

[0062] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0063] Traditional educational methods face the challenge of providing individualized education that addresses children's diverse interests and developmental stages. Furthermore, parents often require significant time and effort to acquire effective parenting skills. In this context, there is a need for systems that utilize technology to enrich children's learning experiences and reduce the burden on parents.

[0064] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0065] In this invention, the server includes means for collecting child attribute information obtained from the user, means for analyzing the collected attribute information and generating personalized educational materials using a generation AI algorithm, and means for transmitting the generated educational materials to the user's device and controlling their display. This provides users with personalized educational content, enabling appropriate education tailored to the child's learning needs. Furthermore, it provides parents with the opportunity to improve their parenting skills through online educational programs, enabling them to gain confidence and improve their parenting skills.

[0066] A "user" refers to a person who uses this system, inputting the child's attribute information and utilizing the generated educational materials and online educational programs.

[0067] "Attribute information" refers to a collection of basic information about a child provided by the user, such as the child's age, interests, developmental stage, and parenting needs.

[0068] A "generative AI algorithm" is one of the artificial intelligence techniques used to generate personalized educational materials based on analyzed attribute information.

[0069] "Educational materials" are personalized learning content created by generative AI algorithms, designed to enhance children's learning experiences.

[0070] "User equipment" refers to electronic devices used by users to view and manipulate educational materials, and includes terminals and personal computers.

[0071] "Evaluation information" refers to feedback provided by users about their results and experiences using the system, and is used to improve the service.

[0072] An "educational program" is a series of learning materials and training sessions provided online with the aim of improving parents' childcare skills.

[0073] This invention is a comprehensive educational support system that provides personalized educational materials for children and improves parents' parenting skills. Users input attribute information about their children using a terminal. This information includes the child's age, interests, developmental stage, and specific parenting needs. This information is transmitted to a server via the internet.

[0074] The server analyzes attribute information and creates personalized educational materials using a generative AI algorithm. For example, if a 3-year-old child is interested in animals, interactive materials will be generated that allow them to learn the names and characteristics of animals. This generative AI algorithm might use a text generation AI model, for instance. At this stage, a specific instruction is input to the generative AI as a prompt: "Generate materials suitable for a 3-year-old child who is interested in animals."

[0075] The generated educational materials are sent from the server to the device, which displays them on the screen. The device incorporates voice recognition AI to allow children to interact with the educational materials. For example, the device can ask questions aloud, such as "What sounds do lions make?" and receive the child's answer.

[0076] Furthermore, the server provides online educational programs optimized for parents. Parents can access these programs through their devices to improve their parenting skills. The programs offered are personalized based on the parents' needs and interests and include online courses and training sessions.

[0077] Users send feedback about their experience using the system from their device to the server. The server analyzes this feedback and uses it to generate future educational materials and improve the service. This allows the system to be continuously optimized and provide more beneficial educational support to users.

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

[0079] Step 1:

[0080] The user enters attribute information about their child using a device. This information includes age, interests, developmental stage, and childcare needs. This input data is formatted in the appropriate data format on the device and sent to the server via a secure communication protocol.

[0081] Step 2:

[0082] The server analyzes attribute information received from the user. The input here is attribute information, and by matching it with the database, it determines which educational materials are most suitable. Based on the analyzed information, it constructs prompt messages to send to the generating AI model. These prompt messages include instructions tailored to the child's age and interests.

[0083] Step 3:

[0084] The server processes prompts using a generative AI model to generate personalized educational materials. This model generates optimal content based on analyzed attribute information. For example, if the model is given the prompt, "Create learning materials for a 3-year-old child who loves animals," it will output interactive games and quiz-style materials.

[0085] Step 4:

[0086] The server sends the generated educational materials to the device. The device receives them and displays them on the screen in a format that children can interact with. Here, the format of the educational materials often includes interactive graphics and audio guides, and is designed for children to directly touch and try out.

[0087] Step 5:

[0088] The device uses conversational AI to facilitate interaction with educational materials. Leveraging voice recognition, it provides feedback as children answer questions or make choices within games. For example, the device might ask a quiz question like, "What sounds do lions make?" and then provide feedback on the child's answer through voice and animation.

[0089] Step 6:

[0090] Users input feedback about the educational materials they have experienced and send it from their device to the server. The server receives this feedback and analyzes its content. The analysis results are used to create future educational materials and improve the service, further increasing the degree of personalization of the system.

[0091] Step 7:

[0092] The server generates and provides online educational programs for parents. Input consists of feedback and evaluation information regarding parents' childcare needs. The program is optimized based on this information, and users can receive training to improve their skills through their devices.

[0093] (Application Example 1)

[0094] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0095] In today's educational environment, there is a demand for educational content tailored to individual needs. However, there is a lack of means to adapt such content to the interests and personalities of the learners, and to effectively promote learning through experiences in augmented reality environments. Conventional systems lack sufficient effective interaction and real-time feedback functions using conversational AI, making it difficult to sustain the learners' interest.

[0096] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0097] In this invention, the server includes means for collecting information on the target of training obtained from the user, means for generating customized educational content using generative AI based on the collected information, and means equipped with conversational AI that enables interaction with the target of training. This makes it possible to generate individualized educational content tailored to the target of training and promote learning while maintaining interest through interaction in an augmented reality environment.

[0098] "Target for development" refers to individuals or animals that are the subject of information collection and education, generally meaning children.

[0099] "Generative AI" refers to artificial intelligence technology that automatically generates optimized content based on collected information.

[0100] "Educational content" is a general term for content that includes teaching materials and activities designed to promote the learning of the target audience.

[0101] A "display device" refers to an electronic device used to visually present generated educational content.

[0102] "Conversational AI" refers to artificial intelligence technology that uses natural language to interact with users or those being trained.

[0103] An "augmented reality environment" refers to a technology that combines the real world with digital information to present it visually.

[0104] A "trainer" refers to a person who provides education and guidance to those being trained.

[0105] An "individualized program" refers to an educational or training plan specifically designed to suit the characteristics and needs of the individuals being trained.

[0106] The system that realizes this invention consists of a user terminal for inputting information, a server for processing data, and a display device for displaying the generated content.

[0107] The user inputs basic information about the person being trained into the device and sends data about their age, interests, and developmental stage to the server. The server uses the received information to customize the generated educational content using a generative AI model (e.g., OpenAI's GPT series). The content is then processed into data for display in an augmented reality environment, tailored to the learning style of the person being trained.

[0108] The server uses generative AI to generate educational content tailored to the target audience. This content is designed for visual learning on a display device and can include, for example, 3D models and audio guides that are adapted to the audience's interests and learning speed. Furthermore, it uses conversational AI to enable real-time interaction and provide feedback based on the audience's responses.

[0109] For example, the server might use a prompt message like, "A 5-year-old child is interested in ants. Please generate interactive content that will teach them about ant ecology," to provide content where the child can learn about ant ecology while observing a 3D model of an ant. As the child answers questions about ants, the conversational AI asks follow-up questions to deepen their learning through interaction.

[0110] Furthermore, the server analyzes user feedback and incorporates it into future content creation. This makes it possible to continuously provide a more personalized educational environment for those being trained.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user enters basic information about the child to be nurtured (age, interests, developmental stage) into the device and sends the data to the server. This input data includes information in text format, and the server prepares to receive and analyze it.

[0114] Step 2:

[0115] The server calls a generation AI model based on the received information about the target being trained, and forms a prompt message. This prompt message is generated in a format such as, "A target being ○ years old is interested in ○○. Please generate content that can be learned about ○○." The server sends this prompt message to the generation AI model, issuing a content generation request.

[0116] Step 3:

[0117] The generation AI model generates educational content according to the prompt text. The generated content is output in a format optimized for the target audience. Specifically, the educational content includes text, images, 3D models, and audio files, and this data is returned to the server.

[0118] Step 4:

[0119] The server formats the generated educational content into a format suitable for the display device and sends it to the terminal. During this process, the data is processed to support display in an augmented reality environment. Specifically, this includes enabling 3D models and adding interactivity, such as conversational AI.

[0120] Step 5:

[0121] The device presents the received educational content to the learner. The learner progresses through visual and auditory interactions. The device continuously interacts with the learner via conversational AI and provides real-time feedback.

[0122] Step 6:

[0123] Users observe their reactions to the content they are being trained on, input feedback into their devices, and send that feedback to the server. The server analyzes this feedback and uses it to create future content, providing a more effective and personalized educational experience.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention features a system that begins with a user inputting information about their child and then provides generated content and advice. The system integrates an emotion engine to enhance interaction based on the emotional states of both the user and the child.

[0126] First, the user uses their device to enter basic information about their child. This information includes age, interests, and learning style. This initial input data is sent to the server, and the data analysis process begins.

[0127] The server analyzes the input data and prepares to generate optimal educational content by referencing the database. Generative AI is used to first select content, and then the generative AI personalizes it. For example, for a 6-year-old child interested in space, it can create an interactive game that allows them to experience a space exploration mission.

[0128] Devices equipped with an emotion engine recognize the emotions of users and children in real time. This engine analyzes voice and facial expression data to understand the user's emotional state and adjusts feedback and content accordingly. For example, if a child becomes excited while playing, the engine can detect this and adjust the game's difficulty level appropriately.

[0129] Furthermore, online courses and training programs are provided via the server to improve parents' parenting skills. These programs are personalized by an emotion engine, with content tailored based on the parents' stress levels and emotional needs. Users can access these educational resources to deepen their knowledge of parenting.

[0130] Finally, feedback from the user's experience is sent to the server, where the emotion engine analyzes it and incorporates it into future content creation and delivery. This entire process ensures that users receive optimal parenting support.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses their device to enter basic information such as the child's age, interests, and learning style. This information is sent to the server via a secure communication protocol.

[0134] Step 2:

[0135] The server analyzes the received information about the child and selects appropriate educational content by referring to a database. Based on this data, the generating AI creates a customized learning plan tailored to each individual child.

[0136] Step 3:

[0137] The server personalizes the generated content and sends it to the user's device. This includes guidelines for interface adjustments by the emotion engine.

[0138] Step 4:

[0139] The device drives an emotion engine that monitors the emotional state of the user and child in real time. It analyzes voice and facial expressions to determine the current emotional state.

[0140] Step 5:

[0141] The device dynamically adjusts the level of educational content and feedback based on acquired emotional information. For example, if a child finds a game difficult, the game's difficulty level is lowered based on the emotional data.

[0142] Step 6:

[0143] Users observe their interactions with their children through the learning program. Based on the feedback provided by the emotion engine, parents can also adapt their responses.

[0144] Step 7:

[0145] The device receives user feedback as input and sends it to the server. This feedback includes opinions on content quality and the learning process.

[0146] Step 8:

[0147] The server analyzes the received feedback and sentiment data. It then makes changes to improve future content generation and processes, continuously enhancing the user experience.

[0148] (Example 2)

[0149] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0150] In the modern education system, there is a problem in providing personalized educational experiences that fully take into account each child's individual characteristics and emotional state. Furthermore, there is a lack of adequate support to effectively improve parents' parenting skills. As a result, children's learning efficiency and parents' sense of security regarding child-rearing are not adequately achieved, which poses a challenge.

[0151] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0152] In this invention, the server includes means for acquiring attribute information of children obtained from users, means for analyzing the acquired information and generating customized educational information using a generative AI model, and means for transmitting and displaying the generated information on a user device. This makes it possible to provide educational experiences tailored to the individual characteristics of each child and to effectively support the improvement of parents' parenting skills.

[0153] A "user" refers to an entity that uses the system to input information about their child and receives the generated educational information and childcare support.

[0154] A "server" refers to a device that receives information from users, analyzes it, generates customized educational information using a generative AI model, and transmits it to the user's device.

