system

Abstract

We provide the system. [Solution] Information input means and A generation means that automatically generates tasks based on individual information obtained from the user via the aforementioned information input means, A presentation means for presenting tasks generated by the generation means to the user, A progress management system for receiving task completion reports from users, An analysis means for analyzing the progress obtained by the progress management means and generating feedback, A feedback presentation means that presents the feedback generated by the analysis means to the user, 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 the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to the description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In a conventional task management system, it is difficult to provide personalized tasks corresponding to the individual needs and progress of children. As a result, there is a problem that the improvement of children's learning motivation and perseverance is not sufficiently achieved. In addition, there is also a problem that parents and educators often lack information to understand the progress of children and provide appropriate support, and the burden of providing continuous support is large.

Means for Solving the Problems

[0005] The system of the present invention includes an information input means for inputting individual information from the user, and a generation means for automatically generating personalized tasks using a generation AI based on the acquired information. Furthermore, by including a presentation means for presenting the generated tasks to the user, an analysis means for managing the user's task progress and generating feedback, and a feedback presentation means for presenting the generated feedback to the user, it is possible to realize optimal task management and learning support for each child. This fosters perseverance, reduces the burden on parents and educators, and enables effective communication and support.

[0006] An "information input means" is an interface or mechanism for obtaining individual information from a user.

[0007] "Generation means" refers to a function or device that automatically generates personalized tasks based on acquired individual information.

[0008] "Presentation means" refers to a device or program for visually or audibly presenting a generated task to the user.

[0009] A "progress management system" is a system that has the functionality to receive task completion reports from users and to track or manage the progress of those tasks.

[0010] "Analysis means" refers to a device or function for analyzing progress data acquired by progress management means and generating feedback.

[0011] A "feedback presentation means" is a means or mechanism for providing generated feedback to the user in a format suitable for that user. [Brief explanation of the drawing]

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

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

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

[0015] In the following embodiments, a processor with a reference numeral (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.

[0016] In the following embodiments, a RAM (Random Access Memory) with a reference numeral is a memory where information is temporarily stored and is used as a work memory by the processor.

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

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

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

[0020] [First Embodiment]

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

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

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

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

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

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

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

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

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

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

[0033] This invention is a digital system that enables personalized task management for children. First, the user (parent or educator) uses an interface on a terminal to input individual information such as the child's age, school learning information, and daily habits. This information is transmitted from the terminal to the server via an information input device.

[0034] The server uses a generation mechanism based on the received information to generate tasks suitable for each child. The generated tasks are individually adjusted based on the learning content, difficulty level, and lifestyle. For example, if information indicates that a child is good at mathematics, tasks including more difficult math problems will be generated.

[0035] Next, the generated task is sent from the server to the terminal via a presentation device and presented to the child. The terminal displays the task content in a way that is easy for the child to understand. This may include visualizations using animations and audio guides.

[0036] The user (child) performs the presented task, and upon completion, sends a completion report to the server via a progress management device through the terminal. The server receives the progress information, analyzes it using an analysis device, and then generates the necessary feedback.

[0037] Finally, the generated feedback is sent to the device via a feedback presentation system and presented to the child. Specifically, it includes praise for task completion and suggestions for the next task to tackle. Through this feedback, the child can recognize their own progress and maintain motivation to continue learning.

[0038] In this way, by providing and managing tasks optimized for each individual child, the system is designed to naturally foster children's perseverance while also making it easier for parents and educators to provide support.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user launches the application on their device and enters individual information such as the child's age, school learning information, and lifestyle habits. The device then sends the entered information to the server via the information input device.

[0042] Step 2:

[0043] The server stores the received user information in a database. The stored information is necessary for the generation mechanism to use in subsequent processing.

[0044] Step 3:

[0045] The server uses a generation mechanism to create personalized tasks suitable for each child, based on individual information about the child obtained from the database. This generation uses an AI algorithm to assign tasks that are appropriate for the child's learning content and lifestyle habits.

[0046] Step 4:

[0047] The server sends the generated task information to the terminal via a presentation device. The terminal receives this information and displays the task on the screen in a way that is easy for children to understand and find interesting.

[0048] Step 5:

[0049] The user (child) works on the presented task. After completion, the user either presses a task completion button on the device or reports the completed task.

[0050] Step 6:

[0051] The terminal sends user task completion information to the server using a progress management system. The transmitted information is received on the server, and the progress is recorded.

[0052] Step 7:

[0053] The server analyzes progress using analytical tools. It determines the degree of task completion and whether follow-up is necessary, and generates feedback for the next learning stage.

[0054] Step 8:

[0055] The server sends the generated feedback to the terminal using a feedback presentation mechanism. The terminal then presents the feedback to the user in a clear and effective manner. This can include motivational messages and advice on the next steps.

[0056] (Example 1)

[0057] 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."

[0058] Providing appropriate educational tasks tailored to the individual age, learning history, and lifestyle of many children is challenging for parents and educators. Furthermore, tracking individual progress and providing effective feedback is also difficult. Traditional systems often fail to adequately provide individualized tasks or generate progress-based feedback, posing challenges in maintaining children's motivation and supporting their continuous learning.

[0059] 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.

[0060] In this invention, the server includes a generation device that automatically generates tasks based on individual information acquired via an information input device, a display device that presents the generated tasks to the user, and an analysis device that analyzes progress and generates feedback. This makes it possible to provide personalized tasks to individual children and to provide educational support with appropriate feedback according to their progress.

[0061] An "information input device" is a device used to acquire individual data from users, and its primary role is to collect necessary information, mainly through an interface.

[0062] A "generation device" is a device that automatically generates tasks suitable for the user based on acquired information, and uses technologies such as generation AI models to construct personalized tasks.

[0063] A "display device" is a device that presents generated tasks to the user in an intuitive and easily understandable format, and includes visual and auditory outputs.

[0064] A "progress management device" is a device that receives and manages reports from users regarding the completion status of tasks, and its role is to record and organize the achievement status of tasks.

[0065] An "analysis device" is a device that analyzes received progress information and generates feedback tailored to the user, such as suggesting the next task to tackle or generating words of praise.

[0066] A "feedback display device" is a device that intuitively presents generated feedback to the user and is equipped with functions to communicate the content of the feedback visually or audibly.

[0067] This invention is a digital system that provides personalized task management for individual children. The system mainly consists of a server, terminals, and users.

[0068] The user (parent or educator) uses a device to input information about their child. The device interface includes forms for the user to input information such as the child's age, learning information, and lifestyle habits. Standard input devices and UI software can be used for this information input. For example, tablet devices and PC browsers are commonly used.

[0069] The terminal sends the entered information to the server as a data package. This transmission uses SSL / TLS protocol to securely encrypt the data and communicate over the internet.

[0070] The server generates tasks using a generative AI model based on the received information. An execution environment for the AI ​​model, written in programming languages ​​such as Python, is provided, and the model is built on machine learning libraries. Specifically, it can automatically generate problem sets tailored to each child's strengths in different subjects.

[0071] The generated tasks are sent to the terminal by the server. This communication also utilizes lightweight data formats such as JSON, enabling rapid data exchange.

[0072] The device displays received tasks in a format that is easy for children to understand. Web technologies such as HTML5, CSS, and JavaScript (registered trademark) are used to provide interactions that include animations and audio guidance.

[0073] As a concrete example, a user could input a prompt into the AI ​​model saying, "Please suggest learning some simple English vocabulary as the next task." Based on this instruction, the server would generate an appropriate task, which would then be presented to the child via the device as animated educational content.

[0074] This system allows users to easily provide tasks tailored to children, and enables children to learn while having fun.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user uses a device to input information about their child through the interface. Specifically, they enter the child's age, learning information, lifestyle habits, etc., into text fields and select boxes. This input data is converted within the device into a specific format (e.g., JSON format) and ready for transmission.

[0078] Step 2:

[0079] The device packages the entered child information, encrypts the data using the SSL / TLS protocol, and securely sends it to the server. This transmission process accesses the server's API endpoint via HTTP requests.

[0080] Step 3:

[0081] The server analyzes the received information and launches a generative AI model. Here, a Python machine learning library is used to generate tasks best suited to the child. In this process, the model is given conditions such as age and preferred subjects as input, and based on this, it generates the optimal set of problems.

[0082] Step 4:

[0083] The tasks generated by the server are converted back into JSON format and sent to the terminal. A lightweight communication format is used for this process to ensure rapid data transfer.

[0084] Step 5:

[0085] The device analyzes received tasks and displays them in a user-friendly format. Specifically, it uses HTML5 and JavaScript to render them on the screen as educational content with animations and audio guides.

[0086] Step 6:

[0087] The user (child) completes the presented task and enters the results into the terminal. The terminal collects these results as progress data and prepares to report them to the server. The reported data includes information such as the degree of task completion and the time taken.

[0088] Step 7:

[0089] The terminal sends a completion report to the server. The report data is again encrypted using the SSL / TLS protocol and securely sent to the server's specified API endpoint.

[0090] Step 8:

[0091] The server receives progress data and uses an analysis device to generate feedback. The generating AI model constructs feedback messages based on the degree of task completion and suggests the next task.

[0092] Step 9:

[0093] The server sends the generated feedback to the device. The device analyzes this feedback and presents it to the user visually or audibly. Specifically, it displays it as a pop-up window or voice message, offering praise for the user's progress and guiding them to the next learning step.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern education, there is a challenge in providing educational content that is not adequately tailored to each child's individual interests and learning pace. This leads to decreased motivation and insufficient educational effectiveness. Furthermore, the lack of mechanisms to appropriately suggest subsequent educational activities based on children's feedback makes it difficult to optimize individual learning experiences.

[0097] 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.

[0098] In this invention, the server includes means for inputting information, means for generating information, and means for analyzing information. This makes it possible to automatically generate personalized educational content based on the user's individual information and present it in an audiovisually easy-to-understand format. Furthermore, by accurately suggesting the next content and learning activities according to the user's interests and progress, it is possible to optimize the individual learning experience and enhance educational effectiveness.

[0099] "Means of inputting information" refers to an interface that allows users to input individual information into an electronic device.

[0100] "Generating means" refers to a function that automatically creates appropriate learning tasks and audiovisual content based on the individual information entered.

[0101] "Means of presentation" refers to functions for presenting generated learning tasks or audiovisual content to users visually or audibly.

[0102] "Means for managing progress" refers to a function that allows users to receive reports on completed tasks and record their progress.

[0103] "Means of analysis" refers to the function of analyzing received progress information and identifying the next feedback or content that should be provided.

[0104] "Means of providing feedback" refers to functions that provide users with feedback generated based on analysis results, either visually or audibly.

[0105] "Audiovisual content" refers to media that conveys information to users visually or aurally, and provides education or entertainment.

[0106] "Learning activities" refer to a series of processes and tasks in which users are involved in order to achieve specific educational goals.

[0107] This document describes a mode for carrying out the invention. The invention is a digital system that provides personalized educational content for children. The system primarily consists of server, terminal, and user interaction.

[0108] First, the user enters individual information such as the child's age, interests, and learning history through the UI (user interface) on their device. This UI is built with React Native and runs on smartphones and tablet devices. The entered information is sent to AWS (registered trademark) servers via a secure communication protocol.

[0109] The server is built using a Python program with Django, and it analyzes the information it receives. Based on the analyzed information, it sends specific prompt messages to a generative AI model, which then generates or selects educational content suitable for the screen. An example of such a prompt message would be, "Based on the user's information, please suggest the most suitable educational content for a 5-year-old with interests in space and an intermediate learning level."

[0110] The generated content is sent to the device and presented to the user through visual effects and audio guidance. This presentation uses streaming technology, including visually easy-to-understand animations. Once the user completes the presented content, their progress is reported back to the server, which analyzes the progress data to suggest the next content or task.

[0111] In this way, it functions as a system that improves children's motivation to learn by continuously providing learning content optimized for them and appropriately managing their learning progress.

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The user enters the child's age, interests, and learning history using a React Native-based UI on their device. The entered information is securely sent to an AWS server using the HTTPS protocol. Input: Individual information of the child; Output: Information sent to the server. Specifically, the user enters or selects text or options into form fields and then clicks the "Submit" button.

[0115] Step 2:

[0116] The server analyzes the individual information it receives. A script implemented in Python analyzes the information and creates prompts to request educational content tailored to the user's learning needs from an AI model. Input: Individual information sent by the user; Output: Generated prompts. Specifically, the algorithm scores the user information and selects the appropriate prompt.

[0117] Step 3:

[0118] The server sends prompt messages to a generation AI model, which then generates and selects appropriate educational content. Once the AI ​​model generates content, the results are returned to the server. Input: prompt messages, Output: generated educational content. Specifically, prompt messages are sent to an external AI service via an API, and the best-matching content links and data are returned.

[0119] Step 4:

[0120] The server sends the generated educational content to the device. The device uses streaming technology to visually present the content to the user. Input: generated educational content, Output: visual presentation. Specifically, a video player is launched on the device and animation playback begins.

[0121] Step 5:

[0122] When a user completes a task, they press a button on their device, and the progress information is reported to the server. Input: Task completion information, Output: Progress report to the server. Specifically, when the "Complete" button is pressed, the progress information is automatically sent to the server.

[0123] Step 6:

[0124] The server receives progress information and analyzes the data. It then generates recommended learning content and tasks. Input: progress information; Output: recommended content. Specifically, it queries the database and performs calculations to select the best next action.

