System

Abstract

An object of a system according to an embodiment is to enable a user to effectively prepare for a future dream or goal.SOLUTION: A system according to an embodiment includes a simulated experience unit, a skill analysis unit, an answer comparison unit, a process learning unit, and a parent-child learning unit. The simulated experience unit allows the user to set a future dream or goal and to have a simulated experience of preparation for realizing the dream or goal in the application. The skill analysis unit analyzes a skill and preparation necessary for realization based on the scenario generated by the simulated experience unit. The answer checking unit checks whether the answer of the user is a majority or a minority with the general answer. The process learning unit provides a learning tool for the user to make a habit of thinking about a problem solving process. The parent-child learning unit provides an educational environment in which a parent and a child can learn and think together.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately support users in effectively preparing for their future dreams and goals, and there is room for improvement.

[0005] The system according to the embodiment aims to enable a user to effectively prepare for his or her future dreams and goals. [Means for solving the problem]

[0006] The system according to the embodiment includes a simulated experience unit, a skill analysis unit, an answer comparison unit, a process learning unit, and a parent-child learning unit. The simulated experience unit allows a user to set future dreams and goals and simulate the preparations needed to achieve them within the app. The skill analysis unit analyzes the skills and preparations needed to achieve those goals based on a scenario generated by the simulated experience unit. The answer comparison unit compares the user's answer with general answers to determine whether it is the majority or minority. The process learning unit provides learning tools that help users develop the habit of thinking about the problem-solving process. The parent-child learning unit provides an educational environment in which parents and children can learn and think together. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to effectively prepare for his or her future dreams and goals. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The educational app according to the embodiment of the present invention is a system that allows users to simulate preparing for their future dreams within the app and learn the skills and preparations necessary to realize them. This allows the user to efficiently prepare for their future dreams and acquire the necessary skills and knowledge.

[0029] An educational app according to an embodiment includes a simulation unit, a skill analysis unit, an answer matching unit, a process learning unit, and a parent-child learning unit. The simulation unit allows a user to set future dreams and goals and simulate preparations for achieving them within the app. For example, a user aspiring to become a doctor can learn the necessary skills and knowledge through simulations of a medical setting. The skill analysis unit analyzes the skills and preparations required for achieving the goal based on a scenario generated by the simulation unit. For example, a user aspiring to become an engineer may be shown to need programming skills and project management knowledge and may be suggested a learning plan based on the scenario. The answer matching unit compares the user's answer with general answers to determine whether it is the majority or minority. For example, when a user answers a problem, the generation AI analyzes the answer and compares it with the answers of other users. The process learning unit provides learning tools to help users develop the habit of thinking about the problem-solving process. For example, the process learning unit presents the user with the steps of "first defining the problem, then coming up with a solution, and finally executing it" for a given task, and guides the user to practice the process. The parent-child learning section provides an educational environment where parents and children can learn and think together. For example, it provides procedures and materials for conducting scientific experiments together, allowing parents and children to learn together. As a result, the educational app according to the embodiment allows users to efficiently prepare for their future dreams and acquire the necessary skills and knowledge.

[0030] The simulation unit can analyze a user's past learning history and interests and generate individually customized scenarios. For example, the simulation unit can analyze a user's past learning history and interests and provide a medical simulation to a user aspiring to be a doctor. For example, it can perform a surgical simulation based on knowledge of anatomy and physiology learned in the past. For a user aspiring to be an engineer, it can analyze a user's past programming history and interests and provide scenarios for specific programming languages ​​or project management. For example, it can generate problem-solving scenarios for actual projects based on algorithms learned in the past. For a user aspiring to be an artist, it can analyze a user's past works and interests and provide an individually customized art project scenario. For example, it can generate a new art project scenario based on paintings and designs created in the past. In this way, it is possible to provide individually customized scenarios based on a user's past learning history and interests.

[0031] The simulation unit can collect behavioral data of the user during the simulation in real time and reflect it in the next scenario. For example, the simulation unit can collect behavioral data in real time when a user aspiring to be a doctor performs a surgical simulation and reflect it in the next scenario. For example, the simulation unit can analyze hand movements and decisions during surgery and provide feedback for the next scenario. Also, the simulation unit can collect behavioral data when a user aspiring to be an engineer performs a programming simulation and reflect it in the next scenario. For example, the simulation unit can analyze how code is written and debugged and suggest areas for improvement in the next scenario. Also, the simulation unit can collect behavioral data when a user aspiring to be an artist performs an art project and reflect it in the next scenario. For example, the simulation unit can analyze how colors are selected and the design process and provide advice for the next scenario. In this way, by collecting user behavioral data in real time and reflecting it in the next scenario, a more realistic experience can be provided.

[0032] The simulation experience section can add scenarios for different occupations and goals, allowing users to experience multiple dreams and goals simultaneously. For example, the simulation experience section can add scenarios for medical-related occupations such as nurses and pharmacists, in addition to doctors, allowing users to experience multiple medical occupations. For example, it can simulate patient care as a nurse or dispensing medicines as a pharmacist. It can also add scenarios for technical occupations such as designers and project managers, in addition to engineers, allowing users to experience multiple technical occupations. For example, it can simulate UI / UX design as a designer or project progress management as a project manager. It can also add scenarios for creative occupations such as musicians and writers, in addition to artists, allowing users to experience multiple creative occupations. For example, it can simulate creating music as a musician or writing a novel as an author. This allows users to experience multiple dreams and goals simultaneously, allowing them to evaluate themselves from multiple perspectives.

[0033] The simulation experience section can add a mode in which parents and children can have a joint simulated experience, thereby promoting parent-child communication. For example, the simulation experience section provides a scenario in which a parent and child simulate a medical setting, with the parent playing the role of a doctor and the child playing the role of a nurse, and working together to complete tasks. For example, the parent performs surgery and the child is responsible for handing over the surgical instruments. It also provides a scenario in which a parent and child work together on a programming project, with the parent playing the role of project manager and the child playing the role of programmer. For example, the parent manages the progress of the project and the child writes the code. It also provides a scenario in which a parent and child work together on an art project, with the parent playing the role of art director and the child playing the role of artist, and working together to create a work. For example, the parent gives instructions on the overall design and the child draws the specific details. This allows parents and children to have a joint simulated experience, thereby promoting parent-child communication.

