System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting an occupation and an area of study desired by a user; means for acquiring related data from a database based on the occupation and the area of study input by the user; means for generating a question and an answer using artificial intelligence using the acquired data; means for presenting the generated question to the user; means for receiving and evaluating the user's answer; and means for providing the evaluation result to the user as feedback.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] Traditional education systems lack a mechanism for effectively providing learning content related to children's future careers. In particular, there is no system that automatically generates questions that take into account practical situations specific to individual careers and provides feedback based on those questions. This means that there is a lack of an environment in which children can learn effectively and enjoyably toward their future goals. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by providing a system including: a means for a user to input a desired future occupation and field of study; a means for retrieving relevant data from a database based on the occupation and field of study; a means for generating questions and answers based on the retrieved data using artificial intelligence; a means for presenting the generated questions to the user; a means for receiving and evaluating the user's answers; and a means for providing the user with feedback based on the evaluation results. The data retrieval means effectively retrieves the relevant data by executing predefined database queries. The generated questions are based on specific situations in the occupation selected by the user, enabling practical and effective learning for the user.

[0006] "User" refers to an individual who uses the system to input learning content and answer questions.

[0007] "Desired occupation in the future" refers to the future occupation that the user desires.

[0008] "Field of Study" refers to a particular academic field that a user wishes to study.

[0009] "Means for inputting" refers to an interface for a user to input occupation and field of study into the system.

[0010] "Means for retrieving relevant data from a database" refers to the process for retrieving the required information from a database based on the occupation and field of study entered by the user.

[0011] "Artificial intelligence" refers to algorithms and technologies that automatically generate questions and answers based on data.

[0012] "Means for generating questions and answers" refers to the process of creating questions and answers for users to learn from based on the acquired data.

[0013] "Means for presenting problems to the user" refers to the interface or method for displaying the generated problems to the user.

[0014] "Means of evaluation" refers to the process by which the system checks the answers entered by the user and determines whether they are correct or not.

[0015] "Means for providing feedback to users" refers to the process of notifying users of the evaluation results and providing information to improve learning outcomes.

[0016] "Database query" refers to a search or retrieval instruction used to obtain desired information from a database.

[0017] A "specific situation" refers to a real-life scene or situation related to the job the user wants to have in the future. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0019] 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.

[0020] First, the terms used in the following description will be explained.

[0021] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0022] 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.

[0023] 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.

[0024] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0025] 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."

[0026] [First embodiment]

[0027] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0028] 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.

[0029] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0030] 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.

[0031] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0032] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0033] 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.

[0034] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0035] 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.

[0036] 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.

[0037] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0038] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0039] The present invention relates to an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[0040] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server.

[0041] The server retrieves relevant data from a database based on the received occupation and field of study. This retrieval process involves executing predefined database queries, for example, to extract symptoms and diagnostic information related to "physician" and "biology."

[0042] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer. As a specific example, it generates the question, "The patient complains of a fever and cough. What illness could it be?" and the answer, "Influenza."

[0043] The server sends the generated questions to the terminal, which then presents them to the user. The user then inputs their answer to the question. For example, the user inputs the answer "influenza." The terminal then sends this answer to the server.

[0044] The server compares the received user's answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback. For example, the feedback may be something like, "Your answer is correct. The patient may have influenza." The server sends this feedback to the terminal, which displays it to the user.

[0045] In this way, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations.

[0046] The processing flow will be explained below.

[0047] Step 1:

[0048] The user inputs the desired career and field of study into the terminal interface. For example, the user inputs "doctor" and "biology."

[0049] Step 2:

[0050] The device receives the user's input, converts it into JSON format, and then sends the data to the server.

[0051] Step 3:

[0052] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[0053] Step 4:

[0054] The server runs database queries based on occupation and field of study to retrieve relevant data, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[0055] Step 5:

[0056] The server applies an artificial intelligence algorithm to the data it acquires to generate questions and answers based on professional situations. For example, it generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[0057] Step 6:

[0058] The server converts the generated questions and answers into JSON format and sends them to the terminal.

[0059] Step 7:

[0060] The device analyzes the received problem and displays it to the user, who can then check the problem on the device.

[0061] Step 8:

[0062] The user inputs the answer to the question into the terminal, for example, "influenza."

[0063] Step 9:

[0064] The device receives the user's response, converts it into JSON format, and sends it to the server.

[0065] Step 10:

[0066] The server analyzes the received user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback. For example, it might say, "Your answer is correct. The patient may have influenza."

[0067] Step 11:

[0068] The server generates feedback and sends it to the device.

[0069] Step 12:

[0070] The device receives the feedback and displays it to the user, who can then check the evaluation results of their answers on the device.

[0071] The above is the specific flow of the system's program processing. By proceeding with the processing in an orderly manner at each step, it is possible to provide a user with an enjoyable learning environment.

[0072] Example 1

[0073] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0074] Conventional educational systems lack the ability to generate customized learning questions tailored to a user's desired future career or field of study and provide immediate, appropriate feedback. This makes it difficult for users to effectively learn practical knowledge directly related to their goals. The present invention aims to solve these problems and provide an educational system that allows users to engage in more practical and effective learning.

[0075] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0076] In this invention, the server includes a means for allowing a user to input a desired future occupation and field of study, a means for retrieving related information from a database based on the occupation and field of study input by the user, and a means for generating questions and answers using an artificial intelligence algorithm based on the retrieved information, thereby enabling the user to instantly receive study questions tailored to their goals and receive accurate feedback.

[0077] "User" refers to a person who uses the system to learn or input information.

[0078] "Terminal" refers to a device used by a user to access the system and enter or receive information.

[0079] "Server" refers to a central control unit that receives data sent by users, processes and communicates with a database, and returns the results to the users.

[0080] "Occupation" refers to the type of job or work that the user wants to do in the future.

[0081] "Field of study" refers to the academic or specialized field that the user wants to study.

[0082] A "database" refers to a storage system in which related information and data are stored in an organized manner and are accessible from a server.

[0083] An "artificial intelligence algorithm" refers to software technology that has a series of calculation procedures for solving problems or making predictions based on data.

[0084] "Questions" refer to questions or tasks that are generated based on the user's input of occupation or field of study to encourage learning.

[0085] "Answer" refers to the solution entered by the user in response to the generated question.

[0086] "Feedback" refers to the evaluation results and additional information provided in response to the answers submitted by the user.

[0087] "Comparing" refers to the process of matching the user's submitted answer with a pre-generated correct answer and evaluating their match and accuracy.

[0088] "Presenting" refers to the act of displaying the generated questions and feedback to the user via a terminal.

[0089] The present invention is an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[0090] First, a user accesses the system using their own device. They reach the interface by opening a web browser and accessing the system's URL. For example, they can use Google Chrome or any other common web browser.

[0091] The user enters their desired career (e.g., "doctor") and field of study (e.g., "biology") into the interface, and the input is sent by the device to the server as an HTTP POST request.

[0092] The server retrieves relevant information from a database based on the occupation and field of study entered by the user. This process involves running predefined SQL queries to extract the required data. For example, a specific query is run against the database to retrieve symptom and diagnosis information related to "doctor" and "biology."

[0093] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired information. Specifically, a generative AI model is used to pass the acquired data as prompts to the AI ​​model, which then generates learning questions and answers. For example, the question "A patient complains of fever and cough. What illness could it be?" and the answer "Influenza" are generated.

[0094] The generated questions are sent from the server to the terminal, and the terminal presents the received questions to the user. The user inputs their answer to the presented questions into the interface. For example, the user inputs the answer "influenza" and sends it back from the terminal to the server.

[0095] The server evaluates the received user's answer by comparing it with an automatically generated correct answer. Once the evaluation process is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." This feedback is sent from the server to the device, which displays it to the user.

[0096] As a specific example of operation, the following prompt sentence is given to the generative AI model:

[0097] Generate educational questions to be created for students who have chosen "Doctor" as their future career and "Biology" as their field of study. Specifically, provide questions and answers related to patient symptoms.

[0098] In this way, the present invention is a system that provides users with a customized learning experience and allows them to deepen their practical knowledge through effective feedback.

[0099] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0100] Step 1:

[0101] Users access the system using their own terminals.

[0102] Input: The user opens a web browser and enters the system's URL.

[0103] Specific behavior: A user launches a web browser such as Google Chrome or Firefox and enters a specific URL (the system's homepage).

[0104] Output: The system login screen or home page is displayed.

[0105] Step 2:

[0106] The user enters their desired career and field of study into the interface.

[0107] Input: Enter your "Occupation" and "Field of Study" in the input fields on the interface.

[0108] Specific behavior: The user enters information such as "doctor" and "biology" and clicks the submit button.

[0109] Output: Data entered by the user is sent from the device to the server.

[0110] Step 3:

[0111] The terminal transmits the user's input information to the server.

[0112] Input: Occupation and field of study data entered by the user.

[0113] Specific operation: The device generates an HTTP POST request and sends data including the input content to the server.

[0114] Output: The server receives the user's input.

[0115] Step 4:

[0116] The server retrieves relevant information from a database based on the user's input information.

[0117] Input: Occupation and field of study data entered by the user.

[0118] What it does: The server runs a predefined SQL query to extract information related to "doctors" and "biology" from the database.

[0119] Output: The extracted information is aggregated on the server.

[0120] Step 5:

[0121] The server uses the information it obtains to generate questions and answers using an artificial intelligence algorithm.

[0122] Input: Relevant information extracted from the database.

[0123] Specific operation: The server uses the generative AI model, passes the extracted data to the AI ​​model as prompts, and generates learning questions and their answers.

[0124] Output: Generated training questions and their answers.

[0125] Step 6:

[0126] The server sends the generated questions to the terminal and presents them to the user.

[0127] Input: The generated training problem.

[0128] Specific operation: The server sends the generated problem as an HTTP response to the device, and the device displays the received content on a web page.

[0129] Output: The study questions are displayed on the user's screen.

[0130] Step 7:

[0131] The user enters their answers to the questions presented in the interface.

[0132] Input: The study question to be answered by the user.

[0133] Specific Action: The user enters "flu" into the answer field on the interface and clicks the submit button.

[0134] Output: The user's answer data is sent from the device to the server.

[0135] Step 8:

[0136] The terminal sends the user's answer to the server.

[0137] Input: User response data.

[0138] Specific operation: The device creates an HTTP POST request and sends the response data to the server.

[0139] Output: The server receives the user's answer data.

[0140] Step 9:

[0141] The server evaluates the received user answer by comparing it with automatically generated correct answers.

[0142] Input: User response data and automatically generated correct answer data.

[0143] Specific operation: The server compares the received user answer with the pre-generated correct answer, and if they match, evaluates it as the "correct answer."

[0144] Output: The evaluation result (correct or incorrect).

[0145] Step 10:

[0146] The server generates feedback based on the evaluation results and provides it to the user.

[0147] Input: Evaluation result.

[0148] Specific behavior: Based on the evaluation results, automatically generate feedback such as "You're correct. The patient may have influenza."

[0149] Output: The generated feedback is sent from the server to the device and displayed to the user.

[0150] (Application example 1)

[0151] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0152] Conventional educational systems have the problem of not having sufficient feedback functions to effectively teach users the practical knowledge necessary for their future careers. They also lack the functionality to systematically record and visualize users' learning history and progress, leaving a need for improved learning efficiency.

[0153] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0154] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for recording and visualizing the user's learning history and progress. This enables the user to effectively learn practical knowledge related to the future occupation, and visualization of the learning progress can improve learning efficiency.

[0155] "User" refers to any individual or corporation that uses this system.

[0156] "Occupation" refers to the job or occupation that the user wants to pursue in the future.

[0157] "Field of study" refers to the academic or technical field in which the user is interested and wants to learn.

[0158] A "database" refers to an information resource that systematically organizes information stored on a server and can be searched and retrieved as needed.

[0159] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and have capabilities such as learning, reasoning, and judgment.

[0160] "Questions" refer to questions that the user must answer as part of the learning content that is generated based on the occupation and field of study entered by the user.

[0161] An "answer" refers to the answer that a user enters to a question.

[0162] "Evaluation" refers to the process of comparing the answers entered by the user with the correct answers and determining whether they are correct or incorrect.

[0163] "Feedback" refers to showing the evaluation results of the user's answers and providing information to assist learning.

[0164] "Study history" refers to a record of the learning activities that a user has undertaken up to now.

[0165] "Progress" refers to information that indicates the current stage of a user's learning goal.

[0166] "Visualization" refers to visually representing data so that it can be easily understood by users.

[0167] A "prompt sentence" refers to an input sentence that prompts an artificial intelligence to respond or generate a specific response.

[0168] The embodiments of the present invention will be specifically described below.

[0169] This system is an educational system consisting of a server and a user terminal. Users access the system through a smartphone application. When users input their desired future career and field of study, the information is sent to the server. The server executes a specified database query to retrieve data related to the entered career and field of study from the database.

[0170] The server uses the acquired data to automatically generate questions and answers using a generative AI model (for example, OpenAI's GPT-3). As a specific example, when the occupation "doctor" and the field of study "biology" are input, the server generates the question "The patient complains of fever and cough. What kind of illness could it be?" and the answer "influenza." An example of a prompt sentence used in this process is as follows:

[0171] text

[0172] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[0173] The server sends the generated questions to the user terminal, which then presents them to the user. The user enters the answer to the question, which is then sent to the server. The server compares the received user answer with the correct answer data and evaluates it. The evaluation result is generated as feedback and sent back to the user terminal. For example, if the user answers "influenza," the following feedback is provided: "That's correct. The patient may have influenza."

[0174] Furthermore, this system has the function of recording and visualizing the user's learning history and progress. This allows users to visually check their learning progress and study efficiently. This visualization is achieved by the server analyzing the learning history recorded in the database and displaying it as graphs and charts on the user's device.

[0175] The hardware used is a smartphone, the server is a web server using the Flask framework, and the database is SQLite, making it possible to build a highly efficient and convenient educational support system.

[0176] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0177] Step 1:

[0178] Users access the system through a smartphone application. On the input screen, users input their desired future occupation (e.g., "doctor") and field of study (e.g., "biology"). This information is sent to the server in JSON format. The user's input, occupation and field of study, is sent to the server, and the server receives it.

[0179] Step 2:

[0180] Based on the received occupation and field of study, the server executes a predefined database query to retrieve relevant data from the database, for example, extracting symptoms and diagnostic information related to "doctor" and "biology." The input at this stage is the occupation and field of study, and the output is the retrieved data.

[0181] Step 3:

[0182] The server creates a prompt for the generative AI model (e.g., OpenAI's GPT-3) based on the acquired data. Specifically, it generates the following prompt:

[0183] text

[0184] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[0185] This prompt is then input into a generative AI model to generate a learning question and its answer. At this stage, the input is the acquired data and the prompt, and the output is the generated question and its answer.

[0186] Step 4:

[0187] The server sends the generated question to the user's terminal. The user's terminal displays the question to the user. The user reads the question and enters their answer. At this stage, the input is the generated question, and the output is the user's answer.

[0188] Step 5:

[0189] The answer entered by the user is sent from the terminal to the server. The server receives this answer and compares it with the generated correct answer data. This comparison evaluates the correctness of the user's answer. At this stage, the input is the user's answer, and the output is the evaluation result.

[0190] Step 6:

[0191] The server generates feedback based on the evaluation result. For example, it generates feedback such as "You are correct. The patient may have influenza." This feedback is sent to the user's terminal and displayed to the user. At this stage, the input is the evaluation result, and the output is the feedback.

[0192] Step 7:

[0193] The server records the user's learning history and progress and periodically updates the data for visualization. This allows the user to check their learning progress in graphs and charts. The input at this stage is learning history and progress data, and the output is visualized information.

[0194] In this way, the system provides an environment in which users can efficiently learn practical knowledge related to the career they want to pursue in the future.

[0195] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0196] This invention relates to an educational system that allows users to input their desired future career and field of study, generates study questions based on the input, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[0197] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server. The server retrieves relevant data from the database based on the received occupation and field of study. This retrieval process involves executing predefined database queries. For example, it extracts symptoms related to "doctor" or basic knowledge related to "biology."