[0155] "Attribute information" refers to personal information necessary for personalizing educational information, such as a child's age, interests, and learning style.

[0156] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized educational information for individual children based on the input information.

[0157] "Educational information" refers to personalized content designed to help children improve their knowledge and skills through learning and activities.

[0158] A "user device" refers to an electronic terminal that, when accessed by a user, displays generated educational information and feedback from the system.

[0159] "Emotion analysis" refers to a technology that analyzes data such as the voice and facial expressions of users and children to detect their emotional state in real time.

[0160] "Feedback" refers to the opinions and information that users provide about their experience using the system, and this feedback is used to improve the system.

[0161] "Nurturing ability" refers to the knowledge and skills that parents possess to effectively support their children's growth and development.

[0162] "Online learning" refers to learning programs and content provided via the internet, representing a format that allows for education regardless of location or time.

[0163] This system is initiated when the user enters their child's attribute information using a terminal. This information includes details such as the child's age, interests, and learning style. This attribute information is transmitted to the server via a communication network.

[0164] The server is equipped with a high-performance processor and database to analyze the received information. A generative AI model is used for the analysis, processing the input data based on a specific algorithm. This model receives prompts and creates educational information tailored to the user's needs. For example, it might use the prompt, "Create a space adventure story that a 6-year-old child can enjoy."

[0165] Next, the generated educational information is sent to the user's device and displayed. The user's device is typically a mobile device such as a tablet or smartphone, allowing for a visual and interactive experience of the content.

[0166] Furthermore, the device incorporates an emotion analysis engine. This engine uses the camera and microphone to analyze the child's facial expressions and vocalizations in real time, detecting their emotional state. Based on the results obtained, the engine can adjust the content and difficulty level of educational information. This allows for adjustments such as increasing the difficulty of a game if the child is excited.

[0167] In addition, the server provides online learning programs aimed at improving parents' parenting skills. These programs are personalized by generative AI models, with content customized according to the parents' current emotional state and needs. As a result, parents can learn to improve their parenting skills regardless of their location.

[0168] As described above, this system provides personalized educational information for children and a range of features to support parents in childcare. This enables an educational experience tailored to each child and effective childcare support for parents.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user uses a device to input attribute information about their child. This attribute information includes the child's age, interests, and learning style. The device converts the input data into a digital format and sends it to the server via a communication network. This input data forms the basis for subsequent analysis and content generation on the server.

[0172] Step 2:

[0173] The server analyzes the received attribute information. This analysis involves issuing queries to the database and matching it with existing records that have similar attribute information. As a result of the analysis, the criteria necessary for selecting educational information tailored to the child's characteristics are extracted. This is then passed on as output data from the server to the next content generation stage.

[0174] Step 3:

[0175] The server uses a generative AI model to generate customized educational information based on analyzed conditions. It provides the AI ​​model with prompts to construct specific learning content. These prompts are specific, such as "Create a space adventure story that a 6-year-old child can enjoy." The AI ​​model responds to this request by creating personalized educational content and generating a digital file. The output educational information is then ready for transmission and moves on to the next step.

[0176] Step 4:

[0177] The server transmits the generated educational information to the user's device. Using a communication protocol, the data is transferred and prepared for real-time display on the user's terminal. The terminal interprets the received data appropriately and displays it to the user in an interactive format. The output of this step is a visual presentation of educational information on the terminal.

[0178] Step 5:

[0179] An emotion analysis engine built into the device analyzes the child's facial expressions and voice in real time. Using input data from the camera and microphone, it executes a specific algorithm to analyze the child's emotional state. Based on the results, it adjusts the content and difficulty level of educational information to optimize the quality of interaction. This analysis result becomes the output for dynamically optimizing the presentation of educational information on the device.

[0180] Step 6:

[0181] Users input feedback to the system via their devices. This feedback includes reactions to children's educational information and parents' experience evaluations. The devices send the input feedback data to a server. The server analyzes the received data and aggregates the feedback as output to help generate future content.

[0182] Step 7:

[0183] The server builds and delivers personalized online learning programs to enhance parents' parenting abilities. Based on parental feedback and emotional states, it uses a generative AI model to customize learning content. As a result, it outputs a specific learning plan for parents to improve their parenting skills. This learning plan is provided in a format that parents can access via their devices.

[0184] (Application Example 2)

[0185] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0186] In today's educational environment, there is a challenge in providing educational content that is not adequately tailored to the individual interests and learning styles of each child. Furthermore, there is a lack of support for home-based education due to insufficient improvement in parents' childcare skills and inadequate product recommendations in virtual environments. Given these challenges, there is a need for a system that provides child-optimized content while simultaneously strengthening childcare support for parents.

[0187] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0188] In this invention, the server includes means for collecting information about children obtained from users, means for generating customized educational content using a generative AI based on the collected information, and means for transmitting and displaying the generated content on the user's terminal. This makes it possible to provide personalized educational content that is tailored to the child.

[0189] "Means for collecting information about children" refers to devices or methods for acquiring individual information about children, such as their age, interests, and learning style.

[0190] "Generative AI" is an artificial intelligence technology that generates optimal content based on input data.

[0191] "Educational content" refers to teaching materials and programs designed to stimulate and support children's learning and interests.

[0192] A "user terminal" is a device that a user can use to receive and interact with content.

[0193] "Conversational AI" refers to artificial intelligence that can interact with people through voice and text, enabling that interaction.

[0194] "Means for collecting and analyzing feedback" refers to methods or systems for gathering user experiences and opinions, analyzing them, and reflecting them in future content creation.

[0195] "A means of analyzing emotions in real time and optimizing educational content" refers to a technology that uses voice and facial expression data to understand the user's emotional state and adjusts the content provided accordingly.

[0196] "A means of dynamically proposing products in a virtual environment" refers to a method of proposing products and services in a virtual space in real time, based on the user's interests and emotions.

[0197] To realize this application, a system should be used in which a server integrates a generative AI model and emotion recognition technology to provide interactive educational content to the user's device. The server receives information about the child obtained from the user and stores it in a database. This includes information such as the child's age, interests, and learning style. This information is input into the generative AI model and serves as the basic data for generating optimal educational content.

[0198] The generated educational content is sent to the user's smartphone or tablet. The device is equipped with conversational AI, enabling interaction through dialogue with the child. Through this dialogue, the educational content is optimized according to the child's interests and responses.

[0199] Furthermore, the server utilizes emotion recognition technology to analyze the user's and child's emotions in real time through cameras and microphones. Emotional data is processed using libraries such as OpenCV and DeepFace. This allows for dynamic adjustments to the difficulty and type of educational content, ensuring children remain engaged.

[0200] As a concrete example, consider a 6-year-old child who is interested in space. Based on this information, a generative AI model can build an interactive game in which the child can experience a space exploration mission. Emotion recognition technology can determine whether the child is excited or bored with the game and adjust the game's difficulty accordingly.

[0201] An example of a prompt is, "My 6-year-old child is interested in space. Please suggest content to deepen his understanding." This prompt allows the generative AI model to create optimized educational content, which is then delivered appropriately by the server.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user uses a device to input information such as the child's age, interests, and learning style. The entered data is sent from the device to the server. The server then stores the information in a database, preparing for subsequent processing.

[0205] Step 2:

[0206] Based on the collected information, the server creates a prompt for the generating AI model and sends it a prompt such as, "Please suggest the best educational content for a 6-year-old child who is interested in space." The generating AI model then uses this prompt to run its algorithm and generate the most suitable educational content. As output, an interactive space exploration mission game or similar is created.

[0207] Step 3:

[0208] The server sends the generated educational content to the user's device. The device receives this content and displays it in the user interface. The user and child can then experience the content on the device.

[0209] Step 4:

[0210] The conversational AI installed in the device initiates a dialogue with the child experiencing the content. The input information obtained here includes the child's voice and facial expressions. The conversational AI analyzes this data in real time and adjusts the interaction according to the child's responses.

[0211] Step 5:

[0212] The server analyzes real-time audio and facial expression data acquired from the terminal using emotion recognition technology. Based on the data obtained as input, it uses libraries such as OpenCV and DeepFace to determine the child's emotional state. The output provides indicators of the child's excitement level and satisfaction level.

[0213] Step 6:

[0214] Based on the emotion recognition results, the server dynamically adjusts the difficulty level and presentation format of the educational content. For example, if the server detects signs that the child is getting bored, it will change the settings, such as altering the game's storyline, to keep the child interested.

[0215] Step 7:

[0216] Ultimately, users provide feedback and send it to the server via their device. The server analyzes this feedback and uses it to improve future content creation. This information is stored in a database and contributes to improving the overall system's performance.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] This invention is a system that begins with the user inputting information about their child, and then provides content and advice generated based on that information.

[0234] First, the user enters basic information about their child via their device, namely their age, interests, developmental stage, and details about specific childcare needs. This information is immediately sent to the server.

[0235] The server analyzes the received information and generates optimal educational content by referring to a database. Generative AI is used here to create individualized learning plans tailored to age and interests. For example, a 3-year-old child interested in animals would be provided with an interactive game where they can learn animal names and characteristics. The server then sends this generated content back to the device.

[0236] The device receives instructions from the server, displays generated content on the screen, and allows children to interact with it. In particular, a conversational AI handles the interaction, asking children questions and receiving answers via voice. For example, the device might display a quiz such as, "What sounds do lions make?" and provide feedback based on the child's answer.

[0237] Furthermore, with the aim of providing ongoing education for parents, the server offers users online courses and training programs to improve their parenting skills. These are customized based on parents' needs, focusing on specific aspects. Users can access these educational resources through their devices and learn at home.

[0238] Finally, users input feedback on their experience and the content they received through the system, which is then sent back to the server. The server analyzes this feedback and incorporates it into future content generation, providing a more personalized service. This creates a system where optimal parenting support is continuously provided to the user.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user uses their device to enter information about their child's age, interests, developmental stage, and specific parenting needs. This information is sent to the server via a secure protocol.

[0242] Step 2:

[0243] The server stores the received information in a database and simultaneously begins analysis. During the analysis, it prepares to generate optimal educational content using AI based on the input information.

[0244] Step 3:

[0245] The server consults a database to determine appropriate educational content. For example, it selects videos, games, or quizzes suitable for a specified age and interests.

[0246] Step 4:

[0247] The server uses generative AI to customize selected educational content, providing children with a more personalized learning experience.

[0248] Step 5:

[0249] The server sends customized content to the terminal, along with the necessary interface configuration information.

[0250] Step 6:

[0251] The device displays the received educational content on the user screen. The interface is optimized for ease of use by children.

[0252] Step 7:

[0253] The device enables conversational AI and initiates two-way interaction with the child. For example, the device uses voice recognition to ask quizzes, understand the child's answers, and provide follow-up support.

[0254] Step 8:

[0255] Users submit feedback on the provided content and learning experience through their devices. This feedback is sent to the server and used to improve future content.

[0256] Step 9:

[0257] The server analyzes the feedback and adjusts the next generation process based on the results. This enables continuous improvement to provide a better user experience.

[0258] (Example 1)

[0259] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] Traditional educational methods face the challenge of providing individualized education that addresses children's diverse interests and developmental stages. Furthermore, parents often require significant time and effort to acquire effective parenting skills. In this context, there is a need for systems that utilize technology to enrich children's learning experiences and reduce the burden on parents.

[0261] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0262] In this invention, the server includes means for collecting child attribute information obtained from the user, means for analyzing the collected attribute information and generating personalized educational materials using a generation AI algorithm, and means for transmitting the generated educational materials to the user's device and controlling their display. This provides users with personalized educational content, enabling appropriate education tailored to the child's learning needs. Furthermore, it provides parents with the opportunity to improve their parenting skills through online educational programs, enabling them to gain confidence and improve their parenting skills.