[0034] The skill analysis unit can evaluate the user's current skill level and provide an individually customized learning plan. For example, the skill analysis unit evaluates the current medical knowledge and technical skills of a user aspiring to be a doctor and provides an individually customized learning plan. For example, it creates a step-by-step learning plan ranging from the basics of anatomy to advanced surgical techniques. Also, the skill analysis unit evaluates the current programming skills and project management abilities of a user aspiring to be an engineer and provides an individually customized learning plan. For example, it creates a learning plan ranging from basic coding to advanced algorithm design. Also, the skill analysis unit evaluates the current art skills and creative abilities of a user aspiring to be an artist and provides an individually customized learning plan. For example, it creates a learning plan ranging from basic drawing to advanced design techniques. In this way, an individually customized learning plan can be provided based on the user's current skill level.

[0035] The skill analysis unit can monitor a user's learning progress in real time and automatically adjust the learning plan as needed. For example, the skill analysis unit can monitor the learning progress of a user aspiring to be a doctor in real time and automatically adjust the learning plan if progress is slow. For example, the skill analysis unit can provide additional learning resources focused on a specific medical field. The skill analysis unit can also monitor the learning progress of a user aspiring to be an engineer in real time and adjust the learning plan to move on to the next step if progress is going well. For example, the skill analysis unit can learn advanced project management skills after mastering basic programming skills. The skill analysis unit can also monitor the learning progress of a user aspiring to be an artist in real time and automatically adjust the learning plan if progress is slow. For example, the skill analysis unit can provide additional practice assignments focused on a specific art technique. This makes it possible to automatically adjust the learning plan according to the user's learning progress.

[0036] The skill analysis unit can provide learning plans that combine different skill sets, allowing users to acquire a wide range of skills. For example, for a user aiming to become a doctor, the skill analysis unit can provide a learning plan that includes not only medical knowledge but also communication skills and leadership skills. For example, learning how to communicate with patients and the role of a team leader. For a user aiming to become an engineer, the skill analysis unit can provide a learning plan that includes not only programming skills but also design skills and marketing skills. For example, learning UI / UX design and market analysis. For a user aiming to become an artist, the skill analysis unit can provide a learning plan that includes not only art skills but also business skills and presentation skills. For example, learning how to sell artwork and how to give effective presentations. This allows users to acquire a wide range of skills.

[0037] The skill analysis unit provides a learning plan that allows parents and children to learn together, thereby promoting cooperation between them. For example, the skill analysis unit provides a collaborative learning plan for parents and children to learn medical knowledge, where the parent plays the role of a doctor and the child plays the role of a nurse. For example, the parent teaches the basics of medicine and the child performs practical tasks. The skill analysis unit also provides a collaborative learning plan for parents and children to learn programming, where the parent plays the role of a project manager and the child plays the role of a programmer. For example, the parent manages the progress of the project and the child writes the code. The skill analysis unit also provides a collaborative learning plan for parents and children to learn art, where the parent plays the role of an art director and the child plays the role of an artist. For example, the parent gives instructions on the overall design and the child draws the specific pictures. This allows parents and children to learn together, thereby promoting cooperation between them.

[0038] The answer matching unit can analyze a user's answers in detail and visualize their relevance to other users' answers. For example, the answer matching unit analyzes the user's answers using a generation AI and visualizes their relevance to other users' answers in graphs or charts. For example, it displays a heat map showing the distribution and trends of answers. It also analyzes the user's answers and visualizes their relevance to other users' answers in a network diagram. For example, it shows similarities and differences between answers with nodes and links. It also analyzes the user's answers and visualizes their relevance to other users' answers in a timeline. For example, it displays the changes and trends in answers along a timeline. This allows for detailed analysis of the user's answers and visualization of their relevance to other users' answers, which can be used as a reference for self-evaluation.

[0039] The answer matching unit can analyze answer trends and patterns based on the user's answer history and use them as a reference for self-assessment. The answer matching unit, for example, analyzes the user's past answer history and identifies answer trends and patterns. For example, it shows the consistency and variation of answers on a specific topic. It also visualizes answer trends and patterns in graphs and charts based on the user's answer history. For example, it displays the frequency of answers and their distribution by topic. It also analyzes the user's answer history and provides feedback that can be used as a reference for self-assessment. For example, it displays advice indicating the strengths of the answers and areas for improvement. In this way, answer trends and patterns can be analyzed based on the user's answer history and used as a reference for self-assessment.

[0040] The answer matching unit can add different question sets to allow users to evaluate themselves from various perspectives. For example, the answer matching unit can add question sets related to different themes or topics to allow users to evaluate themselves from various perspectives. For example, questions related to career aptitude or hobbies can be added. Question sets of different formats can also be added to allow users to evaluate themselves in various ways. For example, multiple choice and open-ended questions can be combined. Question sets of different difficulty levels can also be added to allow users to evaluate themselves at various levels. For example, questions ranging from basic to advanced can be included. This allows users to evaluate themselves from various perspectives, making more comprehensive self-assessment possible.

[0041] The answer matching unit can add a mode in which parents and children jointly answer questions, thereby promoting parent-child communication. The answer matching unit, for example, adds a mode in which parents and children jointly answer questions, providing a scenario in which parents and children cooperate to answer questions. For example, the parent reads out a question, and the child thinks up an answer. In addition, when parents and children jointly answer questions, questions that promote an exchange of opinions between parents and children are provided. For example, the parent and child exchange opinions to decide on an answer. In addition, a mode in which parents and children jointly answer questions is added, providing feedback to promote parent-child communication. For example, a message praising parent-child cooperation is displayed. In this way, parent-child communication can be promoted by parents and children jointly answering questions.

[0042] The process learning unit can analyze the user's problem-solving process in detail and provide individually customized feedback. For example, the process learning unit uses generative AI to analyze behavioral data when the user goes through the problem-solving process and provides individually customized feedback. For example, it provides specific advice on how to define the problem and think about the solution. It can also analyze the user's problem-solving process and provide feedback indicating strengths and areas for improvement at specific steps. For example, it can provide advice on how to implement the solution to a user who is good at analyzing problems. It can also analyze the user's problem-solving process and provide optimal feedback based on past data. For example, it can provide advice that references past success stories and failure stories. This makes it possible to provide individually customized feedback based on the user's problem-solving process.

[0043] The process learning unit can monitor the user's problem-solving process in real time and provide advice as needed. For example, the process learning unit monitors behavioral data in real time when the user is performing the problem-solving process and provides advice as needed. For example, if the problem is not sufficiently defined, it provides advice to redefine it. It also monitors the user's problem-solving process in real time and evaluates progress at a specific step. For example, if the development of a solution is delayed, it provides advice to encourage progress. It also monitors the user's problem-solving process in real time and provides optimal advice based on past data. For example, it provides advice based on past success stories and failure stories. In this way, it is possible to monitor the user's problem-solving process in real time and provide advice as needed.