[0198] Next, the server uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously creates the correct answer. As a specific example, it generates the question "The patient complains of a fever and cough. What illness do you think it could be?" and the answer "influenza." The server sends the generated question to the terminal, which then presents it to the user.

[0199] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice when answering questions to recognize their emotions at any given time. Based on this information, the system dynamically adjusts the difficulty of the questions and the feedback provided. For example, if the user shows a tired expression, the system can lower the difficulty level slightly and provide relatively easy questions.

[0200] When the user enters their answer to the presented question (for example, entering "influenza"), the device sends this answer to the server. The server compares the received user answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." The server sends this feedback to the device, which then displays it to the user.

[0201] The system's built-in emotion engine also adjusts the feedback it provides based on the user's emotions. If the user looks anxious, it can provide more helpful and encouraging feedback, making the learning experience more personalized and effective.

[0202] As such, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations. Furthermore, the combination of an emotion engine further enhances the user's learning experience.

[0203] The processing flow will be explained below.

[0204] Step 1:

[0205] The user inputs their desired career and field of study into the device's interface, for example, "doctor" and "biology."

[0206] Step 2:

[0207] The terminal collects the user's input information, converts it into JSON format, and sends it to the server.

[0208] Step 3:

[0209] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[0210] Step 4:

[0211] The server runs database queries based on occupation and field of study to retrieve relevant data from the database, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[0212] Step 5:

[0213] The server inputs the acquired data into an artificial intelligence (AI) algorithm, which generates questions and answers based on occupational situations. For example, the system generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[0214] Step 6:

[0215] The questions and answers generated by the server are converted back into JSON format and sent to the terminal.

[0216] Step 7:

[0217] The device analyzes the received problem and displays it to the user, who then checks the problem on the device.

[0218] Step 8:

[0219] The user inputs an answer to the question, for example, "influenza."

[0220] Step 9:

[0221] The device collects the user's answers, converts them back into JSON format, and sends them to the server.

[0222] Step 10:

[0223] The server analyzes the user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback based on the results. For example, it generates feedback such as, "Your answer is correct. The patient may have influenza."

[0224] Step 11:

[0225] The server generates feedback and sends it to the device.

[0226] Step 12:

[0227] The device receives the feedback and displays it to the user, who can then check the evaluation results through the device.

[0228] Step 13:

[0229] The emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice data to identify emotions. For example, if the user looks tired, the emotion engine will detect this.

[0230] Step 14:

[0231] The server receives information from the emotion engine and dynamically adjusts the difficulty of the next question and the content of the feedback based on the user's current emotional state. For example, if the user is tired, the difficulty of the question may be slightly reduced or gentle feedback may be provided.

[0232] The above is the specific flow of processing in a system that combines an emotion engine. By performing detailed processing at each step, it is possible to provide an environment in which users can learn in an enjoyable and effective way.

[0233] Example 2

[0234] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0235] Current educational systems do not adequately support users in effectively learning knowledge related to their future careers. Furthermore, feedback designed to improve learning outcomes is generally static and does not dynamically adjust to the user's emotions. This can make it difficult for users to maintain their motivation to learn, potentially hindering effective learning.

[0236] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0237] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions when answering the questions, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for adjusting the content of the feedback according to the user's emotions. This allows the user to effectively learn practical knowledge related to the future occupation and receive personalized feedback according to their emotions.

[0238] A "user" is someone who uses the system to input their desired future career and field of study and work on learning problems.

[0239] "Occupation" refers to the type of work or job that the user wishes to do in the future.

[0240] A "study area" is a particular subject or topic that a user wants to study.

[0241] "Terminal" means a computing device through which a User accesses the System and inputs and receives information.

[0242] "Server" means a central processing unit that processes information sent by users and obtains, generates and evaluates related data.

[0243] A "database" is a collection of data in which information used within a system is organized and stored.

[0244] A "database query" is a search statement for retrieving specific information from a database.

[0245] "Artificial intelligence" refers to machine learning models and algorithms for generating questions and evaluating user answers.

[0246] A "problem" is a question or challenge presented to the user for answer.

[0247] An "answer" is a response that a user enters to a presented question.

[0248] "Feedback" refers to evaluation results and advice provided based on the user's answers.

[0249] An "emotion engine" is a system that analyzes a user's facial expressions and voice to recognize their emotions.

[0250] "Evaluation" is the process of comparing a user's answer with correct answer data to determine whether it is correct or not.

[0251] "Personalization" means optimizing content according to the individual situation and emotions of each user.

[0252] This invention is an educational system that generates study questions based on the user's input of their desired future career and field of study, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[0253] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects this information and sends it to the server.

[0254] The server retrieves relevant data from a database based on the received occupation and field of study. This process involves executing predefined database queries, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology." This retrieval process can be performed using SQL or NoSQL queries.

[0255] The server then uses an artificial intelligence (AI) algorithm to generate learning questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer (e.g., influenza). This is done using a generative AI model (e.g., GPT-3).

[0256] The server sends the generated learning questions to the device, which then presents them to the user. The user then enters their answers to the questions. At this time, the device's built-in emotion engine analyzes in real time how the user works on the questions and recognizes their emotions based on their facial expressions and voice. This recognition result is used as auxiliary information to respond when the user is in trouble or tired.

[0257] When the user enters an answer (e.g., influenza), the device sends this answer to the server. The server compares the received user answer with the correct answer data and evaluates it. Once the evaluation is complete, the server generates feedback. For example, this feedback may be something like, "Your answer is correct. The patient may have influenza."

[0258] Furthermore, the feedback content is dynamically adjusted based on information from the emotion engine. For example, if the user looks anxious, the feedback will be more kind and encouraging (e.g., "Good job, keep trying!").

[0259] Examples of concrete examples and prompts

[0260] Specific examples

[0261] User input: Occupation "Doctor", field of study "Biology"

[0262] Generated question: "A patient complains of fever and cough. What illness could it be?"

[0263] User Answer: "Influenza"

[0264] Feedback: "Correct. The patient may have the flu."

[0265] Prompt Sentence Examples

[0266] Enter the following information:

[0267] 1. What career do you want to have in the future (e.g., doctor)

[0268] 2. Field of study (e.g., biology)

[0269] Based on this information, questions are generated and answers are derived.

[0270] In this way, the system allows users to effectively learn practical knowledge related to their future career aspirations while receiving emotional feedback. This invention makes the learning experience more personalized, increases user motivation, and realizes effective learning.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1: Collecting User Input

[0273] A user accesses the system using a terminal and inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects the information entered by the user and stores it as input data. Specifically, by entering data into the input form and clicking the "Submit" button, the data is temporarily cached. This allows the input information to be obtained as initial data for processing in the next step.

[0274] Input: User-entered occupation and field of study information

[0275] Output: Collected user input data

[0276] Step 2: Retrieving relevant data

[0277] Based on the collected user input data, the device sends the data to the server. The server executes a database query based on the received occupation and field of study information to retrieve relevant data. Specifically, the server executes an SQL query to extract the required data using a command such as "SELECT FROM DATABASE WHERE Occupation = 'Doctor' AND Field = 'Biology'".

[0278] Input: User input data (occupation and field of study)

[0279] Output: Relevant data retrieved

[0280] Step 3: Generate training questions

[0281] The server uses the acquired relevant data to generate learning questions and answers using an artificial intelligence (AI) algorithm. Specifically, it uses a generative AI model (e.g., GPT-3) to automatically generate question statements and correct answers based on symptoms and knowledge. For example, it generates the question, "A patient complains of fever and cough. What kind of illness could it be?" and the answer, "Influenza."

[0282] Input: relevant data

[0283] Output: Generated training questions and answers

[0284] Step 4: Present the learning problem

[0285] The server sends the generated learning questions to the device, which then presents the received learning questions to the user. Specifically, the device displays the question text and an answer form on the device's interface, allowing the user to work on the questions.

[0286] Input: Generated training questions

[0287] Output: The training question presented to the user

[0288] Step 5: Emotion Recognition with the Emotion Engine

[0289] As the user works on the problem, the device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. Specifically, it uses face detection algorithms and voice analysis algorithms to recognize the user's emotions (e.g., confusion, fatigue).

[0290] Input: User's facial expressions and voice data

[0291] Output: Recognized user emotion information

[0292] Step 6: Collect and submit user responses

[0293] When the user enters an answer to a question (e.g., influenza), the terminal collects the answer and sends it to the server. Specifically, the user fills in the answer form and clicks the "Submit" button, which transmits the input data to the server.

[0294] Input: The user's typed answer

[0295] Output: Collected user responses

[0296] Step 7: Evaluate your responses

[0297] The server compares the received user's answer with the automatically generated correct answer data and evaluates it. Specifically, it uses an algorithm to determine whether the answer is correct or not and generates a result. For example, it evaluates it as "correct" based on the comparison logic.

[0298] Input: User answers and correct answer data

[0299] Output: Evaluation results

[0300] Step 8: Generate and provide feedback

[0301] The server generates feedback based on the evaluation results and sends it to the device. The device then displays the received feedback to the user. The content of the feedback is also dynamically adjusted based on information from the emotion engine. Specifically, if the user looks anxious, the system will provide feedback that includes encouraging words.

[0302] Input: Evaluation results and emotional information

[0303] Output: Dynamically adjusted feedback

[0304] This allows users to tackle learning questions related to their future career aspirations and receive feedback based on their answers, personalizing the learning experience based on emotions.

[0305] (Application example 2)

[0306] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0307] Current educational systems make it difficult for users to effectively acquire specialized knowledge related to specific occupations or fields of study. Furthermore, they provide a uniform level of difficulty and feedback without considering the user's emotional state, resulting in a lack of personalized learning experiences and a lack of motivation to learn. In particular, in certain industries, such as the food delivery industry, it is necessary to provide problems based on specific on-site situations.

[0308] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0309] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions, means for dynamically adjusting the difficulty of the questions and the feedback content according to the user's emotions, means for receiving and evaluating the user's answers, and means for providing the user with the evaluation results as feedback. This enables a user to efficiently learn specialized knowledge required for a specific industry and provides a personalized learning experience according to the user's emotions.

[0310] "User" refers to an individual who uses the system to learn.

[0311] "Desired future occupation" refers to a specific occupation that the user is aiming for.

[0312] A "study area" refers to a particular area of ​​knowledge that a user wants to learn.

[0313] "Database" means a collection of information that stores related data based on user-entered information.

[0314] "Artificial intelligence (AI)" refers to the technology that systems use to automatically generate questions and answers.

[0315] "Problem" means content including a problem or question that a user must solve through learning.

[0316] "Answer" refers to a solution provided by a user to a question.

[0317] "Emotion" refers to the psychological state that a user exhibits while learning.

[0318] "Feedback" refers to the evaluation and advice provided by the system in response to the user's answers.

[0319] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state.

[0320] "Dynamic adjustment means" refers to the ability to change the difficulty of questions and the content of feedback in real time according to the user's emotional state.

[0321] "Food delivery industry" means an industry that operates primarily around the delivery of food.

[0322] "Acquiring specialized knowledge" refers to acquiring in-depth knowledge and skills in a specific field.

[0323] The present invention provides an educational system that generates relevant study questions based on a user's desired future career and field of study, and supports learning by taking the user's emotions into consideration. Specific procedures and methods for implementing the present invention are described below.

[0324] System Program

[0325] The system includes the following major components:

[0326] 1. User Input Method

[0327] Users use their smartphones to input their desired future occupation (e.g., "Delivery Manager") and field of study (e.g., "Delivery Route Optimization").

[0328] 2. Data Acquisition Method

[0329] Based on the occupation and field of study information sent from the device, the server retrieves relevant data from a database, which provides information about the knowledge and skills required for the occupation. Retrieval from the database is performed using a predefined database query.

[0330] 3. Problem generation means

[0331] The server uses the acquired data to generate learning questions and answers using an artificial intelligence (AI) algorithm, such as "Select the most efficient delivery route in this area."

[0332] 4. Emotion recognition means

[0333] When the user answers a question, the system uses the smartphone's built-in camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[0334] 5. Dynamic Adjustment Methods

[0335] The server dynamically adjusts the difficulty of the questions and the content of the feedback depending on the emotion recognition results. For example, if the user shows signs of anxiety, it adds encouraging words to the feedback.

[0336] 6. Means of Providing Feedback

[0337] The server evaluates the answers entered by the user and generates and sends the resulting feedback to the device, which then provides the user with appropriate advice and the next challenge.

[0338] Processing Details

[0339] The processing of this system includes the following software and hardware:

[0340] Hardware: Smartphones, servers

[0341] Software: Python (Flask for backend), machine learning libraries (e.g., TensorFlow, EmotionRecognition library)

[0342] The server extracts relevant data from a database based on the received occupation and field of study, and generates questions and answers using an AI model (e.g., a generative AI model). The user's smartphone uses a camera and microphone to analyze the user's facial expressions and voice via an emotion recognition library, and sends the results to the server. The server then dynamically adjusts the difficulty of the questions and the content of the feedback based on this information.

[0343] Specific examples

[0344] For example, if a user selects an occupation as "Delivery Manager" and enters "Delivery Route Optimization" as their field of study, the following example prompt sentence is generated:

[0345] Example prompt sentence:

[0346] "Choose a route that will deliver efficiently in this area. Also consider the number of orders at each point."

[0347] When the user answers the question, their emotions are recognized using the smartphone's camera and microphone, and if the user looks anxious, encouraging feedback such as, "Your answer is a little off, but don't worry, you'll definitely get it right next time!" is provided.

[0348] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0349] Step 1:

[0350] Users input their desired career and field of study in the future.

[0351] Users enter information about their desired career and field of study through a smartphone interface.

[0352] Input: Job title (e.g., "Delivery Manager"), field of study (e.g., "Delivery Route Optimization")

[0353] Output: Input information is transmitted to the server

[0354] Step 2:

[0355] The server retrieves relevant data from a database based on the entered occupation and field of study.

[0356] The server executes predefined database queries based on the received information to extract relevant data.

[0357] Input: Occupation and field of study information

[0358] Output: Relevant data (e.g. information on optimizing delivery routes)

[0359] Step 3:

[0360] The server uses the data it acquires to generate questions and answers using an AI algorithm.

[0361] The server uses a generative AI model to construct questions and answers based on the data it receives.

[0362] Input: Related data

[0363] Output: Generated question and answer (e.g., "Choose an efficient delivery route in this area" and its optimal answer)

[0364] Step 4:

[0365] The server sends the generated questions to the terminal, which then presents them to the user.

[0366] The terminal displays the received questions to the user and allows the user to input answers to the questions.

[0367] Input: Question sent by server

[0368] Output: The problem as seen by the user

[0369] Step 5:

[0370] The user enters the answer to the question, and the terminal sends it to the server.

[0371] The user inputs their answer to the question presented, and the terminal sends the answer to the server.

[0372] Input: User's answer

[0373] Output: User's answer sent to the server

[0374] Step 6:

[0375] The server recognizes the user's emotions and dynamically adjusts the feedback content based on that information.

[0376] The server analyzes the user's facial expressions and voice through the smartphone's camera and microphone, and uses an emotion recognition library to understand the user's emotional state.

[0377] Input: User facial and voice data

[0378] Output: Recognized user emotional state

[0379] Step 7:

[0380] The server evaluates the user's answer by comparing it with automatically generated correct answers.

[0381] The server compares the user's answer with the correct answer data and calculates the evaluation result.

[0382] Input: User's answer, correct answer data

[0383] Output: Evaluation result (e.g., whether it is correct or not)

[0384] Step 8:

[0385] The server generates feedback based on the evaluation results, adjusts the content according to the emotion, and sends it to the device.

[0386] The server modifies the feedback content according to the recognized emotional state and provides appropriate feedback to the user.

[0387] Input: Evaluation result, emotional state

[0388] Output: Dynamically adjusted feedback

[0389] Step 9:

[0390] The terminal displays the feedback received from the server to the user.

[0391] The device receives the feedback sent from the server and displays it to the user, helping them reflect on their learning.

[0392] Input: Feedback data

[0393] Output: Feedback displayed to the user

[0394] 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.

[0395] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0397] [Second embodiment]

[0398] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0399] 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.

[0400] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0401] 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.

[0402] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0404] 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.

[0405] 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.