[0263] A "user" refers to a person who uses this system, inputting the child's attribute information and utilizing the generated educational materials and online educational programs.

[0264] "Attribute information" refers to a collection of basic information about a child provided by the user, such as the child's age, interests, developmental stage, and parenting needs.

[0265] A "generative AI algorithm" is one of the artificial intelligence techniques used to generate personalized educational materials based on analyzed attribute information.

[0266] "Educational materials" are personalized learning content created by generative AI algorithms, designed to enhance children's learning experiences.

[0267] "User equipment" refers to electronic devices used by users to view and manipulate educational materials, and includes terminals and personal computers.

[0268] "Evaluation information" refers to feedback provided by users about their results and experiences using the system, and is used to improve the service.

[0269] An "educational program" is a series of learning materials and training sessions provided online with the aim of improving parents' childcare skills.

[0270] This invention is a comprehensive educational support system that provides personalized educational materials for children and improves parents' parenting skills. Users input attribute information about their children using a terminal. This information includes the child's age, interests, developmental stage, and specific parenting needs. This information is transmitted to a server via the internet.

[0271] The server analyzes attribute information and creates personalized educational materials using a generative AI algorithm. For example, if a 3-year-old child is interested in animals, interactive materials will be generated that allow them to learn the names and characteristics of animals. This generative AI algorithm might use a text generation AI model, for instance. At this stage, a specific instruction is input to the generative AI as a prompt: "Generate materials suitable for a 3-year-old child who is interested in animals."

[0272] The generated educational materials are sent from the server to the device, which displays them on the screen. The device incorporates voice recognition AI to allow children to interact with the educational materials. For example, the device can ask questions aloud, such as "What sounds do lions make?" and receive the child's answer.

[0273] Furthermore, the server provides online educational programs optimized for parents. Parents can access these programs through their devices to improve their parenting skills. The programs offered are personalized based on the parents' needs and interests and include online courses and training sessions.

[0274] Users send feedback about their experience using the system from their device to the server. The server analyzes this feedback and uses it to generate future educational materials and improve the service. This allows the system to be continuously optimized and provide more beneficial educational support to users.

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

[0276] Step 1:

[0277] The user uses the terminal to input the child's attribute information. This information includes age, interests, developmental status, parenting needs, etc. These input data are formatted into an appropriate data format on the terminal and sent to the server via a secure communication protocol.

[0278] Step 2:

[0279] The server analyzes the attribute information received from the user. The input here is the attribute information, and by comparing it with the database, it determines what kind of educational materials are optimal. Based on the analyzed information, a prompt sentence to be sent to the generative AI model is constructed. This prompt sentence includes instructions according to the child's age and interests.

[0280] Step 3:

[0281] The server uses the generative AI model to process the prompt sentence and generate individualized educational materials. This model generates optimal content based on the analyzed attribute information. For example, by giving the model a prompt like "Create learning materials for a 3-year-old child who likes animals", an interactive game or quiz-style teaching material is obtained as the output.

[0282] Step 4:

[0283] The server sends the generated educational materials to the terminal. The terminal receives this and displays it on the screen in a form that can be operated by the child. Here, the form of the educational materials often includes interactive graphics and voice guides and is designed to be directly touched and tried by the child.

[0284] Step 5:

[0285] The terminal executes interactions on educational materials using a conversational AI. By leveraging the speech recognition function, when a child answers a question or makes a selection within a game, corresponding feedback is provided. For example, a quiz such as "What sound does a lion make?" is presented from the terminal, and feedback on the child's answer is returned in the form of speech or animation.

[0286] Step 6:

[0287] The user inputs feedback on the educational materials experienced and sends it from the terminal to the server. The server receives this feedback and analyzes its content. The analysis results are utilized for generating subsequent educational materials and improving the service, further enhancing the degree of personalization of the system.

[0288] Step 7:

[0289] The server generates and provides an online education program for parents. The input is feedback and evaluation information regarding the parenting needs of parents. The program is optimized based on this, and users can receive training for skill improvement through the terminal.

[0290] (Application Example 1)

[0291] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0292] In the modern cultivation environment, there is a demand for providing educational content tailored to individual cultivation targets. However, there is a lack of means to adapt such content to the interests and personalities of the cultivation targets and effectively promote learning through experiences in an extended reality environment. In conventional systems, due to insufficient effective interactions and real-time feedback functions using conversational AI, it is difficult to sustain the interests of the cultivation targets.

[0293] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0294] In this invention, the server includes means for collecting information on the target of training obtained from the user, means for generating customized educational content using generative AI based on the collected information, and means equipped with conversational AI that enables interaction with the target of training. This makes it possible to generate individualized educational content tailored to the target of training and promote learning while maintaining interest through interaction in an augmented reality environment.

[0295] "Target for development" refers to individuals or animals that are the subject of information collection and education, generally meaning children.

[0296] "Generative AI" refers to artificial intelligence technology that automatically generates optimized content based on collected information.

[0297] "Educational content" is a general term for content that includes teaching materials and activities designed to promote the learning of the target audience.

[0298] A "display device" refers to an electronic device used to visually present generated educational content.

[0299] "Conversational AI" refers to artificial intelligence technology that uses natural language to interact with users or those being trained.

[0300] An "augmented reality environment" refers to a technology that combines the real world with digital information to present it visually.

[0301] A "trainer" refers to a person who provides education and guidance to those being trained.

[0302] An "individualized program" refers to an educational or training plan specifically designed to suit the characteristics and needs of the individuals being trained.

[0303] The system for realizing this invention consists of a user terminal for inputting information, a server for data processing, and a display device for displaying the generated content.

[0304] The user inputs the basic information of the training target into the terminal and transmits data on age, interests, and development status to the server. The server utilizes the generated AI model (e.g., OpenAI's GPT series) based on the received information to customize the generated educational content. The content is processed as data for display in an extended reality environment according to the learning style of the training target.

[0305] The server generates educational content suitable for the training target using generative AI. This content is for the purpose of visual learning on the display device and can include, for example, 3D models and voice guides according to the interests and learning speed of the training target. Furthermore, interactive AI is used to enable real-time interaction and provide feedback based on the responses of the training target.

[0306] For example, when the server utilizes a prompt sentence such as "A 5-year-old child is interested in ants. Please generate interactive content for learning about the ecology of ants.", it provides content that allows the child to learn about the ecology while observing a 3D model of an ant. When the training target answers questions about ants, the interactive AI asks follow-up questions to conduct an interaction that deepens learning.

[0307] Furthermore, the server analyzes the feedback provided by the user and reflects it in the next content generation. This makes it possible to continuously provide a more personalized educational environment for the training target.

[0308] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0309] Step 1:

[0310] The user enters basic information about the child to be nurtured (age, interests, developmental stage) into the device and sends the data to the server. This input data includes information in text format, and the server prepares to receive and analyze it.

[0311] Step 2:

[0312] The server calls a generation AI model based on the received information about the target being trained, and forms a prompt message. This prompt message is generated in a format such as, "A target being ○ years old is interested in ○○. Please generate content that can be learned about ○○." The server sends this prompt message to the generation AI model, issuing a content generation request.

[0313] Step 3:

[0314] The generation AI model generates educational content according to the prompt text. The generated content is output in a format optimized for the target audience. Specifically, the educational content includes text, images, 3D models, and audio files, and this data is returned to the server.

[0315] Step 4:

[0316] The server formats the generated educational content into a format suitable for the display device and sends it to the terminal. During this process, the data is processed to support display in an augmented reality environment. Specifically, this includes enabling 3D models and adding interactivity, such as conversational AI.

[0317] Step 5:

[0318] The device presents the received educational content to the learner. The learner progresses through visual and auditory interactions. The device continuously interacts with the learner via conversational AI and provides real-time feedback.

[0319] Step 6:

[0320] Users observe their reactions to the content they are being trained on, input feedback into their devices, and send that feedback to the server. The server analyzes this feedback and uses it to create future content, providing a more effective and personalized educational experience.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] This invention features a system that begins with a user inputting information about their child and then provides generated content and advice. The system integrates an emotion engine to enhance interaction based on the emotional states of both the user and the child.

[0323] First, the user uses their device to enter basic information about their child. This information includes age, interests, and learning style. This initial input data is sent to the server, and the data analysis process begins.

[0324] The server analyzes the input data and prepares to generate optimal educational content by referencing the database. Generative AI is used to first select content, and then the generative AI personalizes it. For example, for a 6-year-old child interested in space, it can create an interactive game that allows them to experience a space exploration mission.

[0325] Devices equipped with an emotion engine recognize the emotions of users and children in real time. This engine analyzes voice and facial expression data to understand the user's emotional state and adjusts feedback and content accordingly. For example, if a child becomes excited while playing, the engine can detect this and adjust the game's difficulty level appropriately.

[0326] Furthermore, online courses and training programs are provided via the server to improve parents' parenting skills. These programs are personalized by an emotion engine, with content tailored based on the parents' stress levels and emotional needs. Users can access these educational resources to deepen their knowledge of parenting.

[0327] Finally, feedback from the user's experience is sent to the server, where the emotion engine analyzes it and incorporates it into future content creation and delivery. This entire process ensures that users receive optimal parenting support.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] The user uses their device to enter basic information such as the child's age, interests, and learning style. This information is sent to the server via a secure communication protocol.

[0331] Step 2:

[0332] The server analyzes the received information about the child and selects appropriate educational content by referring to a database. Based on this data, the generating AI creates a customized learning plan tailored to each individual child.

[0333] Step 3:

[0334] The server personalizes the generated content and sends it to the user's device. This includes guidelines for interface adjustments by the emotion engine.

[0335] Step 4:

[0336] The device drives an emotion engine that monitors the emotional state of the user and child in real time. It analyzes voice and facial expressions to determine the current emotional state.

[0337] Step 5:

[0338] The device dynamically adjusts the level of educational content and feedback based on acquired emotional information. For example, if a child finds a game difficult, the game's difficulty level is lowered based on the emotional data.

[0339] Step 6:

[0340] Users observe their interactions with their children through the learning program. Based on the feedback provided by the emotion engine, parents can also adapt their responses.

[0341] Step 7:

[0342] The device receives user feedback as input and sends it to the server. This feedback includes opinions on content quality and the learning process.

[0343] Step 8:

[0344] The server analyzes the received feedback and sentiment data. It then makes changes to improve future content generation and processes, continuously enhancing the user experience.

[0345] (Example 2)

[0346] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0347] In the modern education system, there is a problem in providing personalized educational experiences that fully take into account each child's individual characteristics and emotional state. Furthermore, there is a lack of adequate support to effectively improve parents' parenting skills. As a result, children's learning efficiency and parents' sense of security regarding child-rearing are not adequately achieved, which poses a challenge.

[0348] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0349] In this invention, the server includes means for acquiring attribute information of children obtained from users, means for analyzing the acquired information and generating customized educational information using a generative AI model, and means for transmitting and displaying the generated information on a user device. This makes it possible to provide educational experiences tailored to the individual characteristics of each child and to effectively support the improvement of parents' parenting skills.

[0350] A "user" refers to an entity that uses the system to input information about their child and receives the generated educational information and childcare support.

[0351] A "server" refers to a device that receives information from users, analyzes it, generates customized educational information using a generative AI model, and transmits it to the user's device.

[0352] "Attribute information" refers to personal information necessary for personalizing educational information, such as a child's age, interests, and learning style.

[0353] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized educational information for individual children based on the input information.

[0354] "Educational information" refers to personalized content designed to help children improve their knowledge and skills through learning and activities.

[0355] A "user device" refers to an electronic terminal that, when accessed by a user, displays generated educational information and feedback from the system.

[0356] "Emotion analysis" refers to a technology that analyzes data such as the voice and facial expressions of users and children to detect their emotional state in real time.