[0044] The process learning unit can add scenarios that allow users to learn different problem-solving processes, thereby enabling users to learn a variety of approaches. The process learning unit, for example, adds scenarios that allow users to learn problem-solving processes in different fields, thereby enabling users to learn a variety of approaches. For example, it provides scenarios for business problem solving and scientific problem solving. It also adds scenarios that allow users to learn problem-solving processes of different difficulty levels, thereby enabling users to learn at a variety of levels. For example, it provides scenarios ranging from basic problem solving to advanced problem solving. It also adds scenarios that allow users to learn problem-solving processes in different formats, thereby enabling users to learn in a variety of ways. For example, it provides scenarios in the form of simulations or practical exercises. This allows users to learn a variety of approaches, thereby improving their problem-solving ability.

[0045] The process learning unit can add a mode in which parents and children solve problems together, thereby promoting cooperation between parents and children. For example, the process learning unit can add a mode in which parents and children solve problems together, and provide a scenario in which parents and children solve problems together. For example, the parent defines the problem, and the child thinks of a solution. The process learning unit can also provide a scenario that promotes an exchange of opinions between parents and children when parents and children solve problems together. For example, the parent and child can exchange opinions and decide on a solution. The process learning unit can also add a mode in which parents and children solve problems together, and provide feedback to promote cooperation between parents and children. For example, a message praising cooperation between parents and children can be displayed. This can promote cooperation between parents and children by allowing them to solve problems together.

[0046] The parent-child learning unit can use generative AI to analyze the learning history and interests of parents and children, and provide individually customized assignments and activities. For example, the parent-child learning unit can analyze the learning history and interests of parents and children, and provide individually customized science experiment assignments for parents and children who are interested in science experiments. For example, it can provide instructions for a simple chemistry experiment for parents and children to do together. It can also analyze the learning history and interests of parents and children, and provide individually customized history activities for parents and children who are interested in history. For example, it can provide a scenario for recreating historical events for parents and children to do together. It can also analyze the learning history and interests of parents and children, and provide individually customized art project assignments for parents and children who are interested in art. For example, it can provide instructions for creating a collaborative artwork for parents and children. This makes it possible to provide individually customized assignments and activities based on the learning history and interests of parents and children.

[0047] The parent-child learning unit can monitor the learning progress of the parent and child in real time and automatically adjust tasks and activities as needed. For example, the parent-child learning unit monitors the learning progress of the parent and child in real time and automatically adjusts tasks and activities if progress is lagging behind. For example, if progress is lagging, easier tasks are added. The parent-child learning unit also monitors the learning progress of the parent and child in real time and adjusts tasks and activities to allow the parent to move on to the next step if progress is going well. For example, if progress is going well, more difficult tasks are added. The parent-child learning unit also monitors the learning progress of the parent and child in real time and provides optimal tasks and activities based on past data. For example, optimal tasks are automatically selected based on past learning history. This makes it possible to automatically adjust tasks and activities according to the learning progress of the parent and child.

[0048] The parent-child learning section can add different learning themes and activities to allow parents and children to enjoy a variety of learning experiences. The parent-child learning section, for example, adds different learning themes to allow parents and children to enjoy a variety of learning experiences. For example, themes such as science, history, and art are provided. Different activities are also added to allow parents and children to learn in a variety of ways. For example, activities such as experiments, crafts, and discussions are provided. Learning themes and activities of different difficulty levels are also added to allow parents and children to learn at a variety of levels. For example, themes ranging from basic to advanced are included. This allows parents and children to enjoy a variety of learning experiences, thereby broadening the scope of learning.

[0049] The parent-child learning module can add a mode for parents and children to work on projects together, thereby promoting cooperation between parents and children. For example, the parent-child learning module can add a mode for parents and children to work on projects together, providing a scenario in which parents and children work together to advance a project. For example, the parent plans the project and the child carries it out. The module can also provide a scenario that promotes the exchange of opinions between parents and children when working on a project together. For example, the parent and child can work on the project while sharing their opinions. The module can also add a mode for parents and children to work on projects together, providing feedback to promote cooperation between parents and children. For example, a message praising cooperation between parents and children can be displayed. This can promote cooperation between parents and children by working on a project together.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] Educational apps allow users to efficiently prepare for their future dreams and acquire the necessary skills and knowledge. For example, a user aiming to become a doctor can learn the necessary skills and knowledge through simulations of medical settings. For a user aiming to become an engineer, apps can show that programming skills and project management knowledge are necessary and suggest a learning plan based on that. They can also provide an educational environment where parents and children can learn and think together. For example, apps can provide procedures and materials for conducting scientific experiments so that parents and children can learn together.

[0052] The simulation section can analyze a user's past learning history and interests to generate individually customized scenarios. For example, a user aspiring to become a doctor can be provided with a medical simulation, or a surgical simulation based on previously learned knowledge of anatomy and physiology. A user aspiring to become an engineer can be provided with scenarios for specific programming languages ​​or project management, based on an analysis of their past programming history and interests. A user aspiring to become an artist can be provided with an individually customized art project scenario, based on an analysis of their past work and interests.

[0053] The simulation unit can collect behavioral data of the user during the simulation in real time and reflect it in the next scenario. For example, behavioral data of a user aspiring to be a doctor performing a surgical simulation can be collected in real time and reflected in the next scenario. The system analyzes hand movements and decisions during surgery and provides feedback for the next scenario. Behavioral data of a user aspiring to be an engineer performing a programming simulation can be collected and reflected in the next scenario. Behavioral data of a user aspiring to be an artist performing an art project can be collected and reflected in the next scenario.

[0054] The simulation experience section can add scenarios for different occupations and goals, allowing users to experience multiple dreams and goals at the same time. For example, scenarios for medical-related occupations such as nurses and pharmacists, rather than just doctors, can be added, allowing users to experience multiple medical occupations. Scenarios for technical occupations such as designers and project managers, rather than just engineers, can be added, allowing users to experience multiple technical occupations. Scenarios for creative occupations such as musicians and writers, rather than just artists, can be added, allowing users to experience multiple creative occupations.