[0125] Step 7:

[0126] The server sends recommended content to the device and provides feedback. The user receives the suggested content as the next step. Input: Recommended content, Output: Presentation of feedback to the user. Specifically, text messages and links to the next content are displayed on the screen.

[0127] 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.

[0128] This invention relates to a personalized task management system that takes user emotions into consideration. This system generates tasks using generative AI based on the user's individual information, but by incorporating a new emotion engine, it achieves more appropriate task presentation and feedback.

[0129] First, the user enters information such as the child's age, interests, learning history, and lifestyle through the device's interface. This information is sent to the server via the input device and stored in the database.

[0130] Next, the server uses an emotion engine to analyze the user's emotions. This emotion analysis uses facial recognition or voice analysis technology to observe the user's reactions when working on tasks and receiving feedback. The results are then fed back into the task generation process.

[0131] The server generates tasks using a generation mechanism, but adjusts them based on the analysis results of the emotion engine. This adjustment optimizes the difficulty and content of the tasks according to the user's current emotional state.

[0132] The generated tasks are sent from the server to the terminal. The terminal presents the tasks in an emotionally sensitive manner, ensuring that the user can engage with them in an appropriate emotional state. For example, if the user is feeling anxious or stressed, a more approachable task can be presented.

[0133] When a user completes a task, the device sends that information to the server using a progress management system. The server analyzes the progress and sentiment data and generates feedback tailored to the user's state using an analysis system.

[0134] Finally, the generated feedback is sent to the device using a feedback presentation mechanism and provided to the user. This feedback celebrates the user's success and efforts, and includes advice and encouragement tailored to their emotional state.

[0135] For example, if a user achieves a goal and their feelings of joy are recognized, the system can then suggest a more challenging task. In this way, the system can provide an optimal learning experience tailored to the user's emotional state, supporting their continuous growth.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] The user uses a device to input individual information about their child, such as their age, learning history, interests, and lifestyle. The device then sends this information to the server using an information input device.

[0139] Step 2:

[0140] The server stores the received individual information in a database. Simultaneously, the emotion engine begins collecting real-time emotional data from the user. This is done by analyzing facial expressions and voice tone using a camera and microphone.

[0141] Step 3:

[0142] The server activates the generation mechanism and automatically generates tasks based on the acquired individual information. At this time, it adjusts the content and difficulty of the tasks according to the user's emotional state based on the emotional data obtained from the emotion engine.

[0143] Step 4:

[0144] The server sends the generated task information to the terminal using a presentation mechanism. The terminal receives it and presents the task in an appropriate format according to the user's emotional state. For example, it can prepare messages or animations that help alleviate stress.

[0145] Step 5:

[0146] The user (child) works on a task. Emotional changes during the task are continuously analyzed by the emotion engine and sent to the server.

[0147] Step 6:

[0148] When a user completes a task, the device sends the completion information to the server using a progress management system.

[0149] Step 7:

[0150] The server analyzes progress information and emotional data using analytical tools. This generates feedback tailored to performance and emotional state.

[0151] Step 8:

[0152] The server sends the generated feedback to the terminal via a feedback presentation mechanism. The terminal then presents the user with encouraging messages and advice for the next task, taking into account their emotional state.

[0153] (Example 2)

[0154] 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".

[0155] Traditional task management systems have a problem where generating tasks without considering individual user attribute data or emotional states can easily lead to decreased user motivation and efficiency. In particular, since a user's emotional state significantly impacts success in learning and work, it is essential to understand and address it appropriately. Therefore, a new system is needed that provides individually optimized tasks that take user emotions into account, enabling users to engage with tasks in the best possible state.

[0156] 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.

[0157] In this invention, the server includes information input means, generation means, and analysis means. This enables the generation of tasks optimized based on the user's individual attribute data and emotional state. Specifically, the user's attribute data is input using a generation AI model, and the task is adjusted considering the emotional state. As a result, the task is presented to the user in an emotionally appropriate manner through the presentation means, and the data collected by the progress management means is analyzed to provide feedback according to the user's emotional state. This makes it possible to support continuous growth while improving the user's motivation and learning efficiency.

[0158] An "information input method" is a mechanism that receives individual attribute data from a user and sends that data to a server.

[0159] A "generative AI model" is an algorithm that generates tasks based on a user's individual attribute data and adjusts the content of those tasks while taking their emotional state into consideration.

[0160] A "generation method" refers to a configuration that uses a generation AI model to create tasks based on user data and has the functionality to optimize the content of those tasks.

[0161] A "presentation method" is a way of displaying generated tasks to the user in an emotionally appropriate manner and providing an interface that takes user reactions into consideration.

[0162] A "progress management system" is a process for receiving task completion reports from users and managing and recording the progress status.

[0163] "Analysis means" refers to a function that integrates and analyzes user progress and sentiment data within the server to generate feedback.

[0164] A "feedback presentation method" is a system that provides users with feedback generated by an analysis method, offering advice and encouragement tailored to their emotional state.

[0165] This invention provides an advanced task management system that takes into account user emotions and individual attribute data. The system consists of a user terminal, a server, and software that works in conjunction with them.

[0166] Users input individual attribute data using the device's user interface. This includes the child's age, interests, learning history, and lifestyle. This data is sent to the server via an application on the device. The server stores the received information in a database and performs sentiment analysis using an emotion engine. This sentiment analysis utilizes facial recognition and voice analysis technologies to identify the user's emotional state (e.g., joy, sadness, stress).

[0167] The server utilizes a generative AI model to generate tasks based on the user's emotional state and attribute data. This AI model employs text generation and data analysis algorithms and is adjusted to provide tasks appropriate to the user's current emotions. For example, if a user is feeling stressed, the system can generate simple tasks that include relaxing content.

[0168] The generated tasks are sent from the server to the terminal and presented to the user. The terminal displays the tasks in a visually clear and emotionally sensitive manner to encourage user engagement. Once the user completes a task, the terminal sends progress information to the server. The server then analyzes the progress and emotional data to generate feedback. This feedback is designed to be positive for the user, including messages that enhance their sense of accomplishment and encouragement for the next step.

[0169] For example, if a user enters "insects" as their interest and their learning history records "good at science," the generated tasks might include an insect-related quiz or creating a simple observation journal. An example of a prompt might be: "An 8-year-old child is interested in insects and is good at science. Please suggest some fun, quiz-style tasks related to insects."

[0170] In this way, the system can perform personalized task management that reflects the user's emotional state and individual attributes. This allows users to approach tasks in an optimal state, enabling them to learn and grow more efficiently.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user uses their device to input individual attribute data such as the child's age, interests, learning history, and lifestyle.

[0174] The entered information is converted into digital data through the terminal's application and transmitted to the server via the information input device. This creates an initial dataset of user attributes.

[0175] Step 2:

[0176] The server saves the received individual attribute data to the database.

[0177] In this process, a unique ID is assigned to each user, and the data is stored in a structured format. The stored data serves as foundational data referenced in subsequent analysis and generation processes.

[0178] Step 3:

[0179] The server utilizes a generative AI model to generate tasks based on the user's individual attribute data.

[0180] In this process, data is input to the AI ​​model using prompt statements (e.g., "An 8-year-old child is interested in insects and is good at science. Please suggest a fun quiz-style task related to insects."), and the generated data returned is formatted appropriately to create an output task suitable for the user.

[0181] Step 4:

[0182] The server uses an emotion engine to analyze the user's emotional state.

[0183] The input consists of images and audio data from a facial recognition camera. Based on this, an emotion analysis algorithm outputs the user's current emotional state (e.g., degree of joy, sadness, stress) as numerical data.

[0184] Step 5:

[0185] The tasks generated by the generation method are adjusted taking into account the results of sentiment analysis.

[0186] The server adjusts the difficulty and content of tasks based on the user's emotional state, and regenerates optimized tasks. This ensures that the tasks are appropriate for the user's current emotional state.

[0187] Step 6:

[0188] Finally, the generated tasks are sent from the server to the terminal and displayed to the user through a presentation mechanism.

[0189] On the device, tasks are presented through a visually intuitive interface, and feedback and animations may be added to take into account the user's emotional state.

[0190] Step 7:

[0191] The user completes the task and enters a completion notification on their device.

[0192] The terminal sends this information to the server through a progress management system, and the completion status is recorded on the server.

[0193] Step 8:

[0194] The server integrates and analyzes the acquired progress data and sentiment data to generate feedback.

[0195] Using analytical tools, the system celebrates user successes and generates advice for the next task. The generated feedback is output as numerical information (such as completion rate) and text messages.

[0196] Step 9:

[0197] The generated feedback is sent to the terminal via a feedback presentation mechanism and presented to the user.

[0198] The device can display feedback in a more easily understandable format and deliver messages of gratitude and encouragement to users.

[0199] (Application Example 2)

[0200] 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 device 14 will be referred to as the "terminal."

[0201] Many task management systems present tasks without considering the user's feelings, sometimes resulting in tasks being presented in an inappropriate way or at an inappropriate time. This can increase user stress and decrease motivation. Furthermore, traditional systems often fail to provide sufficient feedback tailored to individual user responses, making it difficult to support users' continuous growth.

[0202] 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.

[0203] In this invention, the server includes an information input means, an emotion analysis means, a generation means using a generation AI, an adjustment means for adjusting tasks according to emotions, a progress management means, and a feedback presentation means. This makes it possible to instantly adjust tasks based on the user's emotions and present tasks to the user at the optimal timing and with the most appropriate content. Furthermore, it makes it possible to provide appropriate feedback tailored to each individual user, supporting the maintenance of user motivation and growth.

[0204] An "information input means" is a means that has the function of acquiring individual information from end users and providing it to the system.

[0205] A "generation means" is a means that performs the function of automatically generating tasks based on acquired individual information.

[0206] "Presentation means" refers to the means of presenting the generated task to the end user visually or audibly.

[0207] An "emotion analysis tool" is a tool that has the function of analyzing the emotions of end users in real time.

[0208] A "modification tool" is a tool that has the function of adjusting the content and difficulty level of the generated task based on the results of the emotion analysis tool.

[0209] A "progress management system" is a means that has the function of receiving task completion reports from end users and managing the progress status.

[0210] "Analysis means" refers to a means of analyzing progress information obtained by progress management means and generating feedback for end users.

[0211] A "feedback presentation means" is a means that has the function of presenting the feedback generated by the analysis means to the end user.

[0212] This invention realizes a task management system that analyzes the emotions of end users and adjusts tasks based on those emotions. This system is composed of a server, terminals, and users.

[0213] The server first obtains individual information from the user through an information input device, such as age, interests, learning history, and lifestyle habits. This enables the generation of tasks tailored to each user. Based on the acquired information, the server generates tasks using a generation AI model. Furthermore, using emotion analysis devices, the terminal collects emotional data from the user's facial expressions and voice, and adjusts the content and difficulty of the tasks based on that emotional state.

[0214] The terminal presents tasks sent from the server to the end user. These tasks are presented in an appropriate format, taking into account the user's current emotional state, allowing the user to engage with the task in a suitable emotional state. Furthermore, when a task completion report is sent to the server via the progress management system, the server analyzes the progress and emotional data and generates situation-appropriate feedback for the end user.

[0215] For example, if an end-user is browsing specific retail products in a virtual store, and the terminal's emotion analysis system detects joy or excitement, the server can then provide more engaging product suggestions. Conversely, if the user is in a state of displeasure, the system will adjust its recommendations to include relaxing promotions or products with simple purchase procedures.

[0216] By utilizing generative AI models, the system can provide a highly flexible and personalized experience. For example, when a user shows interest in a particular category, the generative AI model optimizes its suggestions using a prompt such as, "Show more products in the category the user is interested in. Recommend premium products when the user is feeling excited, and discount products when they are feeling tired." This allows the system to instantly respond to the user's ever-changing emotions, resulting in a comfortable and meaningful shopping experience.

[0217] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0218] Step 1:

[0219] The server obtains individual information from users through an information input mechanism. This input information includes age, interests, learning history, and lifestyle habits. The obtained information is stored in a database and referenced when generating tasks.

[0220] Step 2:

[0221] The device uses emotion analysis to capture the user's facial expressions and voice in real time and extracts emotion data. Based on this, it determines the user's current emotional state. The acquired emotion data is sent to the server.

[0222] Step 3:

[0223] The server uses acquired individual information and emotional data as input to generate the optimal task using a generative AI model. Prompt statements are utilized in task generation. The generated task is adjusted according to the user's emotional state.

[0224] Step 4:

[0225] The server sends the adjusted task to the terminal. The terminal then presents the task to the user in an appropriate format. For example, when the user is relaxed, the task is presented normally, while when the user is stressed, it is presented in a more user-friendly format.

[0226] Step 5:

[0227] Users work on the assigned tasks and, upon completion, report their completion status to the server through a progress management system. This information is used to verify the task completion status.

[0228] Step 6:

[0229] The server analyzes progress and sentiment data to generate feedback tailored to the user. This feedback includes praise for success and advice for the next steps.

[0230] Step 7:

[0231] The generated feedback is sent to the user through a feedback presentation mechanism. The device receives it and presents it to the user visually or audibly. Based on the feedback, the next steps are devised.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] [Second Embodiment]

[0236] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0237] 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.

[0238] 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).