[0357] "Feedback" refers to the opinions and information that users provide about their experience using the system, and this feedback is used to improve the system.

[0358] "Nurturing ability" refers to the knowledge and skills that parents possess to effectively support their children's growth and development.

[0359] "Online learning" refers to learning programs and content provided via the internet, representing a format that allows for education regardless of location or time.

[0360] This system is initiated when the user enters their child's attribute information using a terminal. This information includes details such as the child's age, interests, and learning style. This attribute information is transmitted to the server via a communication network.

[0361] The server is equipped with a high-performance processor and database to analyze the received information. A generative AI model is used for the analysis, processing the input data based on a specific algorithm. This model receives prompts and creates educational information tailored to the user's needs. For example, it might use the prompt, "Create a space adventure story that a 6-year-old child can enjoy."

[0362] Next, the generated educational information is sent to the user's device and displayed. The user's device is typically a mobile device such as a tablet or smartphone, allowing for a visual and interactive experience of the content.

[0363] Furthermore, the device incorporates an emotion analysis engine. This engine uses the camera and microphone to analyze the child's facial expressions and vocalizations in real time, detecting their emotional state. Based on the results obtained, the engine can adjust the content and difficulty level of educational information. This allows for adjustments such as increasing the difficulty of a game if the child is excited.

[0364] In addition, the server provides online learning programs aimed at improving parents' parenting skills. These programs are personalized by generative AI models, with content customized according to the parents' current emotional state and needs. As a result, parents can learn to improve their parenting skills regardless of their location.

[0365] As described above, this system provides personalized educational information for children and a range of features to support parents in childcare. This enables an educational experience tailored to each child and effective childcare support for parents.

[0366] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0367] Step 1:

[0368] The user uses a device to input attribute information about their child. This attribute information includes the child's age, interests, and learning style. The device converts the input data into a digital format and sends it to the server via a communication network. This input data forms the basis for subsequent analysis and content generation on the server.

[0369] Step 2:

[0370] The server analyzes the received attribute information. This analysis involves issuing queries to the database and matching it with existing records that have similar attribute information. As a result of the analysis, the criteria necessary for selecting educational information tailored to the child's characteristics are extracted. This is then passed on as output data from the server to the next content generation stage.

[0371] Step 3:

[0372] The server uses a generative AI model to generate customized educational information based on analyzed conditions. It provides the AI ​​model with prompts to construct specific learning content. These prompts are specific, such as "Create a space adventure story that a 6-year-old child can enjoy." The AI ​​model responds to this request by creating personalized educational content and generating a digital file. The output educational information is then ready for transmission and moves on to the next step.

[0373] Step 4:

[0374] The server transmits the generated educational information to the user's device. Using a communication protocol, the data is transferred and prepared for real-time display on the user's terminal. The terminal interprets the received data appropriately and displays it to the user in an interactive format. The output of this step is a visual presentation of educational information on the terminal.

[0375] Step 5:

[0376] An emotion analysis engine built into the device analyzes the child's facial expressions and voice in real time. Using input data from the camera and microphone, it executes a specific algorithm to analyze the child's emotional state. Based on the results, it adjusts the content and difficulty level of educational information to optimize the quality of interaction. This analysis result becomes the output for dynamically optimizing the presentation of educational information on the device.

[0377] Step 6:

[0378] Users input feedback to the system via their devices. This feedback includes reactions to children's educational information and parents' experience evaluations. The devices send the input feedback data to a server. The server analyzes the received data and aggregates the feedback as output to help generate future content.

[0379] Step 7:

[0380] The server builds and delivers personalized online learning programs to enhance parents' parenting abilities. Based on parental feedback and emotional states, it uses a generative AI model to customize learning content. As a result, it outputs a specific learning plan for parents to improve their parenting skills. This learning plan is provided in a format that parents can access via their devices.

[0381] (Application Example 2)

[0382] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0383] In today's educational environment, there is a challenge in providing educational content that is not adequately tailored to the individual interests and learning styles of each child. Furthermore, there is a lack of support for home-based education due to insufficient improvement in parents' childcare skills and inadequate product recommendations in virtual environments. Given these challenges, there is a need for a system that provides child-optimized content while simultaneously strengthening childcare support for parents.

[0384] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0385] In this invention, the server includes means for collecting information about children obtained from users, means for generating customized educational content using a generative AI based on the collected information, and means for transmitting and displaying the generated content on the user's terminal. This makes it possible to provide personalized educational content that is tailored to the child.

[0386] "Means for collecting information about children" refers to devices or methods for acquiring individual information about children, such as their age, interests, and learning style.

[0387] "Generative AI" is an artificial intelligence technology that generates optimal content based on input data.

[0388] "Educational content" refers to teaching materials and programs designed to stimulate and support children's learning and interests.

[0389] A "user terminal" is a device that a user can use to receive and interact with content.

[0390] "Conversational AI" refers to artificial intelligence that can interact with people through voice and text, enabling that interaction.

[0391] "Means for collecting and analyzing feedback" refers to methods or systems for gathering user experiences and opinions, analyzing them, and reflecting them in future content creation.

[0392] "A means of analyzing emotions in real time and optimizing educational content" refers to a technology that uses voice and facial expression data to understand the user's emotional state and adjusts the content provided accordingly.

[0393] "A means of dynamically proposing products in a virtual environment" refers to a method of proposing products and services in a virtual space in real time, based on the user's interests and emotions.

[0394] To realize this application, a system should be used in which a server integrates a generative AI model and emotion recognition technology to provide interactive educational content to the user's device. The server receives information about the child obtained from the user and stores it in a database. This includes information such as the child's age, interests, and learning style. This information is input into the generative AI model and serves as the basic data for generating optimal educational content.

[0395] The generated educational content is sent to the user's smartphone or tablet. The device is equipped with conversational AI, enabling interaction through dialogue with the child. Through this dialogue, the educational content is optimized according to the child's interests and responses.

[0396] Furthermore, the server utilizes emotion recognition technology to analyze the user's and child's emotions in real time through cameras and microphones. Emotional data is processed using libraries such as OpenCV and DeepFace. This allows for dynamic adjustments to the difficulty and type of educational content, ensuring children remain engaged.

[0397] As a concrete example, consider a 6-year-old child who is interested in space. Based on this information, a generative AI model can build an interactive game in which the child can experience a space exploration mission. Emotion recognition technology can determine whether the child is excited or bored with the game and adjust the game's difficulty accordingly.

[0398] An example of a prompt is, "My 6-year-old child is interested in space. Please suggest content to deepen his understanding." This prompt allows the generative AI model to create optimized educational content, which is then delivered appropriately by the server.

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The user uses a device to input information such as the child's age, interests, and learning style. The entered data is sent from the device to the server. The server then stores the information in a database, preparing for subsequent processing.

[0402] Step 2:

[0403] Based on the collected information, the server creates a prompt for the generating AI model and sends it a prompt such as, "Please suggest the best educational content for a 6-year-old child who is interested in space." The generating AI model then uses this prompt to run its algorithm and generate the most suitable educational content. As output, an interactive space exploration mission game or similar is created.

[0404] Step 3:

[0405] The server sends the generated educational content to the user's device. The device receives this content and displays it in the user interface. The user and child can then experience the content on the device.

[0406] Step 4:

[0407] The conversational AI installed in the device initiates a dialogue with the child experiencing the content. The input information obtained here includes the child's voice and facial expressions. The conversational AI analyzes this data in real time and adjusts the interaction according to the child's responses.

[0408] Step 5:

[0409] The server analyzes real-time audio and facial expression data acquired from the terminal using emotion recognition technology. Based on the data obtained as input, it uses libraries such as OpenCV and DeepFace to determine the child's emotional state. The output provides indicators of the child's excitement level and satisfaction level.

[0410] Step 6:

[0411] Based on the emotion recognition results, the server dynamically adjusts the difficulty level and presentation format of the educational content. For example, if the server detects signs that the child is getting bored, it will change the settings, such as altering the game's storyline, to keep the child interested.

[0412] Step 7:

[0413] Ultimately, users provide feedback and send it to the server via their device. The server analyzes this feedback and uses it to improve future content creation. This information is stored in a database and contributes to improving the overall system's performance.

[0414] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0415] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0416] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0417] [Third Embodiment]

[0418] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0419] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0420] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0421] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0422] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0423] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0424] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0425] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0426] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0427] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0428] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0429] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0430] This invention is a system that begins with the user inputting information about their child, and then provides content and advice generated based on that information.

[0431] First, the user enters basic information about their child via their device, namely their age, interests, developmental stage, and details about specific childcare needs. This information is immediately sent to the server.

[0432] The server analyzes the received information and generates optimal educational content by referring to a database. Generative AI is used here to create individualized learning plans tailored to age and interests. For example, a 3-year-old child interested in animals would be provided with an interactive game where they can learn animal names and characteristics. The server then sends this generated content back to the device.

[0433] The device receives instructions from the server, displays generated content on the screen, and allows children to interact with it. In particular, a conversational AI handles the interaction, asking children questions and receiving answers via voice. For example, the device might display a quiz such as, "What sounds do lions make?" and provide feedback based on the child's answer.

[0434] Furthermore, with the aim of providing ongoing education for parents, the server offers users online courses and training programs to improve their parenting skills. These are customized based on parents' needs, focusing on specific aspects. Users can access these educational resources through their devices and learn at home.

[0435] Finally, users input feedback on their experience and the content they received through the system, which is then sent back to the server. The server analyzes this feedback and incorporates it into future content generation, providing a more personalized service. This creates a system where optimal parenting support is continuously provided to the user.

[0436] The following describes the processing flow.

[0437] Step 1:

[0438] The user uses their device to enter information about their child's age, interests, developmental stage, and specific parenting needs. This information is sent to the server via a secure protocol.

[0439] Step 2:

[0440] The server stores the received information in a database and simultaneously begins analysis. During the analysis, it prepares to generate optimal educational content using AI based on the input information.

[0441] Step 3:

[0442] The server consults a database to determine appropriate educational content. For example, it selects videos, games, or quizzes suitable for a specified age and interests.

[0443] Step 4:

[0444] The server uses generative AI to customize selected educational content, providing children with a more personalized learning experience.

[0445] Step 5:

[0446] The server sends customized content to the terminal, along with the necessary interface configuration information.

[0447] Step 6:

[0448] The device displays the received educational content on the user screen. The interface is optimized for ease of use by children.

[0449] Step 7:

[0450] The device enables conversational AI and initiates two-way interaction with the child. For example, the device uses voice recognition to ask quizzes, understand the child's answers, and provide follow-up support.

[0451] Step 8:

[0452] Users submit feedback on the provided content and learning experience through their devices. This feedback is sent to the server and used to improve future content.

[0453] Step 9:

[0454] The server analyzes the feedback and adjusts the next generation process based on the results. This enables continuous improvement to provide a better user experience.

[0455] (Example 1)

[0456] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0457] Traditional educational methods face the challenge of providing individualized education that addresses children's diverse interests and developmental stages. Furthermore, parents often require significant time and effort to acquire effective parenting skills. In this context, there is a need for systems that utilize technology to enrich children's learning experiences and reduce the burden on parents.

[0458] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0459] In this invention, the server includes means for collecting child attribute information obtained from the user, means for analyzing the collected attribute information and generating personalized educational materials using a generation AI algorithm, and means for transmitting the generated educational materials to the user's device and controlling their display. This provides users with personalized educational content, enabling appropriate education tailored to the child's learning needs. Furthermore, it provides parents with the opportunity to improve their parenting skills through online educational programs, enabling them to gain confidence and improve their parenting skills.

[0460] A "user" refers to a person who uses this system, inputting the child's attribute information and utilizing the generated educational materials and online educational programs.

[0461] "Attribute information" refers to a collection of basic information about a child provided by the user, such as the child's age, interests, developmental stage, and parenting needs.