[0055] The simulation section adds a mode for parents and children to collaborate on simulated experiences, promoting communication between them. For example, a scenario is provided in which parents and children simulate a medical setting, with the parent playing the role of a doctor and the child playing the role of a nurse, working together to complete tasks. A scenario is provided in which parents and children collaborate on a programming project, with the parent playing the role of project manager and the child playing the role of programmer. A scenario is provided in which parents and children collaborate on an art project, with the parent playing the role of art director and the child playing the role of artist, creating a work of art together.

[0056] The skill analysis unit can evaluate the user's current skill level and provide an individually customized learning plan. For example, it can evaluate the current medical knowledge and technical skills of a user aspiring to be a doctor and provide an individually customized learning plan. It can evaluate the current programming skills and project management abilities of a user aspiring to be an engineer and provide an individually customized learning plan. It can evaluate the current art skills and creative abilities of a user aspiring to be an artist and provide an individually customized learning plan.

[0057] The skill analysis unit can monitor the user's learning progress in real time and automatically adjust the learning plan as needed. For example, the learning progress of a user aiming to become a doctor can be monitored in real time, and the learning plan can be automatically adjusted if progress is slow. The learning progress of a user aiming to become an engineer can be monitored in real time, and if progress is going well, the learning plan can be adjusted to allow the user to move on to the next step. The learning progress of a user aiming to become an artist can be monitored in real time, and if progress is slow, the learning plan can be automatically adjusted.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: In the simulation section, users set their dreams and goals for the future and simulate the preparations they need to achieve them within the app. For example, a user aiming to become a doctor can learn the necessary skills and knowledge through simulations of a medical setting. Step 2: The skills analysis unit analyzes the skills and preparations required to realize the scenario generated by the simulation unit. For example, it shows that a user aiming to become an engineer will need programming skills and project management knowledge, and proposes a learning plan based on that. Step 3: The answer matching unit checks whether the user's answer is in the majority or minority with the general answers. For example, when a user answers a question, the generation AI analyzes the answer and compares it with the answers of other users. Step 4: The process learning section provides learning tools to help users develop the habit of thinking about the problem-solving process. For example, for a given task, the section shows the steps of "first define the problem, then think of a solution, and finally execute it," and guides users through this process. Step 5: The Parent-Child Learning Department provides an educational environment where parents and children can learn and think together. For example, it provides instructions and materials for parent-child science experiments so that parents and children can learn together.

[0060] (Example 2) The educational app according to the embodiment of the present invention is a system that allows users to simulate preparing for their future dreams within the app and learn the skills and preparations necessary to realize them. This allows the user to efficiently prepare for their future dreams and acquire the necessary skills and knowledge.

[0061] An educational app according to an embodiment includes a simulation unit, a skill analysis unit, an answer matching unit, a process learning unit, and a parent-child learning unit. The simulation unit allows a user to set future dreams and goals and simulate preparations for achieving them within the app. For example, a user aspiring to become a doctor can learn the necessary skills and knowledge through simulations of a medical setting. The skill analysis unit analyzes the skills and preparations required for achieving the goal based on a scenario generated by the simulation unit. For example, a user aspiring to become an engineer may be shown to need programming skills and project management knowledge and may be suggested a learning plan based on the scenario. The answer matching unit compares the user's answer with general answers to determine whether it is the majority or minority. For example, when a user answers a problem, the generation AI analyzes the answer and compares it with the answers of other users. The process learning unit provides learning tools to help users develop the habit of thinking about the problem-solving process. For example, the process learning unit presents the user with the steps of "first defining the problem, then coming up with a solution, and finally executing it" for a given task, and guides the user to practice the process. The parent-child learning section provides an educational environment where parents and children can learn and think together. For example, it provides procedures and materials for conducting scientific experiments together, allowing parents and children to learn together. As a result, the educational app according to the embodiment allows users to efficiently prepare for their future dreams and acquire the necessary skills and knowledge.

[0062] The simulation unit can analyze a user's past learning history and interests and generate individually customized scenarios. For example, the simulation unit can analyze a user's past learning history and interests and provide a medical simulation to a user aspiring to be a doctor. For example, it can perform a surgical simulation based on knowledge of anatomy and physiology learned in the past. For a user aspiring to be an engineer, it can analyze a user's past programming history and interests and provide scenarios for specific programming languages ​​or project management. For example, it can generate problem-solving scenarios for actual projects based on algorithms learned in the past. For a user aspiring to be an artist, it can analyze a user's past works and interests and provide an individually customized art project scenario. For example, it can generate a new art project scenario based on paintings and designs created in the past. In this way, it is possible to provide individually customized scenarios based on a user's past learning history and interests.

[0063] The simulation unit can collect behavioral data of the user during the simulation in real time and reflect it in the next scenario. For example, the simulation unit can collect behavioral data in real time when a user aspiring to be a doctor performs a surgical simulation and reflect it in the next scenario. For example, the simulation unit can analyze hand movements and decisions during surgery and provide feedback for the next scenario. Also, the simulation unit can collect behavioral data when a user aspiring to be an engineer performs a programming simulation and reflect it in the next scenario. For example, the simulation unit can analyze how code is written and debugged and suggest areas for improvement in the next scenario. Also, the simulation unit can collect behavioral data when a user aspiring to be an artist performs an art project and reflect it in the next scenario. For example, the simulation unit can analyze how colors are selected and the design process and provide advice for the next scenario. In this way, by collecting user behavioral data in real time and reflecting it in the next scenario, a more realistic experience can be provided.

[0064] The simulation unit can use the emotion estimation function to analyze the user's emotional state and provide scenarios that correspond to their stress and motivation. For example, the simulation unit can use the emotion estimation function to analyze the stress felt by a user aspiring to become a doctor during a surgical simulation, and provide a relaxing scenario if the stress level is high. For example, it can add a scenario set in a post-surgery recovery room. It can also analyze the decrease in motivation felt by a user aspiring to become an engineer during a programming simulation and provide a scenario to increase motivation. For example, it can display success stories and encouraging messages. It can also analyze the emotions felt by a user aspiring to become an artist during an art project and provide a scenario that corresponds to the emotion. For example, if the motivation to create is decreasing, it can add a scenario to gain inspiration. In this way, by providing scenarios that correspond to the user's emotional state, it is possible to reduce the user's stress and maintain their motivation.