[0239] 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.

[0240] 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.

[0241] 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).

[0242] 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.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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.

[0247] 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".

[0248] This invention is a digital system that enables personalized task management for children. First, the user (parent or educator) uses an interface on a terminal to input individual information such as the child's age, school learning information, and daily habits. This information is transmitted from the terminal to the server via an information input device.

[0249] The server uses a generation mechanism based on the received information to generate tasks suitable for each child. The generated tasks are individually adjusted based on the learning content, difficulty level, and lifestyle. For example, if information indicates that a child is good at mathematics, tasks including more difficult math problems will be generated.

[0250] Next, the generated task is sent from the server to the terminal via a presentation device and presented to the child. The terminal displays the task content in a way that is easy for the child to understand. This may include visualizations using animations and audio guides.

[0251] The user (child) performs the presented task, and upon completion, sends a completion report to the server via a progress management device through the terminal. The server receives the progress information, analyzes it using an analysis device, and then generates the necessary feedback.

[0252] Finally, the generated feedback is sent to the device via a feedback presentation system and presented to the child. Specifically, it includes praise for task completion and suggestions for the next task to tackle. Through this feedback, the child can recognize their own progress and maintain motivation to continue learning.

[0253] In this way, by providing and managing tasks optimized for each individual child, the system is designed to naturally foster children's perseverance while also making it easier for parents and educators to provide support.

[0254] The following describes the processing flow.

[0255] Step 1:

[0256] The user launches the application on their device and enters individual information such as the child's age, school learning information, and lifestyle habits. The device then sends the entered information to the server via the information input device.

[0257] Step 2:

[0258] The server stores the received user information in a database. The stored information is necessary for the generation mechanism to use in subsequent processing.

[0259] Step 3:

[0260] The server uses a generation mechanism to create personalized tasks suitable for each child, based on individual information about the child obtained from the database. This generation uses an AI algorithm to assign tasks that are appropriate for the child's learning content and lifestyle habits.

[0261] Step 4:

[0262] The server sends the generated task information to the terminal via a presentation device. The terminal receives this information and displays the task on the screen in a way that is easy for children to understand and find interesting.

[0263] Step 5:

[0264] The user (child) works on the presented task. After completion, the user either presses a task completion button on the device or reports the completed task.

[0265] Step 6:

[0266] The terminal sends user task completion information to the server using a progress management system. The transmitted information is received on the server, and the progress is recorded.

[0267] Step 7:

[0268] The server analyzes progress using analytical tools. It determines the degree of task completion and whether follow-up is necessary, and generates feedback for the next learning stage.

[0269] Step 8:

[0270] The server sends the generated feedback to the terminal using a feedback presentation mechanism. The terminal then presents the feedback to the user in a clear and effective manner. This can include motivational messages and advice on the next steps.

[0271] (Example 1)

[0272] 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."

[0273] Providing appropriate educational tasks tailored to the individual age, learning history, and lifestyle of many children is challenging for parents and educators. Furthermore, tracking individual progress and providing effective feedback is also difficult. Traditional systems often fail to adequately provide individualized tasks or generate progress-based feedback, posing challenges in maintaining children's motivation and supporting their continuous learning.

[0274] 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.

[0275] In this invention, the server includes a generation device that automatically generates tasks based on individual information acquired via an information input device, a display device that presents the generated tasks to the user, and an analysis device that analyzes progress and generates feedback. This makes it possible to provide personalized tasks to individual children and to provide educational support with appropriate feedback according to their progress.

[0276] An "information input device" is a device used to acquire individual data from users, and its primary role is to collect necessary information, mainly through an interface.

[0277] A "generation device" is a device that automatically generates tasks suitable for the user based on acquired information, and uses technologies such as generation AI models to construct personalized tasks.

[0278] A "display device" is a device that presents generated tasks to the user in an intuitive and easily understandable format, and includes visual and auditory outputs.

[0279] The "Progress Management Device" is a device that receives reports on the completion status of tasks from users and manages them, and has the role of recording and organizing the achievement status of tasks.

[0280] The "Analysis Device" is a device that analyzes the received progress information and generates feedback suitable for users, and proposes tasks to be taken next and generates words of praise.

[0281] The "Feedback Display Device" is a device that intuitively presents the generated feedback to users, and has a function for visually or auditorily transmitting the content of the feedback.

[0282] This invention is a digital system that realizes personalized task management for individual children. The system is mainly composed of a server, a terminal, and a user.

[0283] The user (parent or educator) uses the terminal to input information about the child. The interface on the terminal is provided with forms for the user to input the child's age, learning information, living habits, etc. For this information input, standard input devices and UI software can be used. For example, it is common to use a tablet terminal or a browser on a personal computer.

[0284] The terminal transmits the input information to the server as a data package. For the transmission, a technology that encrypts the data safely through the SSL / TLS protocol and communicates via the Internet is used.

[0285] The server uses a generated AI model based on the received information to generate tasks. An execution environment for an AI model using a programming language such as Python is prepared, and the model is constructed based on a machine learning library. Specifically, a problem set can be automatically generated according to the child's favorite subject.

[0286] The generated task is sent by the server to the terminal. The communication here also uses a lightweight data format such as JSON, enabling rapid data exchange.

[0287] The terminal displays the received task in a format that is easy for children to understand. Web technologies such as HTML5, CSS, and JavaScript are used to provide an interaction with animations and audio guides.

[0288] As a specific example, it is conceivable that the user inputs a prompt sentence such as "Please propose simple English word learning as the next task" into the generation AI model. Based on this instruction, the server generates an appropriate task and presents it to children as educational content with animations via the terminal.

[0289] With the system constructed in this way, the user can easily provide tasks suitable for children, and children can continue learning while enjoying themselves.

[0290] The flow of the specific process in Example 1 will be described using FIG. 11.

[0291] Step 1:

[0292] The user uses the terminal to input the child's information from the interface. Specifically, the child's age, learning information, living habits, etc. are input into text fields and select boxes. This input data is converted into a specific format (e.g., JSON format) within the terminal and is ready for transmission.

[0293] Step 2:

[0294] The terminal packages the input child's information, encrypts the data using the SSL / TLS protocol, and securely sends it to the server. In this transmission process, the server's API endpoint is accessed via an HTTP request.

[0295] Step 3:

[0296] The server analyzes the received information and launches a generative AI model. Here, a Python machine learning library is used to generate tasks best suited to the child. In this process, the model is given conditions such as age and preferred subjects as input, and based on this, it generates the optimal set of problems.

[0297] Step 4:

[0298] The tasks generated by the server are converted back into JSON format and sent to the terminal. A lightweight communication format is used for this process to ensure rapid data transfer.

[0299] Step 5:

[0300] The device analyzes received tasks and displays them in a user-friendly format. Specifically, it uses HTML5 and JavaScript to render them on the screen as educational content with animations and audio guides.

[0301] Step 6:

[0302] The user (child) completes the presented task and enters the results into the terminal. The terminal collects these results as progress data and prepares to report them to the server. The reported data includes information such as the degree of task completion and the time taken.

[0303] Step 7:

[0304] The terminal sends a completion report to the server. The report data is again encrypted using the SSL / TLS protocol and securely sent to the server's specified API endpoint.

[0305] Step 8:

[0306] The server receives progress data and uses an analysis device to generate feedback. Based on the generated AI model, a feedback message corresponding to the task completion degree is assembled, and a proposal for the next task is made.

[0307] Step 9:

[0308] The server sends the generated feedback to the terminal. The terminal analyzes this feedback and presents it visually or aurally to the user. Specifically, it is displayed as a pop-up window or a voice message, complimenting the user on their progress and guiding the next learning step.

[0309] (Application Example 1)

[0310] 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".

[0311] In modern educational settings, there is an issue that educational content tailored to children's individual interests and learning progress is not provided sufficiently. As a result, children's learning motivation has declined, and the educational effect has not been fully exerted, which has become a problem. Furthermore, due to the lack of a mechanism to appropriately propose the next educational activity based on children's feedback, it is difficult to optimize individual learning experiences.

[0312] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0313] In this invention, the server includes means for inputting information, means for generating, and means for analyzing. Thereby, it becomes possible to automatically generate personalized educational content based on the individual information of the user and present it in a visually and easily understandable form. Also, by accurately proposing the next content and learning activities according to the user's interests and progress, individual learning experiences can be optimized and the educational effect can be enhanced.

[0314] "Means of inputting information" refers to an interface that allows users to input individual information into an electronic device.

[0315] "Generating means" refers to a function that automatically creates appropriate learning tasks and audiovisual content based on the individual information entered.

[0316] "Means of presentation" refers to functions for presenting generated learning tasks or audiovisual content to users visually or audibly.

[0317] "Means for managing progress" refers to a function that allows users to receive reports on completed tasks and record their progress.

[0318] "Means of analysis" refers to the function of analyzing received progress information and identifying the next feedback or content that should be provided.

[0319] "Means of providing feedback" refers to functions that provide users with feedback generated based on analysis results, either visually or audibly.

[0320] "Audiovisual content" refers to media that conveys information to users visually or aurally, and provides education or entertainment.

[0321] "Learning activities" refer to a series of processes and tasks in which users are involved in order to achieve specific educational goals.

[0322] This document describes a mode for carrying out the invention. The invention is a digital system that provides personalized educational content for children. The system primarily consists of server, terminal, and user interaction.

[0323] First, the user enters individual information such as the child's age, interests, and learning history through the user interface (UI) on their device. This UI is built with React Native and runs on smartphones and tablet devices. The entered information is sent to AWS servers via a secure communication protocol.

[0324] The server is built using a Python program with Django, and it analyzes the information it receives. Based on the analyzed information, it sends specific prompt messages to a generative AI model, which then generates or selects educational content suitable for the screen. An example of such a prompt message would be, "Based on the user's information, please suggest the most suitable educational content for a 5-year-old with interests in space and an intermediate learning level."

[0325] The generated content is sent to the device and presented to the user through visual effects and audio guidance. This presentation uses streaming technology, including visually easy-to-understand animations. Once the user completes the presented content, their progress is reported back to the server, which analyzes the progress data to suggest the next content or task.

[0326] In this way, it functions as a system that improves children's motivation to learn by continuously providing learning content optimized for them and appropriately managing their learning progress.

[0327] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0328] Step 1:

[0329] The user enters the child's age, interests, and learning history using a React Native-based UI on their device. The entered information is securely sent to an AWS server using the HTTPS protocol. Input: Individual information of the child; Output: Information sent to the server. Specifically, the user enters or selects text or options into form fields and then clicks the "Submit" button.

[0330] Step 2:

[0331] The server analyzes the individual information it receives. A script implemented in Python analyzes the information and creates prompts to request educational content tailored to the user's learning needs from an AI model. Input: Individual information sent by the user; Output: Generated prompts. Specifically, the algorithm scores the user information and selects the appropriate prompt.

[0332] Step 3:

[0333] The server sends prompt messages to a generation AI model, which then generates and selects appropriate educational content. Once the AI ​​model generates content, the results are returned to the server. Input: prompt messages, Output: generated educational content. Specifically, prompt messages are sent to an external AI service via an API, and the best-matching content links and data are returned.

[0334] Step 4:

[0335] The server sends the generated educational content to the device. The device uses streaming technology to visually present the content to the user. Input: generated educational content, Output: visual presentation. Specifically, a video player is launched on the device and animation playback begins.

[0336] Step 5:

[0337] When a user completes a task, they press a button on their device, and the progress information is reported to the server. Input: Task completion information, Output: Progress report to the server. Specifically, when the "Complete" button is pressed, the progress information is automatically sent to the server.

[0338] Step 6:

[0339] The server receives progress information and analyzes the data. It then generates recommended learning content and tasks. Input: progress information; Output: recommended content. Specifically, it queries the database and performs calculations to select the best next action.

[0340] Step 7:

[0341] The server sends recommended content to the device and provides feedback. The user receives the suggested content as the next step. Input: Recommended content, Output: Presentation of feedback to the user. Specifically, text messages and links to the next content are displayed on the screen.

[0342] 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.

[0343] This invention relates to a personalized task management system that takes user emotions into consideration. This system generates tasks using generative AI based on the user's individual information, but by incorporating a new emotion engine, it achieves more appropriate task presentation and feedback.

[0344] First, the user enters information such as the child's age, interests, learning history, and lifestyle through the device's interface. This information is sent to the server via the input device and stored in the database.

[0345] Next, the server uses an emotion engine to analyze the user's emotions. This emotion analysis uses facial recognition or voice analysis technology to observe the user's reactions when working on tasks and receiving feedback. The results are then fed back into the task generation process.

[0346] The server generates tasks using a generation mechanism, but adjusts them based on the analysis results of the emotion engine. This adjustment optimizes the difficulty and content of the tasks according to the user's current emotional state.

[0347] The generated tasks are sent from the server to the terminal. The terminal presents the tasks in an emotionally sensitive manner, ensuring that the user can engage with them in an appropriate emotional state. For example, if the user is feeling anxious or stressed, a more approachable task can be presented.

[0348] When a user completes a task, the device sends that information to the server using a progress management system. The server analyzes the progress and sentiment data and generates feedback tailored to the user's state using an analysis system.

[0349] Finally, the generated feedback is sent to the device using a feedback presentation mechanism and provided to the user. This feedback celebrates the user's success and efforts, and includes advice and encouragement tailored to their emotional state.