[0462] A "generative AI algorithm" is one of the artificial intelligence techniques used to generate personalized educational materials based on analyzed attribute information.

[0463] "Educational materials" are personalized learning content created by generative AI algorithms, designed to enhance children's learning experiences.

[0464] "User equipment" refers to electronic devices used by users to view and manipulate educational materials, and includes terminals and personal computers.

[0465] "Evaluation information" refers to feedback provided by users about their results and experiences using the system, and is used to improve the service.

[0466] An "educational program" is a series of learning materials and training sessions provided online with the aim of improving parents' childcare skills.

[0467] This invention is a comprehensive educational support system that provides personalized educational materials for children and improves parents' parenting skills. Users input attribute information about their children using a terminal. This information includes the child's age, interests, developmental stage, and specific parenting needs. This information is transmitted to a server via the internet.

[0468] The server analyzes attribute information and creates personalized educational materials using a generative AI algorithm. For example, if a 3-year-old child is interested in animals, interactive materials will be generated that allow them to learn the names and characteristics of animals. This generative AI algorithm might use a text generation AI model, for instance. At this stage, a specific instruction is input to the generative AI as a prompt: "Generate materials suitable for a 3-year-old child who is interested in animals."

[0469] The generated educational materials are sent from the server to the device, which displays them on the screen. The device incorporates voice recognition AI to allow children to interact with the educational materials. For example, the device can ask questions aloud, such as "What sounds do lions make?" and receive the child's answer.

[0470] Furthermore, the server provides online educational programs optimized for parents. Parents can access these programs through their devices to improve their parenting skills. The programs offered are personalized based on the parents' needs and interests and include online courses and training sessions.

[0471] Users send feedback about their experience using the system from their device to the server. The server analyzes this feedback and uses it to generate future educational materials and improve the service. This allows the system to be continuously optimized and provide more beneficial educational support to users.

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

[0473] Step 1:

[0474] The user enters attribute information about their child using a device. This information includes age, interests, developmental stage, and childcare needs. This input data is formatted in the appropriate data format on the device and sent to the server via a secure communication protocol.

[0475] Step 2:

[0476] The server analyzes attribute information received from the user. The input here is attribute information, and by matching it with the database, it determines which educational materials are most suitable. Based on the analyzed information, it constructs prompt messages to send to the generating AI model. These prompt messages include instructions tailored to the child's age and interests.

[0477] Step 3:

[0478] The server processes prompts using a generative AI model to generate personalized educational materials. This model generates optimal content based on analyzed attribute information. For example, if the model is given the prompt, "Create learning materials for a 3-year-old child who loves animals," it will output interactive games and quiz-style materials.

[0479] Step 4:

[0480] The server sends the generated educational materials to the device. The device receives them and displays them on the screen in a format that children can interact with. Here, the format of the educational materials often includes interactive graphics and audio guides, and is designed for children to directly touch and try out.

[0481] Step 5:

[0482] The device uses conversational AI to facilitate interaction with educational materials. Leveraging voice recognition, it provides feedback as children answer questions or make choices within games. For example, the device might ask a quiz question like, "What sounds do lions make?" and then provide feedback on the child's answer through voice and animation.

[0483] Step 6:

[0484] Users input feedback about the educational materials they have experienced and send it from their device to the server. The server receives this feedback and analyzes its content. The analysis results are used to create future educational materials and improve the service, further increasing the degree of personalization of the system.

[0485] Step 7:

[0486] The server generates and provides online educational programs for parents. Input consists of feedback and evaluation information regarding parents' childcare needs. The program is optimized based on this information, and users can receive training to improve their skills through their devices.

[0487] (Application Example 1)

[0488] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0489] In today's educational environment, there is a demand for educational content tailored to individual needs. However, there is a lack of means to adapt such content to the interests and personalities of the learners, and to effectively promote learning through experiences in augmented reality environments. Conventional systems lack sufficient effective interaction and real-time feedback functions using conversational AI, making it difficult to sustain the learners' interest.

[0490] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0491] In this invention, the server includes means for collecting information on the target of training obtained from the user, means for generating customized educational content using generative AI based on the collected information, and means equipped with conversational AI that enables interaction with the target of training. This makes it possible to generate individualized educational content tailored to the target of training and promote learning while maintaining interest through interaction in an augmented reality environment.

[0492] "Target for development" refers to individuals or animals that are the subject of information collection and education, generally meaning children.

[0493] "Generative AI" refers to artificial intelligence technology that automatically generates optimized content based on collected information.

[0494] "Educational content" is a general term for content that includes teaching materials and activities designed to promote the learning of the target audience.

[0495] A "display device" refers to an electronic device used to visually present generated educational content.

[0496] "Conversational AI" refers to artificial intelligence technology that uses natural language to interact with users or those being trained.

[0497] An "augmented reality environment" refers to a technology that combines the real world with digital information to present it visually.

[0498] A "trainer" refers to a person who provides education and guidance to those being trained.

[0499] An "individualized program" refers to an educational or training plan specifically designed to suit the characteristics and needs of the individuals being trained.

[0500] The system that realizes this invention consists of a user terminal for inputting information, a server for processing data, and a display device for displaying the generated content.

[0501] The user inputs basic information about the person being trained into the device and sends data about their age, interests, and developmental stage to the server. The server uses a generative AI model (e.g., OpenAI's GPT series) based on the received information to customize the generated educational content. The content is then processed into data for display in an augmented reality environment, tailored to the learning style of the person being trained.

[0502] The server uses generative AI to generate educational content tailored to the target audience. This content is designed for visual learning on a display device and can include, for example, 3D models and audio guides that are adapted to the audience's interests and learning speed. Furthermore, it uses conversational AI to enable real-time interaction and provide feedback based on the audience's responses.

[0503] For example, the server might use a prompt message like, "A 5-year-old child is interested in ants. Please generate interactive content that will teach them about ant ecology," to provide content where the child can learn about ant ecology while observing a 3D model of an ant. As the child answers questions about ants, the conversational AI asks follow-up questions to deepen their learning through interaction.

[0504] Furthermore, the server analyzes user feedback and incorporates it into future content creation. This makes it possible to continuously provide a more personalized educational environment for those being trained.

[0505] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0506] Step 1:

[0507] The user enters basic information about the child to be nurtured (age, interests, developmental stage) into the device and sends the data to the server. This input data includes information in text format, and the server prepares to receive and analyze it.

[0508] Step 2:

[0509] The server calls a generation AI model based on the received information about the target being trained, and forms a prompt message. This prompt message is generated in a format such as, "A target being ○ years old is interested in ○○. Please generate content that can be learned about ○○." The server sends this prompt message to the generation AI model, issuing a content generation request.

[0510] Step 3:

[0511] The generation AI model generates educational content according to the prompt text. The generated content is output in a format optimized for the target audience. Specifically, the educational content includes text, images, 3D models, and audio files, and this data is returned to the server.

[0512] Step 4:

[0513] The server formats the generated educational content into a format suitable for the display device and sends it to the terminal. During this process, the data is processed to support display in an augmented reality environment. Specifically, this includes enabling 3D models and adding interactivity, such as conversational AI.

[0514] Step 5:

[0515] The device presents the received educational content to the learner. The learner progresses through visual and auditory interactions. The device continuously interacts with the learner via conversational AI and provides real-time feedback.

[0516] Step 6:

[0517] Users observe their reactions to the content they are being trained on, input feedback into their devices, and send that feedback to the server. The server analyzes this feedback and uses it to create future content, providing a more effective and personalized educational experience.

[0518] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0519] This invention features a system that begins with a user inputting information about their child and then provides generated content and advice. The system integrates an emotion engine to enhance interaction based on the emotional states of both the user and the child.

[0520] First, the user uses their device to enter basic information about their child. This information includes age, interests, and learning style. This initial input data is sent to the server, and the data analysis process begins.

[0521] The server analyzes the input data and prepares to generate optimal educational content by referencing the database. Generative AI is used to first select content, and then the generative AI personalizes it. For example, for a 6-year-old child interested in space, it can create an interactive game that allows them to experience a space exploration mission.

[0522] Devices equipped with an emotion engine recognize the emotions of users and children in real time. This engine analyzes voice and facial expression data to understand the user's emotional state and adjusts feedback and content accordingly. For example, if a child becomes excited while playing, the engine can detect this and adjust the game's difficulty level appropriately.

[0523] Furthermore, online courses and training programs are provided via the server to improve parents' parenting skills. These programs are personalized by an emotion engine, with content tailored based on the parents' stress levels and emotional needs. Users can access these educational resources to deepen their knowledge of parenting.

[0524] Finally, feedback from the user's experience is sent to the server, where the emotion engine analyzes it and incorporates it into future content creation and delivery. This entire process ensures that users receive optimal parenting support.

[0525] The following describes the processing flow.

[0526] Step 1:

[0527] The user uses their device to enter basic information such as the child's age, interests, and learning style. This information is sent to the server via a secure communication protocol.

[0528] Step 2:

[0529] The server analyzes the received information about the child and selects appropriate educational content by referring to a database. Based on this data, the generating AI creates a customized learning plan tailored to each individual child.

[0530] Step 3:

[0531] The server personalizes the generated content and sends it to the user's device. This includes guidelines for interface adjustments by the emotion engine.

[0532] Step 4:

[0533] The device drives an emotion engine that monitors the emotional state of the user and child in real time. It analyzes voice and facial expressions to determine the current emotional state.

[0534] Step 5:

[0535] The device dynamically adjusts the level of educational content and feedback based on acquired emotional information. For example, if a child finds a game difficult, the game's difficulty level is lowered based on the emotional data.

[0536] Step 6:

[0537] Users observe their interactions with their children through the learning program. Based on the feedback provided by the emotion engine, parents can also adapt their responses.

[0538] Step 7:

[0539] The device receives user feedback as input and sends it to the server. This feedback includes opinions on content quality and the learning process.

[0540] Step 8:

[0541] The server analyzes the received feedback and sentiment data. It then makes changes to improve future content generation and processes, continuously enhancing the user experience.

[0542] (Example 2)

[0543] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0544] In the modern education system, there is a problem in providing personalized educational experiences that fully take into account each child's individual characteristics and emotional state. Furthermore, there is a lack of adequate support to effectively improve parents' parenting skills. As a result, children's learning efficiency and parents' sense of security regarding child-rearing are not adequately achieved, which poses a challenge.

[0545] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0546] In this invention, the server includes means for acquiring attribute information of children obtained from users, means for analyzing the acquired information and generating customized educational information using a generative AI model, and means for transmitting and displaying the generated information on a user device. This makes it possible to provide educational experiences tailored to the individual characteristics of each child and to effectively support the improvement of parents' parenting skills.

[0547] A "user" refers to an entity that uses the system to input information about their child and receives the generated educational information and childcare support.

[0548] A "server" refers to a device that receives information from users, analyzes it, generates customized educational information using a generative AI model, and transmits it to the user's device.

[0549] "Attribute information" refers to personal information necessary for personalizing educational information, such as a child's age, interests, and learning style.

[0550] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized educational information for individual children based on the input information.

[0551] "Educational information" refers to personalized content designed to help children improve their knowledge and skills through learning and activities.

[0552] A "user device" refers to an electronic terminal that, when accessed by a user, displays generated educational information and feedback from the system.

[0553] "Emotion analysis" refers to a technology that analyzes data such as the voice and facial expressions of users and children to detect their emotional state in real time.

[0554] "Feedback" refers to the opinions and information that users provide about their experience using the system, and this feedback is used to improve the system.

[0555] "Nurturing ability" refers to the knowledge and skills that parents possess to effectively support their children's growth and development.

[0556] "Online learning" refers to learning programs and content provided via the internet, representing a format that allows for education regardless of location or time.

