System

Abstract

A system is provided.SOLUTION: The system includes a terminal means for receiving an operation of a user, a server means for receiving a request from the terminal means, a content generation means for generating an optimum learning content by the server means based on a learning history of the user, a content distribution means for distributing the learning content to the terminal means, a feedback generation means for analyzing a response of the user and generating a feedback, and a progress recording means for recording a learning progress of the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] There is a demand for English language learning tools for young children that allow them to learn in a fun and efficient way. However, conventional learning tools generally only provide fixed content, and it is difficult to provide feedback or change the content in accordance with each child's learning progress and level of understanding. This can easily lead to child frustration and reduced learning effectiveness. Therefore, an object of the present invention is to provide an environment in which young children can learn English in a fun and efficient way by dynamically changing the content in accordance with each child's learning progress and level of understanding and providing appropriate feedback. [Means for solving the problem]

[0005] The present invention is a system that includes a terminal means for accepting user operations and a server means for receiving requests from the terminal means. The server means includes a content generation means for generating optimal learning content based on the user's learning history and a content distribution means for distributing the learning content to the terminal means. The system further includes a feedback generation means for analyzing the user's answers and generating feedback, and a progress recording means for recording the user's learning progress, thereby providing positive feedback when the user answers correctly and hints or instructions to try again when the user answers incorrectly. The content generation means also has a function for dynamically changing the difficulty level of the problem sets based on the user's learning history, allowing the system to continue providing appropriate problems according to the child's level of understanding. This configuration can provide an environment in which children can maintain their interest in learning and effectively acquire English.

[0006] "User" refers to a child who uses the system and operates the learning tools to learn English.

[0007] "Terminal means" refers to a device that is operated by a user and displays learning content, such as a tablet or smartphone.

[0008] "Server means" refers to a computer system that receives requests from terminal means and manages the generation and distribution of learning content, user progress records, and the like.

[0009] "Study history" refers to data such as questions the user has answered in the past, records of correct and incorrect answers, and learning progress.

[0010] "Content generation means" refers to a program or function installed in the server means for dynamically generating optimal study content based on the study history.

[0011] "Content distribution means" refers to the function of distributing the generated study content to the terminal means and displaying it to the user.

[0012] "Feedback generation means" refers to a program or function that analyzes the user's answers and generates appropriate feedback. Specifically, it generates positive feedback when the answer is correct, and a hint or instructions to try again when the answer is incorrect.

[0013] The "progress recording means" refers to a function for recording the user's learning progress and answer history in a database and managing the user's learning status.

[0014] A "question set" refers to a collection of multiple questions presented to a user as study content.

[0015] "Positive feedback" refers to messages of praise and encouragement that are displayed when a user gets the answer right.

[0016] A "hint" refers to information that helps a user to try again if they get the question wrong.

[0017] "Retry instructions" refers to messages or guidance that encourage the user to try the question again when they get the answer wrong.

[0018] "Dynamic change function" refers to the function that adjusts the difficulty and content of the problem set in real time based on learning history. [Brief explanation of the drawings]

[0019] [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

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention is a system that supports English language learning for young children, allowing young children who are users to operate devices such as tablets and smartphones to effectively learn English using learning content provided by a server.

[0041] System configuration

[0042] The system includes the following main components:

[0043] User: A child who uses the system and operates the terminal to carry out learning activities.

[0044] Terminal: A device operated by a user, such as a tablet or smartphone, that communicates with the server.

[0045] Server: A system that generates and distributes learning content and manages user progress.

[0046] Program processing explanation

[0047] User operations and device roles

[0048] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This causes the device to send the user's operation to the server.

[0049] Server initialization

[0050] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past and information on whether those answers were correct or incorrect.

[0051] Learning content generation and delivery

[0052] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. For example, the question may include a question in which the user selects the correct image for the word "apple." The generated set of questions is sent to the device, which then displays it on the user's screen.

[0053] User responses and server analysis

[0054] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[0055] Track your progress and prepare for your next session

[0056] The server records the user's answers in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, allowing learning to be tailored to the user's level of understanding.

[0057] Specific examples

[0058] For example, consider a situation where a user must select an image of an apple in response to the word "apple." In this case, the user must choose the correct image from among several images of fruits displayed on the tablet screen. If the user selects the image correctly, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects the image incorrectly, the server displays "Try again!" or a simple hint (e.g., "Red fruit").

[0059] This system allows young children to learn English in a fun way without getting bored. In addition, learning progress is recorded in real time and reflected in the next question setting, enabling effective learning.

[0060] The processing flow will be explained below.

[0061] Step 1:

[0062] User: Operate the tablet and tap the "Start" button.

[0063] Step 2:

[0064] Terminal: Detects the start operation and sends a "game start request" to the server.

[0065] Step 3:

[0066] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[0067] Step 4:

[0068] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[0069] Step 5:

[0070] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[0071] Step 6:

[0072] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[0073] Step 7:

[0074] User: Select the correct image from the options displayed on the screen and tap it.

[0075] Step 8:

[0076] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[0077] Step 9:

[0078] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[0079] Step 10:

[0080] Server: If the answer is correct, generate a positive feedback message such as "Great job!". If the answer is incorrect, generate a "Try again!" or a hint. For example, give a simple hint such as "Red fruit."

[0081] Step 11:

[0082] Server: Sends the generated feedback message to the terminal.

[0083] Step 12:

[0084] Terminal: Displays feedback messages received from the server to the user.

[0085] Step 13:

[0086] Server: Records the user's answer results and progress information in a database, including correct / incorrect answers and answer times.

[0087] Step 14:

[0088] Server: Analyzes the user's understanding for the next study session and prepares data to adjust the difficulty of the next set of questions.

[0089] This allows content to be dynamically adjusted according to the child's learning progress, ensuring continuous and effective English learning.

[0090] Example 1

[0091] 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."

[0092] Conventional English learning systems for young children have limited learning effectiveness due to insufficient dynamic content generation based on the user's level of understanding and appropriate management of individual learning progress. In addition, the lack of immediate and appropriate feedback on the user's answers often reduces motivation to learn.

[0093] 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.

[0094] In this invention, the server includes a user operation detection means by a terminal means that sends a request for the user to start a study session, a content generation means that generates optimal study content based on the user's study history, and a progress recording means that stores progress data in a database and uses it to generate questions for the next study session. This makes it possible to accurately manage each user's study progress and provide appropriate feedback and content.

[0095] A "terminal means" is a device that can be operated by a user and is a means for communicating with a server.

[0096] The "server means" is a central management system for receiving requests from the terminal means and executing various processes.

[0097] The "content generation means" is a means for generating optimal study content based on the user's study history.

[0098] The "content distribution means" is a means for distributing study content generated by the server means to the terminal means.

[0099] The "feedback generating means" is a means for analyzing the user's answer and generating appropriate feedback according to whether the answer is correct or incorrect.

[0100] The "progress recording means" is a means for recording the user's learning progress and for use in the next learning session.

[0101] The "user operation detection means" is a means for detecting an operation on the terminal means by the user to send a request to start a learning session.

[0102] The "means for saving progress data in a database" refers to means for recording and saving the learning progress data collected by the progress recording means in a database.

[0103] The present invention is a system for supporting English language learning for young children, in which the user, a young child, can effectively learn English by operating a terminal means such as a tablet or smartphone and using learning content provided by a server means.

[0104] System configuration

[0105] The system includes the following main components:

[0106] 1. User: A child who uses the system and operates the terminal means to carry out learning activities.

[0107] 2. Terminal means: A device operated by the user, such as a tablet or smartphone, that communicates with the server means.