[0350] For example, if a user achieves a goal and their feelings of joy are recognized, the system can then suggest a more challenging task. In this way, the system can provide an optimal learning experience tailored to the user's emotional state, supporting their continuous growth.

[0351] The following describes the processing flow.

[0352] Step 1:

[0353] The user uses a device to input individual information about their child, such as their age, learning history, interests, and lifestyle. The device then sends this information to the server using an information input device.

[0354] Step 2:

[0355] The server stores the received individual information in a database. Simultaneously, the emotion engine begins collecting real-time emotional data from the user. This is done by analyzing facial expressions and voice tone using a camera and microphone.

[0356] Step 3:

[0357] The server activates the generation mechanism and automatically generates tasks based on the acquired individual information. At this time, it adjusts the content and difficulty of the tasks according to the user's emotional state based on the emotional data obtained from the emotion engine.

[0358] Step 4:

[0359] The server sends the generated task information to the terminal using a presentation mechanism. The terminal receives it and presents the task in an appropriate format according to the user's emotional state. For example, it can prepare messages or animations that help alleviate stress.

[0360] Step 5:

[0361] The user (child) works on a task. Emotional changes during the task are continuously analyzed by the emotion engine and sent to the server.

[0362] Step 6:

[0363] When a user completes a task, the device sends the completion information to the server using a progress management system.

[0364] Step 7:

[0365] The server analyzes progress information and emotional data using analytical tools. This generates feedback tailored to performance and emotional state.

[0366] Step 8:

[0367] The server sends the generated feedback to the terminal via a feedback presentation mechanism. The terminal then presents the user with encouraging messages and advice for the next task, taking into account their emotional state.

[0368] (Example 2)

[0369] 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".

[0370] Traditional task management systems have a problem where generating tasks without considering individual user attribute data or emotional states can easily lead to decreased user motivation and efficiency. In particular, since a user's emotional state significantly impacts success in learning and work, it is essential to understand and address it appropriately. Therefore, a new system is needed that provides individually optimized tasks that take user emotions into account, enabling users to engage with tasks in the best possible state.

[0371] 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.

[0372] In this invention, the server includes information input means, generation means, and analysis means. This enables the generation of tasks optimized based on the user's individual attribute data and emotional state. Specifically, the user's attribute data is input using a generation AI model, and the task is adjusted considering the emotional state. As a result, the task is presented to the user in an emotionally appropriate manner through the presentation means, and the data collected by the progress management means is analyzed to provide feedback according to the user's emotional state. This makes it possible to support continuous growth while improving the user's motivation and learning efficiency.

[0373] An "information input method" is a mechanism that receives individual attribute data from a user and sends that data to a server.

[0374] A "generative AI model" is an algorithm that generates tasks based on a user's individual attribute data and adjusts the content of those tasks while taking their emotional state into consideration.

[0375] A "generation method" refers to a configuration that uses a generation AI model to create tasks based on user data and has the functionality to optimize the content of those tasks.

[0376] A "presentation method" is a way of displaying generated tasks to the user in an emotionally appropriate manner and providing an interface that takes user reactions into consideration.

[0377] A "progress management system" is a process for receiving task completion reports from users and managing and recording the progress status.

[0378] "Analysis means" refers to a function that integrates and analyzes user progress and sentiment data within the server to generate feedback.

[0379] A "feedback presentation method" is a system that provides users with feedback generated by an analysis method, offering advice and encouragement tailored to their emotional state.

[0380] This invention provides an advanced task management system that takes into account user emotions and individual attribute data. The system consists of a user terminal, a server, and software that works in conjunction with them.

[0381] Users input individual attribute data using the device's user interface. This includes the child's age, interests, learning history, and lifestyle. This data is sent to the server via an application on the device. The server stores the received information in a database and performs sentiment analysis using an emotion engine. This sentiment analysis utilizes facial recognition and voice analysis technologies to identify the user's emotional state (e.g., joy, sadness, stress).

[0382] The server utilizes a generative AI model to generate tasks based on the user's emotional state and attribute data. This AI model employs text generation and data analysis algorithms and is adjusted to provide tasks appropriate to the user's current emotions. For example, if a user is feeling stressed, the system can generate simple tasks that include relaxing content.

[0383] The generated tasks are sent from the server to the terminal and presented to the user. The terminal displays the tasks in a visually clear and emotionally sensitive manner to encourage user engagement. Once the user completes a task, the terminal sends progress information to the server. The server then analyzes the progress and emotional data to generate feedback. This feedback is designed to be positive for the user, including messages that enhance their sense of accomplishment and encouragement for the next step.

[0384] For example, if a user enters "insects" as their interest and their learning history records "good at science," the generated tasks might include an insect-related quiz or creating a simple observation journal. An example of a prompt might be: "An 8-year-old child is interested in insects and is good at science. Please suggest some fun, quiz-style tasks related to insects."

[0385] In this way, the system can perform personalized task management that reflects the user's emotional state and individual attributes. This allows users to approach tasks in an optimal state, enabling them to learn and grow more efficiently.

[0386] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0387] Step 1:

[0388] The user uses their device to input individual attribute data such as the child's age, interests, learning history, and lifestyle.

[0389] The entered information is converted into digital data through the terminal's application and transmitted to the server via the information input device. This creates an initial dataset of user attributes.

[0390] Step 2:

[0391] The server saves the received individual attribute data to the database.

[0392] In this process, a unique ID is assigned to each user, and the data is stored in a structured format. The stored data serves as foundational data referenced in subsequent analysis and generation processes.

[0393] Step 3:

[0394] The server utilizes a generative AI model to generate tasks based on the user's individual attribute data.

[0395] In this process, data is input to the AI ​​model using prompt statements (e.g., "An 8-year-old child is interested in insects and is good at science. Please suggest a fun quiz-style task related to insects."), and the generated data returned is formatted appropriately to create an output task suitable for the user.

[0396] Step 4:

[0397] The server uses an emotion engine to analyze the user's emotional state.

[0398] The input consists of images and audio data from a facial recognition camera. Based on this, an emotion analysis algorithm outputs the user's current emotional state (e.g., degree of joy, sadness, stress) as numerical data.

[0399] Step 5:

[0400] The tasks generated by the generation method are adjusted taking into account the results of sentiment analysis.

[0401] The server adjusts the difficulty and content of tasks based on the user's emotional state, and regenerates optimized tasks. This ensures that the tasks are appropriate for the user's current emotional state.

[0402] Step 6:

[0403] Finally, the generated tasks are sent from the server to the terminal and displayed to the user through a presentation mechanism.

[0404] On the device, tasks are presented through a visually intuitive interface, and feedback and animations may be added to take into account the user's emotional state.

[0405] Step 7:

[0406] The user completes the task and enters a completion notification on their device.

[0407] The terminal sends this information to the server through a progress management system, and the completion status is recorded on the server.

[0408] Step 8:

[0409] The server integrates and analyzes the acquired progress data and sentiment data to generate feedback.

[0410] Using analytical tools, the system celebrates user successes and generates advice for the next task. The generated feedback is output as numerical information (such as completion rate) and text messages.

[0411] Step 9:

[0412] The generated feedback is sent to the terminal via a feedback presentation mechanism and presented to the user.

[0413] The device can display feedback in a more easily understandable format and deliver messages of gratitude and encouragement to users.

[0414] (Application Example 2)

[0415] 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."

[0416] Many task management systems present tasks without considering the user's feelings, sometimes resulting in tasks being presented in an inappropriate way or at an inappropriate time. This can increase user stress and decrease motivation. Furthermore, traditional systems often fail to provide sufficient feedback tailored to individual user responses, making it difficult to support users' continuous growth.

[0417] 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.

[0418] In this invention, the server includes an information input means, an emotion analysis means, a generation means using a generation AI, an adjustment means for adjusting tasks according to emotions, a progress management means, and a feedback presentation means. This makes it possible to instantly adjust tasks based on the user's emotions and present tasks to the user at the optimal timing and with the most appropriate content. Furthermore, it makes it possible to provide appropriate feedback tailored to each individual user, supporting the maintenance of user motivation and growth.

[0419] An "information input means" is a means that has the function of acquiring individual information from end users and providing it to the system.

[0420] A "generation means" is a means that performs the function of automatically generating tasks based on acquired individual information.

[0421] "Presentation means" refers to the means of presenting the generated task to the end user visually or audibly.

[0422] An "emotion analysis tool" is a tool that has the function of analyzing the emotions of end users in real time.

[0423] A "modification tool" is a tool that has the function of adjusting the content and difficulty level of the generated task based on the results of the emotion analysis tool.

[0424] A "progress management system" is a means that has the function of receiving task completion reports from end users and managing the progress status.

[0425] "Analysis means" refers to a means of analyzing progress information obtained by progress management means and generating feedback for end users.

[0426] A "feedback presentation means" is a means that has the function of presenting the feedback generated by the analysis means to the end user.

[0427] This invention realizes a task management system that analyzes the emotions of end users and adjusts tasks based on those emotions. This system is composed of a server, terminals, and users.

[0428] The server first obtains individual information from the user through an information input device, such as age, interests, learning history, and lifestyle habits. This enables the generation of tasks tailored to each user. Based on the acquired information, the server generates tasks using a generation AI model. Furthermore, using emotion analysis devices, the terminal collects emotional data from the user's facial expressions and voice, and adjusts the content and difficulty of the tasks based on that emotional state.

[0429] The terminal presents tasks sent from the server to the end user. These tasks are presented in an appropriate format, taking into account the user's current emotional state, allowing the user to engage with the task in a suitable emotional state. Furthermore, when a task completion report is sent to the server via the progress management system, the server analyzes the progress and emotional data and generates situation-appropriate feedback for the end user.

[0430] For example, if an end-user is browsing specific retail products in a virtual store, and the terminal's emotion analysis system detects joy or excitement, the server can then provide more engaging product suggestions. Conversely, if the user is in a state of displeasure, the system will adjust its recommendations to include relaxing promotions or products with simple purchase procedures.

[0431] By utilizing generative AI models, the system can provide a highly flexible and personalized experience. For example, when a user shows interest in a particular category, the generative AI model optimizes its suggestions using a prompt such as, "Show more products in the category the user is interested in. Recommend premium products when the user is feeling excited, and discount products when they are feeling tired." This allows the system to instantly respond to the user's ever-changing emotions, resulting in a comfortable and meaningful shopping experience.

[0432] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0433] Step 1:

[0434] The server obtains individual information from users through an information input mechanism. This input information includes age, interests, learning history, and lifestyle habits. The obtained information is stored in a database and referenced when generating tasks.

[0435] Step 2:

[0436] The device uses emotion analysis to capture the user's facial expressions and voice in real time and extracts emotion data. Based on this, it determines the user's current emotional state. The acquired emotion data is sent to the server.

[0437] Step 3:

[0438] The server uses acquired individual information and emotional data as input to generate the optimal task using a generative AI model. Prompt statements are utilized in task generation. The generated task is adjusted according to the user's emotional state.

[0439] Step 4:

[0440] The server sends the adjusted task to the terminal. The terminal then presents the task to the user in an appropriate format. For example, when the user is relaxed, the task is presented normally, while when the user is stressed, it is presented in a more user-friendly format.

[0441] Step 5:

[0442] Users work on the assigned tasks and, upon completion, report their completion status to the server through a progress management system. This information is used to verify the task completion status.

[0443] Step 6:

[0444] The server analyzes progress and sentiment data to generate feedback tailored to the user. This feedback includes praise for success and advice for the next steps.

[0445] Step 7:

[0446] The generated feedback is sent to the user through a feedback presentation mechanism. The device receives it and presents it to the user visually or audibly. Based on the feedback, the next steps are devised.

[0447] 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.

[0448] 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.

[0449] 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.

[0450] [Third Embodiment]

[0451] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0452] 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.

[0453] 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).

[0454] 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.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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.

[0460] 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.

[0461] 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.

[0462] 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".

[0463] This invention is a digital system that enables personalized task management for children. First, the user (parent or educator) uses an interface on a terminal to input individual information such as the child's age, school learning information, and daily habits. This information is transmitted from the terminal to the server via an information input device.

[0464] The server uses a generation mechanism based on the received information to generate tasks suitable for each child. The generated tasks are individually adjusted based on the learning content, difficulty level, and lifestyle. For example, if information indicates that a child is good at mathematics, tasks including more difficult math problems will be generated.

[0465] Next, the generated task is sent from the server to the terminal via a presentation device and presented to the child. The terminal displays the task content in a way that is easy for the child to understand. This may include visualizations using animations and audio guides.

[0466] The user (child) performs the presented task, and upon completion, sends a completion report to the server via a progress management device through the terminal. The server receives the progress information, analyzes it using an analysis device, and then generates the necessary feedback.

[0467] Finally, the generated feedback is sent to the device via a feedback presentation system and presented to the child. Specifically, it includes praise for task completion and suggestions for the next task to tackle. Through this feedback, the child can recognize their own progress and maintain motivation to continue learning.

[0468] In this way, by providing and managing tasks optimized for each individual child, the system is designed to naturally foster children's perseverance while also making it easier for parents and educators to provide support.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The user launches the application on their device and enters individual information such as the child's age, school learning information, and lifestyle habits. The device then sends the entered information to the server via the information input device.