[0557] This system is initiated when the user enters their child's attribute information using a terminal. This information includes details such as the child's age, interests, and learning style. This attribute information is transmitted to the server via a communication network.

[0558] The server is equipped with a high-performance processor and database to analyze the received information. A generative AI model is used for the analysis, processing the input data based on a specific algorithm. This model receives prompts and creates educational information tailored to the user's needs. For example, it might use the prompt, "Create a space adventure story that a 6-year-old child can enjoy."

[0559] Next, the generated educational information is sent to the user's device and displayed. The user's device is typically a mobile device such as a tablet or smartphone, allowing for a visual and interactive experience of the content.

[0560] Furthermore, the device incorporates an emotion analysis engine. This engine uses the camera and microphone to analyze the child's facial expressions and vocalizations in real time, detecting their emotional state. Based on the results obtained, the engine can adjust the content and difficulty level of educational information. This allows for adjustments such as increasing the difficulty of a game if the child is excited.

[0561] In addition, the server provides online learning programs aimed at improving parents' parenting skills. These programs are personalized by generative AI models, with content customized according to the parents' current emotional state and needs. As a result, parents can learn to improve their parenting skills regardless of their location.

[0562] As described above, this system provides personalized educational information for children and a range of features to support parents in childcare. This enables an educational experience tailored to each child and effective childcare support for parents.

[0563] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0564] Step 1:

[0565] The user uses a device to input attribute information about their child. This attribute information includes the child's age, interests, and learning style. The device converts the input data into a digital format and sends it to the server via a communication network. This input data forms the basis for subsequent analysis and content generation on the server.

[0566] Step 2:

[0567] The server analyzes the received attribute information. This analysis involves issuing queries to the database and matching it with existing records that have similar attribute information. As a result of the analysis, the criteria necessary for selecting educational information tailored to the child's characteristics are extracted. This is then passed on as output data from the server to the next content generation stage.

[0568] Step 3:

[0569] The server uses a generative AI model to generate customized educational information based on analyzed conditions. It provides the AI ​​model with prompts to construct specific learning content. These prompts are specific, such as "Create a space adventure story that a 6-year-old child can enjoy." The AI ​​model responds to this request by creating personalized educational content and generating a digital file. The output educational information is then ready for transmission and moves on to the next step.

[0570] Step 4:

[0571] The server transmits the generated educational information to the user's device. Using a communication protocol, the data is transferred and prepared for real-time display on the user's terminal. The terminal interprets the received data appropriately and displays it to the user in an interactive format. The output of this step is a visual presentation of educational information on the terminal.

[0572] Step 5:

[0573] An emotion analysis engine built into the device analyzes the child's facial expressions and voice in real time. Using input data from the camera and microphone, it executes a specific algorithm to analyze the child's emotional state. Based on the results, it adjusts the content and difficulty level of educational information to optimize the quality of interaction. This analysis result becomes the output for dynamically optimizing the presentation of educational information on the device.

[0574] Step 6:

[0575] Users input feedback to the system via their devices. This feedback includes reactions to children's educational information and parents' experience evaluations. The devices send the input feedback data to a server. The server analyzes the received data and aggregates the feedback as output to help generate future content.

[0576] Step 7:

[0577] The server builds and delivers personalized online learning programs to enhance parents' parenting abilities. Based on parental feedback and emotional states, it uses a generative AI model to customize learning content. As a result, it outputs a specific learning plan for parents to improve their parenting skills. This learning plan is provided in a format that parents can access via their devices.

[0578] (Application Example 2)

[0579] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0580] In today's educational environment, there is a challenge in providing educational content that is not adequately tailored to the individual interests and learning styles of each child. Furthermore, there is a lack of support for home-based education due to insufficient improvement in parents' childcare skills and inadequate product recommendations in virtual environments. Given these challenges, there is a need for a system that provides child-optimized content while simultaneously strengthening childcare support for parents.

[0581] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0582] In this invention, the server includes means for collecting information about children obtained from users, means for generating customized educational content using a generative AI based on the collected information, and means for transmitting and displaying the generated content on the user's terminal. This makes it possible to provide personalized educational content that is tailored to the child.

[0583] "Means for collecting information about children" refers to devices or methods for acquiring individual information about children, such as their age, interests, and learning style.

[0584] "Generative AI" is an artificial intelligence technology that generates optimal content based on input data.

[0585] "Educational content" refers to teaching materials and programs designed to stimulate and support children's learning and interests.

[0586] A "user terminal" is a device that a user can use to receive and interact with content.

[0587] "Conversational AI" refers to artificial intelligence that can interact with people through voice and text, enabling that interaction.

[0588] "Means for collecting and analyzing feedback" refers to methods or systems for gathering user experiences and opinions, analyzing them, and reflecting them in future content creation.

[0589] "A means of analyzing emotions in real time and optimizing educational content" refers to a technology that uses voice and facial expression data to understand the user's emotional state and adjusts the content provided accordingly.

[0590] "A means of dynamically proposing products in a virtual environment" refers to a method of proposing products and services in a virtual space in real time, based on the user's interests and emotions.

[0591] To realize this application, a system should be used in which a server integrates a generative AI model and emotion recognition technology to provide interactive educational content to the user's device. The server receives information about the child obtained from the user and stores it in a database. This includes information such as the child's age, interests, and learning style. This information is input into the generative AI model and serves as the basic data for generating optimal educational content.

[0592] The generated educational content is sent to the user's smartphone or tablet. The device is equipped with conversational AI, enabling interaction through dialogue with the child. Through this dialogue, the educational content is optimized according to the child's interests and responses.

[0593] Furthermore, the server utilizes emotion recognition technology to analyze the user's and child's emotions in real time through cameras and microphones. Emotional data is processed using libraries such as OpenCV and DeepFace. This allows for dynamic adjustments to the difficulty and type of educational content, ensuring children remain engaged.

[0594] As a concrete example, consider a 6-year-old child who is interested in space. Based on this information, a generative AI model can build an interactive game in which the child can experience a space exploration mission. Emotion recognition technology can determine whether the child is excited or bored with the game and adjust the game's difficulty accordingly.

[0595] An example of a prompt is, "My 6-year-old child is interested in space. Please suggest content to deepen his understanding." This prompt allows the generative AI model to create optimized educational content, which is then delivered appropriately by the server.

[0596] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0597] Step 1:

[0598] The user uses a device to input information such as the child's age, interests, and learning style. The entered data is sent from the device to the server. The server then stores the information in a database, preparing for subsequent processing.

[0599] Step 2:

[0600] Based on the collected information, the server creates a prompt for the generating AI model and sends it a prompt such as, "Please suggest the best educational content for a 6-year-old child who is interested in space." The generating AI model then uses this prompt to run its algorithm and generate the most suitable educational content. As output, an interactive space exploration mission game or similar is created.

[0601] Step 3:

[0602] The server sends the generated educational content to the user's device. The device receives this content and displays it in the user interface. The user and child can then experience the content on the device.

[0603] Step 4:

[0604] The conversational AI installed in the device initiates a dialogue with the child experiencing the content. The input information obtained here includes the child's voice and facial expressions. The conversational AI analyzes this data in real time and adjusts the interaction according to the child's responses.

[0605] Step 5:

[0606] The server analyzes real-time audio and facial expression data acquired from the terminal using emotion recognition technology. Based on the data obtained as input, it uses libraries such as OpenCV and DeepFace to determine the child's emotional state. The output provides indicators of the child's excitement level and satisfaction level.

[0607] Step 6:

[0608] Based on the emotion recognition results, the server dynamically adjusts the difficulty level and presentation format of the educational content. For example, if the server detects signs that the child is getting bored, it will change the settings, such as altering the game's storyline, to keep the child interested.

[0609] Step 7:

[0610] Ultimately, users provide feedback and send it to the server via their device. The server analyzes this feedback and uses it to improve future content creation. This information is stored in a database and contributes to improving the overall system's performance.

[0611] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0612] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0613] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0614] [Fourth Embodiment]

[0615] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0616] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0617] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0618] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0619] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0620] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0621] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0622] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0623] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0624] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0625] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0626] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0627] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0628] This invention is a system that begins with the user inputting information about their child, and then provides content and advice generated based on that information.

[0629] First, the user enters basic information about their child via their device, namely their age, interests, developmental stage, and details about specific childcare needs. This information is immediately sent to the server.

[0630] The server analyzes the received information and generates optimal educational content by referring to a database. Generative AI is used here to create individualized learning plans tailored to age and interests. For example, a 3-year-old child interested in animals would be provided with an interactive game where they can learn animal names and characteristics. The server then sends this generated content back to the device.

[0631] The device receives instructions from the server, displays generated content on the screen, and allows children to interact with it. In particular, a conversational AI handles the interaction, asking children questions and receiving answers via voice. For example, the device might display a quiz such as, "What sounds do lions make?" and provide feedback based on the child's answer.

[0632] Furthermore, with the aim of providing ongoing education for parents, the server offers users online courses and training programs to improve their parenting skills. These are customized based on parents' needs, focusing on specific aspects. Users can access these educational resources through their devices and learn at home.

[0633] Finally, users input feedback on their experience and the content they received through the system, which is then sent back to the server. The server analyzes this feedback and incorporates it into future content generation, providing a more personalized service. This creates a system where optimal parenting support is continuously provided to the user.

[0634] The following describes the processing flow.

[0635] Step 1:

[0636] The user uses their device to enter information about their child's age, interests, developmental stage, and specific parenting needs. This information is sent to the server via a secure protocol.

[0637] Step 2:

[0638] The server stores the received information in a database and simultaneously begins analysis. During the analysis, it prepares to generate optimal educational content using AI based on the input information.

[0639] Step 3:

[0640] The server consults a database to determine appropriate educational content. For example, it selects videos, games, or quizzes suitable for a specified age and interests.

[0641] Step 4:

[0642] The server uses generative AI to customize selected educational content, providing children with a more personalized learning experience.

[0643] Step 5:

[0644] The server sends customized content to the terminal, along with the necessary interface configuration information.

[0645] Step 6:

[0646] The device displays the received educational content on the user screen. The interface is optimized for ease of use by children.

[0647] Step 7:

[0648] The device enables conversational AI and initiates two-way interaction with the child. For example, the device uses voice recognition to ask quizzes, understand the child's answers, and provide follow-up support.

[0649] Step 8:

[0650] Users submit feedback on the provided content and learning experience through their devices. This feedback is sent to the server and used to improve future content.

[0651] Step 9:

[0652] The server analyzes the feedback and adjusts the next generation process based on the results. This enables continuous improvement to provide a better user experience.

[0653] (Example 1)

[0654] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0655] Traditional educational methods face the challenge of providing individualized education that addresses children's diverse interests and developmental stages. Furthermore, parents often require significant time and effort to acquire effective parenting skills. In this context, there is a need for systems that utilize technology to enrich children's learning experiences and reduce the burden on parents.

[0656] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0657] In this invention, the server includes means for collecting child attribute information obtained from the user, means for analyzing the collected attribute information and generating personalized educational materials using a generation AI algorithm, and means for transmitting the generated educational materials to the user's device and controlling their display. This provides users with personalized educational content, enabling appropriate education tailored to the child's learning needs. Furthermore, it provides parents with the opportunity to improve their parenting skills through online educational programs, enabling them to gain confidence and improve their parenting skills.

[0658] A "user" refers to a person who uses this system, inputting the child's attribute information and utilizing the generated educational materials and online educational programs.

[0659] "Attribute information" refers to a collection of basic information about a child provided by the user, such as the child's age, interests, developmental stage, and parenting needs.

[0660] A "generative AI algorithm" is one of the artificial intelligence techniques used to generate personalized educational materials based on analyzed attribute information.

[0661] "Educational materials" are personalized learning content created by generative AI algorithms, designed to enhance children's learning experiences.