[0108] 3. Server: A system that generates and distributes learning content, manages user progress, etc. It uses Python and machine learning libraries (e.g., scikit-learn).

[0109] Processing flow and specific system operation

[0110] User operations and the role of terminal means

[0111] The user operates the terminal means to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This operation information is transmitted from the terminal means to the server means.

[0112] Initial processing of the server means

[0113] The server analyzes the "game start request" received from the terminal and retrieves the user's learning history from the database. This learning history includes information on the questions the user has previously tackled and whether they were correct or incorrect.

[0114] Learning content generation and delivery

[0115] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. During this process, it uses Python and machine learning libraries (e.g., scikit-learn) to select appropriate questions. The generated set of questions is sent to the terminal and displayed on the user's screen.

[0116] Obtaining user answers and sending them to the server

[0117] The user answers the displayed questions. For example, when the word "apple" is displayed, the user taps on the image of an apple. The terminal means transmits this answer information to the server means. The answer data includes the user ID, question ID, and answer content.

[0118] Server-based response analysis and feedback generation

[0119] The server analyzes the received answer data and determines whether it is correct. If the answer is correct, it generates a positive feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a simple hint such as "Try again!" or "Red fruit" or an instruction to try again.

[0120] Track your progress and prepare for your next session

[0121] The server means records the user's answers in a database, which is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session.

[0122] Specific examples

[0123] For example, consider a situation where a user selects an image of an apple for the word "apple." In this case, the user selects the correct one from multiple images displayed on the tablet screen. If the user selects the image correctly, the server means generates a message saying "Great job!" and sends it to the terminal means to display it to the user. If the user selects an incorrect image, a hint such as "Try again!" or "Red fruit" is displayed on the terminal means.

[0124] Example prompts for generative AI models

[0125] "I'm thinking of a system that allows young children to learn English using a tablet. In this system, a server receives requests from the device operated by the user, generates content based on the user's learning history, and delivers it to the device. The server analyzes the user's responses and provides feedback to the device. Please tell me the specific processing flow of this system."

[0126] In this way, the present invention realizes optimal content generation and feedback provision to improve the user's learning ability throughout the entire system.

[0127] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0128] Step 1:

[0129] The user taps the "Start" button displayed on the screen of the terminal means. The terminal means detects the user's tapping action, generates a "game start request" and sends it to the server means. The input is the user's tapping action, and the output is the "game start request." This request data includes the user ID.

[0130] Step 2:

[0131] The server analyzes the received "game start request" and retrieves the user's learning history from the database. The input is the "game start request" and the output is the user's learning history. The learning history (past questions, correct / incorrect data) is retrieved through an SQL query to the database.

[0132] Step 3:

[0133] The server generates a problem set appropriate to the user's level of understanding based on their learning history. The input is the learning history, and the output is the generated problem set. Using Python scripts and machine learning libraries (e.g., scikit-learn), the server extracts optimal problems and generates the set.

[0134] Step 4:

[0135] The server means distributes the generated problem set to the terminal means. The input is the generated problem set, and the output is the display of the problem set by the terminal means. The problem set is sent as an HTTP response and displayed on the screen of the terminal means.

[0136] Step 5:

[0137] The user answers the question. For example, by tapping on an image of an apple. The terminal means transmits this answer information to the server means. The input is the user's answer operation, and the output is answer data (user ID, question ID, answer content). This includes the operation of obtaining the tap position on the touch screen and creating a data package.

[0138] Step 6:

[0139] The server analyzes the received response data and determines whether it is correct or incorrect. The input is the response data, and the output is a feedback message. The analysis uses conditional branching to determine whether it is correct or incorrect, and generates feedback such as "Great job!" or "Try again!"

[0140] Step 7:

[0141] The server means sends the generated feedback message to the terminal means. The input is the feedback message and the output is a screen display for the user. An HTTP response is created and the feedback message is sent to the terminal means for display.

[0142] Step 8:

[0143] The server means records the answer results in a database and updates the progress information for the next learning session. The input is the answer results and the output is the updated progress data. The progress information is saved in the database using an SQL query.

[0144] In this way, each processing step proceeds with specific inputs and outputs, and the server means and terminal means work together to provide the user with an optimal learning experience.

[0145] (Application example 1)

[0146] 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."

[0147] Conventional English learning systems for young children are limited to learning content provided on devices, and have little connection to the real world, making it difficult to provide real-life English usage experiences. Furthermore, they lack interactive elements to maintain users' motivation to learn, and there has been a demand for educational entertainment, especially in brick-and-mortar stores.

[0148] 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.

[0149] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for the server means to generate optimal study content based on the user's study history, content distribution means for distributing study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's study progress, and means for providing interactive study activities using codes installed in physical stores. This enables English learning to be linked to the real world, increasing the user's motivation to study.

[0150] The "terminal means for accepting user operations" is a device such as a tablet or smartphone that is operated by the user to input data, and is a means for communicating with the server.

[0151] The "server means for receiving a request from the terminal means" refers to a server and its system for receiving a request based on a user's operation via a network and performing appropriate processing in response to the request.

[0152] "Content generation means by which the server means generates optimal learning content based on the user's learning history" refers to a function or module within the server that generates the most suitable learning material for a user based on the user's past learning history.

[0153] The "content distribution means for distributing learning content to the terminal means" is a function for transmitting the generated learning materials to the user's terminal device via a network.

[0154] The "feedback generating means for analyzing the user's answer and generating feedback" is a function for analyzing the answer given by the user and providing an appropriate response or advice in response to the answer.

[0155] The "progress recording means for recording the user's learning progress" is a function for storing the user's learning content and progress status in a database for future reference or for adjusting the learning plan.

[0156] "Means for providing interactive learning activities using codes installed in physical stores" refers to a means for unlocking specific learning content by scanning QR codes (registered trademark) installed in the physical world, enabling interactive English learning.

[0157] This invention is a system for supporting English learning for young children, providing advanced data processing and interactive learning experiences similar to technologies such as modules installed in self-driving vehicles, logistics management systems in distribution centers, and control platforms for factory robots. The main components are terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal learning content based on the user's learning history, content distribution means for distributing the generated learning content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's learning progress, and means for providing interactive learning activities using codes installed in physical stores.

[0158] Specific examples of programs

[0159] 1. Server Role

[0160] Receiving a request from a terminal means: The server receives a learning content start request sent from the user's tablet or smartphone.

[0161] Content generation: Refer to the user's past learning history database to generate learning content of appropriate difficulty.

[0162] Delivery and feedback: The generated learning content is delivered to the user's terminal means, and after receiving the user's answer, the correctness of the answer is judged and appropriate feedback is generated.

[0163] Progress Record: The user's answers are recorded in a database to allow for continuation of learning and progress management.

[0164] 2. Role of the terminal

[0165] User interface: The user initiates the learning game via a terminal device such as a tablet or smartphone, for example by tapping the "Start" button on the screen.

[0166] Content display: The learning content received from the server is displayed to the user, allowing the user to answer questions.

[0167] Sending and receiving: Sending the user's answers to the server and displaying the feedback provided by the server.

[0168] 3. Interactive learning

[0169] In-store code redemption: Scanning QR codes in physical stores unlocks specific learning activities, allowing users to learn English through real-world experiences.

[0170] Interactive learning activities: Questions are presented that utilize in-store products and displays, and users can earn points and badges if they answer correctly.

[0171] Hardware and software used

[0172] Hardware

[0173] Terminal means such as tablets and smartphones

[0174] QR code installed in a physical store

[0175] software

[0176] Backend: Ruby on Rails

[0177] Frontend: React Native

[0178] Specific examples

[0179] For example, a user scans a QR code in a store and the following question appears on the tablet: "Find the red fruit in the store and tap its image." In this case, the user goes to the fruit section of the store and taps the image of an apple. The app checks the answer and displays the message "Great job!" The next question may also be an English question about a different product.