[0406] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] 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.

[0408] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0409] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0410] The present invention relates to an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[0411] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server.

[0412] The server retrieves relevant data from a database based on the received occupation and field of study. This retrieval process involves executing predefined database queries, for example, to extract symptoms and diagnostic information related to "physician" and "biology."

[0413] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer. As a specific example, it generates the question, "The patient complains of a fever and cough. What illness could it be?" and the answer, "Influenza."

[0414] The server sends the generated questions to the terminal, which then presents them to the user. The user then inputs their answer to the question. For example, the user inputs the answer "influenza." The terminal then sends this answer to the server.

[0415] The server compares the received user's answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback. For example, the feedback may be something like, "Your answer is correct. The patient may have influenza." The server sends this feedback to the terminal, which displays it to the user.

[0416] In this way, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations.

[0417] The processing flow will be explained below.

[0418] Step 1:

[0419] The user inputs the desired career and field of study into the terminal interface. For example, the user inputs "doctor" and "biology."

[0420] Step 2:

[0421] The device receives the user's input, converts it into JSON format, and then sends the data to the server.

[0422] Step 3:

[0423] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[0424] Step 4:

[0425] The server runs database queries based on occupation and field of study to retrieve relevant data, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[0426] Step 5:

[0427] The server applies an artificial intelligence algorithm to the data it acquires to generate questions and answers based on professional situations. For example, it generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[0428] Step 6:

[0429] The server converts the generated questions and answers into JSON format and sends them to the terminal.

[0430] Step 7:

[0431] The device analyzes the received problem and displays it to the user, who can then check the problem on the device.

[0432] Step 8:

[0433] The user inputs the answer to the question into the terminal, for example, "influenza."

[0434] Step 9:

[0435] The device receives the user's response, converts it into JSON format, and sends it to the server.

[0436] Step 10:

[0437] The server analyzes the received user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback. For example, it might say, "Your answer is correct. The patient may have influenza."

[0438] Step 11:

[0439] The server generates feedback and sends it to the device.

[0440] Step 12:

[0441] The device receives the feedback and displays it to the user, who can then check the evaluation results of their answers on the device.

[0442] The above is the specific flow of the system's program processing. By proceeding with the processing in an orderly manner at each step, it is possible to provide a user with an enjoyable learning environment.

[0443] Example 1

[0444] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0445] Conventional educational systems lack the ability to generate customized learning questions tailored to a user's desired future career or field of study and provide immediate, appropriate feedback. This makes it difficult for users to effectively learn practical knowledge directly related to their goals. The present invention aims to solve these problems and provide an educational system that allows users to engage in more practical and effective learning.

[0446] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0447] In this invention, the server includes a means for allowing a user to input a desired future occupation and field of study, a means for retrieving related information from a database based on the occupation and field of study input by the user, and a means for generating questions and answers using an artificial intelligence algorithm based on the retrieved information, thereby enabling the user to instantly receive study questions tailored to their goals and receive accurate feedback.

[0448] "User" refers to a person who uses the system to learn or input information.

[0449] "Terminal" refers to a device used by a user to access the system and enter or receive information.

[0450] "Server" refers to a central control unit that receives data sent by users, processes and communicates with a database, and returns the results to the users.

[0451] "Occupation" refers to the type of job or work that the user wants to do in the future.

[0452] "Field of study" refers to the academic or specialized field that the user wants to study.

[0453] A "database" refers to a storage system in which related information and data are stored in an organized manner and are accessible from a server.

[0454] An "artificial intelligence algorithm" refers to software technology that has a series of calculation procedures for solving problems or making predictions based on data.

[0455] "Questions" refer to questions or tasks that are generated based on the user's input of occupation or field of study to encourage learning.

[0456] "Answer" refers to the solution entered by the user in response to the generated question.

[0457] "Feedback" refers to the evaluation results and additional information provided in response to the answers submitted by the user.

[0458] "Comparing" refers to the process of matching the user's submitted answer with a pre-generated correct answer and evaluating their match and accuracy.

[0459] "Presenting" refers to the act of displaying the generated questions and feedback to the user via a terminal.

[0460] The present invention is an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[0461] First, a user accesses the system using their own device. They reach the interface by opening a web browser and accessing the system's URL. For example, they can use Google Chrome or any other common web browser.

[0462] The user enters their desired career (e.g., "doctor") and field of study (e.g., "biology") into the interface, and the input is sent by the device to the server as an HTTP POST request.

[0463] The server retrieves relevant information from a database based on the occupation and field of study entered by the user. This process involves running predefined SQL queries to extract the required data. For example, a specific query is run against the database to retrieve symptom and diagnosis information related to "doctor" and "biology."

[0464] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired information. Specifically, a generative AI model is used to pass the acquired data as prompts to the AI ​​model, which then generates learning questions and answers. For example, the question "A patient complains of fever and cough. What illness could it be?" and the answer "Influenza" are generated.

[0465] The generated questions are sent from the server to the terminal, and the terminal presents the received questions to the user. The user inputs their answer to the presented questions into the interface. For example, the user inputs the answer "influenza" and sends it back from the terminal to the server.

[0466] The server evaluates the received user's answer by comparing it with an automatically generated correct answer. Once the evaluation process is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." This feedback is sent from the server to the device, which displays it to the user.

[0467] As a specific example of operation, the following prompt sentence is given to the generative AI model:

[0468] Generate educational questions to be created for students who have chosen "Doctor" as their future career and "Biology" as their field of study. Specifically, provide questions and answers related to patient symptoms.

[0469] In this way, the present invention is a system that provides users with a customized learning experience and allows them to deepen their practical knowledge through effective feedback.

[0470] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0471] Step 1:

[0472] Users access the system using their own terminals.

[0473] Input: The user opens a web browser and enters the system's URL.

[0474] Specific behavior: A user launches a web browser such as Google Chrome or Firefox and enters a specific URL (the system's homepage).

[0475] Output: The system login screen or home page is displayed.

[0476] Step 2:

[0477] The user enters their desired career and field of study into the interface.

[0478] Input: Enter your "Occupation" and "Field of Study" in the input fields on the interface.

[0479] Specific behavior: The user enters information such as "doctor" and "biology" and clicks the submit button.

[0480] Output: Data entered by the user is sent from the device to the server.

[0481] Step 3:

[0482] The terminal transmits the user's input information to the server.

[0483] Input: Occupation and field of study data entered by the user.

[0484] Specific operation: The device generates an HTTP POST request and sends data including the input content to the server.

[0485] Output: The server receives the user's input.

[0486] Step 4:

[0487] The server retrieves relevant information from a database based on the user's input information.

[0488] Input: Occupation and field of study data entered by the user.

[0489] What it does: The server runs a predefined SQL query to extract information related to "doctors" and "biology" from the database.

[0490] Output: The extracted information is aggregated on the server.

[0491] Step 5:

[0492] The server uses the information it obtains to generate questions and answers using an artificial intelligence algorithm.

[0493] Input: Relevant information extracted from the database.

[0494] Specific operation: The server uses the generative AI model, passes the extracted data to the AI ​​model as prompts, and generates learning questions and their answers.

[0495] Output: Generated training questions and their answers.

[0496] Step 6:

[0497] The server sends the generated questions to the terminal and presents them to the user.

[0498] Input: The generated training problem.

[0499] Specific operation: The server sends the generated problem as an HTTP response to the device, and the device displays the received content on a web page.

[0500] Output: The study questions are displayed on the user's screen.

[0501] Step 7:

[0502] The user enters their answers to the questions presented in the interface.

[0503] Input: The study question to be answered by the user.

[0504] Specific Action: The user enters "flu" into the answer field on the interface and clicks the submit button.

[0505] Output: The user's answer data is sent from the device to the server.

[0506] Step 8:

[0507] The terminal sends the user's answer to the server.

[0508] Input: User response data.

[0509] Specific operation: The device creates an HTTP POST request and sends the response data to the server.

[0510] Output: The server receives the user's answer data.

[0511] Step 9:

[0512] The server evaluates the received user answer by comparing it with automatically generated correct answers.

[0513] Input: User response data and automatically generated correct answer data.

[0514] Specific operation: The server compares the received user answer with the pre-generated correct answer, and if they match, evaluates it as the "correct answer."

[0515] Output: The evaluation result (correct or incorrect).

[0516] Step 10:

[0517] The server generates feedback based on the evaluation results and provides it to the user.

[0518] Input: Evaluation result.

[0519] Specific behavior: Based on the evaluation results, automatically generate feedback such as "You're correct. The patient may have influenza."

[0520] Output: The generated feedback is sent from the server to the device and displayed to the user.

[0521] (Application example 1)

[0522] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0523] Conventional educational systems have the problem of not having sufficient feedback functions to effectively teach users the practical knowledge necessary for their future careers. They also lack the functionality to systematically record and visualize users' learning history and progress, leaving a need for improved learning efficiency.

[0524] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0525] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for recording and visualizing the user's learning history and progress. This enables the user to effectively learn practical knowledge related to the future occupation, and visualization of the learning progress can improve learning efficiency.

[0526] "User" refers to any individual or corporation that uses this system.

[0527] "Occupation" refers to the job or occupation that the user wants to pursue in the future.

[0528] "Field of study" refers to the academic or technical field in which the user is interested and wants to learn.

[0529] A "database" refers to an information resource that systematically organizes information stored on a server and can be searched and retrieved as needed.

[0530] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and have capabilities such as learning, reasoning, and judgment.

[0531] "Questions" refer to questions that the user must answer as part of the learning content that is generated based on the occupation and field of study entered by the user.

[0532] An "answer" refers to the answer that a user enters to a question.

[0533] "Evaluation" refers to the process of comparing the answers entered by the user with the correct answers and determining whether they are correct or incorrect.

[0534] "Feedback" refers to showing the evaluation results of the user's answers and providing information to assist learning.

[0535] "Study history" refers to a record of the learning activities that a user has undertaken up to now.

[0536] "Progress" refers to information that indicates the current stage of a user's learning goal.

[0537] "Visualization" refers to visually representing data so that it can be easily understood by users.

[0538] A "prompt sentence" refers to an input sentence that prompts an artificial intelligence to respond or generate a specific response.

[0539] The embodiments of the present invention will be specifically described below.

[0540] This system is an educational system consisting of a server and a user terminal. Users access the system through a smartphone application. When users input their desired future career and field of study, the information is sent to the server. The server executes a specified database query to retrieve data related to the entered career and field of study from the database.

[0541] The server uses the acquired data to automatically generate questions and answers using a generative AI model (for example, OpenAI's GPT-3). As a specific example, when the occupation "doctor" and the field of study "biology" are input, the server generates the question "The patient complains of fever and cough. What kind of illness could it be?" and the answer "influenza." An example of a prompt sentence used in this process is as follows:

[0542] text

[0543] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[0544] The server sends the generated questions to the user terminal, which then presents them to the user. The user enters the answer to the question, which is then sent to the server. The server compares the received user answer with the correct answer data and evaluates it. The evaluation result is generated as feedback and sent back to the user terminal. For example, if the user answers "influenza," the following feedback is provided: "That's correct. The patient may have influenza."

[0545] Furthermore, this system has the function of recording and visualizing the user's learning history and progress. This allows users to visually check their learning progress and study efficiently. This visualization is achieved by the server analyzing the learning history recorded in the database and displaying it as graphs and charts on the user's device.

[0546] The hardware used is a smartphone, the server is a web server using the Flask framework, and the database is SQLite, making it possible to build a highly efficient and convenient educational support system.

[0547] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0548] Step 1:

[0549] Users access the system through a smartphone application. On the input screen, users input their desired future occupation (e.g., "doctor") and field of study (e.g., "biology"). This information is sent to the server in JSON format. The user's input, occupation and field of study, is sent to the server, and the server receives it.

[0550] Step 2:

[0551] Based on the received occupation and field of study, the server executes a predefined database query to retrieve relevant data from the database, for example, extracting symptoms and diagnostic information related to "doctor" and "biology." The input at this stage is the occupation and field of study, and the output is the retrieved data.

[0552] Step 3:

[0553] The server creates a prompt for the generative AI model (e.g., OpenAI's GPT-3) based on the acquired data. Specifically, it generates the following prompt:

[0554] text

[0555] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[0556] This prompt is then input into a generative AI model to generate a learning question and its answer. At this stage, the input is the acquired data and the prompt, and the output is the generated question and its answer.

[0557] Step 4:

[0558] The server sends the generated question to the user's terminal. The user's terminal displays the question to the user. The user reads the question and enters their answer. At this stage, the input is the generated question, and the output is the user's answer.

[0559] Step 5:

[0560] The answer entered by the user is sent from the terminal to the server. The server receives this answer and compares it with the generated correct answer data. This comparison evaluates the correctness of the user's answer. At this stage, the input is the user's answer, and the output is the evaluation result.

[0561] Step 6:

[0562] The server generates feedback based on the evaluation result. For example, it generates feedback such as "You are correct. The patient may have influenza." This feedback is sent to the user's terminal and displayed to the user. At this stage, the input is the evaluation result, and the output is the feedback.

[0563] Step 7:

[0564] The server records the user's learning history and progress and periodically updates the data for visualization. This allows the user to check their learning progress in graphs and charts. The input at this stage is learning history and progress data, and the output is visualized information.

[0565] In this way, the system provides an environment in which users can efficiently learn practical knowledge related to the career they want to pursue in the future.

[0566] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0567] This invention relates to an educational system that allows users to input their desired future career and field of study, generates study questions based on the input, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[0568] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server. The server retrieves relevant data from the database based on the received occupation and field of study. This retrieval process involves executing predefined database queries. For example, it extracts symptoms related to "doctor" or basic knowledge related to "biology."

[0569] Next, the server uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously creates the correct answer. As a specific example, it generates the question "The patient complains of a fever and cough. What illness do you think it could be?" and the answer "influenza." The server sends the generated question to the terminal, which then presents it to the user.

[0570] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice when answering questions to recognize their emotions at any given time. Based on this information, the system dynamically adjusts the difficulty of the questions and the feedback provided. For example, if the user shows a tired expression, the system can lower the difficulty level slightly and provide relatively easy questions.

[0571] When the user enters their answer to the presented question (for example, entering "influenza"), the device sends this answer to the server. The server compares the received user answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." The server sends this feedback to the device, which then displays it to the user.

[0572] The system's built-in emotion engine also adjusts the feedback it provides based on the user's emotions. If the user looks anxious, it can provide more helpful and encouraging feedback, making the learning experience more personalized and effective.

[0573] As such, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations. Furthermore, the combination of an emotion engine further enhances the user's learning experience.

[0574] The processing flow will be explained below.

[0575] Step 1:

[0576] The user inputs their desired career and field of study into the device's interface, for example, "doctor" and "biology."

[0577] Step 2:

[0578] The terminal collects the user's input information, converts it into JSON format, and sends it to the server.

[0579] Step 3:

[0580] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[0581] Step 4:

[0582] The server runs database queries based on occupation and field of study to retrieve relevant data from the database, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[0583] Step 5:

[0584] The server inputs the acquired data into an artificial intelligence (AI) algorithm, which generates questions and answers based on occupational situations. For example, the system generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[0585] Step 6:

[0586] The questions and answers generated by the server are converted back into JSON format and sent to the terminal.

[0587] Step 7:

[0588] The device analyzes the received problem and displays it to the user, who then checks the problem on the device.

[0589] Step 8:

[0590] The user inputs an answer to the question, for example, "influenza."

[0591] Step 9:

[0592] The device collects the user's answers, converts them back into JSON format, and sends them to the server.

[0593] Step 10:

[0594] The server analyzes the user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback based on the results. For example, it generates feedback such as, "Your answer is correct. The patient may have influenza."

[0595] Step 11:

[0596] The server generates feedback and sends it to the device.

[0597] Step 12:

[0598] The device receives the feedback and displays it to the user, who can then check the evaluation results through the device.

[0599] Step 13:

[0600] The emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice data to identify emotions. For example, if the user looks tired, the emotion engine will detect this.

[0601] Step 14:

[0602] The server receives information from the emotion engine and dynamically adjusts the difficulty of the next question and the content of the feedback based on the user's current emotional state. For example, if the user is tired, the difficulty of the question may be slightly reduced or gentle feedback may be provided.