[0472] Step 2:

[0473] The server stores the received user information in a database. The stored information is necessary for the generation mechanism to use in subsequent processing.

[0474] Step 3:

[0475] The server uses a generation mechanism to create personalized tasks suitable for each child, based on individual information about the child obtained from the database. This generation uses an AI algorithm to assign tasks that are appropriate for the child's learning content and lifestyle habits.

[0476] Step 4:

[0477] The server sends the generated task information to the terminal via a presentation device. The terminal receives this information and displays the task on the screen in a way that is easy for children to understand and find interesting.

[0478] Step 5:

[0479] The user (child) works on the presented task. After completion, the user either presses a task completion button on the device or reports the completed task.

[0480] Step 6:

[0481] The terminal sends user task completion information to the server using a progress management system. The transmitted information is received on the server, and the progress is recorded.

[0482] Step 7:

[0483] The server analyzes progress using analytical tools. It determines the degree of task completion and whether follow-up is necessary, and generates feedback for the next learning stage.

[0484] Step 8:

[0485] The server sends the generated feedback to the terminal using a feedback presentation mechanism. The terminal then presents the feedback to the user in a clear and effective manner. This can include motivational messages and advice on the next steps.

[0486] (Example 1)

[0487] 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."

[0488] Providing appropriate educational tasks tailored to the individual age, learning history, and lifestyle of many children is challenging for parents and educators. Furthermore, tracking individual progress and providing effective feedback is also difficult. Traditional systems often fail to adequately provide individualized tasks or generate progress-based feedback, posing challenges in maintaining children's motivation and supporting their continuous learning.

[0489] 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.

[0490] In this invention, the server includes a generation device that automatically generates tasks based on individual information acquired via an information input device, a display device that presents the generated tasks to the user, and an analysis device that analyzes progress and generates feedback. This makes it possible to provide personalized tasks to individual children and to provide educational support with appropriate feedback according to their progress.

[0491] An "information input device" is a device used to acquire individual data from users, and its primary role is to collect necessary information, mainly through an interface.

[0492] A "generation device" is a device that automatically generates tasks suitable for the user based on acquired information, and uses technologies such as generation AI models to construct personalized tasks.

[0493] A "display device" is a device that presents generated tasks to the user in an intuitive and easily understandable format, and includes visual and auditory outputs.

[0494] A "progress management device" is a device that receives and manages reports from users regarding the completion status of tasks, and its role is to record and organize the achievement status of tasks.

[0495] An "analysis device" is a device that analyzes received progress information and generates feedback tailored to the user, such as suggesting the next task to tackle or generating words of praise.

[0496] A "feedback display device" is a device that intuitively presents generated feedback to the user and is equipped with functions to communicate the content of the feedback visually or audibly.

[0497] This invention is a digital system that provides personalized task management for individual children. The system mainly consists of a server, terminals, and users.

[0498] The user (parent or educator) uses a device to input information about their child. The device interface includes forms for the user to input information such as the child's age, learning information, and lifestyle habits. Standard input devices and UI software can be used for this information input. For example, tablet devices and PC browsers are commonly used.

[0499] The terminal sends the entered information to the server as a data package. This transmission uses SSL / TLS protocol to securely encrypt the data and communicate over the internet.

[0500] The server generates tasks using a generative AI model based on the received information. An execution environment for the AI ​​model, written in programming languages ​​such as Python, is provided, and the model is built on machine learning libraries. Specifically, it can automatically generate problem sets tailored to each child's strengths in different subjects.

[0501] The generated tasks are sent to the terminal by the server. This communication also utilizes lightweight data formats such as JSON, enabling rapid data exchange.

[0502] The device displays received tasks in a format that is easy for children to understand. Web technologies such as HTML5, CSS, and JavaScript are used to provide interactions that include animations and audio guidance.

[0503] As a concrete example, a user could input a prompt into the AI ​​model saying, "Please suggest learning some simple English vocabulary as the next task." Based on this instruction, the server would generate an appropriate task, which would then be presented to the child via the device as animated educational content.

[0504] This system allows users to easily provide tasks tailored to children, and enables children to learn while having fun.

[0505] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0506] Step 1:

[0507] The user uses a device to input information about their child through the interface. Specifically, they enter the child's age, learning information, lifestyle habits, etc., into text fields and select boxes. This input data is converted within the device into a specific format (e.g., JSON format) and ready for transmission.

[0508] Step 2:

[0509] The device packages the entered child information, encrypts the data using the SSL / TLS protocol, and securely sends it to the server. This transmission process accesses the server's API endpoint via HTTP requests.

[0510] Step 3:

[0511] The server analyzes the received information and launches a generative AI model. Here, a Python machine learning library is used to generate tasks best suited to the child. In this process, the model is given conditions such as age and preferred subjects as input, and based on this, it generates the optimal set of problems.

[0512] Step 4:

[0513] The tasks generated by the server are converted back into JSON format and sent to the terminal. A lightweight communication format is used for this process to ensure rapid data transfer.

[0514] Step 5:

[0515] The device analyzes received tasks and displays them in a user-friendly format. Specifically, it uses HTML5 and JavaScript to render them on the screen as educational content with animations and audio guides.

[0516] Step 6:

[0517] The user (child) completes the presented task and enters the results into the terminal. The terminal collects these results as progress data and prepares to report them to the server. The reported data includes information such as the degree of task completion and the time taken.

[0518] Step 7:

[0519] The terminal sends a completion report to the server. The report data is again encrypted using the SSL / TLS protocol and securely sent to the server's specified API endpoint.

[0520] Step 8:

[0521] The server receives progress data and uses an analysis device to generate feedback. The generating AI model constructs feedback messages based on the degree of task completion and suggests the next task.

[0522] Step 9:

[0523] The server sends the generated feedback to the device. The device analyzes this feedback and presents it to the user visually or audibly. Specifically, it displays it as a pop-up window or voice message, offering praise for the user's progress and guiding them to the next learning step.

[0524] (Application Example 1)

[0525] 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."

[0526] In modern education, there is a challenge in providing educational content that is not adequately tailored to each child's individual interests and learning pace. This leads to decreased motivation and insufficient educational effectiveness. Furthermore, the lack of mechanisms to appropriately suggest subsequent educational activities based on children's feedback makes it difficult to optimize individual learning experiences.

[0527] 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.

[0528] In this invention, the server includes means for inputting information, means for generating information, and means for analyzing information. This makes it possible to automatically generate personalized educational content based on the user's individual information and present it in an audiovisually easy-to-understand format. Furthermore, by accurately suggesting the next content and learning activities according to the user's interests and progress, it is possible to optimize the individual learning experience and enhance educational effectiveness.

[0529] "Means of inputting information" refers to an interface that allows users to input individual information into an electronic device.

[0530] "Generating means" refers to a function that automatically creates appropriate learning tasks and audiovisual content based on the individual information entered.

[0531] "Means of presentation" refers to functions for presenting generated learning tasks or audiovisual content to users visually or audibly.

[0532] "Means for managing progress" refers to a function that allows users to receive reports on completed tasks and record their progress.

[0533] "Means of analysis" refers to the function of analyzing received progress information and identifying the next feedback or content that should be provided.

[0534] "Means of providing feedback" refers to functions that provide users with feedback generated based on analysis results, either visually or audibly.

[0535] "Audiovisual content" refers to media that conveys information to users visually or aurally, and provides education or entertainment.

[0536] "Learning activities" refer to a series of processes and tasks in which users are involved in order to achieve specific educational goals.

[0537] This document describes a mode for carrying out the invention. The invention is a digital system that provides personalized educational content for children. The system primarily consists of server, terminal, and user interaction.

[0538] First, the user enters individual information such as the child's age, interests, and learning history through the user interface (UI) on their device. This UI is built with React Native and runs on smartphones and tablet devices. The entered information is sent to AWS servers via a secure communication protocol.

[0539] The server is built using a Python program with Django, and it analyzes the information it receives. Based on the analyzed information, it sends specific prompt messages to a generative AI model, which then generates or selects educational content suitable for the screen. An example of such a prompt message would be, "Based on the user's information, please suggest the most suitable educational content for a 5-year-old with interests in space and an intermediate learning level."

[0540] The generated content is sent to the device and presented to the user through visual effects and audio guidance. This presentation uses streaming technology, including visually easy-to-understand animations. Once the user completes the presented content, their progress is reported back to the server, which analyzes the progress data to suggest the next content or task.

[0541] In this way, it functions as a system that improves children's motivation to learn by continuously providing learning content optimized for them and appropriately managing their learning progress.

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The user enters the child's age, interests, and learning history using a React Native-based UI on their device. The entered information is securely sent to an AWS server using the HTTPS protocol. Input: Individual information of the child; Output: Information sent to the server. Specifically, the user enters or selects text or options into form fields and then clicks the "Submit" button.

[0545] Step 2:

[0546] The server analyzes the individual information it receives. A script implemented in Python analyzes the information and creates prompts to request educational content tailored to the user's learning needs from an AI model. Input: Individual information sent by the user; Output: Generated prompts. Specifically, the algorithm scores the user information and selects the appropriate prompt.

[0547] Step 3:

[0548] The server sends prompt messages to a generation AI model, which then generates and selects appropriate educational content. Once the AI ​​model generates content, the results are returned to the server. Input: prompt messages, Output: generated educational content. Specifically, prompt messages are sent to an external AI service via an API, and the best-matching content links and data are returned.

[0549] Step 4:

[0550] The server sends the generated educational content to the device. The device uses streaming technology to visually present the content to the user. Input: generated educational content, Output: visual presentation. Specifically, a video player is launched on the device and animation playback begins.

[0551] Step 5:

[0552] When a user completes a task, they press a button on their device, and the progress information is reported to the server. Input: Task completion information, Output: Progress report to the server. Specifically, when the "Complete" button is pressed, the progress information is automatically sent to the server.

[0553] Step 6:

[0554] The server receives progress information and analyzes the data. It then generates recommended learning content and tasks. Input: progress information; Output: recommended content. Specifically, it queries the database and performs calculations to select the best next action.

[0555] Step 7:

[0556] The server sends recommended content to the device and provides feedback. The user receives the suggested content as the next step. Input: Recommended content, Output: Presentation of feedback to the user. Specifically, text messages and links to the next content are displayed on the screen.

[0557] 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.

[0558] This invention relates to a personalized task management system that takes user emotions into consideration. This system generates tasks using generative AI based on the user's individual information, but by incorporating a new emotion engine, it achieves more appropriate task presentation and feedback.

[0559] First, the user enters information such as the child's age, interests, learning history, and lifestyle through the device's interface. This information is sent to the server via the input device and stored in the database.

[0560] Next, the server uses an emotion engine to analyze the user's emotions. This emotion analysis uses facial recognition or voice analysis technology to observe the user's reactions when working on tasks and receiving feedback. The results are then fed back into the task generation process.

[0561] The server generates tasks using a generation mechanism, but adjusts them based on the analysis results of the emotion engine. This adjustment optimizes the difficulty and content of the tasks according to the user's current emotional state.

[0562] The generated tasks are sent from the server to the terminal. The terminal presents the tasks in an emotionally sensitive manner, ensuring that the user can engage with them in an appropriate emotional state. For example, if the user is feeling anxious or stressed, a more approachable task can be presented.

[0563] When a user completes a task, the device sends that information to the server using a progress management system. The server analyzes the progress and sentiment data and generates feedback tailored to the user's state using an analysis system.

[0564] Finally, the generated feedback is sent to the device using a feedback presentation mechanism and provided to the user. This feedback celebrates the user's success and efforts, and includes advice and encouragement tailored to their emotional state.

[0565] For example, if a user achieves a goal and their feelings of joy are recognized, the system can then suggest a more challenging task. In this way, the system can provide an optimal learning experience tailored to the user's emotional state, supporting their continuous growth.

[0566] The following describes the processing flow.

[0567] Step 1:

[0568] The user uses a device to input individual information about their child, such as their age, learning history, interests, and lifestyle. The device then sends this information to the server using an information input device.

[0569] Step 2:

[0570] The server stores the received individual information in a database. Simultaneously, the emotion engine begins collecting real-time emotional data from the user. This is done by analyzing facial expressions and voice tone using a camera and microphone.

[0571] Step 3:

[0572] The server activates the generation mechanism and automatically generates tasks based on the acquired individual information. At this time, it adjusts the content and difficulty of the tasks according to the user's emotional state based on the emotional data obtained from the emotion engine.

[0573] Step 4:

[0574] The server sends the generated task information to the terminal using a presentation mechanism. The terminal receives it and presents the task in an appropriate format according to the user's emotional state. For example, it can prepare messages or animations that help alleviate stress.

[0575] Step 5:

[0576] The user (child) works on a task. Emotional changes during the task are continuously analyzed by the emotion engine and sent to the server.

[0577] Step 6:

[0578] When a user completes a task, the device sends the completion information to the server using a progress management system.

[0579] Step 7:

[0580] The server analyzes progress information and emotional data using analytical tools. This generates feedback tailored to performance and emotional state.

[0581] Step 8:

[0582] The server sends the generated feedback to the terminal via a feedback presentation mechanism. The terminal then presents the user with encouraging messages and advice for the next task, taking into account their emotional state.

[0583] (Example 2)

[0584] 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."