[0180] Prompt Sentence Examples

[0181] "Please provide an English learning question where the user scans a QR code with a tablet and finds an image of a fruit. Please also include a simple hint in the question."

[0182] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0183] Step 1:

[0184] The user operates the terminal device, such as a tablet or smartphone, and taps the "Start" button to start the learning game. This operation causes the terminal device to send a "game start request" to the server.

[0185] Input: User taps

[0186] Output: Sending "Game Start Request"

[0187] Specific operation: When the user taps the "Start" button on the screen, the corresponding request generation function runs within the terminal means, and a request is sent to the server.

[0188] Step 2:

[0189] The server receives the "game start request" and retrieves the user's learning history from the database. The server analyzes the user's answer data and progress information to determine the user's level of understanding.

[0190] Input: "Game start request", learning history data

[0191] Output: Analysis results based on user's learning comprehension information

[0192] Specific operation: The server analyzes the request received, reads past learning history from the database based on the user ID, analyzes progress information, and evaluates the level of understanding.

[0193] Step 3:

[0194] The server generates optimal learning content based on the user's learning history and comprehension level. Specifically, it creates problem sets for words that the user has often made mistakes on in the past and new words that the user should learn.

[0195] Input: learning history data, comprehension information

[0196] Output: Generated learning content (problem set)

[0197] Specific operation: Based on the information obtained from the database, the content generation algorithm in the server works to automatically generate an appropriate problem set.

[0198] Step 4:

[0199] The server distributes the generated learning content to the terminal means, and at this time, data is transferred to the terminal through the network.

[0200] Input: Generated learning content

[0201] Output: Data delivered to the terminal

[0202] Specific operation: The server sends the generated content to the terminal means as an HTTP response, and the terminal means receives this data.

[0203] Step 5:

[0204] The terminal means displays the received learning content on the user's screen, and the user answers the questions by, for example, tapping on the correct image for the displayed word.

[0205] Input: Learning content, user actions

[0206] Output: User response data

[0207] Specific operation: The terminal means displays the content received from the server on the screen, and the user responds by touch operation.

[0208] Step 6:

[0209] The user's answer is transmitted from the terminal means to the server, which analyzes the answer, determines whether the answer is correct, and generates appropriate feedback.

[0210] Input: User response data

[0211] Output: Feedback message

[0212] Specific operation: The server analyzes the received answer and determines whether it is correct. If it is correct, it generates a feedback message such as "Great job!", if it is incorrect, it generates a feedback message such as "Try again!"

[0213] Step 7:

[0214] The server sends the generated feedback message to the terminal means, which displays it to the user, and the server records the user's response results in a database and prepares for the next session.

[0215] Input: Feedback message, response result data

[0216] Output: Show feedback, update progress record

[0217] Specific operation: The server sends the generated feedback message to the terminal means, which displays it on the screen, and at the same time, the server records the user's answer results in the database.

[0218] Step 8:

[0219] When a user scans a QR code installed in a physical store, a specific learning activity is unlocked. The server receives this request, generates the corresponding learning content, and delivers it to the terminal device.

[0220] Input: QR code scan request

[0221] Output: Unlocked learning content

[0222] Specific operation: A user scans a QR code in a store, and the terminal device sends the information to the server. The server receives this request, generates new learning content, and delivers it to the terminal device.

[0223] 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.

[0224] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. The user, a young child, operates a device such as a tablet or smartphone, making use of learning content provided by a server, and by combining this with an emotion engine, more effective and adaptable learning is possible.

[0225] System configuration

[0226] The system includes the following main components:

[0227] User: A young child operating a learning tool.

[0228] Device: A device operated by a user, such as a tablet or smartphone.

[0229] Server: A system that generates and distributes learning content, manages user progress, and recognizes emotions.

[0230] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[0231] Program processing explanation

[0232] User operations and device roles

[0233] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session.

[0234] Server initialization

[0235] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether those answers were correct or incorrect.

[0236] The role of the emotional engine

[0237] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state, which is classified into multiple categories such as "happiness," "sadness," and "surprise."

[0238] Learning content generation and delivery

[0239] The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question where the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[0240] User responses and server analysis

[0241] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[0242] Adjusting feedback based on emotional state

[0243] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, the server will reinforce the message of encouragement to motivate the user to try again.

[0244] Track your progress and prepare for your next session

[0245] The server records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, thereby providing optimal learning based on the user's level of understanding and emotional state.

[0246] Specific examples

[0247] For example, consider a scenario where a user selects an image of an apple in response to the word "apple." In this case, the user selects the correct image from multiple fruit images displayed on the tablet screen. If the user selects the correct image, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects an incorrect image, the server displays "Try again!" or a simple hint (e.g., "Red fruit"). If the emotion engine recognizes that the user's facial expression indicates disappointment, it adds an encouraging message.

[0248] This system allows young children to learn English in a fun and engaging way, and it also records their progress in real time and adjusts feedback based on their emotional state, making learning more effective.

[0249] The processing flow will be explained below.

[0250] Step 1:

[0251] User: Operate the tablet and tap the "Start" button.

[0252] Step 2:

[0253] Terminal: Detects the start operation and sends a "game start request" to the server.

[0254] Step 3:

[0255] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[0256] Step 4:

[0257] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[0258] Step 5:

[0259] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[0260] Step 6:

[0261] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[0262] Step 7:

[0263] Emotion engine: The device's camera and microphone are used to collect the user's facial expressions and voice data in real time, and the emotional state is analyzed. The analysis results are stored internally on the device.

[0264] Step 8:

[0265] User: Select the correct image from the options displayed on the screen and tap it.

[0266] Step 9:

[0267] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[0268] Step 10:

[0269] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[0270] Step 11:

[0271] Emotion engine: Sends the analyzed emotional state to the server, for example, whether the user is disappointed or happy.

[0272] Step 12:

[0273] Server: Generates a positive feedback message such as "Great job!" if the answer is correct, or "Try again!" or a hint if the answer is incorrect.

[0274] Step 13:

[0275] Server: Adjust the content and tone of the feedback based on the received emotional state. For example, if the user is sad, add an additional encouraging message.

[0276] Step 14:

[0277] Server: Sends the generated feedback message to the terminal.

[0278] Step 15:

[0279] Terminal: Displays feedback messages received from the server to the user.

[0280] Step 16:

[0281] Server: Records the user's answers, emotional state, and progress information in a database. This includes correct / incorrect answers, response time, emotional state, etc.

[0282] Step 17:

[0283] Server: Analyzes the user's comprehension and emotional state for the next learning session and prepares data to adjust the difficulty of the next set of questions.

[0284] This allows content and feedback to be dynamically adjusted according to the child's learning progress and emotional state, ensuring continuous and effective English learning.

[0285] Example 2

[0286] 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."

[0287] While conventional English learning systems have the ability to generate learning content and provide feedback based on a user's learning history, they lack the ability to adjust the feedback and learning content based on the user's emotional state. As a result, they are unable to address the stress and loss of motivation that users experience while studying, making it difficult for them to continue studying effectively. The objective of this invention is to provide an English learning system that recognizes a user's emotional state in real time and provides appropriate feedback and learning content based on that.

[0288] 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.

[0289] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal study content based on the user's learning history and emotional state, content distribution means for distributing the study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, emotion recognition means for recognizing the user's emotional state and adjusting the content and intensity of the feedback, and progress recording means for recording the user's study progress. This makes it possible to provide appropriate feedback and study content according to the user's emotional state, enabling effective and continuous learning.