[0603] The above is the specific flow of processing in a system that combines an emotion engine. By performing detailed processing at each step, it is possible to provide an environment in which users can learn in an enjoyable and effective way.

[0604] Example 2

[0605] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0606] Current educational systems do not adequately support users in effectively learning knowledge related to their future careers. Furthermore, feedback designed to improve learning outcomes is generally static and does not dynamically adjust to the user's emotions. This can make it difficult for users to maintain their motivation to learn, potentially hindering effective learning.

[0607] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0608] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions when answering the questions, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for adjusting the content of the feedback according to the user's emotions. This allows the user to effectively learn practical knowledge related to the future occupation and receive personalized feedback according to their emotions.

[0609] A "user" is someone who uses the system to input their desired future career and field of study and work on learning problems.

[0610] "Occupation" refers to the type of work or job that the user wishes to do in the future.

[0611] A "study area" is a particular subject or topic that a user wants to study.

[0612] "Terminal" means a computing device through which a User accesses the System and inputs and receives information.

[0613] "Server" means a central processing unit that processes information sent by users and obtains, generates and evaluates related data.

[0614] A "database" is a collection of data in which information used within a system is organized and stored.

[0615] A "database query" is a search statement for retrieving specific information from a database.

[0616] "Artificial intelligence" refers to machine learning models and algorithms for generating questions and evaluating user answers.

[0617] A "problem" is a question or challenge presented to the user for answer.

[0618] An "answer" is a response that a user enters to a presented question.

[0619] "Feedback" refers to evaluation results and advice provided based on the user's answers.

[0620] An "emotion engine" is a system that analyzes a user's facial expressions and voice to recognize their emotions.

[0621] "Evaluation" is the process of comparing a user's answer with correct answer data to determine whether it is correct or not.

[0622] "Personalization" means optimizing content according to the individual situation and emotions of each user.

[0623] This invention is an educational system that generates study questions based on the user's input of their desired future career and field of study, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[0624] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects this information and sends it to the server.

[0625] The server retrieves relevant data from a database based on the received occupation and field of study. This process involves executing predefined database queries, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology." This retrieval process can be performed using SQL or NoSQL queries.

[0626] The server then uses an artificial intelligence (AI) algorithm to generate learning questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer (e.g., influenza). This is done using a generative AI model (e.g., GPT-3).

[0627] The server sends the generated learning questions to the device, which then presents them to the user. The user then enters their answers to the questions. At this time, the device's built-in emotion engine analyzes in real time how the user works on the questions and recognizes their emotions based on their facial expressions and voice. This recognition result is used as auxiliary information to respond when the user is in trouble or tired.

[0628] When the user enters an answer (e.g., influenza), the device sends this answer to the server. The server compares the received user answer with the correct answer data and evaluates it. Once the evaluation is complete, the server generates feedback. For example, this feedback may be something like, "Your answer is correct. The patient may have influenza."

[0629] Furthermore, the feedback content is dynamically adjusted based on information from the emotion engine. For example, if the user looks anxious, the feedback will be more kind and encouraging (e.g., "Good job, keep trying!").

[0630] Examples of concrete examples and prompts

[0631] Specific examples

[0632] User input: Occupation "Doctor", field of study "Biology"

[0633] Generated question: "A patient complains of fever and cough. What illness could it be?"

[0634] User Answer: "Influenza"

[0635] Feedback: "Correct. The patient may have the flu."

[0636] Prompt Sentence Examples

[0637] Enter the following information:

[0638] 1. What career do you want to have in the future (e.g., doctor)

[0639] 2. Field of study (e.g., biology)

[0640] Based on this information, questions are generated and answers are derived.

[0641] In this way, the system allows users to effectively learn practical knowledge related to their future career aspirations while receiving emotional feedback. This invention makes the learning experience more personalized, increases user motivation, and realizes effective learning.

[0642] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0643] Step 1: Collecting User Input

[0644] A user accesses the system using a terminal and inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects the information entered by the user and stores it as input data. Specifically, by entering data into the input form and clicking the "Submit" button, the data is temporarily cached. This allows the input information to be obtained as initial data for processing in the next step.

[0645] Input: User-entered occupation and field of study information

[0646] Output: Collected user input data

[0647] Step 2: Retrieving relevant data

[0648] Based on the collected user input data, the device sends the data to the server. The server executes a database query based on the received occupation and field of study information to retrieve relevant data. Specifically, the server executes an SQL query to extract the required data using a command such as "SELECT FROM DATABASE WHERE Occupation = 'Doctor' AND Field = 'Biology'".

[0649] Input: User input data (occupation and field of study)

[0650] Output: Relevant data retrieved

[0651] Step 3: Generate training questions

[0652] The server uses the acquired relevant data to generate learning questions and answers using an artificial intelligence (AI) algorithm. Specifically, it uses a generative AI model (e.g., GPT-3) to automatically generate question statements and correct answers based on symptoms and knowledge. For example, it generates the question, "A patient complains of fever and cough. What kind of illness could it be?" and the answer, "Influenza."

[0653] Input: relevant data

[0654] Output: Generated training questions and answers

[0655] Step 4: Present the learning problem

[0656] The server sends the generated learning questions to the device, which then presents the received learning questions to the user. Specifically, the device displays the question text and an answer form on the device's interface, allowing the user to work on the questions.

[0657] Input: Generated training questions

[0658] Output: The training question presented to the user

[0659] Step 5: Emotion Recognition with the Emotion Engine

[0660] As the user works on the problem, the device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. Specifically, it uses face detection algorithms and voice analysis algorithms to recognize the user's emotions (e.g., confusion, fatigue).

[0661] Input: User's facial expressions and voice data

[0662] Output: Recognized user emotion information

[0663] Step 6: Collect and submit user responses

[0664] When the user enters an answer to a question (e.g., influenza), the terminal collects the answer and sends it to the server. Specifically, the user fills in the answer form and clicks the "Submit" button, which transmits the input data to the server.

[0665] Input: The user's typed answer

[0666] Output: Collected user responses

[0667] Step 7: Evaluate your responses

[0668] The server compares the received user's answer with the automatically generated correct answer data and evaluates it. Specifically, it uses an algorithm to determine whether the answer is correct or not and generates a result. For example, it evaluates it as "correct" based on the comparison logic.

[0669] Input: User answers and correct answer data

[0670] Output: Evaluation results

[0671] Step 8: Generate and provide feedback

[0672] The server generates feedback based on the evaluation results and sends it to the device. The device then displays the received feedback to the user. The content of the feedback is also dynamically adjusted based on information from the emotion engine. Specifically, if the user looks anxious, the system will provide feedback that includes encouraging words.

[0673] Input: Evaluation results and emotional information

[0674] Output: Dynamically adjusted feedback

[0675] This allows users to tackle learning questions related to their future career aspirations and receive feedback based on their answers, personalizing the learning experience based on emotions.

[0676] (Application example 2)

[0677] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0678] Current educational systems make it difficult for users to effectively acquire specialized knowledge related to specific occupations or fields of study. Furthermore, they provide a uniform level of difficulty and feedback without considering the user's emotional state, resulting in a lack of personalized learning experiences and a lack of motivation to learn. In particular, in certain industries, such as the food delivery industry, it is necessary to provide problems based on specific on-site situations.

[0679] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0680] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions, means for dynamically adjusting the difficulty of the questions and the feedback content according to the user's emotions, means for receiving and evaluating the user's answers, and means for providing the user with the evaluation results as feedback. This enables a user to efficiently learn specialized knowledge required for a specific industry and provides a personalized learning experience according to the user's emotions.

[0681] "User" refers to an individual who uses the system to learn.

[0682] "Desired future occupation" refers to a specific occupation that the user is aiming for.

[0683] A "study area" refers to a particular area of ​​knowledge that a user wants to learn.

[0684] "Database" means a collection of information that stores related data based on user-entered information.

[0685] "Artificial intelligence (AI)" refers to the technology that systems use to automatically generate questions and answers.

[0686] "Problem" means content including a problem or question that a user must solve through learning.

[0687] "Answer" refers to a solution provided by a user to a question.

[0688] "Emotion" refers to the psychological state that a user exhibits while learning.

[0689] "Feedback" refers to the evaluation and advice provided by the system in response to the user's answers.

[0690] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state.

[0691] "Dynamic adjustment means" refers to the ability to change the difficulty of questions and the content of feedback in real time according to the user's emotional state.

[0692] "Food delivery industry" means an industry that operates primarily around the delivery of food.

[0693] "Acquiring specialized knowledge" refers to acquiring in-depth knowledge and skills in a specific field.

[0694] The present invention provides an educational system that generates relevant study questions based on a user's desired future career and field of study, and supports learning by taking the user's emotions into consideration. Specific procedures and methods for implementing the present invention are described below.

[0695] System Program

[0696] The system includes the following major components:

[0697] 1. User Input Method

[0698] Users use their smartphones to input their desired future occupation (e.g., "Delivery Manager") and field of study (e.g., "Delivery Route Optimization").

[0699] 2. Data Acquisition Method

[0700] Based on the occupation and field of study information sent from the device, the server retrieves relevant data from a database, which provides information about the knowledge and skills required for the occupation. Retrieval from the database is performed using a predefined database query.

[0701] 3. Problem generation means

[0702] The server uses the acquired data to generate learning questions and answers using an artificial intelligence (AI) algorithm, such as "Select the most efficient delivery route in this area."

[0703] 4. Emotion recognition means

[0704] When the user answers a question, the system uses the smartphone's built-in camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[0705] 5. Dynamic Adjustment Methods

[0706] The server dynamically adjusts the difficulty of the questions and the content of the feedback depending on the emotion recognition results. For example, if the user shows signs of anxiety, it adds encouraging words to the feedback.

[0707] 6. Means of Providing Feedback

[0708] The server evaluates the answers entered by the user and generates and sends the resulting feedback to the device, which then provides the user with appropriate advice and the next challenge.

[0709] Processing Details

[0710] The processing of this system includes the following software and hardware:

[0711] Hardware: Smartphones, servers

[0712] Software: Python (Flask for backend), machine learning libraries (e.g., TensorFlow, EmotionRecognition library)

[0713] The server extracts relevant data from a database based on the received occupation and field of study, and generates questions and answers using an AI model (e.g., a generative AI model). The user's smartphone uses a camera and microphone to analyze the user's facial expressions and voice via an emotion recognition library, and sends the results to the server. The server then dynamically adjusts the difficulty of the questions and the content of the feedback based on this information.

[0714] Specific examples

[0715] For example, if a user selects an occupation as a "Delivery Manager" and enters "Delivery Route Optimization" as their field of study, the following example prompt sentence will be generated:

[0716] Example prompt sentence:

[0717] "Choose a route that will deliver efficiently in this area. Also consider the number of orders at each point."

[0718] When the user answers the question, their emotions are recognized using the smartphone's camera and microphone, and if the user looks anxious, encouraging feedback such as, "Your answer is a little off, but don't worry, you'll definitely get it right next time!" is provided.

[0719] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0720] Step 1:

[0721] Users input their desired career and field of study in the future.

[0722] Users enter information about their desired career and field of study through a smartphone interface.

[0723] Input: Job title (e.g., "Delivery Manager"), field of study (e.g., "Delivery Route Optimization")

[0724] Output: Input information is transmitted to the server

[0725] Step 2:

[0726] The server retrieves relevant data from a database based on the entered occupation and field of study.

[0727] The server executes predefined database queries based on the received information to extract relevant data.

[0728] Input: Occupation and field of study information

[0729] Output: Relevant data (e.g. information on optimizing delivery routes)

[0730] Step 3:

[0731] The server uses the data it acquires to generate questions and answers using an AI algorithm.

[0732] The server uses a generative AI model to construct questions and answers based on the data it receives.

[0733] Input: Related data

[0734] Output: Generated question and answer (e.g., "Choose an efficient delivery route in this area" and its optimal answer)

[0735] Step 4:

[0736] The server sends the generated questions to the terminal, which then presents them to the user.

[0737] The terminal displays the received questions to the user and allows the user to input answers to the questions.

[0738] Input: Question sent by server

[0739] Output: The problem as seen by the user

[0740] Step 5:

[0741] The user enters the answer to the question, and the terminal sends it to the server.

[0742] The user inputs their answer to the question presented, and the terminal sends the answer to the server.

[0743] Input: User's answer

[0744] Output: User's answer sent to the server

[0745] Step 6:

[0746] The server recognizes the user's emotions and dynamically adjusts the feedback content based on that information.

[0747] The server analyzes the user's facial expressions and voice through the smartphone's camera and microphone, and uses an emotion recognition library to understand the user's emotional state.

[0748] Input: User facial and voice data

[0749] Output: Recognized user emotional state

[0750] Step 7:

[0751] The server evaluates the user's answer by comparing it with automatically generated correct answers.

[0752] The server compares the user's answer with the correct answer data and calculates the evaluation result.

[0753] Input: User's answer, correct answer data

[0754] Output: Evaluation result (e.g., whether it is correct or not)

[0755] Step 8:

[0756] The server generates feedback based on the evaluation results, adjusts the content according to the emotion, and sends it to the device.

[0757] The server modifies the feedback content according to the recognized emotional state and provides appropriate feedback to the user.

[0758] Input: Evaluation result, emotional state

[0759] Output: Dynamically adjusted feedback

[0760] Step 9:

[0761] The terminal displays the feedback received from the server to the user.

[0762] The device receives the feedback sent from the server and displays it to the user, helping them reflect on their learning.

[0763] Input: Feedback data

[0764] Output: Feedback displayed to the user

[0765] 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.

[0766] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0767] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0768] [Third embodiment]

[0769] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0770] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0771] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0772] 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.

[0773] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0774] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0775] 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.

[0776] 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.

[0777] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0778] 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.

[0779] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0780] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0781] The present invention relates to an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[0782] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server.

[0783] The server retrieves relevant data from a database based on the received occupation and field of study. This retrieval process involves executing predefined database queries, for example, to extract symptoms and diagnostic information related to "physician" and "biology."

[0784] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer. As a specific example, it generates the question, "The patient complains of a fever and cough. What illness could it be?" and the answer, "Influenza."

[0785] The server sends the generated questions to the terminal, which then presents them to the user. The user then inputs their answer to the question. For example, the user inputs the answer "influenza." The terminal then sends this answer to the server.

[0786] The server compares the received user's answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback. For example, the feedback may be something like, "Your answer is correct. The patient may have influenza." The server sends this feedback to the terminal, which displays it to the user.

[0787] In this way, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations.

[0788] The processing flow will be explained below.

[0789] Step 1:

[0790] The user inputs the desired career and field of study into the terminal interface. For example, the user inputs "doctor" and "biology."

[0791] Step 2:

[0792] The device receives the user's input, converts it into JSON format, and then sends the data to the server.

[0793] Step 3:

[0794] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[0795] Step 4:

[0796] The server runs database queries based on occupation and field of study to retrieve relevant data, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[0797] Step 5:

[0798] The server applies an artificial intelligence algorithm to the data it acquires to generate questions and answers based on professional situations. For example, it generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[0799] Step 6:

[0800] The server converts the generated questions and answers into JSON format and sends them to the terminal.

[0801] Step 7:

[0802] The device analyzes the received problem and displays it to the user, who can then check the problem on the device.

[0803] Step 8:

[0804] The user inputs the answer to the question into the terminal, for example, "influenza."

[0805] Step 9:

[0806] The device receives the user's response, converts it into JSON format, and sends it to the server.

[0807] Step 10:

[0808] The server analyzes the received user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback. For example, it might say, "Your answer is correct. The patient may have influenza."

[0809] Step 11:

[0810] The server generates feedback and sends it to the device.

[0811] Step 12:

[0812] The device receives the feedback and displays it to the user, who can then check the evaluation results of their answers on the device.

[0813] The above is the specific flow of the system's program processing. By proceeding with the processing in an orderly manner at each step, it is possible to provide a user with an enjoyable learning environment.

[0814] Example 1

[0815] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0816] Conventional educational systems lack the ability to generate customized learning questions tailored to a user's desired future career or field of study and provide immediate, appropriate feedback. This makes it difficult for users to effectively learn practical knowledge directly related to their goals. The present invention aims to solve these problems and provide an educational system that allows users to engage in more practical and effective learning.