[0662] "User equipment" refers to electronic devices used by users to view and manipulate educational materials, and includes terminals and personal computers.

[0663] "Evaluation information" refers to feedback provided by users about their results and experiences using the system, and is used to improve the service.

[0664] An "educational program" is a series of learning materials and training sessions provided online with the aim of improving parents' childcare skills.

[0665] This invention is a comprehensive educational support system that provides personalized educational materials for children and improves parents' parenting skills. Users input attribute information about their children using a terminal. This information includes the child's age, interests, developmental stage, and specific parenting needs. This information is transmitted to a server via the internet.

[0666] The server analyzes attribute information and creates personalized educational materials using a generative AI algorithm. For example, if a 3-year-old child is interested in animals, interactive materials will be generated that allow them to learn the names and characteristics of animals. This generative AI algorithm might use a text generation AI model, for instance. At this stage, a specific instruction is input to the generative AI as a prompt: "Generate materials suitable for a 3-year-old child who is interested in animals."

[0667] The generated educational materials are sent from the server to the device, which displays them on the screen. The device incorporates voice recognition AI to allow children to interact with the educational materials. For example, the device can ask questions aloud, such as "What sounds do lions make?" and receive the child's answer.

[0668] Furthermore, the server provides online educational programs optimized for parents. Parents can access these programs through their devices to improve their parenting skills. The programs offered are personalized based on the parents' needs and interests and include online courses and training sessions.

[0669] Users send feedback about their experience using the system from their device to the server. The server analyzes this feedback and uses it to generate future educational materials and improve the service. This allows the system to be continuously optimized and provide more beneficial educational support to users.

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

[0671] Step 1:

[0672] The user enters attribute information about their child using a device. This information includes age, interests, developmental stage, and childcare needs. This input data is formatted in the appropriate data format on the device and sent to the server via a secure communication protocol.

[0673] Step 2:

[0674] The server analyzes attribute information received from the user. The input here is attribute information, and by matching it with the database, it determines which educational materials are most suitable. Based on the analyzed information, it constructs prompt messages to send to the generating AI model. These prompt messages include instructions tailored to the child's age and interests.

[0675] Step 3:

[0676] The server processes prompts using a generative AI model to generate personalized educational materials. This model generates optimal content based on analyzed attribute information. For example, if the model is given the prompt, "Create learning materials for a 3-year-old child who loves animals," it will output interactive games and quiz-style materials.

[0677] Step 4:

[0678] The server sends the generated educational materials to the device. The device receives them and displays them on the screen in a format that children can interact with. Here, the format of the educational materials often includes interactive graphics and audio guides, and is designed for children to directly touch and try out.

[0679] Step 5:

[0680] The device uses conversational AI to facilitate interaction with educational materials. Leveraging voice recognition, it provides feedback as children answer questions or make choices within games. For example, the device might ask a quiz question like, "What sounds do lions make?" and then provide feedback on the child's answer through voice and animation.

[0681] Step 6:

[0682] Users input feedback about the educational materials they have experienced and send it from their device to the server. The server receives this feedback and analyzes its content. The analysis results are used to create future educational materials and improve the service, further increasing the degree of personalization of the system.

[0683] Step 7:

[0684] The server generates and provides online educational programs for parents. Input consists of feedback and evaluation information regarding parents' childcare needs. The program is optimized based on this information, and users can receive training to improve their skills through their devices.

[0685] (Application Example 1)

[0686] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] In today's educational environment, there is a demand for educational content tailored to individual needs. However, there is a lack of means to adapt such content to the interests and personalities of the learners, and to effectively promote learning through experiences in augmented reality environments. Conventional systems lack sufficient effective interaction and real-time feedback functions using conversational AI, making it difficult to sustain the learners' interest.

[0688] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0689] In this invention, the server includes means for collecting information on the target of training obtained from the user, means for generating customized educational content using generative AI based on the collected information, and means equipped with conversational AI that enables interaction with the target of training. This makes it possible to generate individualized educational content tailored to the target of training and promote learning while maintaining interest through interaction in an augmented reality environment.

[0690] "Target for development" refers to individuals or animals that are the subject of information collection and education, generally meaning children.

[0691] "Generative AI" refers to artificial intelligence technology that automatically generates optimized content based on collected information.

[0692] "Educational content" is a general term for content that includes teaching materials and activities designed to promote the learning of the target audience.

[0693] A "display device" refers to an electronic device used to visually present generated educational content.

[0694] "Conversational AI" refers to artificial intelligence technology that uses natural language to interact with users or those being trained.

[0695] An "augmented reality environment" refers to a technology that combines the real world with digital information to present it visually.

[0696] A "trainer" refers to a person who provides education and guidance to those being trained.

[0697] An "individualized program" refers to an educational or training plan specifically designed to suit the characteristics and needs of the individuals being trained.

[0698] The system that realizes this invention consists of a user terminal for inputting information, a server for processing data, and a display device for displaying the generated content.

[0699] The user inputs basic information about the person being trained into the device and sends data about their age, interests, and developmental stage to the server. The server uses a generative AI model (e.g., OpenAI's GPT series) based on the received information to customize the generated educational content. The content is then processed into data for display in an augmented reality environment, tailored to the learning style of the person being trained.

[0700] The server uses generative AI to generate educational content tailored to the target audience. This content is designed for visual learning on a display device and can include, for example, 3D models and audio guides that are adapted to the audience's interests and learning speed. Furthermore, it uses conversational AI to enable real-time interaction and provide feedback based on the audience's responses.

[0701] For example, the server might use a prompt message like, "A 5-year-old child is interested in ants. Please generate interactive content that will teach them about ant ecology," to provide content where the child can learn about ant ecology while observing a 3D model of an ant. As the child answers questions about ants, the conversational AI asks follow-up questions to deepen their learning through interaction.

[0702] Furthermore, the server analyzes user feedback and incorporates it into future content creation. This makes it possible to continuously provide a more personalized educational environment for those being trained.

[0703] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0704] Step 1:

[0705] The user enters basic information about the child to be nurtured (age, interests, developmental stage) into the device and sends the data to the server. This input data includes information in text format, and the server prepares to receive and analyze it.

[0706] Step 2:

[0707] The server calls a generation AI model based on the received information about the target being trained, and forms a prompt message. This prompt message is generated in a format such as, "A target being ○ years old is interested in ○○. Please generate content that can be learned about ○○." The server sends this prompt message to the generation AI model, issuing a content generation request.

[0708] Step 3:

[0709] The generation AI model generates educational content according to the prompt text. The generated content is output in a format optimized for the target audience. Specifically, the educational content includes text, images, 3D models, and audio files, and this data is returned to the server.

[0710] Step 4:

[0711] The server formats the generated educational content into a format suitable for the display device and sends it to the terminal. During this process, the data is processed to support display in an augmented reality environment. Specifically, this includes enabling 3D models and adding interactivity, such as conversational AI.

[0712] Step 5:

[0713] The device presents the received educational content to the learner. The learner progresses through visual and auditory interactions. The device continuously interacts with the learner via conversational AI and provides real-time feedback.

[0714] Step 6:

[0715] Users observe their reactions to the content they are being trained on, input feedback into their devices, and send that feedback to the server. The server analyzes this feedback and uses it to create future content, providing a more effective and personalized educational experience.

[0716] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0717] This invention features a system that begins with a user inputting information about their child and then provides generated content and advice. The system integrates an emotion engine to enhance interaction based on the emotional states of both the user and the child.

[0718] First, the user uses their device to enter basic information about their child. This information includes age, interests, and learning style. This initial input data is sent to the server, and the data analysis process begins.

[0719] The server analyzes the input data and prepares to generate optimal educational content by referencing the database. Generative AI is used to first select content, and then the generative AI personalizes it. For example, for a 6-year-old child interested in space, it can create an interactive game that allows them to experience a space exploration mission.

[0720] Devices equipped with an emotion engine recognize the emotions of users and children in real time. This engine analyzes voice and facial expression data to understand the user's emotional state and adjusts feedback and content accordingly. For example, if a child becomes excited while playing, the engine can detect this and adjust the game's difficulty level appropriately.

[0721] Furthermore, online courses and training programs are provided via the server to improve parents' parenting skills. These programs are personalized by an emotion engine, with content tailored based on the parents' stress levels and emotional needs. Users can access these educational resources to deepen their knowledge of parenting.

[0722] Finally, feedback from the user's experience is sent to the server, where the emotion engine analyzes it and incorporates it into future content creation and delivery. This entire process ensures that users receive optimal parenting support.

[0723] The following describes the processing flow.

[0724] Step 1:

[0725] The user uses their device to enter basic information such as the child's age, interests, and learning style. This information is sent to the server via a secure communication protocol.

[0726] Step 2:

[0727] The server analyzes the received information about the child and selects appropriate educational content by referring to a database. Based on this data, the generating AI creates a customized learning plan tailored to each individual child.

[0728] Step 3:

[0729] The server personalizes the generated content and sends it to the user's device. This includes guidelines for interface adjustments by the emotion engine.

[0730] Step 4:

[0731] The device drives an emotion engine that monitors the emotional state of the user and child in real time. It analyzes voice and facial expressions to determine the current emotional state.

[0732] Step 5:

[0733] The device dynamically adjusts the level of educational content and feedback based on acquired emotional information. For example, if a child finds a game difficult, the game's difficulty level is lowered based on the emotional data.

[0734] Step 6:

[0735] Users observe their interactions with their children through the learning program. Based on the feedback provided by the emotion engine, parents can also adapt their responses.

[0736] Step 7:

[0737] The device receives user feedback as input and sends it to the server. This feedback includes opinions on content quality and the learning process.

[0738] Step 8:

[0739] The server analyzes the received feedback and sentiment data. It then makes changes to improve future content generation and processes, continuously enhancing the user experience.

[0740] (Example 2)

[0741] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0742] In the modern education system, there is a problem in providing personalized educational experiences that fully take into account each child's individual characteristics and emotional state. Furthermore, there is a lack of adequate support to effectively improve parents' parenting skills. As a result, children's learning efficiency and parents' sense of security regarding child-rearing are not adequately achieved, which poses a challenge.

[0743] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0744] In this invention, the server includes means for acquiring attribute information of children obtained from users, means for analyzing the acquired information and generating customized educational information using a generative AI model, and means for transmitting and displaying the generated information on a user device. This makes it possible to provide educational experiences tailored to the individual characteristics of each child and to effectively support the improvement of parents' parenting skills.

[0745] A "user" refers to an entity that uses the system to input information about their child and receives the generated educational information and childcare support.

[0746] A "server" refers to a device that receives information from users, analyzes it, generates customized educational information using a generative AI model, and transmits it to the user's device.

[0747] "Attribute information" refers to personal information necessary for personalizing educational information, such as a child's age, interests, and learning style.

[0748] A "generative AI model" refers to an algorithm that uses artificial intelligence to generate personalized educational information for individual children based on the input information.

[0749] "Educational information" refers to personalized content designed to help children improve their knowledge and skills through learning and activities.

[0750] A "user device" refers to an electronic terminal that, when accessed by a user, displays generated educational information and feedback from the system.

[0751] "Emotion analysis" refers to a technology that analyzes data such as the voice and facial expressions of users and children to detect their emotional state in real time.

[0752] "Feedback" refers to the opinions and information that users provide about their experience using the system, and this feedback is used to improve the system.

[0753] "Nurturing ability" refers to the knowledge and skills that parents possess to effectively support their children's growth and development.

[0754] "Online learning" refers to learning programs and content provided via the internet, representing a format that allows for education regardless of location or time.

[0755] This system is initiated when the user enters their child's attribute information using a terminal. This information includes details such as the child's age, interests, and learning style. This attribute information is transmitted to the server via a communication network.