[0290] The "terminal means for accepting user operations" refers to a device operated by a user, specifically a tablet or smartphone, which has an input device and an interface.

[0291] A "server means for receiving requests from a terminal means" is a computing device that is responsible for receiving and processing information or requests sent from a terminal means.

[0292] The "content generation means for generating optimal learning content based on the user's learning history and emotional state" is an algorithm or program that selects and generates appropriate learning materials based on the user's past learning history and emotional state recognized in real time.

[0293] The "content distribution means for distributing study content to terminal means" has the function of transferring the generated study content to terminal means and displaying it on a device operated by the user.

[0294] The "feedback generation means for analyzing the user's answers and generating feedback" has the function of analyzing the results of the user's answers to the study questions and generating an appropriate feedback message based on the results.

[0295] "Emotion recognition means that recognizes the user's emotional state and adjusts the content and intensity of feedback" refers to an algorithm or program that identifies the user's emotional state by analyzing their facial expressions and voice, and appropriately adjusts the content and intensity of feedback according to that state.

[0296] The "progress recording means for recording the user's learning progress" refers to a database or program for storing a record of the user's learning activities and managing the learning progress.

[0297] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. This system allows the user, a small child, to operate a device such as a tablet or smartphone, access learning content provided by a server, and, by combining this with an emotion engine, enables more effective and adaptive learning.

[0298] The system consists of the following main components:

[0299] User: A young child operating a learning tool.

[0300] Device: A device operated by a user, such as a tablet or smartphone.

[0301] Server: A central system that generates and distributes learning content, manages user progress, and recognizes emotions.

[0302] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[0303] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session. The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether they answered correctly or incorrectly.

[0304] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state. These emotional states are classified into multiple categories, such as "happiness," "sadness," and "surprise." The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question in which the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[0305] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[0306] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, it will reinforce the encouraging messages to motivate them to try again. The server also records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session. This allows the server to provide optimal learning based on the user's level of understanding and emotional state.

[0307] Below is an example of a prompt sentence to input to the generative AI model.

[0308] markdown

[0309] Describe a scenario in which a user is asked to select the correct image (an apple) for the word "apple."

[0310] Specific steps:

[0311] 1. A user sees the word "apple" displayed on a tablet screen and selects an apple from a selection of fruit images.

[0312] 2. The server verifies the answer and displays a positive message: "Great job!"

[0313] If you answer incorrectly, the server will display the message "Try again!" and a simple hint: "Red fruit."

[0314] The role of the Emotion Engine:

[0315] It recognizes the user's emotions and adds an encouraging message if, for example, they are feeling down.

[0316] The user's learning history and emotional state are recorded in a database and the questions presented in the next session are adjusted accordingly.

[0317] In this way, the present invention is a system that recognizes a user's emotions in real time and optimizes learning content and feedback, thereby enabling more effective and sustainable learning.

[0318] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0319] Step 1:

[0320] User operation start

[0321] The user taps the "Start" button displayed on the device screen. The user's tapping operation is the input, which sends a "game start request" to the server. The device detects this operation and sends the request to the server.

[0322] Step 2:

[0323] Server initialization

[0324] The server receives the "game start request" and retrieves past learning history from the database based on the user ID. The input data is the user ID and past session history, and the output is the user's learning history. The server uses this to evaluate the user's current learning situation.

[0325] Step 3:

[0326] Emotion Engine Operation

[0327] The emotion engine uses the device's camera and microphone to capture the user's facial expressions and voice data. The input data is image data from the camera and voice data from the microphone, and the output is the user's emotional state. The emotion engine analyzes this and classifies it into categories such as "joy," "sadness," and "surprise."

[0328] Step 4:

[0329] Generating learning content

[0330] The server generates an optimal problem set based on the user's learning history and emotional state. The input data are the learning history and emotional state, and the output is the generated problem set. For example, a problem set containing questions about selecting the correct image for the word "apple" is generated.

[0331] Step 5:

[0332] Delivery of learning content

[0333] The generated problem set is sent to the terminal, which displays it on the user's screen. The input data is the generated problem set, and the output is the displayed learning content.

[0334] Step 6:

[0335] User Answers

[0336] The user answers questions displayed on the screen by tapping on an image of an apple when asked the word "apple." The input data is the user's response, and the device sends this information to the server.

[0337] Step 7:

[0338] Server response analysis

[0339] The server analyzes the received user answer data and determines whether it is correct or incorrect. The input data is the user's answer, and the output is the result of the correct answer. If the answer is correct, the server generates a feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a hint such as "Try again!" or "Red fruit."

[0340] Step 8:

[0341] Feedback adjustment based on emotional state

[0342] The server adjusts the content and intensity of the feedback based on the user's emotional state obtained from the emotion engine. The input data is the emotional state and the result of the correct / incorrect judgment, and the output is the adjusted feedback message. For example, if the user is feeling depressed, the message of encouragement is strengthened.

[0343] Step 9:

[0344] Record your learning progress

[0345] The server records the user's answers and emotional state in a database. The input data are the answers and emotional state, and the output is an updated learning history. Based on this, the server adjusts the content of the next learning session and provides optimal learning according to the user's level of understanding and emotional state.

[0346] (Application example 2)

[0347] 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."

[0348] Improving worker productivity and motivation in factories is essential for maintaining an efficient work environment. However, conventional systems lack the means to properly grasp workers' emotional states and adjust feedback accordingly, which means they are unable to provide adaptive support according to their emotions. Therefore, there is a need for a system that can improve work efficiency and motivation by recognizing workers' emotional states in real time and adjusting feedback based on that information.

[0349] 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.

[0350] In this invention, the server includes a terminal means for accepting user operations, a server means for receiving requests from the terminal means, and a feedback generation means for the server means to generate optimal feedback based on the user's work history. This makes it possible to recognize the emotional state of the worker in real time and adjust the feedback based on that, thereby maintaining the motivation of the worker and providing an efficient work environment.

[0351] The "terminal means" is a device that accepts user operations, and includes, for example, a tablet or a smartphone.

[0352] The "server means" is a server system having the function of receiving requests from the terminal means and performing various processes.

[0353] The "content generation means" has the function of generating optimal learning content and feedback based on the user's learning history and work history.

[0354] The "content distribution means" has the function of distributing the generated learning content and feedback to the terminal means.

[0355] The "feedback generation means" has a function of analyzing the user's answers and operations and generating feedback based on them.

[0356] The "progress recording means" has the function of recording the user's learning progress and work progress.

[0357] The "emotion recognition means" has a function of recognizing the user's emotional state in real time using the terminal means.

[0358] The "feedback adjustment means" has a function of adjusting the content of the feedback based on the emotional state acquired from the emotion recognition means.

[0359] Overall system picture

[0360] The present invention is a system for improving the productivity and motivation of workers in a factory, and includes a terminal means, a server means, an emotion recognition means, a feedback generation means, and a feedback adjustment means.

[0361] Hardware and Software Configuration

[0362] Hardware

[0363] Terminal means: A device operated by a user, i.e., a worker, and includes small computers and smart devices. Examples include tablets and smartphones.

[0364] Server means: A server that manages and processes work history and feedback information.

[0365] Camera: Its role is to acquire image data to recognize the emotions of workers.

[0366] Robots: Provide feedback to workers and assist them in their work.

[0367] software

[0368] Content generation means: A program that generates optimal feedback and work instructions based on work history.

[0369] Content distribution means: A program that distributes generated feedback and work instructions to terminal means.