[0817] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0818] In this invention, the server includes a means for allowing a user to input a desired future occupation and field of study, a means for retrieving related information from a database based on the occupation and field of study input by the user, and a means for generating questions and answers using an artificial intelligence algorithm based on the retrieved information, thereby enabling the user to instantly receive study questions tailored to their goals and receive accurate feedback.

[0819] "User" refers to a person who uses the system to learn or input information.

[0820] "Terminal" refers to a device used by a user to access the system and enter or receive information.

[0821] "Server" refers to a central control unit that receives data sent by users, processes and communicates with a database, and returns the results to the users.

[0822] "Occupation" refers to the type of job or work that the user wants to do in the future.

[0823] "Field of study" refers to the academic or specialized field that the user wants to study.

[0824] A "database" refers to a storage system in which related information and data are stored in an organized manner and are accessible from a server.

[0825] An "artificial intelligence algorithm" refers to software technology that has a series of calculation procedures for solving problems or making predictions based on data.

[0826] "Questions" refer to questions or tasks that are generated based on the user's input of occupation or field of study to encourage learning.

[0827] "Answer" refers to the solution entered by the user in response to the generated question.

[0828] "Feedback" refers to the evaluation results and additional information provided in response to the answers submitted by the user.

[0829] "Comparing" refers to the process of matching the user's submitted answer with a pre-generated correct answer and evaluating their match and accuracy.

[0830] "Presenting" refers to the act of displaying the generated questions and feedback to the user via a terminal.

[0831] The present invention is an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[0832] First, a user accesses the system using their own device. They reach the interface by opening a web browser and accessing the system's URL. For example, they can use Google Chrome or any other common web browser.

[0833] The user enters their desired career (e.g., "doctor") and field of study (e.g., "biology") into the interface, and the input is sent by the device to the server as an HTTP POST request.

[0834] The server retrieves relevant information from a database based on the occupation and field of study entered by the user. This process involves running predefined SQL queries to extract the required data. For example, a specific query is run against the database to retrieve symptom and diagnosis information related to "doctor" and "biology."

[0835] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired information. Specifically, a generative AI model is used to pass the acquired data as prompts to the AI ​​model, which then generates learning questions and answers. For example, the question "A patient complains of fever and cough. What illness could it be?" and the answer "Influenza" are generated.

[0836] The generated questions are sent from the server to the terminal, and the terminal presents the received questions to the user. The user inputs their answer to the presented questions into the interface. For example, the user inputs the answer "influenza" and sends it back from the terminal to the server.

[0837] The server evaluates the received user's answer by comparing it with an automatically generated correct answer. Once the evaluation process is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." This feedback is sent from the server to the device, which displays it to the user.

[0838] As a specific example of operation, the following prompt sentence is given to the generative AI model:

[0839] Generate educational questions to be created for students who have chosen "Doctor" as their future career and "Biology" as their field of study. Specifically, provide questions and answers related to patient symptoms.

[0840] In this way, the present invention is a system that provides users with a customized learning experience and allows them to deepen their practical knowledge through effective feedback.

[0841] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0842] Step 1:

[0843] Users access the system using their own terminals.

[0844] Input: The user opens a web browser and enters the system's URL.

[0845] Specific behavior: A user launches a web browser such as Google Chrome or Firefox and enters a specific URL (the system's homepage).

[0846] Output: The system login screen or home page is displayed.

[0847] Step 2:

[0848] The user enters their desired career and field of study into the interface.

[0849] Input: Enter your "Occupation" and "Field of Study" in the input fields on the interface.

[0850] Specific behavior: The user enters information such as "doctor" and "biology" and clicks the submit button.

[0851] Output: Data entered by the user is sent from the device to the server.

[0852] Step 3:

[0853] The terminal transmits the user's input information to the server.

[0854] Input: Occupation and field of study data entered by the user.

[0855] Specific operation: The device generates an HTTP POST request and sends data including the input content to the server.

[0856] Output: The server receives the user's input.

[0857] Step 4:

[0858] The server retrieves relevant information from a database based on the user's input information.

[0859] Input: Occupation and field of study data entered by the user.

[0860] What it does: The server runs a predefined SQL query to extract information related to "doctors" and "biology" from the database.

[0861] Output: The extracted information is aggregated on the server.

[0862] Step 5:

[0863] The server uses the information it obtains to generate questions and answers using an artificial intelligence algorithm.

[0864] Input: Relevant information extracted from the database.

[0865] Specific operation: The server uses the generative AI model, passes the extracted data to the AI ​​model as prompts, and generates learning questions and their answers.

[0866] Output: Generated training questions and their answers.

[0867] Step 6:

[0868] The server sends the generated questions to the terminal and presents them to the user.

[0869] Input: The generated training problem.

[0870] Specific operation: The server sends the generated problem as an HTTP response to the device, and the device displays the received content on a web page.

[0871] Output: The study questions are displayed on the user's screen.

[0872] Step 7:

[0873] The user enters their answers to the questions presented in the interface.

[0874] Input: The study question to be answered by the user.

[0875] Specific Action: The user enters "flu" into the answer field on the interface and clicks the submit button.

[0876] Output: The user's answer data is sent from the device to the server.

[0877] Step 8:

[0878] The terminal sends the user's answer to the server.

[0879] Input: User response data.

[0880] Specific operation: The device creates an HTTP POST request and sends the response data to the server.

[0881] Output: The server receives the user's answer data.

[0882] Step 9:

[0883] The server evaluates the received user answer by comparing it with automatically generated correct answers.

[0884] Input: User response data and automatically generated correct answer data.

[0885] Specific operation: The server compares the received user answer with the pre-generated correct answer, and if they match, evaluates it as the "correct answer."

[0886] Output: The evaluation result (correct or incorrect).

[0887] Step 10:

[0888] The server generates feedback based on the evaluation results and provides it to the user.

[0889] Input: Evaluation result.

[0890] Specific behavior: Based on the evaluation results, automatically generate feedback such as "You're correct. The patient may have influenza."

[0891] Output: The generated feedback is sent from the server to the device and displayed to the user.

[0892] (Application example 1)

[0893] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0894] Conventional educational systems have the problem of not having sufficient feedback functions to effectively teach users the practical knowledge necessary for their future careers. They also lack the functionality to systematically record and visualize users' learning history and progress, leaving a need for improved learning efficiency.

[0895] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0896] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for recording and visualizing the user's learning history and progress. This enables the user to effectively learn practical knowledge related to the future occupation, and visualization of the learning progress can improve learning efficiency.

[0897] "User" refers to any individual or corporation that uses this system.

[0898] "Occupation" refers to the job or occupation that the user wants to pursue in the future.

[0899] "Field of study" refers to the academic or technical field in which the user is interested and wants to learn.

[0900] A "database" refers to an information resource that systematically organizes information stored on a server and can be searched and retrieved as needed.

[0901] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and have capabilities such as learning, reasoning, and judgment.

[0902] "Questions" refer to questions that the user must answer as part of the learning content that is generated based on the occupation and field of study entered by the user.

[0903] An "answer" refers to the answer that a user enters to a question.

[0904] "Evaluation" refers to the process of comparing the answers entered by the user with the correct answers and determining whether they are correct or incorrect.

[0905] "Feedback" refers to showing the evaluation results of the user's answers and providing information to assist learning.

[0906] "Study history" refers to a record of the learning activities that a user has undertaken up to now.

[0907] "Progress" refers to information that indicates the current stage of a user's learning goal.

[0908] "Visualization" refers to visually representing data so that it can be easily understood by users.

[0909] A "prompt sentence" refers to an input sentence that prompts an artificial intelligence to respond or generate a specific response.

[0910] The embodiments of the present invention will be specifically described below.

[0911] This system is an educational system consisting of a server and a user terminal. Users access the system through a smartphone application. When users input their desired future career and field of study, the information is sent to the server. The server executes a specified database query to retrieve data related to the entered career and field of study from the database.

[0912] The server uses the acquired data to automatically generate questions and answers using a generative AI model (for example, OpenAI's GPT-3). As a specific example, when the occupation "doctor" and the field of study "biology" are input, the server generates the question "The patient complains of fever and cough. What kind of illness could it be?" and the answer "influenza." An example of a prompt sentence used in this process is as follows:

[0913] text

[0914] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[0915] The server sends the generated questions to the user terminal, which then presents them to the user. The user enters the answer to the question, which is then sent to the server. The server compares the received user answer with the correct answer data and evaluates it. The evaluation result is generated as feedback and sent back to the user terminal. For example, if the user answers "influenza," the following feedback is provided: "That's correct. The patient may have influenza."

[0916] Furthermore, this system has the function of recording and visualizing the user's learning history and progress. This allows users to visually check their learning progress and study efficiently. This visualization is achieved by the server analyzing the learning history recorded in the database and displaying it as graphs and charts on the user's device.

[0917] The hardware used is a smartphone, the server is a web server using the Flask framework, and the database is SQLite, making it possible to build a highly efficient and convenient educational support system.

[0918] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0919] Step 1:

[0920] Users access the system through a smartphone application. On the input screen, users input their desired future occupation (e.g., "doctor") and field of study (e.g., "biology"). This information is sent to the server in JSON format. The user's input, occupation and field of study, is sent to the server, and the server receives it.

[0921] Step 2:

[0922] Based on the received occupation and field of study, the server executes a predefined database query to retrieve relevant data from the database, for example, extracting symptoms and diagnostic information related to "doctor" and "biology." The input at this stage is the occupation and field of study, and the output is the retrieved data.

[0923] Step 3:

[0924] The server creates a prompt for the generative AI model (e.g., OpenAI's GPT-3) based on the acquired data. Specifically, it generates the following prompt:

[0925] text

[0926] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[0927] This prompt is then input into a generative AI model to generate a learning question and its answer. At this stage, the input is the acquired data and the prompt, and the output is the generated question and its answer.

[0928] Step 4:

[0929] The server sends the generated question to the user's terminal. The user's terminal displays the question to the user. The user reads the question and enters their answer. At this stage, the input is the generated question, and the output is the user's answer.

[0930] Step 5:

[0931] The answer entered by the user is sent from the terminal to the server. The server receives this answer and compares it with the generated correct answer data. This comparison evaluates the correctness of the user's answer. At this stage, the input is the user's answer, and the output is the evaluation result.

[0932] Step 6:

[0933] The server generates feedback based on the evaluation result. For example, it generates feedback such as "You are correct. The patient may have influenza." This feedback is sent to the user's terminal and displayed to the user. At this stage, the input is the evaluation result, and the output is the feedback.

[0934] Step 7:

[0935] The server records the user's learning history and progress and periodically updates the data for visualization. This allows the user to check their learning progress in graphs and charts. The input at this stage is learning history and progress data, and the output is visualized information.

[0936] In this way, the system provides an environment in which users can efficiently learn practical knowledge related to the career they want to pursue in the future.

[0937] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0938] This invention relates to an educational system that allows users to input their desired future career and field of study, generates study questions based on the input, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[0939] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server. The server retrieves relevant data from the database based on the received occupation and field of study. This retrieval process involves executing predefined database queries. For example, it extracts symptoms related to "doctor" or basic knowledge related to "biology."

[0940] Next, the server uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously creates the correct answer. As a specific example, it generates the question "The patient complains of a fever and cough. What illness do you think it could be?" and the answer "influenza." The server sends the generated question to the terminal, which then presents it to the user.

[0941] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice when answering questions to recognize their emotions at any given time. Based on this information, the system dynamically adjusts the difficulty of the questions and the feedback provided. For example, if the user shows a tired expression, the system can lower the difficulty level slightly and provide relatively easy questions.

[0942] When the user enters their answer to the presented question (for example, entering "influenza"), the device sends this answer to the server. The server compares the received user answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." The server sends this feedback to the device, which then displays it to the user.

[0943] The system's built-in emotion engine also adjusts the feedback it provides based on the user's emotions. If the user looks anxious, it can provide more helpful and encouraging feedback, making the learning experience more personalized and effective.

[0944] As such, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations. Furthermore, the combination of an emotion engine further enhances the user's learning experience.

[0945] The processing flow will be explained below.

[0946] Step 1:

[0947] The user inputs their desired career and field of study into the device's interface, for example, "doctor" and "biology."

[0948] Step 2:

[0949] The terminal collects the user's input information, converts it into JSON format, and sends it to the server.

[0950] Step 3:

[0951] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[0952] Step 4:

[0953] The server runs database queries based on occupation and field of study to retrieve relevant data from the database, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[0954] Step 5:

[0955] The server inputs the acquired data into an artificial intelligence (AI) algorithm, which generates questions and answers based on occupational situations. For example, the system generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[0956] Step 6:

[0957] The questions and answers generated by the server are converted back into JSON format and sent to the terminal.

[0958] Step 7:

[0959] The device analyzes the received problem and displays it to the user, who then checks the problem on the device.

[0960] Step 8:

[0961] The user inputs an answer to the question, for example, "influenza."

[0962] Step 9:

[0963] The device collects the user's answers, converts them back into JSON format, and sends them to the server.

[0964] Step 10:

[0965] The server analyzes the user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback based on the results. For example, it generates feedback such as, "Your answer is correct. The patient may have influenza."

[0966] Step 11:

[0967] The server generates feedback and sends it to the device.

[0968] Step 12:

[0969] The device receives the feedback and displays it to the user, who can then check the evaluation results through the device.

[0970] Step 13:

[0971] The emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice data to identify emotions. For example, if the user looks tired, the emotion engine will detect this.

[0972] Step 14:

[0973] The server receives information from the emotion engine and dynamically adjusts the difficulty of the next question and the content of the feedback based on the user's current emotional state. For example, if the user is tired, the difficulty of the question may be slightly reduced or gentle feedback may be provided.

[0974] The above is the specific flow of processing in a system that combines an emotion engine. By performing detailed processing at each step, it is possible to provide an environment in which users can learn in an enjoyable and effective way.

[0975] Example 2

[0976] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0977] Current educational systems do not adequately support users in effectively learning knowledge related to their future careers. Furthermore, feedback designed to improve learning outcomes is generally static and does not dynamically adjust to the user's emotions. This can make it difficult for users to maintain their motivation to learn, potentially hindering effective learning.

[0978] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0979] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions when answering the questions, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for adjusting the content of the feedback according to the user's emotions. This allows the user to effectively learn practical knowledge related to the future occupation and receive personalized feedback according to their emotions.

[0980] A "user" is someone who uses the system to input their desired future career and field of study and work on learning problems.

[0981] "Occupation" refers to the type of work or job that the user wishes to do in the future.

[0982] A "study area" is a particular subject or topic that a user wants to study.

[0983] "Terminal" means a computing device through which a User accesses the System and inputs and receives information.

[0984] "Server" means a central processing unit that processes information sent by users and obtains, generates and evaluates related data.

[0985] A "database" is a collection of data in which information used within a system is organized and stored.

[0986] A "database query" is a search statement for retrieving specific information from a database.

[0987] "Artificial intelligence" refers to machine learning models and algorithms for generating questions and evaluating user answers.

[0988] A "problem" is a question or challenge presented to the user for answer.

[0989] An "answer" is a response that a user enters to a presented question.

[0990] "Feedback" refers to evaluation results and advice provided based on the user's answers.

[0991] An "emotion engine" is a system that analyzes a user's facial expressions and voice to recognize their emotions.

[0992] "Evaluation" is the process of comparing a user's answer with correct answer data to determine whether it is correct or not.

[0993] "Personalization" means optimizing content according to the individual situation and emotions of each user.

[0994] This invention is an educational system that generates study questions based on the user's input of their desired future career and field of study, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[0995] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects this information and sends it to the server.

[0996] The server retrieves relevant data from a database based on the received occupation and field of study. This process involves executing predefined database queries, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology." This retrieval process can be performed using SQL or NoSQL queries.

[0997] The server then uses an artificial intelligence (AI) algorithm to generate learning questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer (e.g., influenza). This is done using a generative AI model (e.g., GPT-3).