[0585] Traditional task management systems have a problem where generating tasks without considering individual user attribute data or emotional states can easily lead to decreased user motivation and efficiency. In particular, since a user's emotional state significantly impacts success in learning and work, it is essential to understand and address it appropriately. Therefore, a new system is needed that provides individually optimized tasks that take user emotions into account, enabling users to engage with tasks in the best possible state.

[0586] 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.

[0587] In this invention, the server includes information input means, generation means, and analysis means. This enables the generation of tasks optimized based on the user's individual attribute data and emotional state. Specifically, the user's attribute data is input using a generation AI model, and the task is adjusted considering the emotional state. As a result, the task is presented to the user in an emotionally appropriate manner through the presentation means, and the data collected by the progress management means is analyzed to provide feedback according to the user's emotional state. This makes it possible to support continuous growth while improving the user's motivation and learning efficiency.

[0588] An "information input method" is a mechanism that receives individual attribute data from a user and sends that data to a server.

[0589] A "generative AI model" is an algorithm that generates tasks based on a user's individual attribute data and adjusts the content of those tasks while taking their emotional state into consideration.

[0590] A "generation method" refers to a configuration that uses a generation AI model to create tasks based on user data and has the functionality to optimize the content of those tasks.

[0591] A "presentation method" is a way of displaying generated tasks to the user in an emotionally appropriate manner and providing an interface that takes user reactions into consideration.

[0592] A "progress management system" is a process for receiving task completion reports from users and managing and recording the progress status.

[0593] "Analysis means" refers to a function that integrates and analyzes user progress and sentiment data within the server to generate feedback.

[0594] A "feedback presentation method" is a system that provides users with feedback generated by an analysis method, offering advice and encouragement tailored to their emotional state.

[0595] This invention provides an advanced task management system that takes into account user emotions and individual attribute data. The system consists of a user terminal, a server, and software that works in conjunction with them.

[0596] Users input individual attribute data using the device's user interface. This includes the child's age, interests, learning history, and lifestyle. This data is sent to the server via an application on the device. The server stores the received information in a database and performs sentiment analysis using an emotion engine. This sentiment analysis utilizes facial recognition and voice analysis technologies to identify the user's emotional state (e.g., joy, sadness, stress).

[0597] The server utilizes a generative AI model to generate tasks based on the user's emotional state and attribute data. This AI model employs text generation and data analysis algorithms and is adjusted to provide tasks appropriate to the user's current emotions. For example, if a user is feeling stressed, the system can generate simple tasks that include relaxing content.

[0598] The generated tasks are sent from the server to the terminal and presented to the user. The terminal displays the tasks in a visually clear and emotionally sensitive manner to encourage user engagement. Once the user completes a task, the terminal sends progress information to the server. The server then analyzes the progress and emotional data to generate feedback. This feedback is designed to be positive for the user, including messages that enhance their sense of accomplishment and encouragement for the next step.

[0599] For example, if a user enters "insects" as their interest and their learning history records "good at science," the generated tasks might include an insect-related quiz or creating a simple observation journal. An example of a prompt might be: "An 8-year-old child is interested in insects and is good at science. Please suggest some fun, quiz-style tasks related to insects."

[0600] In this way, the system can perform personalized task management that reflects the user's emotional state and individual attributes. This allows users to approach tasks in an optimal state, enabling them to learn and grow more efficiently.

[0601] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0602] Step 1:

[0603] The user uses their device to input individual attribute data such as the child's age, interests, learning history, and lifestyle.

[0604] The entered information is converted into digital data through the terminal's application and transmitted to the server via the information input device. This creates an initial dataset of user attributes.

[0605] Step 2:

[0606] The server saves the received individual attribute data to the database.

[0607] In this process, a unique ID is assigned to each user, and the data is stored in a structured format. The stored data serves as foundational data referenced in subsequent analysis and generation processes.

[0608] Step 3:

[0609] The server utilizes a generative AI model to generate tasks based on the user's individual attribute data.

[0610] In this process, data is input to the AI ​​model using prompt statements (e.g., "An 8-year-old child is interested in insects and is good at science. Please suggest a fun quiz-style task related to insects."), and the generated data returned is formatted appropriately to create an output task suitable for the user.

[0611] Step 4:

[0612] The server uses an emotion engine to analyze the user's emotional state.

[0613] The input consists of images and audio data from a facial recognition camera. Based on this, an emotion analysis algorithm outputs the user's current emotional state (e.g., degree of joy, sadness, stress) as numerical data.

[0614] Step 5:

[0615] The tasks generated by the generation method are adjusted taking into account the results of sentiment analysis.

[0616] The server adjusts the difficulty and content of tasks based on the user's emotional state, and regenerates optimized tasks. This ensures that the tasks are appropriate for the user's current emotional state.

[0617] Step 6:

[0618] Finally, the generated tasks are sent from the server to the terminal and displayed to the user through a presentation mechanism.

[0619] On the device, tasks are presented through a visually intuitive interface, and feedback and animations may be added to take into account the user's emotional state.

[0620] Step 7:

[0621] The user completes the task and enters a completion notification on their device.

[0622] The terminal sends this information to the server through a progress management system, and the completion status is recorded on the server.

[0623] Step 8:

[0624] The server integrates and analyzes the acquired progress data and sentiment data to generate feedback.

[0625] Using analytical tools, the system celebrates user successes and generates advice for the next task. The generated feedback is output as numerical information (such as completion rate) and text messages.

[0626] Step 9:

[0627] The generated feedback is sent to the terminal via a feedback presentation mechanism and presented to the user.

[0628] The device can display feedback in a more easily understandable format and deliver messages of gratitude and encouragement to users.

[0629] (Application Example 2)

[0630] 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."

[0631] Many task management systems present tasks without considering the user's feelings, sometimes resulting in tasks being presented in an inappropriate way or at an inappropriate time. This can increase user stress and decrease motivation. Furthermore, traditional systems often fail to provide sufficient feedback tailored to individual user responses, making it difficult to support users' continuous growth.

[0632] 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.

[0633] In this invention, the server includes an information input means, an emotion analysis means, a generation means using a generation AI, an adjustment means for adjusting tasks according to emotions, a progress management means, and a feedback presentation means. This makes it possible to instantly adjust tasks based on the user's emotions and present tasks to the user at the optimal timing and with the most appropriate content. Furthermore, it makes it possible to provide appropriate feedback tailored to each individual user, supporting the maintenance of user motivation and growth.

[0634] An "information input means" is a means that has the function of acquiring individual information from end users and providing it to the system.

[0635] A "generation means" is a means that performs the function of automatically generating tasks based on acquired individual information.

[0636] "Presentation means" refers to the means of presenting the generated task to the end user visually or audibly.

[0637] An "emotion analysis tool" is a tool that has the function of analyzing the emotions of end users in real time.

[0638] A "modification tool" is a tool that has the function of adjusting the content and difficulty level of the generated task based on the results of the emotion analysis tool.

[0639] A "progress management system" is a means that has the function of receiving task completion reports from end users and managing the progress status.

[0640] "Analysis means" refers to a means of analyzing progress information obtained by progress management means and generating feedback for end users.

[0641] A "feedback presentation means" is a means that has the function of presenting the feedback generated by the analysis means to the end user.

[0642] This invention realizes a task management system that analyzes the emotions of end users and adjusts tasks based on those emotions. This system is composed of a server, terminals, and users.

[0643] The server first obtains individual information from the user through an information input device, such as age, interests, learning history, and lifestyle habits. This enables the generation of tasks tailored to each user. Based on the acquired information, the server generates tasks using a generation AI model. Furthermore, using emotion analysis devices, the terminal collects emotional data from the user's facial expressions and voice, and adjusts the content and difficulty of the tasks based on that emotional state.

[0644] The terminal presents tasks sent from the server to the end user. These tasks are presented in an appropriate format, taking into account the user's current emotional state, allowing the user to engage with the task in a suitable emotional state. Furthermore, when a task completion report is sent to the server via the progress management system, the server analyzes the progress and emotional data and generates situation-appropriate feedback for the end user.

[0645] For example, if an end-user is browsing specific retail products in a virtual store, and the terminal's emotion analysis system detects joy or excitement, the server can then provide more engaging product suggestions. Conversely, if the user is in a state of displeasure, the system will adjust its recommendations to include relaxing promotions or products with simple purchase procedures.

[0646] By utilizing generative AI models, the system can provide a highly flexible and personalized experience. For example, when a user shows interest in a particular category, the generative AI model optimizes its suggestions using a prompt such as, "Show more products in the category the user is interested in. Recommend premium products when the user is feeling excited, and discount products when they are feeling tired." This allows the system to instantly respond to the user's ever-changing emotions, resulting in a comfortable and meaningful shopping experience.

[0647] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0648] Step 1:

[0649] The server obtains individual information from users through an information input mechanism. This input information includes age, interests, learning history, and lifestyle habits. The obtained information is stored in a database and referenced when generating tasks.

[0650] Step 2:

[0651] The device uses emotion analysis to capture the user's facial expressions and voice in real time and extracts emotion data. Based on this, it determines the user's current emotional state. The acquired emotion data is sent to the server.

[0652] Step 3:

[0653] The server uses acquired individual information and emotional data as input to generate the optimal task using a generative AI model. Prompt statements are utilized in task generation. The generated task is adjusted according to the user's emotional state.

[0654] Step 4:

[0655] The server sends the adjusted task to the terminal. The terminal then presents the task to the user in an appropriate format. For example, when the user is relaxed, the task is presented normally, while when the user is stressed, it is presented in a more user-friendly format.

[0656] Step 5:

[0657] Users work on the assigned tasks and, upon completion, report their completion status to the server through a progress management system. This information is used to verify the task completion status.

[0658] Step 6:

[0659] The server analyzes progress and sentiment data to generate feedback tailored to the user. This feedback includes praise for success and advice for the next steps.

[0660] Step 7:

[0661] The generated feedback is sent to the user through a feedback presentation mechanism. The device receives it and presents it to the user visually or audibly. Based on the feedback, the next steps are devised.

[0662] 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.

[0663] 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.

[0664] 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.

[0665] [Fourth Embodiment]

[0666] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0667] 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.

[0668] 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).

[0669] 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.

[0670] 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.

[0671] 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).

[0672] 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.

[0673] 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.

[0674] 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.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] 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".

[0679] This invention is a digital system that enables personalized task management for children. First, the user (parent or educator) uses an interface on a terminal to input individual information such as the child's age, school learning information, and daily habits. This information is transmitted from the terminal to the server via an information input device.

[0680] The server uses a generation mechanism based on the received information to generate tasks suitable for each child. The generated tasks are individually adjusted based on the learning content, difficulty level, and lifestyle. For example, if information indicates that a child is good at mathematics, tasks including more difficult math problems will be generated.

[0681] Next, the generated task is sent from the server to the terminal via a presentation device and presented to the child. The terminal displays the task content in a way that is easy for the child to understand. This may include visualizations using animations and audio guides.

[0682] The user (child) performs the presented task, and upon completion, sends a completion report to the server via a progress management device through the terminal. The server receives the progress information, analyzes it using an analysis device, and then generates the necessary feedback.

[0683] Finally, the generated feedback is sent to the device via a feedback presentation system and presented to the child. Specifically, it includes praise for task completion and suggestions for the next task to tackle. Through this feedback, the child can recognize their own progress and maintain motivation to continue learning.

[0684] In this way, by providing and managing tasks optimized for each individual child, the system is designed to naturally foster children's perseverance while also making it easier for parents and educators to provide support.

[0685] The following describes the processing flow.

[0686] Step 1:

[0687] The user launches the application on their device and enters individual information such as the child's age, school learning information, and lifestyle habits. The device then sends the entered information to the server via the information input device.

[0688] Step 2:

[0689] The server stores the received user information in a database. The stored information is necessary for the generation mechanism to use in subsequent processing.

[0690] Step 3:

[0691] The server uses a generation mechanism to create personalized tasks suitable for each child, based on individual information about the child obtained from the database. This generation uses an AI algorithm to assign tasks that are appropriate for the child's learning content and lifestyle habits.

[0692] Step 4:

[0693] The server sends the generated task information to the terminal via a presentation device. The terminal receives this information and displays the task on the screen in a way that is easy for children to understand and find interesting.

[0694] Step 5:

[0695] The user (child) works on the presented task. After completion, the user either presses a task completion button on the device or reports the completed task.

[0696] Step 6:

[0697] The terminal sends user task completion information to the server using a progress management system. The transmitted information is received on the server, and the progress is recorded.

[0698] Step 7:

[0699] The server analyzes progress using analytical tools. It determines the degree of task completion and whether follow-up is necessary, and generates feedback for the next learning stage.

[0700] Step 8:

[0701] The server sends the generated feedback to the terminal using a feedback presentation mechanism. The terminal then presents the feedback to the user in a clear and effective manner. This can include motivational messages and advice on the next steps.

[0702] (Example 1)

[0703] 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".

[0704] Providing appropriate educational tasks tailored to the individual age, learning history, and lifestyle of many children is challenging for parents and educators. Furthermore, tracking individual progress and providing effective feedback is also difficult. Traditional systems often fail to adequately provide individualized tasks or generate progress-based feedback, posing challenges in maintaining children's motivation and supporting their continuous learning.

[0705] 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.

[0706] In this invention, the server includes a generation device that automatically generates tasks based on individual information acquired via an information input device, a display device that presents the generated tasks to the user, and an analysis device that analyzes progress and generates feedback. This makes it possible to provide personalized tasks to individual children and to provide educational support with appropriate feedback according to their progress.