[0370] Feedback generation means: A program that generates appropriate feedback based on the user's operation and emotional state. Libraries used include DeepFace for emotion recognition and an SDK for robot operation (e.g., SoftBank Robotics' SDK).

[0371] Progress recording means: A program that records and manages work progress in a database.

[0372] Processing flow

[0373] The terminal means accepts operations by the worker. When the user starts operating the terminal, the information is sent to the server means, which then obtains the user's progress from the work history database and generates optimal feedback. At this time, the server means uses a camera to recognize the user's emotions in real time and uses the results to adjust the content of the feedback.

[0374] Specific examples

[0375] For example, a worker operates a tablet and the robot provides feedback such as "Keep working, you're almost there!" If the robot detects that the user looks discouraged from the camera's facial expression, it will provide an additional encouraging message (e.g., "You can do it! Do your best!").

[0376] Prompt Sentence Examples

[0377] The following is an example of a prompt provided to the generative AI model for emotion recognition in this system:

[0378] Provide a face image as input and analyze the dominant emotion of this face. The analysis result will include emotion categories such as "happy", "sad", "angry", and "neutral".

[0379] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0380] Step 1:

[0381] The user operates the terminal means to start a work session.

[0382] Input: User action (e.g. tapping the Start button)

[0383] Data processing: Receiving and recording operation events

[0384] Output: Sends a start work request to the server

[0385] Specific operation: The user taps the "Start" button displayed on the screen of the terminal means, and the operation event is sent by the terminal to the server.

[0386] Step 2:

[0387] The server receives the work start request and retrieves the user's work history from the database.

[0388] Input: Work start request, user ID

[0389] Data processing: Query the user's progress from the work history database

[0390] Output: User's work history data

[0391] Specific operation: When the server receives a request to start a task, it retrieves the past task history from the database based on the user ID.

[0392] Step 3:

[0393] The server uses a camera to recognize the user's emotions in real time.

[0394] Input: Camera video data

[0395] Data processing: Sentiment analysis using the DeepFace library

[0396] Output: User's emotional state (e.g. "happy", "sad", "angry", "neutral")

[0397] Specific operation: The server acquires video from the device's camera, analyzes the video using the DeepFace library, and recognizes the user's main emotions.

[0398] Step 4:

[0399] The server generates optimal feedback based on the user's task history and emotional state.

[0400] Input: Work history data, emotional state data

[0401] Data processing: Applying feedback generation algorithms based on work history and emotions

[0402] Output: Generated feedback

[0403] Specific operation: The server analyzes the user's work history and emotional state, and generates an adapted feedback message.

[0404] Step 5:

[0405] The server distributes the generated feedback to the terminal means.

[0406] Input: Feedback

[0407] Data processing: Sending feedback messages to devices

[0408] Output: Feedback presented to the user on their device, either visually or audibly.

[0409] Specific operation: The server sends the generated feedback message to the terminal means and transmits it to the user through the screen or speaker of the terminal means.

[0410] Step 6:

[0411] Evaluate the effectiveness of the feedback based on the user's emotional state and adjust as needed.

[0412] Input: Additional emotional state data of the user

[0413] Data processing: Reassessing and adjusting emotional states after feedback

[0414] Output: Adjusted feedback content

[0415] Specific behavior: After the user receives feedback, the camera footage is analyzed again, and if the emotional state has not improved, further encouragement or adaptive feedback is provided.

[0416] 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.

[0417] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0418] 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.

[0419] [Second embodiment]

[0420] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0421] 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.

[0422] 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).

[0423] 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.

[0424] 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.

[0425] 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).

[0426] 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.

[0427] 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.

[0428] 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.

[0429] 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.

[0430] 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.

[0431] 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."

[0432] The present invention is a system that supports English language learning for young children, allowing young children who are users to operate devices such as tablets and smartphones to effectively learn English using learning content provided by a server.

[0433] System configuration

[0434] The system includes the following main components:

[0435] User: A child who uses the system and operates the terminal to carry out learning activities.

[0436] Terminal: A device operated by a user, such as a tablet or smartphone, that communicates with the server.

[0437] Server: A system that generates and distributes learning content and manages user progress.

[0438] Program processing explanation

[0439] User operations and device roles

[0440] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This causes the device to send the user's operation to the server.

[0441] Server initialization

[0442] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past and information on whether those answers were correct or incorrect.

[0443] Learning content generation and delivery

[0444] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. For example, the question may include a question in which the user selects the correct image for the word "apple." The generated set of questions is sent to the device, which then displays it on the user's screen.

[0445] User responses and server analysis

[0446] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[0447] Track your progress and prepare for your next session

[0448] The server records the user's answers in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, allowing learning to be tailored to the user's level of understanding.

[0449] Specific examples

[0450] For example, consider a situation where a user must select an image of an apple in response to the word "apple." In this case, the user must choose the correct image from among several images of fruits displayed on the tablet screen. If the user selects the image correctly, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects the image incorrectly, the server displays "Try again!" or a simple hint (e.g., "Red fruit").

[0451] This system allows young children to learn English in a fun way without getting bored. In addition, learning progress is recorded in real time and reflected in the next question setting, enabling effective learning.

[0452] The processing flow will be explained below.

[0453] Step 1:

[0454] User: Operate the tablet and tap the "Start" button.

[0455] Step 2:

[0456] Terminal: Detects the start operation and sends a "game start request" to the server.

[0457] Step 3:

[0458] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[0459] Step 4:

[0460] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[0461] Step 5:

[0462] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[0463] Step 6:

[0464] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[0465] Step 7:

[0466] User: Select the correct image from the options displayed on the screen and tap it.

[0467] Step 8:

[0468] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[0469] Step 9:

[0470] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[0471] Step 10:

[0472] Server: If the answer is correct, generate a positive feedback message such as "Great job!". If the answer is incorrect, generate a "Try again!" or a hint. For example, give a simple hint such as "Red fruit."

[0473] Step 11:

[0474] Server: Sends the generated feedback message to the terminal.

[0475] Step 12:

[0476] Terminal: Displays feedback messages received from the server to the user.

[0477] Step 13:

[0478] Server: Records the user's answer results and progress information in a database, including correct / incorrect answers and answer times.

[0479] Step 14:

[0480] Server: Analyzes the user's understanding for the next study session and prepares data to adjust the difficulty of the next set of questions.

[0481] This allows content to be dynamically adjusted according to the child's learning progress, ensuring continuous and effective English learning.

[0482] Example 1

[0483] 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."

[0484] Conventional English learning systems for young children have limited learning effectiveness due to insufficient dynamic content generation based on the user's level of understanding and appropriate management of individual learning progress. In addition, the lack of immediate and appropriate feedback on the user's answers often reduces motivation to learn.

[0485] 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.

[0486] In this invention, the server includes a user operation detection means by a terminal means that sends a request for the user to start a study session, a content generation means that generates optimal study content based on the user's study history, and a progress recording means that stores progress data in a database and uses it to generate questions for the next study session. This makes it possible to accurately manage each user's study progress and provide appropriate feedback and content.

[0487] A "terminal means" is a device that can be operated by a user and is a means for communicating with a server.

[0488] The "server means" is a central management system for receiving requests from the terminal means and executing various processes.

[0489] The "content generation means" is a means for generating optimal study content based on the user's study history.

[0490] The "content distribution means" is a means for distributing study content generated by the server means to the terminal means.

[0491] The "feedback generating means" is a means for analyzing the user's answer and generating appropriate feedback according to whether the answer is correct or incorrect.

[0492] The "progress recording means" is a means for recording the user's learning progress and for use in the next learning session.

[0493] The "user operation detection means" is a means for detecting an operation on the terminal means by the user to send a request to start a learning session.