[0065] The simulation experience section can add scenarios for different occupations and goals, allowing users to experience multiple dreams and goals simultaneously. For example, the simulation experience section can add scenarios for medical-related occupations such as nurses and pharmacists, in addition to doctors, allowing users to experience multiple medical occupations. For example, it can simulate patient care as a nurse or dispensing medicines as a pharmacist. It can also add scenarios for technical occupations such as designers and project managers, in addition to engineers, allowing users to experience multiple technical occupations. For example, it can simulate UI / UX design as a designer or project progress management as a project manager. It can also add scenarios for creative occupations such as musicians and writers, in addition to artists, allowing users to experience multiple creative occupations. For example, it can simulate creating music as a musician or writing a novel as an author. This allows users to experience multiple dreams and goals simultaneously, allowing them to evaluate themselves from multiple perspectives.

[0066] The simulation experience section can add a mode in which parents and children can have a joint simulated experience, thereby promoting parent-child communication. For example, the simulation experience section provides a scenario in which a parent and child simulate a medical setting, with the parent playing the role of a doctor and the child playing the role of a nurse, and working together to complete tasks. For example, the parent performs surgery and the child is responsible for handing over the surgical instruments. It also provides a scenario in which a parent and child work together on a programming project, with the parent playing the role of project manager and the child playing the role of programmer. For example, the parent manages the progress of the project and the child writes the code. It also provides a scenario in which a parent and child work together on an art project, with the parent playing the role of art director and the child playing the role of artist, and working together to create a work. For example, the parent gives instructions on the overall design and the child draws the specific details. This allows parents and children to have a joint simulated experience, thereby promoting parent-child communication.

[0067] The simulated experience unit can use the emotion estimation function to analyze the emotional state of parents and children and provide scenarios that can be enjoyed by both parents and children. For example, when a parent and child simulate a medical setting, the simulated experience unit uses the emotion estimation function to analyze the emotional state of the parent and child and provide a relaxing scenario if stress levels are high. For example, a scenario set in a recovery room after surgery can be added. Also, when a parent and child work on a programming project, the emotion estimation function can analyze the emotional state of the parent and child and display an encouraging message if motivation is low. For example, a message praising success stories or cooperation between parent and child can be displayed. Also, when a parent and child work on an art project, the emotion estimation function can analyze the emotional state of the parent and child and provide a scenario for inspiration if motivation to create is low. For example, a scenario of a parent and child visiting an art museum can be added. In this way, by providing scenarios that correspond to the emotional state of the parent and child, an experience that can be enjoyed by both parents and children can be provided.

[0068] The skill analysis unit can evaluate the user's current skill level and provide an individually customized learning plan. For example, the skill analysis unit evaluates the current medical knowledge and technical skills of a user aspiring to be a doctor and provides an individually customized learning plan. For example, it creates a step-by-step learning plan ranging from the basics of anatomy to advanced surgical techniques. Also, the skill analysis unit evaluates the current programming skills and project management abilities of a user aspiring to be an engineer and provides an individually customized learning plan. For example, it creates a learning plan ranging from basic coding to advanced algorithm design. Also, the skill analysis unit evaluates the current art skills and creative abilities of a user aspiring to be an artist and provides an individually customized learning plan. For example, it creates a learning plan ranging from basic drawing to advanced design techniques. In this way, an individually customized learning plan can be provided based on the user's current skill level.

[0069] The skill analysis unit can monitor a user's learning progress in real time and automatically adjust the learning plan as needed. For example, the skill analysis unit can monitor the learning progress of a user aspiring to be a doctor in real time and automatically adjust the learning plan if progress is slow. For example, the skill analysis unit can provide additional learning resources focused on a specific medical field. The skill analysis unit can also monitor the learning progress of a user aspiring to be an engineer in real time and adjust the learning plan to move on to the next step if progress is going well. For example, the skill analysis unit can learn advanced project management skills after mastering basic programming skills. The skill analysis unit can also monitor the learning progress of a user aspiring to be an artist in real time and automatically adjust the learning plan if progress is slow. For example, the skill analysis unit can provide additional practice assignments focused on a specific art technique. This makes it possible to automatically adjust the learning plan according to the user's learning progress.

[0070] The skill analysis unit can use the emotion estimation function to analyze the user's emotional state while studying and provide feedback to maintain motivation. For example, the skill analysis unit can analyze the emotional state of a user aspiring to be a doctor while studying, and provide relaxation feedback if stress levels are high. For example, the skill analysis unit can suggest breathing techniques or stretching exercises to help the user relax. The skill analysis unit can also analyze the emotional state of a user aspiring to be an engineer while studying, and provide encouraging messages if motivation levels are low. For example, the skill analysis unit can display success stories and encouraging words. The skill analysis unit can also analyze the emotional state of a user aspiring to be an artist while studying, and provide feedback to help the user gain inspiration if their motivation levels are low. For example, the skill analysis unit can introduce the work of other artists. This allows the user to maintain motivation by providing feedback based on their emotional state while studying.

[0071] The skill analysis unit can provide learning plans that combine different skill sets, allowing users to acquire a wide range of skills. For example, for a user aiming to become a doctor, the skill analysis unit can provide a learning plan that includes not only medical knowledge but also communication skills and leadership skills. For example, learning how to communicate with patients and the role of a team leader. For a user aiming to become an engineer, the skill analysis unit can provide a learning plan that includes not only programming skills but also design skills and marketing skills. For example, learning UI / UX design and market analysis. For a user aiming to become an artist, the skill analysis unit can provide a learning plan that includes not only art skills but also business skills and presentation skills. For example, learning how to sell artwork and how to give effective presentations. This allows users to acquire a wide range of skills.

[0072] The skill analysis unit provides a learning plan that allows parents and children to learn together, thereby promoting cooperation between them. For example, the skill analysis unit provides a collaborative learning plan for parents and children to learn medical knowledge, where the parent plays the role of a doctor and the child plays the role of a nurse. For example, the parent teaches the basics of medicine and the child performs practical tasks. The skill analysis unit also provides a collaborative learning plan for parents and children to learn programming, where the parent plays the role of a project manager and the child plays the role of a programmer. For example, the parent manages the progress of the project and the child writes the code. The skill analysis unit also provides a collaborative learning plan for parents and children to learn art, where the parent plays the role of an art director and the child plays the role of an artist. For example, the parent gives instructions on the overall design and the child draws the specific pictures. This allows parents and children to learn together, thereby promoting cooperation between them.