[0756] The server is equipped with a high-performance processor and database to analyze the received information. A generative AI model is used for the analysis, processing the input data based on a specific algorithm. This model receives prompts and creates educational information tailored to the user's needs. For example, it might use the prompt, "Create a space adventure story that a 6-year-old child can enjoy."

[0757] Next, the generated educational information is sent to the user's device and displayed. The user's device is typically a mobile device such as a tablet or smartphone, allowing for a visual and interactive experience of the content.

[0758] Furthermore, the device incorporates an emotion analysis engine. This engine uses the camera and microphone to analyze the child's facial expressions and vocalizations in real time, detecting their emotional state. Based on the results obtained, the engine can adjust the content and difficulty level of educational information. This allows for adjustments such as increasing the difficulty of a game if the child is excited.

[0759] In addition, the server provides online learning programs aimed at improving parents' parenting skills. These programs are personalized by generative AI models, with content customized according to the parents' current emotional state and needs. As a result, parents can learn to improve their parenting skills regardless of their location.

[0760] As described above, this system provides personalized educational information for children and a range of features to support parents in childcare. This enables an educational experience tailored to each child and effective childcare support for parents.

[0761] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0762] Step 1:

[0763] The user uses a device to input attribute information about their child. This attribute information includes the child's age, interests, and learning style. The device converts the input data into a digital format and sends it to the server via a communication network. This input data forms the basis for subsequent analysis and content generation on the server.

[0764] Step 2:

[0765] The server analyzes the received attribute information. This analysis involves issuing queries to the database and matching it with existing records that have similar attribute information. As a result of the analysis, the criteria necessary for selecting educational information tailored to the child's characteristics are extracted. This is then passed on as output data from the server to the next content generation stage.

[0766] Step 3:

[0767] The server uses a generative AI model to generate customized educational information based on analyzed conditions. It provides the AI ​​model with prompts to construct specific learning content. These prompts are specific, such as "Create a space adventure story that a 6-year-old child can enjoy." The AI ​​model responds to this request by creating personalized educational content and generating a digital file. The output educational information is then ready for transmission and moves on to the next step.

[0768] Step 4:

[0769] The server transmits the generated educational information to the user's device. Using a communication protocol, the data is transferred and prepared for real-time display on the user's terminal. The terminal interprets the received data appropriately and displays it to the user in an interactive format. The output of this step is a visual presentation of educational information on the terminal.

[0770] Step 5:

[0771] An emotion analysis engine built into the device analyzes the child's facial expressions and voice in real time. Using input data from the camera and microphone, it executes a specific algorithm to analyze the child's emotional state. Based on the results, it adjusts the content and difficulty level of educational information to optimize the quality of interaction. This analysis result becomes the output for dynamically optimizing the presentation of educational information on the device.

[0772] Step 6:

[0773] Users input feedback to the system via their devices. This feedback includes reactions to children's educational information and parents' experience evaluations. The devices send the input feedback data to a server. The server analyzes the received data and aggregates the feedback as output to help generate future content.

[0774] Step 7:

[0775] The server builds and delivers personalized online learning programs to enhance parents' parenting abilities. Based on parental feedback and emotional states, it uses a generative AI model to customize learning content. As a result, it outputs a specific learning plan for parents to improve their parenting skills. This learning plan is provided in a format that parents can access via their devices.

[0776] (Application Example 2)

[0777] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0778] In today's educational environment, there is a challenge in providing educational content that is not adequately tailored to the individual interests and learning styles of each child. Furthermore, there is a lack of support for home-based education due to insufficient improvement in parents' childcare skills and inadequate product recommendations in virtual environments. Given these challenges, there is a need for a system that provides child-optimized content while simultaneously strengthening childcare support for parents.

[0779] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0780] In this invention, the server includes means for collecting information about children obtained from users, means for generating customized educational content using a generative AI based on the collected information, and means for transmitting and displaying the generated content on the user's terminal. This makes it possible to provide personalized educational content that is tailored to the child.

[0781] "Means for collecting information about children" refers to devices or methods for acquiring individual information about children, such as their age, interests, and learning style.

[0782] "Generative AI" is an artificial intelligence technology that generates optimal content based on input data.

[0783] "Educational content" refers to teaching materials and programs designed to stimulate and support children's learning and interests.

[0784] A "user terminal" is a device that a user can use to receive and interact with content.

[0785] "Conversational AI" refers to artificial intelligence that can interact with people through voice and text, enabling that interaction.

[0786] "Means for collecting and analyzing feedback" refers to methods or systems for gathering user experiences and opinions, analyzing them, and reflecting them in future content creation.

[0787] "A means of analyzing emotions in real time and optimizing educational content" refers to a technology that uses voice and facial expression data to understand the user's emotional state and adjusts the content provided accordingly.

[0788] "A means of dynamically proposing products in a virtual environment" refers to a method of proposing products and services in a virtual space in real time, based on the user's interests and emotions.

[0789] To realize this application, a system should be used in which a server integrates a generative AI model and emotion recognition technology to provide interactive educational content to the user's device. The server receives information about the child obtained from the user and stores it in a database. This includes information such as the child's age, interests, and learning style. This information is input into the generative AI model and serves as the basic data for generating optimal educational content.

[0790] The generated educational content is sent to the user's smartphone or tablet. The device is equipped with conversational AI, enabling interaction through dialogue with the child. Through this dialogue, the educational content is optimized according to the child's interests and responses.

[0791] Furthermore, the server utilizes emotion recognition technology to analyze the user's and child's emotions in real time through cameras and microphones. Emotional data is processed using libraries such as OpenCV and DeepFace. This allows for dynamic adjustments to the difficulty and type of educational content, ensuring children remain engaged.

[0792] As a concrete example, consider a 6-year-old child who is interested in space. Based on this information, a generative AI model can build an interactive game in which the child can experience a space exploration mission. Emotion recognition technology can determine whether the child is excited or bored with the game and adjust the game's difficulty accordingly.

[0793] An example of a prompt is, "My 6-year-old child is interested in space. Please suggest content to deepen his understanding." This prompt allows the generative AI model to create optimized educational content, which is then delivered appropriately by the server.

[0794] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0795] Step 1:

[0796] The user uses a device to input information such as the child's age, interests, and learning style. The entered data is sent from the device to the server. The server then stores the information in a database, preparing for subsequent processing.

[0797] Step 2:

[0798] Based on the collected information, the server creates a prompt for the generating AI model and sends it a prompt such as, "Please suggest the best educational content for a 6-year-old child who is interested in space." The generating AI model then uses this prompt to run its algorithm and generate the most suitable educational content. As output, an interactive space exploration mission game or similar is created.

[0799] Step 3:

[0800] The server sends the generated educational content to the user's device. The device receives this content and displays it in the user interface. The user and child can then experience the content on the device.

[0801] Step 4:

[0802] The conversational AI installed in the device initiates a dialogue with the child experiencing the content. The input information obtained here includes the child's voice and facial expressions. The conversational AI analyzes this data in real time and adjusts the interaction according to the child's responses.

[0803] Step 5:

[0804] The server analyzes real-time audio and facial expression data acquired from the terminal using emotion recognition technology. Based on the data obtained as input, it uses libraries such as OpenCV and DeepFace to determine the child's emotional state. The output provides indicators of the child's excitement level and satisfaction level.

[0805] Step 6:

[0806] Based on the emotion recognition results, the server dynamically adjusts the difficulty level and presentation format of the educational content. For example, if the server detects signs that the child is getting bored, it will change the settings, such as altering the game's storyline, to keep the child interested.

[0807] Step 7:

[0808] Ultimately, users provide feedback and send it to the server via their device. The server analyzes this feedback and uses it to improve future content creation. This information is stored in a database and contributes to improving the overall system's performance.

[0809] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0810] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0811] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0812] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0813] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0814] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0815] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0816] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0817] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0818] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0819] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0820] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0821] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0822] 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.

[0823] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0824] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0825] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0826] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0827] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0828] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0829] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0830] The following is further disclosed regarding the embodiments described above.

[0831] (Claim 1)

[0832] A means of collecting information about children obtained from users,

[0833] A means of generating customized educational content using generative AI based on collected information,

[0834] A means for sending and displaying the generated content on the user's terminal,

[0835] A device equipped with conversational AI that enables interaction with children,

[0836] A means of collecting and analyzing user feedback,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, comprising means for curating generated educational content based on age, interests, and learning style.

[0840] (Claim 3)

[0841] The system according to claim 1, comprising means for providing personalized online courses and training programs to improve parents' parenting skills.

[0842] "Example 1"

[0843] (Claim 1)

[0844] A means of collecting child attribute information obtained from the user,

[0845] A means for analyzing collected attribute information and generating personalized educational materials using a generation AI algorithm,

[0846] A means for transmitting the generated educational materials to the user's device and controlling their display,

[0847] A method incorporating a voice recognition AI that interacts with children,

[0848] A means of collecting and analyzing user evaluation information,

[0849] A system that includes this.

[0850] (Claim 2)

[0851] The system according to claim 1, comprising means for selecting generated educational materials based on age, interests, and learning characteristics.

[0852] (Claim 3)

[0853] The system according to claim 1, comprising means for optimizing and providing online educational programs to improve parents' childcare skills.

[0854] "Application Example 1"

[0855] (Claim 1)

[0856] A means of collecting information on the target of training obtained from the user,

[0857] A means of generating customized educational content using generative AI based on collected information,

[0858] A means for transmitting and displaying the generated content on a display device,

[0859] A means equipped with an interactive AI that enables dialogue with the subjects being trained,

[0860] A means of collecting and analyzing user feedback,

[0861] A means for presenting generated educational content in an augmented reality environment via a display device,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, comprising means for curating generated educational content based on age, interests, and learning style, and for displaying the content in an augmented reality environment.

[0865] (Claim 3)

[0866] The system according to claim 1, comprising means for providing personalized online courses and training programs to improve the skills of trainers, and for influencing the next program based on the learning outcomes of the trainees.

[0867] "Example 2 of combining an emotion engine"

[0868] (Claim 1)

[0869] A means of obtaining child attribute information obtained from the user,

[0870] A means for analyzing acquired information and generating customized educational information using a generative AI model,

[0871] A means for transmitting and displaying the generated information on a user device,

[0872] A system equipped with emotion analysis that enables real-time interaction with children based on their emotions,

[0873] A means of collecting and analyzing user feedback,

[0874] A system that includes this.

[0875] (Claim 2)

[0876] The system according to claim 1, comprising means for curating generated educational information based on age, interests, and learning style.

[0877] (Claim 3)

[0878] The system according to claim 1, comprising means for providing personalized online learning and training programs to improve parents' parenting abilities.

[0879] "Application example 2 when combining with an emotional engine"

[0880] (Claim 1)

[0881] A means of collecting information about children obtained from users,

[0882] A means of generating customized educational content using generative AI based on collected information,

[0883] A means for sending and displaying the generated content on the user's terminal,

[0884] A device equipped with conversational AI that enables interaction with children,

[0885] A means of collecting and analyzing user feedback,

[0886] A means to analyze emotions in real time and optimize educational content,

[0887] A means of dynamically presenting product proposals in a virtual environment,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, comprising means for curating generated educational content based on age, interests, and learning style.

[0891] (Claim 3)

[0892] The system according to claim 1, comprising means for providing personalized online courses and training programs to improve parents' parenting skills. [Explanation of Symbols]

[0893] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information about children obtained from users, A means of generating customized educational content using generative AI based on collected information, A means for sending and displaying the generated content on the user's terminal, A device equipped with conversational AI that enables interaction with children, A means of collecting and analyzing user feedback, A system that includes this.

2. The system according to claim 1, comprising means for curating generated educational content based on age, interests, and learning style.

3. The system according to claim 1, comprising means for providing personalized online courses and training programs to improve parents' childcare skills.

Images