[0494] The "means for saving progress data in a database" refers to means for recording and saving the learning progress data collected by the progress recording means in a database.

[0495] The present invention is a system for supporting English language learning for young children, in which the user, a young child, can effectively learn English by operating a terminal means such as a tablet or smartphone and using learning content provided by a server means.

[0496] System configuration

[0497] The system includes the following main components:

[0498] 1. User: A child who uses the system and operates the terminal means to carry out learning activities.

[0499] 2. Terminal means: A device operated by the user, such as a tablet or smartphone, that communicates with the server means.

[0500] 3. Server: A system that generates and distributes learning content, manages user progress, etc. It uses Python and machine learning libraries (e.g., scikit-learn).

[0501] Processing flow and specific system operation

[0502] User operations and the role of terminal means

[0503] The user operates the terminal means to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This operation information is transmitted from the terminal means to the server means.

[0504] Initial processing of the server means

[0505] The server analyzes the "game start request" received from the terminal and retrieves the user's learning history from the database. This learning history includes information on the questions the user has previously tackled and whether they were correct or incorrect.

[0506] Learning content generation and delivery

[0507] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. During this process, it uses Python and machine learning libraries (e.g., scikit-learn) to select appropriate questions. The generated set of questions is sent to the terminal and displayed on the user's screen.

[0508] Obtaining user answers and sending them to the server

[0509] The user answers the displayed questions. For example, when the word "apple" is displayed, the user taps on the image of an apple. The terminal means transmits this answer information to the server means. The answer data includes the user ID, question ID, and answer content.

[0510] Server-based response analysis and feedback generation

[0511] The server analyzes the received answer data and determines whether it is correct. If the answer is correct, it generates a positive feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a simple hint such as "Try again!" or "Red fruit" or an instruction to try again.

[0512] Track your progress and prepare for your next session

[0513] The server means records the user's answers in a database, which is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session.

[0514] Specific examples

[0515] For example, consider a situation where a user selects an image of an apple for the word "apple." In this case, the user selects the correct one from multiple images displayed on the tablet screen. If the user selects the image correctly, the server means generates a message saying "Great job!" and sends it to the terminal means to display it to the user. If the user selects an incorrect image, a hint such as "Try again!" or "Red fruit" is displayed on the terminal means.

[0516] Example prompts for generative AI models

[0517] "I'm thinking of a system that allows young children to learn English using a tablet. In this system, a server receives requests from the device operated by the user, generates content based on the user's learning history, and delivers it to the device. The server analyzes the user's responses and provides feedback to the device. Please tell me the specific processing flow of this system."

[0518] In this way, the present invention realizes optimal content generation and feedback provision to improve the user's learning ability throughout the entire system.

[0519] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0520] Step 1:

[0521] The user taps the "Start" button displayed on the screen of the terminal means. The terminal means detects the user's tapping action, generates a "game start request" and sends it to the server means. The input is the user's tapping action, and the output is the "game start request." This request data includes the user ID.

[0522] Step 2:

[0523] The server analyzes the received "game start request" and retrieves the user's learning history from the database. The input is the "game start request" and the output is the user's learning history. The learning history (past questions, correct / incorrect data) is retrieved through an SQL query to the database.

[0524] Step 3:

[0525] The server generates a problem set appropriate to the user's level of understanding based on their learning history. The input is the learning history, and the output is the generated problem set. Using Python scripts and machine learning libraries (e.g., scikit-learn), the server extracts optimal problems and generates the set.

[0526] Step 4:

[0527] The server means distributes the generated problem set to the terminal means. The input is the generated problem set, and the output is the display of the problem set by the terminal means. The problem set is sent as an HTTP response and displayed on the screen of the terminal means.

[0528] Step 5:

[0529] The user answers the question. For example, by tapping on an image of an apple. The terminal means transmits this answer information to the server means. The input is the user's answer operation, and the output is answer data (user ID, question ID, answer content). This includes the operation of obtaining the tap position on the touch screen and creating a data package.

[0530] Step 6:

[0531] The server analyzes the received response data and determines whether it is correct or incorrect. The input is the response data, and the output is a feedback message. The analysis uses conditional branching to determine whether it is correct or incorrect, and generates feedback such as "Great job!" or "Try again!"

[0532] Step 7:

[0533] The server means sends the generated feedback message to the terminal means. The input is the feedback message and the output is a screen display for the user. An HTTP response is created and the feedback message is sent to the terminal means for display.

[0534] Step 8:

[0535] The server means records the answer results in a database and updates the progress information for the next learning session. The input is the answer results and the output is the updated progress data. The progress information is saved in the database using an SQL query.

[0536] In this way, each processing step proceeds with specific inputs and outputs, and the server means and terminal means work together to provide the user with an optimal learning experience.

[0537] (Application example 1)

[0538] 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."

[0539] Conventional English learning systems for young children are limited to learning content provided on devices, and have little connection to the real world, making it difficult to provide real-life English usage experiences. Furthermore, they lack interactive elements to maintain users' motivation to learn, and there has been a demand for educational entertainment, especially in brick-and-mortar stores.

[0540] 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.

[0541] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for the server means to generate optimal study content based on the user's study history, content distribution means for distributing study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's study progress, and means for providing interactive study activities using codes installed in physical stores. This enables English learning to be linked to the real world, increasing the user's motivation to study.

[0542] The "terminal means for accepting user operations" is a device such as a tablet or smartphone that is operated by the user to input data, and is a means for communicating with the server.

[0543] The "server means for receiving a request from the terminal means" refers to a server and its system for receiving a request based on a user's operation via a network and performing appropriate processing in response to the request.

[0544] "Content generation means by which the server means generates optimal learning content based on the user's learning history" refers to a function or module within the server that generates the most suitable learning material for a user based on the user's past learning history.

[0545] The "content distribution means for distributing learning content to the terminal means" is a function for transmitting the generated learning materials to the user's terminal device via a network.

[0546] The "feedback generating means for analyzing the user's answer and generating feedback" is a function for analyzing the answer given by the user and providing an appropriate response or advice in response to the answer.

[0547] The "progress recording means for recording the user's learning progress" is a function for storing the user's learning content and progress status in a database for future reference or for adjusting the learning plan.

[0548] "Means for providing interactive learning activities using codes installed in physical stores" refers to a means for unlocking specific learning content by scanning QR codes or other codes installed in the physical world, enabling interactive English learning.

[0549] This invention is a system for supporting English learning for young children, providing advanced data processing and interactive learning experiences similar to technologies such as modules installed in self-driving vehicles, logistics management systems in distribution centers, and control platforms for factory robots. The main components are terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal learning content based on the user's learning history, content distribution means for distributing the generated learning content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's learning progress, and means for providing interactive learning activities using codes installed in physical stores.

[0550] Specific examples of programs

[0551] 1. Server Role

[0552] Receiving a request from a terminal means: The server receives a learning content start request sent from the user's tablet or smartphone.

[0553] Content generation: Refer to the user's past learning history database to generate learning content of appropriate difficulty.

[0554] Delivery and feedback: The generated learning content is delivered to the user's terminal means, and after receiving the user's answer, the correctness of the answer is judged and appropriate feedback is generated.

[0555] Progress Record: The user's answers are recorded in a database to allow for continuation of learning and progress management.

[0556] 2. Role of the terminal

[0557] User interface: The user initiates the learning game via a terminal device such as a tablet or smartphone, for example by tapping the "Start" button on the screen.

[0558] Content display: The learning content received from the server is displayed to the user, allowing the user to answer questions.

[0559] Sending and receiving: Sending the user's answers to the server and displaying the feedback provided by the server.