[0073] The skill analysis unit can use the emotion estimation function to analyze the emotional state of parents and children while they are learning, and provide a learning plan that both parents and children can enjoy. For example, when a parent and child are learning medical knowledge together, the skill analysis unit can analyze the emotional state of the parent and child using the emotion estimation function, and provide a learning plan that allows them to relax if stress levels are high. For example, it can provide a break time for relaxation. When a parent and child are learning programming together, the emotion estimation function can analyze the emotional state of the parent and child, and display an encouraging message if motivation is low. For example, it can display a message praising success stories or cooperation between parent and child. When a parent and child are learning art together, the emotion estimation function can analyze the emotional state of the parent and child, and provide a learning plan to inspire them if their motivation to create is low. For example, it can add a scenario in which the parent and child visit an art museum. This makes it possible to provide a learning experience that both parents and children can enjoy by providing a learning plan that matches the emotional state of the parent and child.

[0074] The answer matching unit can analyze a user's answers in detail and visualize their relevance to other users' answers. For example, the answer matching unit analyzes the user's answers using a generation AI and visualizes their relevance to other users' answers in graphs or charts. For example, it displays a heat map showing the distribution and trends of answers. It also analyzes the user's answers and visualizes their relevance to other users' answers in a network diagram. For example, it shows similarities and differences between answers with nodes and links. It also analyzes the user's answers and visualizes their relevance to other users' answers in a timeline. For example, it displays the changes and trends in answers along a timeline. This allows for detailed analysis of the user's answers and visualization of their relevance to other users' answers, which can be used as a reference for self-evaluation.

[0075] The answer matching unit can analyze answer trends and patterns based on the user's answer history and use them as a reference for self-assessment. The answer matching unit, for example, analyzes the user's past answer history and identifies answer trends and patterns. For example, it shows the consistency and variation of answers on a specific topic. It also visualizes answer trends and patterns in graphs and charts based on the user's answer history. For example, it displays the frequency of answers and their distribution by topic. It also analyzes the user's answer history and provides feedback that can be used as a reference for self-assessment. For example, it displays advice indicating the strengths of the answers and areas for improvement. In this way, answer trends and patterns can be analyzed based on the user's answer history and used as a reference for self-assessment.

[0076] The answer matching unit can use the emotion estimation function to analyze the emotional state of the user when answering and provide feedback based on the emotion. The answer matching unit, for example, analyzes the emotional state of the user when answering and provides feedback based on the emotion. For example, if the user is under high stress, it may provide advice to relax. The answer matching unit may also analyze the emotional state of the user when answering and provide an encouraging message if the user's emotion is strong positive. For example, it may display success stories or positive feedback. The answer matching unit may also analyze the emotional state of the user when answering and provide feedback indicating areas for improvement if the user's emotion is strong negative. For example, it may suggest ways to review or improve the answer. In this way, by providing feedback based on the user's emotional state when answering, it is possible to provide appropriate advice according to the user's emotion.

[0077] The answer matching unit can add different question sets to allow users to evaluate themselves from various perspectives. For example, the answer matching unit can add question sets related to different themes or topics to allow users to evaluate themselves from various perspectives. For example, questions related to career aptitude or hobbies can be added. Question sets of different formats can also be added to allow users to evaluate themselves in various ways. For example, multiple choice and open-ended questions can be combined. Question sets of different difficulty levels can also be added to allow users to evaluate themselves at various levels. For example, questions ranging from basic to advanced can be included. This allows users to evaluate themselves from various perspectives, making more comprehensive self-assessment possible.

[0078] The answer matching unit can add a mode in which parents and children jointly answer questions, thereby promoting parent-child communication. The answer matching unit, for example, adds a mode in which parents and children jointly answer questions, providing a scenario in which parents and children cooperate to answer questions. For example, the parent reads out a question, and the child thinks up an answer. In addition, when parents and children jointly answer questions, questions that promote an exchange of opinions between parents and children are provided. For example, the parent and child exchange opinions to decide on an answer. In addition, a mode in which parents and children jointly answer questions is added, providing feedback to promote parent-child communication. For example, a message praising parent-child cooperation is displayed. In this way, parent-child communication can be promoted by parents and children jointly answering questions.

[0079] The answer matching unit can use the emotion estimation function to analyze the emotional state of the parent and child when they answer questions, and provide a question set that can be enjoyed by both parents and children. For example, when a parent and child answer questions together, the answer matching unit uses the emotion estimation function to analyze the emotional state of the parent and child, and provides a question set that helps them relax if they are under high stress. For example, it can provide a break time for relaxation. When a parent and child answer questions together, the emotion estimation function can analyze the emotional state of the parent and child, and display an encouraging message if their motivation is low. For example, it can display a success story or a message praising the cooperation between the parent and child. When a parent and child answer questions together, the emotion estimation function can analyze the emotional state of the parent and child, and provide a question set to help them gain inspiration if their motivation to create is low. For example, it can add a scenario in which a parent and child visit an art museum. In this way, a question set that matches the emotional state of the parent and child can be provided, providing an experience that can be enjoyed by both parents and children.

[0080] The process learning unit can analyze the user's problem-solving process in detail and provide individually customized feedback. For example, the process learning unit uses generative AI to analyze behavioral data when the user goes through the problem-solving process and provides individually customized feedback. For example, it provides specific advice on how to define the problem and think about the solution. It can also analyze the user's problem-solving process and provide feedback indicating strengths and areas for improvement at specific steps. For example, it can provide advice on how to implement the solution to a user who is good at analyzing problems. It can also analyze the user's problem-solving process and provide optimal feedback based on past data. For example, it can provide advice that references past success stories and failure stories. This makes it possible to provide individually customized feedback based on the user's problem-solving process.

[0081] The process learning unit can monitor the user's problem-solving process in real time and provide advice as needed. For example, the process learning unit monitors behavioral data in real time when the user is performing the problem-solving process and provides advice as needed. For example, if the problem is not sufficiently defined, it provides advice to redefine it. It also monitors the user's problem-solving process in real time and evaluates progress at a specific step. For example, if the development of a solution is delayed, it provides advice to encourage progress. It also monitors the user's problem-solving process in real time and provides optimal advice based on past data. For example, it provides advice based on past success stories and failure stories. In this way, it is possible to monitor the user's problem-solving process in real time and provide advice as needed.

[0082] The process learning unit can use the emotion estimation function to analyze the user's emotional state while solving a problem and provide support according to stress and motivation. For example, the process learning unit analyzes the user's emotional state when performing a problem-solving process, and provides support to help them relax if they are under high stress. For example, it suggests breathing techniques or stretching exercises to help them relax. It also analyzes the user's emotional state while solving a problem and provides encouraging messages if their motivation is low. For example, it displays success stories and words of encouragement. It also analyzes the user's emotional state while solving a problem and provides support to help them gain inspiration if their motivation is low. For example, it introduces success stories of other users. In this way, by providing support according to the user's emotional state while solving a problem, it is possible to reduce the user's stress and maintain their motivation.