[0707] An "information input device" is a device used to acquire individual data from users, and its primary role is to collect necessary information, mainly through an interface.

[0708] A "generation device" is a device that automatically generates tasks suitable for the user based on acquired information, and uses technologies such as generation AI models to construct personalized tasks.

[0709] A "display device" is a device that presents generated tasks to the user in an intuitive and easily understandable format, and includes visual and auditory outputs.

[0710] A "progress management device" is a device that receives and manages reports from users regarding the completion status of tasks, and its role is to record and organize the achievement status of tasks.

[0711] An "analysis device" is a device that analyzes received progress information and generates feedback tailored to the user, such as suggesting the next task to tackle or generating words of praise.

[0712] A "feedback display device" is a device that intuitively presents generated feedback to the user and is equipped with functions to communicate the content of the feedback visually or audibly.

[0713] This invention is a digital system that provides personalized task management for individual children. The system mainly consists of a server, terminals, and users.

[0714] The user (parent or educator) uses a device to input information about their child. The device interface includes forms for the user to input information such as the child's age, learning information, and lifestyle habits. Standard input devices and UI software can be used for this information input. For example, tablet devices and PC browsers are commonly used.

[0715] The terminal sends the entered information to the server as a data package. This transmission uses SSL / TLS protocol to securely encrypt the data and communicate over the internet.

[0716] The server generates tasks using a generative AI model based on the received information. An execution environment for the AI ​​model, written in programming languages ​​such as Python, is provided, and the model is built on machine learning libraries. Specifically, it can automatically generate problem sets tailored to each child's strengths in different subjects.

[0717] The generated tasks are sent to the terminal by the server. This communication also utilizes lightweight data formats such as JSON, enabling rapid data exchange.

[0718] The device displays received tasks in a format that is easy for children to understand. Web technologies such as HTML5, CSS, and JavaScript are used to provide interactions that include animations and audio guidance.

[0719] As a concrete example, a user could input a prompt into the AI ​​model saying, "Please suggest learning some simple English vocabulary as the next task." Based on this instruction, the server would generate an appropriate task, which would then be presented to the child via the device as animated educational content.

[0720] This system allows users to easily provide tasks tailored to children, and enables children to learn while having fun.

[0721] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0722] Step 1:

[0723] The user uses a device to input information about their child through the interface. Specifically, they enter the child's age, learning information, lifestyle habits, etc., into text fields and select boxes. This input data is converted within the device into a specific format (e.g., JSON format) and ready for transmission.

[0724] Step 2:

[0725] The device packages the entered child information, encrypts the data using the SSL / TLS protocol, and securely sends it to the server. This transmission process accesses the server's API endpoint via HTTP requests.

[0726] Step 3:

[0727] The server analyzes the received information and launches a generative AI model. Here, a Python machine learning library is used to generate tasks best suited to the child. In this process, the model is given conditions such as age and preferred subjects as input, and based on this, it generates the optimal set of problems.

[0728] Step 4:

[0729] The tasks generated by the server are converted back into JSON format and sent to the terminal. A lightweight communication format is used for this process to ensure rapid data transfer.

[0730] Step 5:

[0731] The device analyzes received tasks and displays them in a user-friendly format. Specifically, it uses HTML5 and JavaScript to render them on the screen as educational content with animations and audio guides.

[0732] Step 6:

[0733] The user (child) completes the presented task and enters the results into the terminal. The terminal collects these results as progress data and prepares to report them to the server. The reported data includes information such as the degree of task completion and the time taken.

[0734] Step 7:

[0735] The terminal sends a completion report to the server. The report data is again encrypted using the SSL / TLS protocol and securely sent to the server's specified API endpoint.

[0736] Step 8:

[0737] The server receives progress data and uses an analysis device to generate feedback. The generating AI model constructs feedback messages based on the degree of task completion and suggests the next task.

[0738] Step 9:

[0739] The server sends the generated feedback to the device. The device analyzes this feedback and presents it to the user visually or audibly. Specifically, it displays it as a pop-up window or voice message, offering praise for the user's progress and guiding them to the next learning step.

[0740] (Application Example 1)

[0741] 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".

[0742] In modern education, there is a challenge in providing educational content that is not adequately tailored to each child's individual interests and learning pace. This leads to decreased motivation and insufficient educational effectiveness. Furthermore, the lack of mechanisms to appropriately suggest subsequent educational activities based on children's feedback makes it difficult to optimize individual learning experiences.

[0743] 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.

[0744] In this invention, the server includes means for inputting information, means for generating information, and means for analyzing information. This makes it possible to automatically generate personalized educational content based on the user's individual information and present it in an audiovisually easy-to-understand format. Furthermore, by accurately suggesting the next content and learning activities according to the user's interests and progress, it is possible to optimize the individual learning experience and enhance educational effectiveness.

[0745] "Means of inputting information" refers to an interface that allows users to input individual information into an electronic device.

[0746] "Generating means" refers to a function that automatically creates appropriate learning tasks and audiovisual content based on the individual information entered.

[0747] "Means of presentation" refers to functions for presenting generated learning tasks or audiovisual content to users visually or audibly.

[0748] "Means for managing progress" refers to a function that allows users to receive reports on completed tasks and record their progress.

[0749] "Means of analysis" refers to the function of analyzing received progress information and identifying the next feedback or content that should be provided.

[0750] "Means of providing feedback" refers to functions that provide users with feedback generated based on analysis results, either visually or audibly.

[0751] "Audiovisual content" refers to media that conveys information to users visually or aurally, and provides education or entertainment.

[0752] "Learning activities" refer to a series of processes and tasks in which users are involved in order to achieve specific educational goals.

[0753] This document describes a mode for carrying out the invention. The invention is a digital system that provides personalized educational content for children. The system primarily consists of server, terminal, and user interaction.

[0754] First, the user enters individual information such as the child's age, interests, and learning history through the user interface (UI) on their device. This UI is built with React Native and runs on smartphones and tablet devices. The entered information is sent to AWS servers via a secure communication protocol.

[0755] The server is built using a Python program with Django, and it analyzes the information it receives. Based on the analyzed information, it sends specific prompt messages to a generative AI model, which then generates or selects educational content suitable for the screen. An example of such a prompt message would be, "Based on the user's information, please suggest the most suitable educational content for a 5-year-old with interests in space and an intermediate learning level."

[0756] The generated content is sent to the device and presented to the user through visual effects and audio guidance. This presentation uses streaming technology, including visually easy-to-understand animations. Once the user completes the presented content, their progress is reported back to the server, which analyzes the progress data to suggest the next content or task.

[0757] In this way, it functions as a system that improves children's motivation to learn by continuously providing learning content optimized for them and appropriately managing their learning progress.

[0758] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0759] Step 1:

[0760] The user enters the child's age, interests, and learning history using a React Native-based UI on their device. The entered information is securely sent to an AWS server using the HTTPS protocol. Input: Individual information of the child; Output: Information sent to the server. Specifically, the user enters or selects text or options into form fields and then clicks the "Submit" button.

[0761] Step 2:

[0762] The server analyzes the individual information it receives. A script implemented in Python analyzes the information and creates prompts to request educational content tailored to the user's learning needs from an AI model. Input: Individual information sent by the user; Output: Generated prompts. Specifically, the algorithm scores the user information and selects the appropriate prompt.

[0763] Step 3:

[0764] The server sends prompt messages to a generation AI model, which then generates and selects appropriate educational content. Once the AI ​​model generates content, the results are returned to the server. Input: prompt messages, Output: generated educational content. Specifically, prompt messages are sent to an external AI service via an API, and the best-matching content links and data are returned.

[0765] Step 4:

[0766] The server sends the generated educational content to the device. The device uses streaming technology to visually present the content to the user. Input: generated educational content, Output: visual presentation. Specifically, a video player is launched on the device and animation playback begins.

[0767] Step 5:

[0768] When a user completes a task, they press a button on their device, and the progress information is reported to the server. Input: Task completion information, Output: Progress report to the server. Specifically, when the "Complete" button is pressed, the progress information is automatically sent to the server.

[0769] Step 6:

[0770] The server receives progress information and analyzes the data. It then generates recommended learning content and tasks. Input: progress information, Output: recommended content. Specifically, it queries the database and performs calculations to select the best next action.

[0771] Step 7:

[0772] The server sends recommended content to the device and provides feedback. The user receives the suggested content as the next step. Input: Recommended content, Output: Presentation of feedback to the user. Specifically, text messages and links to the next content are displayed on the screen.

[0773] 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.

[0774] This invention relates to a personalized task management system that takes user emotions into consideration. This system generates tasks using generative AI based on the user's individual information, but by incorporating a new emotion engine, it achieves more appropriate task presentation and feedback.

[0775] First, the user enters information such as the child's age, interests, learning history, and lifestyle through the device's interface. This information is sent to the server via the input device and stored in the database.

[0776] Next, the server uses an emotion engine to analyze the user's emotions. This emotion analysis uses facial recognition or voice analysis technology to observe the user's reactions when working on tasks and receiving feedback. The results are then fed back into the task generation process.

[0777] The server generates tasks using a generation mechanism, but adjusts them based on the analysis results of the emotion engine. This adjustment optimizes the difficulty and content of the tasks according to the user's current emotional state.

[0778] The generated tasks are sent from the server to the terminal. The terminal presents the tasks in an emotionally sensitive manner, ensuring that the user can work on them in an appropriate emotional state. For example, if the user is feeling anxious or stressed, a more approachable task can be presented.

[0779] When a user completes a task, the device sends that information to the server using a progress management system. The server analyzes the progress and sentiment data and generates feedback tailored to the user's state using an analysis system.

[0780] Finally, the generated feedback is sent to the device using a feedback presentation mechanism and provided to the user. This feedback celebrates the user's success and efforts, and includes advice and encouragement tailored to their emotional state.

[0781] For example, if a user achieves a goal and their feelings of joy are recognized, the system can then suggest a more challenging task. In this way, the system can provide an optimal learning experience tailored to the user's emotional state, supporting their continuous growth.

[0782] The following describes the processing flow.

[0783] Step 1:

[0784] The user uses a device to input individual information about their child, such as their age, learning history, interests, and lifestyle. The device then sends this information to the server using an information input device.

[0785] Step 2:

[0786] The server stores the received individual information in a database. Simultaneously, the emotion engine begins collecting real-time emotional data from the user. This is done by analyzing facial expressions and voice tone using a camera and microphone.

[0787] Step 3:

[0788] The server activates the generation mechanism and automatically generates tasks based on the acquired individual information. At this time, it adjusts the content and difficulty of the tasks according to the user's emotional state based on the emotional data obtained from the emotion engine.

[0789] Step 4:

[0790] The server sends the generated task information to the terminal using a presentation mechanism. The terminal receives it and presents the task in an appropriate format according to the user's emotional state. For example, it can prepare messages or animations that help alleviate stress.

[0791] Step 5:

[0792] The user (child) works on a task. Emotional changes during the task are continuously analyzed by the emotion engine and sent to the server.

[0793] Step 6:

[0794] When a user completes a task, the device sends the completion information to the server using a progress management system.

[0795] Step 7:

[0796] The server analyzes progress information and emotional data using analytical tools. This generates feedback tailored to performance and emotional state.

[0797] Step 8:

[0798] The server sends the generated feedback to the terminal via a feedback presentation mechanism. The terminal then presents the user with encouraging messages and advice for the next task, taking into account their emotional state.

[0799] (Example 2)

[0800] 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".

[0801] Traditional task management systems have a problem where generating tasks without considering individual user attribute data or emotional states can easily lead to decreased user motivation and efficiency. In particular, since a user's emotional state significantly impacts success in learning and work, it is essential to understand and address it appropriately. Therefore, a new system is needed that provides individually optimized tasks that take user emotions into account, enabling users to engage with tasks in the best possible state.

[0802] 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.

[0803] In this invention, the server includes information input means, generation means, and analysis means. This enables the generation of tasks optimized based on the user's individual attribute data and emotional state. Specifically, the user's attribute data is input using a generation AI model, and the task is adjusted considering the emotional state. As a result, the task is presented to the user in an emotionally appropriate manner through the presentation means, and the data collected by the progress management means is analyzed to provide feedback according to the user's emotional state. This makes it possible to support continuous growth while improving the user's motivation and learning efficiency.

[0804] An "information input method" is a mechanism that receives individual attribute data from a user and sends that data to a server.

[0805] A "generative AI model" is an algorithm that generates tasks based on a user's individual attribute data and adjusts the content of those tasks while taking their emotional state into consideration.

[0806] A "generation method" refers to a configuration that uses a generation AI model to create tasks based on user data and has the functionality to optimize the content of those tasks.

[0807] A "presentation method" is a way of displaying generated tasks to the user in an emotionally appropriate manner and providing an interface that takes user reactions into consideration.

[0808] A "progress management system" is a process for receiving task completion reports from users and managing and recording the progress status.

[0809] "Analysis means" refers to a function that integrates and analyzes user progress and sentiment data within the server to generate feedback.

[0810] A "feedback presentation method" is a system that provides users with feedback generated by an analysis method, offering advice and encouragement tailored to their emotional state.

[0811] This invention provides an advanced task management system that takes into account user emotions and individual attribute data. The system consists of a user terminal, a server, and software that works in conjunction with them.