[0560] 3. Interactive learning

[0561] In-store code redemption: Scanning QR codes in physical stores unlocks specific learning activities, allowing users to learn English through real-world experiences.

[0562] Interactive learning activities: Questions are presented that utilize in-store products and displays, and users can earn points and badges if they answer correctly.

[0563] Hardware and software used

[0564] Hardware

[0565] Terminal means such as tablets and smartphones

[0566] QR code installed in a physical store

[0567] software

[0568] Backend: Ruby on Rails

[0569] Frontend: React Native

[0570] Specific examples

[0571] For example, a user scans a QR code in a store and the following question appears on the tablet: "Find the red fruit in the store and tap its image." In this case, the user goes to the fruit section of the store and taps the image of an apple. The app checks the answer and displays the message "Great job!" The next question may also be an English question about a different product.

[0572] Prompt Sentence Examples

[0573] "Please provide an English learning question where the user scans a QR code with a tablet and finds an image of a fruit. Please also include a simple hint in the question."

[0574] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0575] Step 1:

[0576] The user operates the terminal device, such as a tablet or smartphone, and taps the "Start" button to start the learning game. This operation causes the terminal device to send a "game start request" to the server.

[0577] Input: User taps

[0578] Output: Sending "Game Start Request"

[0579] Specific operation: When the user taps the "Start" button on the screen, the corresponding request generation function runs within the terminal means, and a request is sent to the server.

[0580] Step 2:

[0581] The server receives the "game start request" and retrieves the user's learning history from the database. The server analyzes the user's answer data and progress information to determine the user's level of understanding.

[0582] Input: "Game start request", learning history data

[0583] Output: Analysis results based on user's learning comprehension information

[0584] Specific operation: The server analyzes the request received, reads past learning history from the database based on the user ID, analyzes progress information, and evaluates the level of understanding.

[0585] Step 3:

[0586] The server generates optimal learning content based on the user's learning history and comprehension level. Specifically, it creates problem sets for words that the user has often made mistakes on in the past and new words that the user should learn.

[0587] Input: learning history data, comprehension information

[0588] Output: Generated learning content (problem set)

[0589] Specific operation: Based on the information obtained from the database, the content generation algorithm in the server works to automatically generate an appropriate problem set.

[0590] Step 4:

[0591] The server distributes the generated learning content to the terminal means, and at this time, data is transferred to the terminal through the network.

[0592] Input: Generated learning content

[0593] Output: Data delivered to the terminal

[0594] Specific operation: The server sends the generated content to the terminal means as an HTTP response, and the terminal means receives this data.

[0595] Step 5:

[0596] The terminal means displays the received learning content on the user's screen, and the user answers the questions by, for example, tapping on the correct image for the displayed word.

[0597] Input: Learning content, user actions

[0598] Output: User response data

[0599] Specific operation: The terminal means displays the content received from the server on the screen, and the user responds by touch operation.

[0600] Step 6:

[0601] The user's answer is transmitted from the terminal means to the server, which analyzes the answer, determines whether the answer is correct, and generates appropriate feedback.

[0602] Input: User response data

[0603] Output: Feedback message

[0604] Specific operation: The server analyzes the received answer and determines whether it is correct. If it is correct, it generates a feedback message such as "Great job!", if it is incorrect, it generates a feedback message such as "Try again!"

[0605] Step 7:

[0606] The server sends the generated feedback message to the terminal means, which displays it to the user, and the server records the user's response results in a database and prepares for the next session.

[0607] Input: Feedback message, response result data

[0608] Output: Show feedback, update progress record

[0609] Specific operation: The server sends the generated feedback message to the terminal means, which displays it on the screen, and at the same time, the server records the user's answer results in the database.

[0610] Step 8:

[0611] When a user scans a QR code installed in a physical store, a specific learning activity is unlocked. The server receives this request, generates the corresponding learning content, and delivers it to the terminal device.

[0612] Input: QR code scan request

[0613] Output: Unlocked learning content

[0614] Specific operation: A user scans a QR code in a store, and the terminal device sends the information to the server. The server receives this request, generates new learning content, and delivers it to the terminal device.

[0615] 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.

[0616] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. The user, a young child, operates a device such as a tablet or smartphone, making use of learning content provided by a server, and by combining this with an emotion engine, more effective and adaptable learning is possible.

[0617] System configuration

[0618] The system includes the following main components:

[0619] User: A young child operating a learning tool.

[0620] Device: A device operated by a user, such as a tablet or smartphone.

[0621] Server: A system that generates and distributes learning content, manages user progress, and recognizes emotions.

[0622] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[0623] Program processing explanation

[0624] User operations and device roles

[0625] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session.

[0626] Server initialization

[0627] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether those answers were correct or incorrect.

[0628] The role of the emotional engine

[0629] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state, which is classified into multiple categories such as "happiness," "sadness," and "surprise."

[0630] Learning content generation and delivery

[0631] The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question where the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[0632] User responses and server analysis

[0633] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[0634] Adjusting feedback based on emotional state

[0635] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, the server will reinforce the message of encouragement to motivate the user to try again.

[0636] Track your progress and prepare for your next session

[0637] The server records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, thereby providing optimal learning based on the user's level of understanding and emotional state.

[0638] Specific examples

[0639] For example, consider a scenario where a user selects an image of an apple in response to the word "apple." In this case, the user selects the correct image from multiple fruit images displayed on the tablet screen. If the user selects the correct image, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects an incorrect image, the server displays "Try again!" or a simple hint (e.g., "Red fruit"). If the emotion engine recognizes that the user's facial expression indicates disappointment, it adds an encouraging message.

[0640] This system allows young children to learn English in a fun and engaging way, and it also records their progress in real time and adjusts feedback based on their emotional state, making learning more effective.

[0641] The processing flow will be explained below.

[0642] Step 1:

[0643] User: Operate the tablet and tap the "Start" button.

[0644] Step 2:

[0645] Terminal: Detects the start operation and sends a "game start request" to the server.

[0646] Step 3:

[0647] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[0648] Step 4:

[0649] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[0650] Step 5:

[0651] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[0652] Step 6:

[0653] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[0654] Step 7:

[0655] Emotion engine: The device's camera and microphone are used to collect the user's facial expressions and voice data in real time, and the emotional state is analyzed. The analysis results are stored internally on the device.

[0656] Step 8:

[0657] User: Select the correct image from the options displayed on the screen and tap it.

[0658] Step 9:

[0659] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[0660] Step 10:

[0661] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[0662] Step 11:

[0663] Emotion engine: Sends the analyzed emotional state to the server, for example, whether the user is disappointed or happy.

[0664] Step 12:

[0665] Server: Generates a positive feedback message such as "Great job!" if the answer is correct, or "Try again!" or a hint if the answer is incorrect.

[0666] Step 13:

[0667] Server: Adjust the content and tone of the feedback based on the received emotional state. For example, if the user is sad, add an additional encouraging message.

[0668] Step 14:

[0669] Server: Sends the generated feedback message to the terminal.

[0670] Step 15:

[0671] Terminal: Displays feedback messages received from the server to the user.

[0672] Step 16:

[0673] Server: Records the user's answers, emotional state, and progress information in a database. This includes correct / incorrect answers, response time, emotional state, etc.

[0674] Step 17:

[0675] Server: Analyzes the user's comprehension and emotional state for the next learning session and prepares data to adjust the difficulty of the next set of questions.

[0676] This allows content and feedback to be dynamically adjusted according to the child's learning progress and emotional state, ensuring continuous and effective English learning.

[0677] Example 2

[0678] 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."