[0083] The process learning unit can add scenarios that allow users to learn different problem-solving processes, thereby enabling users to learn a variety of approaches. The process learning unit, for example, adds scenarios that allow users to learn problem-solving processes in different fields, thereby enabling users to learn a variety of approaches. For example, it provides scenarios for business problem solving and scientific problem solving. It also adds scenarios that allow users to learn problem-solving processes of different difficulty levels, thereby enabling users to learn at a variety of levels. For example, it provides scenarios ranging from basic problem solving to advanced problem solving. It also adds scenarios that allow users to learn problem-solving processes in different formats, thereby enabling users to learn in a variety of ways. For example, it provides scenarios in the form of simulations or practical exercises. This allows users to learn a variety of approaches, thereby improving their problem-solving ability.

[0084] The process learning unit can add a mode in which parents and children solve problems together, thereby promoting cooperation between parents and children. For example, the process learning unit can add a mode in which parents and children solve problems together, and provide a scenario in which parents and children solve problems together. For example, the parent defines the problem, and the child thinks of a solution. The process learning unit can also provide a scenario that promotes an exchange of opinions between parents and children when parents and children solve problems together. For example, the parent and child can exchange opinions and decide on a solution. The process learning unit can also add a mode in which parents and children solve problems together, and provide feedback to promote cooperation between parents and children. For example, a message praising cooperation between parents and children can be displayed. This can promote cooperation between parents and children by allowing them to solve problems together.

[0085] The process learning unit can use the emotion estimation function to analyze the emotional state of a parent and child while they are solving a problem, and provide a scenario that the parent and child can enjoy together. For example, when a parent and child are solving a problem together, the process learning unit can analyze the emotional state of the parent and child using the emotion estimation function, and provide a scenario that helps the parent and child relax if stress levels are high. For example, the process learning unit can provide a break time to relax. When a parent and child are solving a problem together, the emotion estimation function can analyze the emotional state of the parent and child, and display an encouraging message if motivation levels are low. For example, the process learning unit can display a success story or a message praising the cooperation between the parent and child. When a parent and child are solving a problem together, the emotion estimation function can analyze the emotional state of the parent and child, and provide a scenario for inspiration if motivation levels are low. For example, a scenario of a parent and child visiting an art museum can be added. This allows for an enjoyable experience for the parent and child by providing a scenario that matches the emotional state of the parent and child.

[0086] The parent-child learning unit can use generative AI to analyze the learning history and interests of parents and children, and provide individually customized assignments and activities. For example, the parent-child learning unit can analyze the learning history and interests of parents and children, and provide individually customized science experiment assignments for parents and children who are interested in science experiments. For example, it can provide instructions for a simple chemistry experiment for parents and children to do together. It can also analyze the learning history and interests of parents and children, and provide individually customized history activities for parents and children who are interested in history. For example, it can provide a scenario for recreating historical events for parents and children to do together. It can also analyze the learning history and interests of parents and children, and provide individually customized art project assignments for parents and children who are interested in art. For example, it can provide instructions for creating a collaborative artwork for parents and children. This makes it possible to provide individually customized assignments and activities based on the learning history and interests of parents and children.

[0087] The parent-child learning unit can monitor the learning progress of the parent and child in real time and automatically adjust tasks and activities as needed. For example, the parent-child learning unit monitors the learning progress of the parent and child in real time and automatically adjusts tasks and activities if progress is lagging behind. For example, if progress is lagging, easier tasks are added. The parent-child learning unit also monitors the learning progress of the parent and child in real time and adjusts tasks and activities to allow the parent to move on to the next step if progress is going well. For example, if progress is going well, more difficult tasks are added. The parent-child learning unit also monitors the learning progress of the parent and child in real time and provides optimal tasks and activities based on past data. For example, optimal tasks are automatically selected based on past learning history. This makes it possible to automatically adjust tasks and activities according to the learning progress of the parent and child.

[0088] The parent-child learning unit can use the emotion estimation function to analyze the emotional state of the parent and child while they are learning and provide feedback to maintain motivation. For example, the parent-child learning unit can analyze the emotional state of the parent and child while they are learning, and provide feedback to help them relax if they are under high stress. For example, it can suggest breathing techniques or stretching exercises to help them relax. It can also analyze the emotional state of the parent and child while they are learning, and provide encouraging messages if their motivation is low. For example, it can display success stories or words of encouragement. It can also analyze the emotional state of the parent and child while they are learning, and provide feedback to help them gain inspiration if their motivation to create is low. For example, it can introduce success stories of other parents and children. In this way, motivation can be maintained by providing feedback based on the emotional state of the parent and child while they are learning.

[0089] The parent-child learning section can add different learning themes and activities to allow parents and children to enjoy a variety of learning experiences. The parent-child learning section, for example, adds different learning themes to allow parents and children to enjoy a variety of learning experiences. For example, themes such as science, history, and art are provided. Different activities are also added to allow parents and children to learn in a variety of ways. For example, activities such as experiments, crafts, and discussions are provided. Learning themes and activities of different difficulty levels are also added to allow parents and children to learn at a variety of levels. For example, themes ranging from basic to advanced are included. This allows parents and children to enjoy a variety of learning experiences, thereby broadening the scope of learning.

[0090] The parent-child learning module can add a mode for parents and children to work on projects together, thereby promoting cooperation between parents and children. For example, the parent-child learning module can add a mode for parents and children to work on projects together, providing a scenario in which parents and children work together to advance a project. For example, the parent plans the project and the child carries it out. The module can also provide a scenario that promotes the exchange of opinions between parents and children when working on a project together. For example, the parent and child can work on the project while sharing their opinions. The module can also add a mode for parents and children to work on projects together, providing feedback to promote cooperation between parents and children. For example, a message praising cooperation between parents and children can be displayed. This can promote cooperation between parents and children by working on a project together.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] Educational apps allow users to efficiently prepare for their future dreams and acquire the necessary skills and knowledge. For example, a user aiming to become a doctor can learn the necessary skills and knowledge through simulations of medical settings. For a user aiming to become an engineer, apps can show that programming skills and project management knowledge are necessary and suggest a learning plan based on that. They can also provide an educational environment where parents and children can learn and think together. For example, apps can provide procedures and materials for conducting scientific experiments so that parents and children can learn together.