[0812] Users input individual attribute data using the device's user interface. This includes the child's age, interests, learning history, and lifestyle. This data is sent to the server via an application on the device. The server stores the received information in a database and performs sentiment analysis using an emotion engine. This sentiment analysis utilizes facial recognition and voice analysis technologies to identify the user's emotional state (e.g., joy, sadness, stress).

[0813] The server utilizes a generative AI model to generate tasks based on the user's emotional state and attribute data. This AI model employs text generation and data analysis algorithms and is adjusted to provide tasks appropriate to the user's current emotions. For example, if a user is feeling stressed, the system can generate simple tasks that include relaxing content.

[0814] The generated tasks are sent from the server to the terminal and presented to the user. The terminal displays the tasks in a visually clear and emotionally sensitive manner to encourage user engagement. Once the user completes a task, the terminal sends progress information to the server. The server then analyzes the progress and emotional data to generate feedback. This feedback is designed to be positive for the user, including messages that enhance their sense of accomplishment and encouragement for the next step.

[0815] For example, if a user enters "insects" as their interest and their learning history records "good at science," the generated tasks might include an insect-related quiz or creating a simple observation journal. An example of a prompt might be: "An 8-year-old child is interested in insects and is good at science. Please suggest some fun, quiz-style tasks related to insects."

[0816] In this way, the system can perform personalized task management that reflects the user's emotional state and individual attributes. This allows users to approach tasks in an optimal state, enabling them to learn and grow more efficiently.

[0817] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0818] Step 1:

[0819] The user uses their device to input individual attribute data such as the child's age, interests, learning history, and lifestyle.

[0820] The entered information is converted into digital data through the terminal's application and transmitted to the server via the information input device. This creates an initial dataset of user attributes.

[0821] Step 2:

[0822] The server saves the received individual attribute data to the database.

[0823] In this process, a unique ID is assigned to each user, and the data is stored in a structured format. The stored data serves as foundational data referenced in subsequent analysis and generation processes.

[0824] Step 3:

[0825] The server utilizes a generative AI model to generate tasks based on the user's individual attribute data.

[0826] In this process, data is input to the AI ​​model using prompt statements (e.g., "An 8-year-old child is interested in insects and is good at science. Please suggest a fun quiz-style task related to insects."), and the generated data returned is formatted appropriately to create an output task suitable for the user.

[0827] Step 4:

[0828] The server uses an emotion engine to analyze the user's emotional state.

[0829] The input consists of images and audio data from a facial recognition camera. Based on this, an emotion analysis algorithm outputs the user's current emotional state (e.g., degree of joy, sadness, stress) as numerical data.

[0830] Step 5:

[0831] The tasks generated by the generation method are adjusted taking into account the results of sentiment analysis.

[0832] The server adjusts the difficulty and content of tasks based on the user's emotional state, and regenerates optimized tasks. This ensures that the tasks are appropriate for the user's current emotional state.

[0833] Step 6:

[0834] Finally, the generated tasks are sent from the server to the terminal and displayed to the user through a presentation mechanism.

[0835] On the device, tasks are presented through a visually intuitive interface, and feedback and animations may be added to take into account the user's emotional state.

[0836] Step 7:

[0837] The user completes the task and enters a completion notification on their device.

[0838] The terminal sends this information to the server through a progress management system, and the completion status is recorded on the server.

[0839] Step 8:

[0840] The server integrates and analyzes the acquired progress data and sentiment data to generate feedback.

[0841] Using analytical tools, the system celebrates user successes and generates advice for the next task. The generated feedback is output as numerical information (such as completion rate) and text messages.

[0842] Step 9:

[0843] The generated feedback is sent to the terminal via a feedback presentation mechanism and presented to the user.

[0844] The device can display feedback in a more easily understandable format and deliver messages of gratitude and encouragement to users.

[0845] (Application Example 2)

[0846] 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".

[0847] Many task management systems present tasks without considering the user's feelings, sometimes resulting in tasks being presented in an inappropriate way or at an inappropriate time. This can increase user stress and decrease motivation. Furthermore, traditional systems often fail to provide sufficient feedback tailored to individual user responses, making it difficult to support users' continuous growth.

[0848] 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.

[0849] In this invention, the server includes an information input means, an emotion analysis means, a generation means using a generation AI, an adjustment means for adjusting tasks according to emotions, a progress management means, and a feedback presentation means. This makes it possible to instantly adjust tasks based on the user's emotions and present tasks to the user at the optimal timing and with the most appropriate content. Furthermore, it makes it possible to provide appropriate feedback tailored to each individual user, supporting the maintenance of user motivation and growth.

[0850] An "information input means" is a means that has the function of acquiring individual information from end users and providing it to the system.

[0851] A "generation means" is a means that performs the function of automatically generating tasks based on acquired individual information.

[0852] "Presentation means" refers to the means of presenting the generated task to the end user visually or audibly.

[0853] An "emotion analysis tool" is a tool that has the function of analyzing the emotions of end users in real time.

[0854] A "modification tool" is a tool that has the function of adjusting the content and difficulty level of the generated task based on the results of the emotion analysis tool.

[0855] A "progress management system" is a means that has the function of receiving task completion reports from end users and managing the progress status.

[0856] "Analysis means" refers to a means of analyzing progress information obtained by progress management means and generating feedback for end users.

[0857] A "feedback presentation means" is a means that has the function of presenting the feedback generated by the analysis means to the end user.

[0858] This invention realizes a task management system that analyzes the emotions of end users and adjusts tasks based on those emotions. This system is composed of a server, terminals, and users.

[0859] The server first obtains individual information from the user through an information input device, such as age, interests, learning history, and lifestyle habits. This enables the generation of tasks tailored to each user. Based on the acquired information, the server generates tasks using a generation AI model. Furthermore, using emotion analysis devices, the terminal collects emotional data from the user's facial expressions and voice, and adjusts the content and difficulty of the tasks based on that emotional state.

[0860] The terminal presents tasks sent from the server to the end user. These tasks are presented in an appropriate format, taking into account the user's current emotional state, allowing the user to engage with the task in a suitable emotional state. Furthermore, when a task completion report is sent to the server via the progress management system, the server analyzes the progress and emotional data and generates situation-appropriate feedback for the end user.

[0861] For example, if an end-user is browsing specific retail products in a virtual store, and the terminal's emotion analysis system detects joy or excitement, the server can then provide more engaging product suggestions. Conversely, if the user is in a state of displeasure, the system will adjust its recommendations to include relaxing promotions or products with simple purchase procedures.

[0862] By utilizing generative AI models, the system can provide a highly flexible and personalized experience. For example, when a user shows interest in a particular category, the generative AI model optimizes its suggestions using a prompt such as, "Show more products in the category the user is interested in. Recommend premium products when the user is feeling excited, and discount products when they are feeling tired." This allows the system to instantly respond to the user's ever-changing emotions, resulting in a comfortable and meaningful shopping experience.

[0863] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0864] Step 1:

[0865] The server obtains individual information from users through an information input mechanism. This input information includes age, interests, learning history, and lifestyle habits. The obtained information is stored in a database and referenced when generating tasks.

[0866] Step 2:

[0867] The device uses emotion analysis to capture the user's facial expressions and voice in real time and extracts emotion data. Based on this, it determines the user's current emotional state. The acquired emotion data is sent to the server.

[0868] Step 3:

[0869] The server uses acquired individual information and emotional data as input to generate the optimal task using a generative AI model. Prompt statements are utilized in task generation. The generated task is adjusted according to the user's emotional state.

[0870] Step 4:

[0871] The server sends the adjusted task to the terminal. The terminal then presents the task to the user in an appropriate format. For example, when the user is relaxed, the task is presented normally, while when the user is stressed, it is presented in a more user-friendly format.

[0872] Step 5:

[0873] Users work on the assigned tasks and, upon completion, report their completion status to the server through a progress management system. This information is used to verify the task completion status.

[0874] Step 6:

[0875] The server analyzes progress and sentiment data to generate feedback tailored to the user. This feedback includes praise for success and advice for the next steps.

[0876] Step 7:

[0877] The generated feedback is sent to the user through a feedback presentation mechanism. The device receives it and presents it to the user visually or audibly. Based on the feedback, the next steps are devised.

[0878] 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.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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."

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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 as being incorporated by reference.

[0899] The following is further disclosed regarding the embodiments described above.

[0900] (Claim 1)

[0901] Information input means and

[0902] A generation means that automatically generates tasks based on individual information obtained from the user via the aforementioned information input means,

[0903] A presentation means for presenting tasks generated by the generation means to the user,

[0904] A progress management system for receiving task completion reports from users,

[0905] An analysis means for analyzing the progress obtained by the progress management means and generating feedback,

[0906] A feedback presentation means that presents the feedback generated by the analysis means to the user,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, characterized in that the generation means adjusts tasks based on the user's age and learning history.

[0910] (Claim 3)

[0911] The system according to claim 1, characterized in that the analysis means makes the next task suggestion according to the user's learning progress.

[0912] "Example 1"

[0913] (Claim 1)

[0914] Information input device and

[0915] A generation device that automatically generates tasks based on individual pieces of information obtained from the user via the aforementioned information input device,

[0916] A display device that presents tasks generated by the generation device to the user,

[0917] A progress management device that receives task completion reports from users,

[0918] An analysis device that analyzes the progress acquired by the progress management device and generates feedback,

[0919] A feedback display device that presents feedback generated by the analysis device to the user,

[0920] A system that includes this.

[0921] (Claim 2)

[0922] The system according to claim 1, characterized in that the generating device adjusts tasks based on lifestyle information as well as the user's age and learning history, and utilizes a generating AI model.

[0923] (Claim 3)

[0924] The system according to claim 1, characterized in that the analysis device makes the next task suggestion based on the user's learning progress and task completion level, and generates a feedback message to the user that includes words of praise.

[0925] "Application Example 1"

[0926] (Claim 1)

[0927] Means of inputting information,

[0928] A means for automatically generating tasks based on individual information obtained from the user via the means for inputting the aforementioned information,

[0929] A means for presenting the work generated by the aforementioned generation means to the user,

[0930] A means of managing progress by receiving reports from users that the work has been completed,

[0931] An analysis means for analyzing the progress obtained by the means for managing the progress and generating feedback,

[0932] A means for presenting feedback to the user, which presents the feedback generated by the aforementioned analysis means.

[0933] The means for analyzing the content to be viewed next and for suggesting the next learning activity based on the user's interests,

[0934] A system that includes this.

[0935] (Claim 2)

[0936] The system according to claim 1, characterized in that the generating means adjusts the work based on the user's age and learning history.

[0937] (Claim 3)

[0938] The system according to claim 1, characterized in that the means for analysis proposes the following audiovisual content according to the user's interests and learning progress.

[0939] "Example 2 of combining an emotion engine"

[0940] (Claim 1)

[0941] Information input means and

[0942] A generation means that generates tasks using a generation AI model based on individual attribute data obtained from the user via the aforementioned information input means, and adjusts the tasks considering the user's emotional state.

[0943] A presentation means that presents the tasks generated by the generation means to the user and displays them adaptively, taking into consideration the user's emotional response,

[0944] A progress management system that receives task completion reports from users and records the progress status,

[0945] An analysis means analyzes the progress status and user emotion data obtained by the aforementioned progress management means and generates feedback according to the user's state,

[0946] A feedback presentation means that presents feedback generated by the analysis means to the user and provides encouragement and advice corresponding to the user's emotional state,

[0947] A system that includes this.

[0948] (Claim 2)

[0949] The system according to claim 1, characterized in that the generation means optimizes tasks based on the user's age, interests, learning history, and emotional state.

[0950] (Claim 3)

[0951] The system according to claim 1, characterized in that the analysis means makes the next task suggestion taking into account the user's emotional state and level of achievement.

[0952] "Application example 2 when combining with an emotional engine"

[0953] (Claim 1)

[0954] Information input means and

[0955] A generation means that automatically generates tasks based on individual information obtained from end users via the aforementioned information input means,

[0956] A presentation means for presenting tasks generated by the generation means to the end user,

[0957] A sentiment analysis tool that analyzes end-user emotions in real time,

[0958] An adjustment means for adjusting tasks generated based on the analysis results of the emotion analysis means,

[0959] A progress management method for receiving task completion reports from end users,

[0960] An analysis means for analyzing the progress obtained by the progress management means and generating feedback,

[0961] A feedback presentation means that presents the feedback generated by the analysis means to the end user,

[0962] A system that includes this.

[0963] (Claim 2)

[0964] The system according to claim 1, characterized in that the generation means adjusts tasks based on the end user's age and history information.

[0965] (Claim 3)

[0966] The system according to claim 1, characterized in that the analysis means makes the next task suggestion according to the end user's learning progress. [Explanation of Symbols]

[0967] 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. Information input means and A generation means that automatically generates tasks based on individual information obtained from the user via the aforementioned information input means, A presentation means for presenting tasks generated by the generation means to the user, A progress management system for receiving task completion reports from users, An analysis means for analyzing the progress obtained by the progress management means and generating feedback, A feedback presentation means that presents the feedback generated by the analysis means to the user, A system that includes this.

2. The system according to claim 1, characterized in that the generation means adjusts tasks based on the user's age and learning history.

3. The system according to claim 1, characterized in that the analysis means makes the next task suggestion according to the user's learning progress.

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