[0998] The server sends the generated learning questions to the device, which then presents them to the user. The user then enters their answers to the questions. At this time, the device's built-in emotion engine analyzes in real time how the user works on the questions and recognizes their emotions based on their facial expressions and voice. This recognition result is used as auxiliary information to respond when the user is in trouble or tired.

[0999] When the user enters an answer (e.g., influenza), the device sends this answer to the server. The server compares the received user answer with the correct answer data and evaluates it. Once the evaluation is complete, the server generates feedback. For example, this feedback may be something like, "Your answer is correct. The patient may have influenza."

[1000] Furthermore, the feedback content is dynamically adjusted based on information from the emotion engine. For example, if the user looks anxious, the feedback will be more kind and encouraging (e.g., "Good job, keep trying!").

[1001] Examples of concrete examples and prompts

[1002] Specific examples

[1003] User input: Occupation "Doctor", field of study "Biology"

[1004] Generated question: "A patient complains of fever and cough. What illness could it be?"

[1005] User Answer: "Influenza"

[1006] Feedback: "Correct. The patient may have the flu."

[1007] Prompt Sentence Examples

[1008] Enter the following information:

[1009] 1. What career do you want to have in the future (e.g., doctor)

[1010] 2. Field of study (e.g., biology)

[1011] Based on this information, questions are generated and answers are derived.

[1012] In this way, the system allows users to effectively learn practical knowledge related to their future career aspirations while receiving emotional feedback. This invention makes the learning experience more personalized, increases user motivation, and realizes effective learning.

[1013] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1014] Step 1: Collecting User Input

[1015] A user accesses the system using a terminal and inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects the information entered by the user and stores it as input data. Specifically, by entering data into the input form and clicking the "Submit" button, the data is temporarily cached. This allows the input information to be obtained as initial data for processing in the next step.

[1016] Input: User-entered occupation and field of study information

[1017] Output: Collected user input data

[1018] Step 2: Retrieving relevant data

[1019] Based on the collected user input data, the device sends the data to the server. The server executes a database query based on the received occupation and field of study information to retrieve relevant data. Specifically, the server executes an SQL query to extract the required data using a command such as "SELECT FROM DATABASE WHERE Occupation = 'Doctor' AND Field = 'Biology'".

[1020] Input: User input data (occupation and field of study)

[1021] Output: Relevant data retrieved

[1022] Step 3: Generate training questions

[1023] The server uses the acquired relevant data to generate learning questions and answers using an artificial intelligence (AI) algorithm. Specifically, it uses a generative AI model (e.g., GPT-3) to automatically generate question statements and correct answers based on symptoms and knowledge. For example, it generates the question, "A patient complains of fever and cough. What kind of illness could it be?" and the answer, "Influenza."

[1024] Input: relevant data

[1025] Output: Generated training questions and answers

[1026] Step 4: Present the learning problem

[1027] The server sends the generated learning questions to the device, which then presents the received learning questions to the user. Specifically, the device displays the question text and an answer form on the device's interface, allowing the user to work on the questions.

[1028] Input: Generated training questions

[1029] Output: The training question presented to the user

[1030] Step 5: Emotion Recognition with the Emotion Engine

[1031] As the user works on the problem, the device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. Specifically, it uses face detection algorithms and voice analysis algorithms to recognize the user's emotions (e.g., confusion, fatigue).

[1032] Input: User's facial expressions and voice data

[1033] Output: Recognized user emotion information

[1034] Step 6: Collect and submit user responses

[1035] When the user enters an answer to a question (e.g., influenza), the terminal collects the answer and sends it to the server. Specifically, the user fills in the answer form and clicks the "Submit" button, which transmits the input data to the server.

[1036] Input: The user's typed answer

[1037] Output: Collected user responses

[1038] Step 7: Evaluate your responses

[1039] The server compares the received user's answer with the automatically generated correct answer data and evaluates it. Specifically, it uses an algorithm to determine whether the answer is correct or not and generates a result. For example, it evaluates it as "correct" based on the comparison logic.

[1040] Input: User answers and correct answer data

[1041] Output: Evaluation results

[1042] Step 8: Generate and provide feedback

[1043] The server generates feedback based on the evaluation results and sends it to the device. The device then displays the received feedback to the user. The content of the feedback is also dynamically adjusted based on information from the emotion engine. Specifically, if the user looks anxious, the system will provide feedback that includes encouraging words.

[1044] Input: Evaluation results and emotional information

[1045] Output: Dynamically adjusted feedback

[1046] This allows users to tackle learning questions related to their future career aspirations and receive feedback based on their answers, personalizing the learning experience based on emotions.

[1047] (Application example 2)

[1048] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1049] Current educational systems make it difficult for users to effectively acquire specialized knowledge related to specific occupations or fields of study. Furthermore, they provide a uniform level of difficulty and feedback without considering the user's emotional state, resulting in a lack of personalized learning experiences and a lack of motivation to learn. In particular, in certain industries, such as the food delivery industry, it is necessary to provide problems based on specific on-site situations.

[1050] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1051] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions, means for dynamically adjusting the difficulty of the questions and the feedback content according to the user's emotions, means for receiving and evaluating the user's answers, and means for providing the user with the evaluation results as feedback. This enables a user to efficiently learn specialized knowledge required for a specific industry and provides a personalized learning experience according to the user's emotions.

[1052] "User" refers to an individual who uses the system to learn.

[1053] "Desired future occupation" refers to a specific occupation that the user is aiming for.

[1054] A "study area" refers to a particular area of ​​knowledge that a user wants to learn.

[1055] "Database" means a collection of information that stores related data based on user-entered information.

[1056] "Artificial intelligence (AI)" refers to the technology that systems use to automatically generate questions and answers.

[1057] "Problem" means content including a problem or question that a user must solve through learning.

[1058] "Answer" refers to a solution provided by a user to a question.

[1059] "Emotion" refers to the psychological state that a user exhibits while learning.

[1060] "Feedback" refers to the evaluation and advice provided by the system in response to the user's answers.

[1061] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state.

[1062] "Dynamic adjustment means" refers to the ability to change the difficulty of questions and the content of feedback in real time according to the user's emotional state.

[1063] "Food delivery industry" means an industry that operates primarily around the delivery of food.

[1064] "Acquiring specialized knowledge" refers to acquiring in-depth knowledge and skills in a specific field.

[1065] The present invention provides an educational system that generates relevant study questions based on a user's desired future career and field of study, and supports learning by taking the user's emotions into consideration. Specific procedures and methods for implementing the present invention are described below.

[1066] System Program

[1067] The system includes the following major components:

[1068] 1. User Input Method

[1069] Users use their smartphones to input their desired future occupation (e.g., "Delivery Manager") and field of study (e.g., "Delivery Route Optimization").

[1070] 2. Data Acquisition Method

[1071] Based on the occupation and field of study information sent from the device, the server retrieves relevant data from a database, which provides information about the knowledge and skills required for the occupation. Retrieval from the database is performed using a predefined database query.

[1072] 3. Problem generation means

[1073] The server uses the acquired data to generate learning questions and answers using an artificial intelligence (AI) algorithm, such as "Select the most efficient delivery route in this area."

[1074] 4. Emotion recognition means

[1075] When the user answers a question, the system uses the smartphone's built-in camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[1076] 5. Dynamic Adjustment Methods

[1077] The server dynamically adjusts the difficulty of the questions and the content of the feedback depending on the emotion recognition results. For example, if the user shows signs of anxiety, it adds encouraging words to the feedback.

[1078] 6. Means of Providing Feedback

[1079] The server evaluates the answers entered by the user and generates and sends the resulting feedback to the device, which then provides the user with appropriate advice and the next challenge.

[1080] Processing Details

[1081] The processing of this system includes the following software and hardware:

[1082] Hardware: Smartphones, servers

[1083] Software: Python (Flask for backend), machine learning libraries (e.g., TensorFlow, EmotionRecognition library)

[1084] The server extracts relevant data from a database based on the received occupation and field of study, and generates questions and answers using an AI model (e.g., a generative AI model). The user's smartphone uses a camera and microphone to analyze the user's facial expressions and voice via an emotion recognition library, and sends the results to the server. The server then dynamically adjusts the difficulty of the questions and the content of the feedback based on this information.

[1085] Specific examples

[1086] For example, if a user selects an occupation as "Delivery Manager" and enters "Delivery Route Optimization" as their field of study, the following example prompt sentence will be generated:

[1087] Example prompt sentence:

[1088] "Choose a route that will deliver efficiently in this area. Also consider the number of orders at each point."

[1089] When the user answers the question, their emotions are recognized using the smartphone's camera and microphone, and if the user looks anxious, encouraging feedback such as, "Your answer is a little off, but don't worry, you'll definitely get it right next time!" is provided.

[1090] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1091] Step 1:

[1092] Users input their desired career and field of study in the future.

[1093] Users enter information about their desired career and field of study through a smartphone interface.

[1094] Input: Job title (e.g., "Delivery Manager"), field of study (e.g., "Delivery Route Optimization")

[1095] Output: Input information is transmitted to the server

[1096] Step 2:

[1097] The server retrieves relevant data from a database based on the entered occupation and field of study.

[1098] The server executes predefined database queries based on the received information to extract relevant data.

[1099] Input: Occupation and field of study information

[1100] Output: Relevant data (e.g. information on optimizing delivery routes)

[1101] Step 3:

[1102] The server uses the data it acquires to generate questions and answers using an AI algorithm.

[1103] The server uses a generative AI model to construct questions and answers based on the data it receives.

[1104] Input: Related data

[1105] Output: Generated question and answer (e.g., "Choose an efficient delivery route in this area" and its optimal answer)

[1106] Step 4:

[1107] The server sends the generated questions to the terminal, which then presents them to the user.

[1108] The terminal displays the received questions to the user and allows the user to input answers to the questions.

[1109] Input: Question sent by server

[1110] Output: The problem as seen by the user

[1111] Step 5:

[1112] The user enters the answer to the question, and the terminal sends it to the server.

[1113] The user inputs their answer to the question presented, and the terminal sends the answer to the server.

[1114] Input: User's answer

[1115] Output: User's answer sent to the server

[1116] Step 6:

[1117] The server recognizes the user's emotions and dynamically adjusts the feedback content based on that information.

[1118] The server analyzes the user's facial expressions and voice through the smartphone's camera and microphone, and uses an emotion recognition library to understand the user's emotional state.

[1119] Input: User facial and voice data

[1120] Output: Recognized user emotional state

[1121] Step 7:

[1122] The server evaluates the user's answer by comparing it with automatically generated correct answers.

[1123] The server compares the user's answer with the correct answer data and calculates the evaluation result.

[1124] Input: User's answer, correct answer data

[1125] Output: Evaluation result (e.g., whether it is correct or not)

[1126] Step 8:

[1127] The server generates feedback based on the evaluation results, adjusts the content according to the emotion, and sends it to the device.

[1128] The server modifies the feedback content according to the recognized emotional state and provides appropriate feedback to the user.

[1129] Input: Evaluation result, emotional state

[1130] Output: Dynamically adjusted feedback

[1131] Step 9:

[1132] The terminal displays the feedback received from the server to the user.

[1133] The device receives the feedback sent from the server and displays it to the user, helping them reflect on their learning.

[1134] Input: Feedback data

[1135] Output: Feedback displayed to the user

[1136] 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.

[1137] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1138] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1139] [Fourth embodiment]

[1140] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1141] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1143] 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.

[1144] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1146] 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.

[1147] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1148] 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.

[1149] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1150] 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.

[1151] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1152] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1153] The present invention relates to an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[1154] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server.

[1155] The server retrieves relevant data from a database based on the received occupation and field of study. This retrieval process involves executing predefined database queries, for example, to extract symptoms and diagnostic information related to "physician" and "biology."

[1156] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer. As a specific example, it generates the question, "The patient complains of a fever and cough. What illness could it be?" and the answer, "Influenza."

[1157] The server sends the generated questions to the terminal, which then presents them to the user. The user then inputs their answer to the question. For example, the user inputs the answer "influenza." The terminal then sends this answer to the server.

[1158] The server compares the received user's answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback. For example, the feedback may be something like, "Your answer is correct. The patient may have influenza." The server sends this feedback to the terminal, which displays it to the user.

[1159] In this way, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations.

[1160] The processing flow will be explained below.

[1161] Step 1:

[1162] The user inputs the desired career and field of study into the terminal interface. For example, the user inputs "doctor" and "biology."

[1163] Step 2:

[1164] The device receives the user's input, converts it into JSON format, and then sends the data to the server.

[1165] Step 3:

[1166] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[1167] Step 4:

[1168] The server runs database queries based on occupation and field of study to retrieve relevant data, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[1169] Step 5:

[1170] The server applies an artificial intelligence algorithm to the data it acquires to generate questions and answers based on professional situations. For example, it generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[1171] Step 6:

[1172] The server converts the generated questions and answers into JSON format and sends them to the terminal.

[1173] Step 7:

[1174] The device analyzes the received problem and displays it to the user, who can then check the problem on the device.

[1175] Step 8:

[1176] The user inputs the answer to the question into the terminal, for example, "influenza."

[1177] Step 9:

[1178] The device receives the user's response, converts it into JSON format, and sends it to the server.

[1179] Step 10:

[1180] The server analyzes the received user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback. For example, it might say, "Your answer is correct. The patient may have influenza."

[1181] Step 11:

[1182] The server generates feedback and sends it to the device.

[1183] Step 12:

[1184] The device receives the feedback and displays it to the user, who can then check the evaluation results of their answers on the device.

[1185] The above is the specific flow of the system's program processing. By proceeding with the processing in an orderly manner at each step, it is possible to provide a user with an enjoyable learning environment.

[1186] Example 1

[1187] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1188] Conventional educational systems lack the ability to generate customized learning questions tailored to a user's desired future career or field of study and provide immediate, appropriate feedback. This makes it difficult for users to effectively learn practical knowledge directly related to their goals. The present invention aims to solve these problems and provide an educational system that allows users to engage in more practical and effective learning.

[1189] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1190] In this invention, the server includes a means for allowing a user to input a desired future occupation and field of study, a means for retrieving related information from a database based on the occupation and field of study input by the user, and a means for generating questions and answers using an artificial intelligence algorithm based on the retrieved information, thereby enabling the user to instantly receive study questions tailored to their goals and receive accurate feedback.

[1191] "User" refers to a person who uses the system to learn or input information.

[1192] "Terminal" refers to a device used by a user to access the system and enter or receive information.

[1193] "Server" refers to a central control unit that receives data sent by users, processes and communicates with a database, and returns the results to the users.

[1194] "Occupation" refers to the type of job or work that the user wants to do in the future.

[1195] "Field of study" refers to the academic or specialized field that the user wants to study.

[1196] A "database" refers to a storage system in which related information and data are stored in an organized manner and are accessible from a server.

[1197] An "artificial intelligence algorithm" refers to software technology that has a series of calculation procedures for solving problems or making predictions based on data.

[1198] "Questions" refer to questions or tasks that are generated based on the user's input of occupation or field of study to encourage learning.

[1199] "Answer" refers to the solution entered by the user in response to the generated question.

[1200] "Feedback" refers to the evaluation results and additional information provided in response to the answers submitted by the user.

[1201] "Comparing" refers to the process of matching the user's submitted answer with a pre-generated correct answer and evaluating their match and accuracy.

[1202] "Presenting" refers to the act of displaying the generated questions and feedback to the user via a terminal.

[1203] The present invention is an educational system that allows a user to input their desired future career and field of study, generates corresponding study questions, and provides feedback based on the user's answers. Specific embodiments of the system are described below.

[1204] First, a user accesses the system using their own device. They reach the interface by opening a web browser and accessing the system's URL. For example, they can use Google Chrome or any other common web browser.

[1205] The user enters their desired career (e.g., "doctor") and field of study (e.g., "biology") into the interface, and the input is sent by the device to the server as an HTTP POST request.

[1206] The server retrieves relevant information from a database based on the occupation and field of study entered by the user. This process involves running predefined SQL queries to extract the required data. For example, a specific query is run against the database to retrieve symptom and diagnosis information related to "doctor" and "biology."

[1207] The server then uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired information. Specifically, a generative AI model is used to pass the acquired data as prompts to the AI ​​model, which then generates learning questions and answers. For example, the question "A patient complains of fever and cough. What illness could it be?" and the answer "Influenza" are generated.