[0093] The simulation section can analyze a user's past learning history and interests to generate individually customized scenarios. For example, a user aspiring to become a doctor can be provided with a medical simulation, or a surgical simulation based on previously learned knowledge of anatomy and physiology. A user aspiring to become an engineer can be provided with scenarios for specific programming languages ​​or project management, based on an analysis of their past programming history and interests. A user aspiring to become an artist can be provided with an individually customized art project scenario, based on an analysis of their past work and interests.

[0094] The simulation unit can collect behavioral data of the user during the simulation in real time and reflect it in the next scenario. For example, behavioral data of a user aspiring to be a doctor performing a surgical simulation can be collected in real time and reflected in the next scenario. The system analyzes hand movements and decisions during surgery and provides feedback for the next scenario. Behavioral data of a user aspiring to be an engineer performing a programming simulation can be collected and reflected in the next scenario. Behavioral data of a user aspiring to be an artist performing an art project can be collected and reflected in the next scenario.

[0095] The simulation unit can use the emotion estimation function to analyze the user's emotional state and provide scenarios that correspond to their stress and motivation. For example, the emotion estimation function can analyze the stress felt by a user aspiring to become a doctor during a surgical simulation and provide a scenario that helps them relax if the stress level is high. The function can analyze the decrease in motivation felt by a user aspiring to become an engineer during a programming simulation and provide a scenario that increases motivation. The function can analyze the emotions felt by a user aspiring to become an artist during an art project and provide a scenario that corresponds to the emotions.

[0096] The simulation experience section can add scenarios for different occupations and goals, allowing users to experience multiple dreams and goals at the same time. For example, scenarios for medical-related occupations such as nurses and pharmacists, rather than just doctors, can be added, allowing users to experience multiple medical occupations. Scenarios for technical occupations such as designers and project managers, rather than just engineers, can be added, allowing users to experience multiple technical occupations. Scenarios for creative occupations such as musicians and writers, rather than just artists, can be added, allowing users to experience multiple creative occupations.

[0097] The simulation section adds a mode for parents and children to collaborate on simulated experiences, promoting communication between them. For example, a scenario is provided in which parents and children simulate a medical setting, with the parent playing the role of a doctor and the child playing the role of a nurse, working together to complete tasks. A scenario is provided in which parents and children collaborate on a programming project, with the parent playing the role of project manager and the child playing the role of programmer. A scenario is provided in which parents and children collaborate on an art project, with the parent playing the role of art director and the child playing the role of artist, creating a work of art together.

[0098] The simulation unit can use the emotion estimation function to analyze the emotional state of parents and children and provide scenarios that parents and children can enjoy together. For example, when a parent and child are simulating a medical setting, the emotion estimation function can analyze the emotional state of the parent and child and provide a relaxing scenario if stress levels are high. When a parent and child are working on a programming project together, the emotion estimation function can analyze the emotional state of the parent and child and display an encouraging message if motivation levels are low. When a parent and child are working on an art project together, the emotion estimation function can analyze the emotional state of the parent and child and provide a scenario to inspire if motivation levels are low.

[0099] The skill analysis unit can evaluate the user's current skill level and provide an individually customized learning plan. For example, it can evaluate the current medical knowledge and technical skills of a user aspiring to be a doctor and provide an individually customized learning plan. It can evaluate the current programming skills and project management abilities of a user aspiring to be an engineer and provide an individually customized learning plan. It can evaluate the current art skills and creative abilities of a user aspiring to be an artist and provide an individually customized learning plan.

[0100] The skill analysis unit can monitor the user's learning progress in real time and automatically adjust the learning plan as needed. For example, the learning progress of a user aiming to become a doctor can be monitored in real time, and the learning plan can be automatically adjusted if progress is slow. The learning progress of a user aiming to become an engineer can be monitored in real time, and if progress is going well, the learning plan can be adjusted to allow the user to move on to the next step. The learning progress of a user aiming to become an artist can be monitored in real time, and if progress is slow, the learning plan can be automatically adjusted.

[0101] The skill analysis unit can use the emotion estimation function to analyze the user's emotional state while studying and provide feedback to maintain motivation. For example, the skill analysis unit can analyze the emotional state of a user aspiring to be a doctor while studying and provide relaxing feedback if stress levels are high. The skill analysis unit can analyze the emotional state of a user aspiring to be an engineer while studying and provide encouraging messages if motivation levels are low. The skill analysis unit can analyze the emotional state of a user aspiring to be an artist while studying and provide inspirational feedback if motivation levels are low.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: In the simulation section, users set their dreams and goals for the future and simulate the preparations they need to achieve them within the app. For example, a user aiming to become a doctor can learn the necessary skills and knowledge through simulations of a medical setting. Step 2: The skills analysis unit analyzes the skills and preparations required to realize the scenario generated by the simulation unit. For example, it shows that a user aiming to become an engineer will need programming skills and project management knowledge, and proposes a learning plan based on that. Step 3: The answer matching unit checks whether the user's answer is in the majority or minority with the general answers. For example, when a user answers a question, the generation AI analyzes the answer and compares it with the answers of other users. Step 4: The process learning section provides learning tools to help users develop the habit of thinking about the problem-solving process. For example, for a given task, the section shows the steps of "first define the problem, then think of a solution, and finally execute it," and guides users through this process. Step 5: The Parent-Child Learning Department provides an educational environment where parents and children can learn and think together. For example, it provides instructions and materials for parent-child science experiments so that parents and children can learn together.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] 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.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0132] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] 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.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The app also features a simulated experience section where users can set their dreams and goals for the future and simulate the preparations they need to make to achieve them. a skill analysis unit that analyzes the skills and preparations required for realizing the scenario based on the scenario generated by the simulation unit; an answer collating unit that compares the user's answer with general answers to determine whether the answer is a majority or a minority; a process learning unit that provides learning tools to help users develop the habit of thinking about the process of problem solving; A parent-child learning department that provides an educational environment where parents and children can learn and think together. A system characterized by:

2. The simulation experience section includes: Analyzing the user's past learning history and interests to generate an individually customized scenario 2. The system of claim 1.

3. The simulation experience section includes: Collecting behavioral data of the user during the simulated experience in real time and reflecting it in the next scenario 2. The system of claim 1.

4. The simulation experience section includes: Analyzing the emotional state of the user and providing the scenario according to stress and motivation 2. The system of claim 1.

5. The simulation experience section includes: Add scenarios for different jobs and goals, allowing the user to experience multiple dreams and goals simultaneously.

2. The system of claim 1.

Images