[1208] The generated questions are sent from the server to the terminal, and the terminal presents the received questions to the user. The user inputs their answer to the presented questions into the interface. For example, the user inputs the answer "influenza" and sends it back from the terminal to the server.

[1209] The server evaluates the received user's answer by comparing it with an automatically generated correct answer. Once the evaluation process is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." This feedback is sent from the server to the device, which displays it to the user.

[1210] As a specific example of operation, the following prompt sentence is given to the generative AI model:

[1211] Generate educational questions to be created for students who have chosen "Doctor" as their future career and "Biology" as their field of study. Specifically, provide questions and answers related to patient symptoms.

[1212] In this way, the present invention is a system that provides users with a customized learning experience and allows them to deepen their practical knowledge through effective feedback.

[1213] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1214] Step 1:

[1215] Users access the system using their own terminals.

[1216] Input: The user opens a web browser and enters the system's URL.

[1217] Specific behavior: A user launches a web browser such as Google Chrome or Firefox and enters a specific URL (the system's homepage).

[1218] Output: The system login screen or home page is displayed.

[1219] Step 2:

[1220] The user enters their desired career and field of study into the interface.

[1221] Input: Enter your "Occupation" and "Field of Study" in the input fields on the interface.

[1222] Specific behavior: The user enters information such as "doctor" and "biology" and clicks the submit button.

[1223] Output: Data entered by the user is sent from the device to the server.

[1224] Step 3:

[1225] The terminal transmits the user's input information to the server.

[1226] Input: Occupation and field of study data entered by the user.

[1227] Specific operation: The device generates an HTTP POST request and sends data including the input content to the server.

[1228] Output: The server receives the user's input.

[1229] Step 4:

[1230] The server retrieves relevant information from a database based on the user's input information.

[1231] Input: Occupation and field of study data entered by the user.

[1232] What it does: The server runs a predefined SQL query to extract information related to "doctors" and "biology" from the database.

[1233] Output: The extracted information is aggregated on the server.

[1234] Step 5:

[1235] The server uses the information it obtains to generate questions and answers using an artificial intelligence algorithm.

[1236] Input: Relevant information extracted from the database.

[1237] Specific operation: The server uses the generative AI model, passes the extracted data to the AI ​​model as prompts, and generates learning questions and their answers.

[1238] Output: Generated training questions and their answers.

[1239] Step 6:

[1240] The server sends the generated questions to the terminal and presents them to the user.

[1241] Input: The generated training problem.

[1242] Specific operation: The server sends the generated problem as an HTTP response to the device, and the device displays the received content on a web page.

[1243] Output: The study questions are displayed on the user's screen.

[1244] Step 7:

[1245] The user enters their answers to the questions presented in the interface.

[1246] Input: The study question to be answered by the user.

[1247] Specific Action: The user enters "flu" into the answer field on the interface and clicks the submit button.

[1248] Output: The user's answer data is sent from the device to the server.

[1249] Step 8:

[1250] The terminal sends the user's answer to the server.

[1251] Input: User response data.

[1252] Specific operation: The device creates an HTTP POST request and sends the response data to the server.

[1253] Output: The server receives the user's answer data.

[1254] Step 9:

[1255] The server evaluates the received user answer by comparing it with automatically generated correct answers.

[1256] Input: User response data and automatically generated correct answer data.

[1257] Specific operation: The server compares the received user answer with the pre-generated correct answer, and if they match, evaluates it as the "correct answer."

[1258] Output: The evaluation result (correct or incorrect).

[1259] Step 10:

[1260] The server generates feedback based on the evaluation results and provides it to the user.

[1261] Input: Evaluation result.

[1262] Specific behavior: Based on the evaluation results, automatically generate feedback such as "You're correct. The patient may have influenza."

[1263] Output: The generated feedback is sent from the server to the device and displayed to the user.

[1264] (Application example 1)

[1265] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1266] Conventional educational systems have the problem of not having sufficient feedback functions to effectively teach users the practical knowledge necessary for their future careers. They also lack the functionality to systematically record and visualize users' learning history and progress, leaving a need for improved learning efficiency.

[1267] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1268] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for recording and visualizing the user's learning history and progress. This enables the user to effectively learn practical knowledge related to the future occupation, and visualization of the learning progress can improve learning efficiency.

[1269] "User" refers to any individual or corporation that uses this system.

[1270] "Occupation" refers to the job or occupation that the user wants to pursue in the future.

[1271] "Field of study" refers to the academic or technical field in which the user is interested and wants to learn.

[1272] A "database" refers to an information resource that systematically organizes information stored on a server and can be searched and retrieved as needed.

[1273] "Artificial intelligence" refers to computer systems and algorithms that mimic human intelligence and have capabilities such as learning, reasoning, and judgment.

[1274] "Questions" refer to questions that the user must answer as part of the learning content that is generated based on the occupation and field of study entered by the user.

[1275] An "answer" refers to the answer that a user enters to a question.

[1276] "Evaluation" refers to the process of comparing the answers entered by the user with the correct answers and determining whether they are correct or incorrect.

[1277] "Feedback" refers to showing the evaluation results of the user's answers and providing information to assist learning.

[1278] "Study history" refers to a record of the learning activities that a user has undertaken up to now.

[1279] "Progress" refers to information that indicates the current stage of a user's learning goal.

[1280] "Visualization" refers to visually representing data so that it can be easily understood by users.

[1281] A "prompt sentence" refers to an input sentence that prompts an artificial intelligence to respond or generate a specific response.

[1282] The embodiments of the present invention will be specifically described below.

[1283] This system is an educational system consisting of a server and a user terminal. Users access the system through a smartphone application. When users input their desired future career and field of study, the information is sent to the server. The server executes a specified database query to retrieve data related to the entered career and field of study from the database.

[1284] The server uses the acquired data to automatically generate questions and answers using a generative AI model (for example, OpenAI's GPT-3). As a specific example, when the occupation "doctor" and the field of study "biology" are input, the server generates the question "The patient complains of fever and cough. What kind of illness could it be?" and the answer "influenza." An example of a prompt sentence used in this process is as follows:

[1285] text

[1286] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[1287] The server sends the generated questions to the user terminal, which then presents them to the user. The user enters the answer to the question, which is then sent to the server. The server compares the received user answer with the correct answer data and evaluates it. The evaluation result is generated as feedback and sent back to the user terminal. For example, if the user answers "influenza," the following feedback is provided: "That's correct. The patient may have influenza."

[1288] Furthermore, this system has the function of recording and visualizing the user's learning history and progress. This allows users to visually check their learning progress and study efficiently. This visualization is achieved by the server analyzing the learning history recorded in the database and displaying it as graphs and charts on the user's device.

[1289] The hardware used is a smartphone, the server is a web server using the Flask framework, and the database is SQLite, making it possible to build a highly efficient and convenient educational support system.

[1290] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1291] Step 1:

[1292] Users access the system through a smartphone application. On the input screen, users input their desired future occupation (e.g., "doctor") and field of study (e.g., "biology"). This information is sent to the server in JSON format. The user's input, occupation and field of study, is sent to the server, and the server receives it.

[1293] Step 2:

[1294] Based on the received occupation and field of study, the server executes a predefined database query to retrieve relevant data from the database, for example, extracting symptoms and diagnostic information related to "doctor" and "biology." The input at this stage is the occupation and field of study, and the output is the retrieved data.

[1295] Step 3:

[1296] The server creates a prompt for the generative AI model (e.g., OpenAI's GPT-3) based on the acquired data. Specifically, it generates the following prompt:

[1297] text

[1298] Create a learning question for someone aspiring to be a doctor in the field of biology. Example: A patient complains of fever and cough. What disease might it be?

[1299] This prompt is then input into a generative AI model to generate a learning question and its answer. At this stage, the input is the acquired data and the prompt, and the output is the generated question and its answer.

[1300] Step 4:

[1301] The server sends the generated question to the user's terminal. The user's terminal displays the question to the user. The user reads the question and enters their answer. At this stage, the input is the generated question, and the output is the user's answer.

[1302] Step 5:

[1303] The answer entered by the user is sent from the terminal to the server. The server receives this answer and compares it with the generated correct answer data. This comparison evaluates the correctness of the user's answer. At this stage, the input is the user's answer, and the output is the evaluation result.

[1304] Step 6:

[1305] The server generates feedback based on the evaluation result. For example, it generates feedback such as "You are correct. The patient may have influenza." This feedback is sent to the user's terminal and displayed to the user. At this stage, the input is the evaluation result, and the output is the feedback.

[1306] Step 7:

[1307] The server records the user's learning history and progress and periodically updates the data for visualization. This allows the user to check their learning progress in graphs and charts. The input at this stage is learning history and progress data, and the output is visualized information.

[1308] In this way, the system provides an environment in which users can efficiently learn practical knowledge related to the career they want to pursue in the future.

[1309] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1310] This invention relates to an educational system that allows users to input their desired future career and field of study, generates study questions based on the input, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[1311] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., "doctor") and field of study (e.g., "biology") into the interface. The terminal collects this input information and sends it to the server. The server retrieves relevant data from the database based on the received occupation and field of study. This retrieval process involves executing predefined database queries. For example, it extracts symptoms related to "doctor" or basic knowledge related to "biology."

[1312] Next, the server uses an artificial intelligence (AI) algorithm to generate questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously creates the correct answer. As a specific example, it generates the question "The patient complains of a fever and cough. What illness do you think it could be?" and the answer "influenza." The server sends the generated question to the terminal, which then presents it to the user.

[1313] This system incorporates an emotion engine that recognizes the user's emotions. The emotion engine analyzes the user's facial expressions and voice when answering questions to recognize their emotions at any given time. Based on this information, the system dynamically adjusts the difficulty of the questions and the feedback provided. For example, if the user shows a tired expression, the system can lower the difficulty level slightly and provide relatively easy questions.

[1314] When the user enters their answer to the presented question (for example, entering "influenza"), the device sends this answer to the server. The server compares the received user answer with the automatically generated correct answer and evaluates it. Once the evaluation is complete, the server generates feedback, such as "Your answer is correct. The patient may have influenza." The server sends this feedback to the device, which then displays it to the user.

[1315] The system's built-in emotion engine also adjusts the feedback it provides based on the user's emotions. If the user looks anxious, it can provide more helpful and encouraging feedback, making the learning experience more personalized and effective.

[1316] As such, the present invention provides a fun learning environment for children, enabling them to effectively acquire practical knowledge related to their future careers. Through a series of processes incorporating concrete examples, users can deepen their knowledge by solving problems based on actual career situations. Furthermore, the combination of an emotion engine further enhances the user's learning experience.

[1317] The processing flow will be explained below.

[1318] Step 1:

[1319] The user inputs their desired career and field of study into the device's interface, for example, "doctor" and "biology."

[1320] Step 2:

[1321] The terminal collects the user's input information, converts it into JSON format, and sends it to the server.

[1322] Step 3:

[1323] The server analyzes the received data and extracts the occupation and field of study entered by the user.

[1324] Step 4:

[1325] The server runs database queries based on occupation and field of study to retrieve relevant data from the database, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology."

[1326] Step 5:

[1327] The server inputs the acquired data into an artificial intelligence (AI) algorithm, which generates questions and answers based on occupational situations. For example, the system generates the question, "A patient complains of fever and cough. What illness could it be?" and the answer, "Influenza."

[1328] Step 6:

[1329] The questions and answers generated by the server are converted back into JSON format and sent to the terminal.

[1330] Step 7:

[1331] The device analyzes the received problem and displays it to the user, who then checks the problem on the device.

[1332] Step 8:

[1333] The user inputs an answer to the question, for example, "influenza."

[1334] Step 9:

[1335] The device collects the user's answers, converts them back into JSON format, and sends them to the server.

[1336] Step 10:

[1337] The server analyzes the user's answer and compares it with the automatically generated correct answer. It generates an evaluation result and creates feedback based on the results. For example, it generates feedback such as, "Your answer is correct. The patient may have influenza."

[1338] Step 11:

[1339] The server generates feedback and sends it to the device.

[1340] Step 12:

[1341] The device receives the feedback and displays it to the user, who can then check the evaluation results through the device.

[1342] Step 13:

[1343] The emotion engine that recognizes the user's emotions analyzes the user's facial expressions and voice data to identify emotions. For example, if the user looks tired, the emotion engine will detect this.

[1344] Step 14:

[1345] The server receives information from the emotion engine and dynamically adjusts the difficulty of the next question and the content of the feedback based on the user's current emotional state. For example, if the user is tired, the difficulty of the question may be slightly reduced or gentle feedback may be provided.

[1346] The above is the specific flow of processing in a system that combines an emotion engine. By performing detailed processing at each step, it is possible to provide an environment in which users can learn in an enjoyable and effective way.

[1347] Example 2

[1348] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1349] Current educational systems do not adequately support users in effectively learning knowledge related to their future careers. Furthermore, feedback designed to improve learning outcomes is generally static and does not dynamically adjust to the user's emotions. This can make it difficult for users to maintain their motivation to learn, potentially hindering effective learning.

[1350] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1351] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions when answering the questions, means for receiving and evaluating the user's answers, means for providing the evaluation results to the user as feedback, and means for adjusting the content of the feedback according to the user's emotions. This allows the user to effectively learn practical knowledge related to the future occupation and receive personalized feedback according to their emotions.

[1352] A "user" is someone who uses the system to input their desired future career and field of study and work on learning problems.

[1353] "Occupation" refers to the type of work or job that the user wishes to do in the future.

[1354] A "study area" is a particular subject or topic that a user wants to study.

[1355] "Terminal" means a computing device through which a User accesses the System and inputs and receives information.

[1356] "Server" means a central processing unit that processes information sent by users and obtains, generates and evaluates related data.

[1357] A "database" is a collection of data in which information used within a system is organized and stored.

[1358] A "database query" is a search statement for retrieving specific information from a database.

[1359] "Artificial intelligence" refers to machine learning models and algorithms for generating questions and evaluating user answers.

[1360] A "problem" is a question or challenge presented to the user for answer.

[1361] An "answer" is a response that a user enters to a presented question.

[1362] "Feedback" refers to evaluation results and advice provided based on the user's answers.

[1363] An "emotion engine" is a system that analyzes a user's facial expressions and voice to recognize their emotions.

[1364] "Evaluation" is the process of comparing a user's answer with correct answer data to determine whether it is correct or not.

[1365] "Personalization" means optimizing content according to the individual situation and emotions of each user.

[1366] This invention is an educational system that generates study questions based on the user's input of their desired future career and field of study, and provides feedback based on the user's answers. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, it is possible to provide dynamic learning support that responds to the user's emotions.

[1367] First, a user accesses the system using a terminal. The user inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects this information and sends it to the server.

[1368] The server retrieves relevant data from a database based on the received occupation and field of study. This process involves executing predefined database queries, for example, extracting symptoms related to "doctors" or basic knowledge related to "biology." This retrieval process can be performed using SQL or NoSQL queries.

[1369] The server then uses an artificial intelligence (AI) algorithm to generate learning questions and answers based on the acquired data. For example, it generates a question about "diagnosis based on the patient's symptoms" and simultaneously generates the correct answer (e.g., influenza). This is done using a generative AI model (e.g., GPT-3).

[1370] The server sends the generated learning questions to the device, which then presents them to the user. The user then enters their answers to the questions. At this time, the device's built-in emotion engine analyzes in real time how the user works on the questions and recognizes their emotions based on their facial expressions and voice. This recognition result is used as auxiliary information to respond when the user is in trouble or tired.

[1371] When the user enters an answer (e.g., influenza), the device sends this answer to the server. The server compares the received user answer with the correct answer data and evaluates it. Once the evaluation is complete, the server generates feedback. For example, this feedback may be something like, "Your answer is correct. The patient may have influenza."

[1372] Furthermore, the feedback content is dynamically adjusted based on information from the emotion engine. For example, if the user looks anxious, the feedback will be more kind and encouraging (e.g., "Good job, keep trying!").

[1373] Examples of concrete examples and prompts

[1374] Specific examples

[1375] User input: Occupation "Doctor", field of study "Biology"

[1376] Generated question: "A patient complains of fever and cough. What illness could it be?"

[1377] User Answer: "Influenza"

[1378] Feedback: "Correct. The patient may have the flu."

[1379] Prompt Sentence Examples

[1380] Enter the following information:

[1381] 1. What career do you want to have in the future (e.g., doctor)

[1382] 2. Field of study (e.g., biology)

[1383] Based on this information, questions are generated and answers are derived.

[1384] In this way, the system allows users to effectively learn practical knowledge related to their future career aspirations while receiving emotional feedback. This invention makes the learning experience more personalized, increases user motivation, and realizes effective learning.

[1385] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1386] Step 1: Collecting User Input

[1387] A user accesses the system using a terminal and inputs their desired future occupation (e.g., doctor) and field of study (e.g., biology) into the interface. The terminal collects the information entered by the user and stores it as input data. Specifically, by entering data into the input form and clicking the "Submit" button, the data is temporarily cached. This allows the input information to be obtained as initial data for processing in the next step.

[1388] Input: User-entered occupation and field of study information

[1389] Output: Collected user input data

[1390] Step 2: Retrieving relevant data

[1391] Based on the collected user input data, the device sends the data to the server. The server executes a database query based on the received occupation and field of study information to retrieve relevant data. Specifically, the server executes an SQL query to extract the required data using a command such as "SELECT FROM DATABASE WHERE Occupation = 'Doctor' AND Field = 'Biology'".

[1392] Input: User input data (occupation and field of study)

[1393] Output: Relevant data retrieved

[1394] Step 3: Generate training questions

[1395] The server uses the acquired relevant data to generate learning questions and answers using an artificial intelligence (AI) algorithm. Specifically, it uses a generative AI model (e.g., GPT-3) to automatically generate question statements and correct answers based on symptoms and knowledge. For example, it generates the question, "A patient complains of fever and cough. What kind of illness could it be?" and the answer, "Influenza."

[1396] Input: relevant data

[1397] Output: Generated training questions and answers

[1398] Step 4: Present the learning problem

[1399] The server sends the generated learning questions to the device, which then presents the received learning questions to the user. Specifically, the device displays the question text and an answer form on the device's interface, allowing the user to work on the questions.

[1400] Input: Generated training questions

[1401] Output: The training question presented to the user

[1402] Step 5: Emotion Recognition with the Emotion Engine

[1403] As the user works on the problem, the device's built-in emotion engine uses the camera and microphone to analyze the user's facial expressions and voice in real time. Specifically, it uses face detection algorithms and voice analysis algorithms to recognize the user's emotions (e.g., confusion, fatigue).

[1404] Input: User's facial expressions and voice data

[1405] Output: Recognized user emotion information

[1406] Step 6: Collect and submit user responses

[1407] When the user enters an answer to a question (e.g., influenza), the terminal collects the answer and sends it to the server. Specifically, the user fills in the answer form and clicks the "Submit" button, which transmits the input data to the server.

[1408] Input: The user's typed answer

[1409] Output: Collected user responses

[1410] Step 7: Evaluate your responses

[1411] The server compares the received user's answer with the automatically generated correct answer data and evaluates it. Specifically, it uses an algorithm to determine whether the answer is correct or not and generates a result. For example, it evaluates it as "correct" based on the comparison logic.

[1412] Input: User answers and correct answer data

[1413] Output: Evaluation results

[1414] Step 8: Generate and provide feedback

[1415] The server generates feedback based on the evaluation results and sends it to the device. The device then displays the received feedback to the user. The content of the feedback is also dynamically adjusted based on information from the emotion engine. Specifically, if the user looks anxious, the system will provide feedback that includes encouraging words.

[1416] Input: Evaluation results and emotional information

[1417] Output: Dynamically adjusted feedback

[1418] This allows users to tackle learning questions related to their future career aspirations and receive feedback based on their answers, personalizing the learning experience based on emotions.

[1419] (Application example 2)

[1420] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1421] Current educational systems make it difficult for users to effectively acquire specialized knowledge related to specific occupations or fields of study. Furthermore, they provide a uniform level of difficulty and feedback without considering the user's emotional state, resulting in a lack of personalized learning experiences and a lack of motivation to learn. In particular, in certain industries, such as the food delivery industry, it is necessary to provide problems based on specific on-site situations.

[1422] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1423] In this invention, the server includes means for a user to input a desired future occupation and field of study, means for retrieving related data from a database based on the occupation and field of study input by the user, means for generating questions and answers using artificial intelligence with the retrieved data, means for presenting the generated questions to the user, means for recognizing the user's emotions, means for dynamically adjusting the difficulty of the questions and the feedback content according to the user's emotions, means for receiving and evaluating the user's answers, and means for providing the user with the evaluation results as feedback. This enables a user to efficiently learn specialized knowledge required for a specific industry and provides a personalized learning experience according to the user's emotions.

[1424] "User" refers to an individual who uses the system to learn.

[1425] "Desired future occupation" refers to a specific occupation that the user is aiming for.

[1426] A "study area" refers to a particular area of ​​knowledge that a user wants to learn.

[1427] "Database" means a collection of information that stores related data based on user-entered information.

[1428] "Artificial intelligence (AI)" refers to the technology that systems use to automatically generate questions and answers.

[1429] "Problem" means content including a problem or question that a user must solve through learning.

[1430] "Answer" refers to a solution provided by a user to a question.

[1431] "Emotion" refers to the psychological state that a user exhibits while learning.

[1432] "Feedback" refers to the evaluation and advice provided by the system in response to the user's answers.

[1433] "Means for recognizing emotions" refers to technology that analyzes a user's facial expressions and voice to understand their emotional state.

[1434] "Dynamic adjustment means" refers to the ability to change the difficulty of questions and the content of feedback in real time according to the user's emotional state.

[1435] "Food delivery industry" means an industry that operates primarily around the delivery of food.

[1436] "Acquiring specialized knowledge" refers to acquiring in-depth knowledge and skills in a specific field.

[1437] The present invention provides an educational system that generates relevant study questions based on a user's desired future career and field of study, and supports learning by taking the user's emotions into consideration. Specific procedures and methods for implementing the present invention are described below.

[1438] System Program

[1439] The system includes the following major components:

[1440] 1. User Input Method

[1441] Users use their smartphones to input their desired future occupation (e.g., "Delivery Manager") and field of study (e.g., "Delivery Route Optimization").

[1442] 2. Data Acquisition Method

[1443] Based on the occupation and field of study information sent from the device, the server retrieves relevant data from a database, which provides information about the knowledge and skills required for the occupation. Retrieval from the database is performed using a predefined database query.

[1444] 3. Problem generation means

[1445] The server uses the acquired data to generate learning questions and answers using an artificial intelligence (AI) algorithm, such as "Select the most efficient delivery route in this area."

[1446] 4. Emotion recognition means

[1447] When the user answers a question, the system uses the smartphone's built-in camera and microphone to analyze facial expressions and voice to recognize the user's emotions.

[1448] 5. Dynamic Adjustment Methods

[1449] The server dynamically adjusts the difficulty of the questions and the content of the feedback depending on the emotion recognition results. For example, if the user shows signs of anxiety, it adds encouraging words to the feedback.

[1450] 6. Means of Providing Feedback

[1451] The server evaluates the answers entered by the user and generates and sends the resulting feedback to the device, which then provides the user with appropriate advice and the next challenge.

[1452] Processing Details

[1453] The processing of this system includes the following software and hardware:

[1454] Hardware: Smartphones, servers

[1455] Software: Python (Flask for backend), machine learning libraries (e.g., TensorFlow, EmotionRecognition library)

[1456] The server extracts relevant data from a database based on the received occupation and field of study, and generates questions and answers using an AI model (e.g., a generative AI model). The user's smartphone uses a camera and microphone to analyze the user's facial expressions and voice via an emotion recognition library, and sends the results to the server. The server then dynamically adjusts the difficulty of the questions and the content of the feedback based on this information.

[1457] Specific examples

[1458] For example, if a user selects an occupation as "Delivery Manager" and enters "Delivery Route Optimization" as their field of study, the following example prompt sentence is generated:

[1459] Example prompt sentence:

[1460] "Choose a route that will deliver efficiently in this area. Also consider the number of orders at each point."

[1461] When the user answers the question, their emotions are recognized using the smartphone's camera and microphone, and if the user looks anxious, encouraging feedback such as, "Your answer is a little off, but don't worry, you'll definitely get it right next time!" is provided.

[1462] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1463] Step 1:

[1464] Users input their desired career and field of study in the future.

[1465] Users enter information about their desired career and field of study through a smartphone interface.

[1466] Input: Job title (e.g., "Delivery Manager"), field of study (e.g., "Delivery Route Optimization")

[1467] Output: Input information is transmitted to the server

[1468] Step 2:

[1469] The server retrieves relevant data from a database based on the entered occupation and field of study.

[1470] The server executes predefined database queries based on the received information to extract relevant data.

[1471] Input: Occupation and field of study information

[1472] Output: Relevant data (e.g. information on optimizing delivery routes)

[1473] Step 3:

[1474] The server uses the data it acquires to generate questions and answers using an AI algorithm.

[1475] The server uses a generative AI model to construct questions and answers based on the data it receives.

[1476] Input: Related data

[1477] Output: Generated question and answer (e.g., "Choose an efficient delivery route in this area" and its optimal answer)

[1478] Step 4:

[1479] The server sends the generated questions to the terminal, which then presents them to the user.

[1480] The terminal displays the received questions to the user and allows the user to input answers to the questions.

[1481] Input: Question sent by server

[1482] Output: The problem as seen by the user

[1483] Step 5:

[1484] The user enters the answer to the question, and the terminal sends it to the server.

[1485] The user inputs their answer to the question presented, and the terminal sends the answer to the server.

[1486] Input: User's answer

[1487] Output: User's answer sent to the server

[1488] Step 6:

[1489] The server recognizes the user's emotions and dynamically adjusts the feedback content based on that information.

[1490] The server analyzes the user's facial expressions and voice through the smartphone's camera and microphone, and uses an emotion recognition library to understand the user's emotional state.

[1491] Input: User facial and voice data

[1492] Output: Recognized user emotional state

[1493] Step 7:

[1494] The server evaluates the user's answer by comparing it with automatically generated correct answers.

[1495] The server compares the user's answer with the correct answer data and calculates the evaluation result.

[1496] Input: User's answer, correct answer data

[1497] Output: Evaluation result (e.g., whether it is correct or not)

[1498] Step 8:

[1499] The server generates feedback based on the evaluation results, adjusts the content according to the emotion, and sends it to the device.

[1500] The server modifies the feedback content according to the recognized emotional state and provides appropriate feedback to the user.

[1501] Input: Evaluation result, emotional state

[1502] Output: Dynamically adjusted feedback

[1503] Step 9:

[1504] The terminal displays the feedback received from the server to the user.

[1505] The device receives the feedback sent from the server and displays it to the user, helping them reflect on their learning.

[1506] Input: Feedback data

[1507] Output: Feedback displayed to the user

[1508] 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.

[1509] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1510] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1511] 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.

[1512] 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 includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1513] 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.

[1514] 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).

[1515] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1516] 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."

[1517] 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.

[1518] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1519] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1524] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1525] 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.

[1526] 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.

[1527] 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.

[1528] 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.

[1529] The following is further disclosed regarding the above embodiment.

[1530] (Claim 1)

[1531] a means for the user to input his / her desired career and field of study;

[1532] means for retrieving relevant data from a database based on the occupation and field of study entered by the user;

[1533] A means for generating questions and answers using the acquired data and utilizing artificial intelligence;

[1534] means for presenting the generated questions to a user;

[1535] means for receiving and evaluating the user's responses;

[1536] means for providing the evaluation result to the user as feedback;

[1537] A system including:

[1538] (Claim 2)

[1539] 10. The system of claim 1, wherein the means for retrieving data related to occupations and fields of study from a database further comprises means for executing a predefined database query.

[1540] (Claim 3)

[1541] 2. The system of claim 1, wherein the generated questions are based on situations in a user-selected occupation.

[1542] "Example 1"

[1543] (Claim 1)

[1544] a means for the user to input his / her desired career and field of study;

[1545] means for retrieving relevant information from a database based on the occupation and field of study entered by the user;

[1546] means for generating questions and answers using an artificial intelligence algorithm using the acquired information;

[1547] means for presenting the generated questions to a user;

[1548] means for receiving and evaluating the user's answer against the generated correct answer;

[1549] means for providing the evaluation result to the user as feedback;

[1550] A system including:

[1551] (Claim 2)

[1552] 10. The system of claim 1, wherein the means for retrieving information related to occupations and fields of study from a database further comprises means for executing a predefined database query.

[1553] (Claim 3)

[1554] 10. The system of claim 1, wherein the generated questions are based on situations in a user-selected occupation.

[1555] "Application Example 1"

[1556] (Claim 1)

[1557] a means for the user to input his / her desired career and field of study;

[1558] means for retrieving relevant data from a database based on the occupation and field of study entered by the user;

[1559] A means for generating questions and answers using the acquired data and utilizing artificial intelligence;

[1560] means for presenting the generated questions to a user;

[1561] means for receiving and evaluating the user's responses;

[1562] means for providing the evaluation result to a user as feedback;

[1563] A means for recording and visualizing a user's learning history and progress;

[1564] A system including:

[1565] (Claim 2)

[1566] 10. The system of claim 1, wherein the means for retrieving data related to occupations and fields of study from a database further comprises means for executing a predefined database query.

[1567] (Claim 3)

[1568] 2. The system of claim 1, wherein the generated questions are based on situations in a user-selected occupation.

[1569] (Claim 4)

[1570] The system of claim 1 , wherein the generated question includes a prompt sentence.

[1571] "Example 2: Combining Emotion Engines"

[1572] (Claim 1)

[1573] a means for the user to input his / her desired career and field of study;

[1574] means for retrieving relevant data from a database based on the occupation and field of study entered by the user;

[1575] A means for generating questions and answers using the acquired data and utilizing artificial intelligence;

[1576] means for presenting the generated questions to a user;

[1577] means for recognizing the user's emotions when answering the questions;

[1578] means for receiving and evaluating the user's responses;

[1579] means for providing the evaluation result to the user as feedback;

[1580] means for adjusting the content of the feedback in accordance with the user's emotions;

[1581] A system including:

[1582] (Claim 2)

[1583] 10. The system of claim 1, wherein the means for retrieving data related to occupations and fields of study from a database further comprises means for executing a predefined database query.

[1584] (Claim 3)

[1585] 2. The system of claim 1, wherein the generated questions are based on situations in a user-selected occupation.

[1586] "Application example 2 when combining emotion engines"

[1587] (Claim 1)

[1588] a means for the user to input his / her desired career and field of study;

[1589] means for retrieving relevant data from a database based on the occupation and field of study entered by the user;

[1590] A means for generating questions and answers using the acquired data and utilizing artificial intelligence;

[1591] means for presenting the generated questions to a user;

[1592] means for recognizing a user's emotion;

[1593] means for dynamically adjusting the difficulty of questions and the content of feedback according to the user's emotions;

[1594] means for receiving and evaluating the user's responses;

[1595] means for providing the evaluation result to the user as feedback;

[1596] A system including:

[1597] (Claim 2)

[1598] 10. The system of claim 1, wherein the means for retrieving data related to occupations and fields of study from a database further comprises means for executing a predefined database query.

[1599] (Claim 3)

[1600] 2. The system of claim 1, wherein the generated questions are based on situations in a user-selected occupation.

[1601] (Claim 4)

[1602] 2. The system of claim 1, wherein the generated questions relate to acquiring specialized knowledge in a particular industry (e.g., the food delivery industry). [Explanation of symbols]

[1603] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for the user to input his / her desired career and field of study; means for retrieving relevant data from a database based on the occupation and field of study entered by the user; A means for generating questions and answers using the acquired data and utilizing artificial intelligence; means for presenting the generated questions to a user; means for receiving and evaluating the user's responses; means for providing the evaluation result to the user as feedback; A system including:

2. 2. The system of claim 1, wherein the means for retrieving data related to occupations and fields of study from a database further comprises means for executing a predefined database query.

3. 2. The system of claim 1, wherein the generated questions are based on situations in a user-selected occupation.

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