[0679] While conventional English learning systems have the ability to generate learning content and provide feedback based on a user's learning history, they lack the ability to adjust the feedback and learning content based on the user's emotional state. As a result, they are unable to address the stress and loss of motivation that users experience while studying, making it difficult for them to continue studying effectively. The objective of this invention is to provide an English learning system that recognizes a user's emotional state in real time and provides appropriate feedback and learning content based on that.

[0680] 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.

[0681] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal study content based on the user's learning history and emotional state, content distribution means for distributing the study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, emotion recognition means for recognizing the user's emotional state and adjusting the content and intensity of the feedback, and progress recording means for recording the user's study progress. This makes it possible to provide appropriate feedback and study content according to the user's emotional state, enabling effective and continuous learning.

[0682] The "terminal means for accepting user operations" refers to a device operated by a user, specifically a tablet or smartphone, which has an input device and an interface.

[0683] A "server means for receiving requests from a terminal means" is a computing device that is responsible for receiving and processing information or requests sent from a terminal means.

[0684] The "content generation means for generating optimal learning content based on the user's learning history and emotional state" is an algorithm or program that selects and generates appropriate learning materials based on the user's past learning history and emotional state recognized in real time.

[0685] The "content distribution means for distributing study content to terminal means" has the function of transferring the generated study content to terminal means and displaying it on a device operated by the user.

[0686] The "feedback generation means for analyzing the user's answers and generating feedback" has the function of analyzing the results of the user's answers to the study questions and generating an appropriate feedback message based on the results.

[0687] "Emotion recognition means that recognizes the user's emotional state and adjusts the content and intensity of feedback" refers to an algorithm or program that identifies the user's emotional state by analyzing their facial expressions and voice, and appropriately adjusts the content and intensity of feedback according to that state.

[0688] The "progress recording means for recording the user's learning progress" refers to a database or program for storing a record of the user's learning activities and managing the learning progress.

[0689] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. This system allows the user, a small child, to operate a device such as a tablet or smartphone, access learning content provided by a server, and, by combining this with an emotion engine, enables more effective and adaptive learning.

[0690] The system consists of the following main components:

[0691] User: A young child operating a learning tool.

[0692] Device: A device operated by a user, such as a tablet or smartphone.

[0693] Server: A central system that generates and distributes learning content, manages user progress, and recognizes emotions.

[0694] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[0695] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session. The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether they answered correctly or incorrectly.

[0696] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state. These emotional states are classified into multiple categories, such as "happiness," "sadness," and "surprise." The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question in which the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[0697] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[0698] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, it will reinforce the encouraging messages to motivate them to try again. The server also records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session. This allows the server to provide optimal learning based on the user's level of understanding and emotional state.

[0699] Below is an example of a prompt sentence to input to the generative AI model.

[0700] markdown

[0701] Describe a scenario in which a user is asked to select the correct image (an apple) for the word "apple."

[0702] Specific steps:

[0703] 1. A user sees the word "apple" displayed on a tablet screen and selects an apple from a selection of fruit images.

[0704] 2. The server verifies the answer and displays a positive message: "Great job!"

[0705] If you answer incorrectly, the server will display the message "Try again!" and a simple hint: "Red fruit."

[0706] The role of the Emotion Engine:

[0707] It recognizes the user's emotions and adds an encouraging message if, for example, they are feeling down.

[0708] The user's learning history and emotional state are recorded in a database and the questions presented in the next session are adjusted accordingly.

[0709] In this way, the present invention is a system that recognizes a user's emotions in real time and optimizes learning content and feedback, thereby enabling more effective and sustainable learning.

[0710] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0711] Step 1:

[0712] User operation start

[0713] The user taps the "Start" button displayed on the device screen. The user's tapping operation is the input, which sends a "game start request" to the server. The device detects this operation and sends the request to the server.

[0714] Step 2:

[0715] Server initialization

[0716] The server receives the "game start request" and retrieves past learning history from the database based on the user ID. The input data is the user ID and past session history, and the output is the user's learning history. The server uses this to evaluate the user's current learning situation.

[0717] Step 3:

[0718] Emotion Engine Operation

[0719] The emotion engine uses the device's camera and microphone to capture the user's facial expressions and voice data. The input data is image data from the camera and voice data from the microphone, and the output is the user's emotional state. The emotion engine analyzes this and classifies it into categories such as "joy," "sadness," and "surprise."

[0720] Step 4:

[0721] Generating learning content

[0722] The server generates an optimal problem set based on the user's learning history and emotional state. The input data are the learning history and emotional state, and the output is the generated problem set. For example, a problem set containing questions about selecting the correct image for the word "apple" is generated.

[0723] Step 5:

[0724] Delivery of learning content

[0725] The generated problem set is sent to the terminal, which displays it on the user's screen. The input data is the generated problem set, and the output is the displayed learning content.

[0726] Step 6:

[0727] User Answers

[0728] The user answers questions displayed on the screen by tapping on an image of an apple when asked the word "apple." The input data is the user's response, and the device sends this information to the server.

[0729] Step 7:

[0730] Server response analysis

[0731] The server analyzes the received user answer data and determines whether it is correct or incorrect. The input data is the user's answer, and the output is the result of the correct answer. If the answer is correct, the server generates a feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a hint such as "Try again!" or "Red fruit."

[0732] Step 8:

[0733] Feedback adjustment based on emotional state

[0734] The server adjusts the content and intensity of the feedback based on the user's emotional state obtained from the emotion engine. The input data is the emotional state and the result of the correct / incorrect judgment, and the output is the adjusted feedback message. For example, if the user is feeling depressed, the message of encouragement is strengthened.

[0735] Step 9:

[0736] Record your learning progress

[0737] The server records the user's answers and emotional state in a database. The input data are the answers and emotional state, and the output is an updated learning history. Based on this, the server adjusts the content of the next learning session and provides optimal learning according to the user's level of understanding and emotional state.

[0738] (Application example 2)

[0739] 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."

[0740] Improving worker productivity and motivation in factories is essential for maintaining an efficient work environment. However, conventional systems lack the means to properly grasp workers' emotional states and adjust feedback accordingly, which means they are unable to provide adaptive support according to their emotions. Therefore, there is a need for a system that can improve work efficiency and motivation by recognizing workers' emotional states in real time and adjusting feedback based on that information.

[0741] 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.

[0742] In this invention, the server includes a terminal means for accepting user operations, a server means for receiving requests from the terminal means, and a feedback generation means for the server means to generate optimal feedback based on the user's work history. This makes it possible to recognize the emotional state of the worker in real time and adjust the feedback based on that, thereby maintaining the motivation of the worker and providing an efficient work environment.

[0743] The "terminal means" is a device that accepts user operations, and includes, for example, a tablet or a smartphone.

[0744] The "server means" is a server system having the function of receiving requests from the terminal means and performing various processes.

[0745] The "content generation means" has the function of generating optimal learning content and feedback based on the user's learning history and work history.

[0746] The "content distribution means" has the function of distributing the generated learning content and feedback to the terminal means.

[0747] The "feedback generation means" has a function of analyzing the user's answers and operations and generating feedback based on them.

[0748] The "progress recording means" has the function of recording the user's learning progress and work progress.

[0749] The "emotion recognition means" has a function of recognizing the user's emotional state in real time using the terminal means.

[0750] The "feedback adjustment means" has a function of adjusting the content of the feedback based on the emotional state acquired from the emotion recognition means.

[0751] Overall system picture

[0752] The present invention is a system for improving the productivity and motivation of workers in a factory, and includes a terminal means, a server means, an emotion recognition means, a feedback generation means, and a feedback adjustment means.

[0753] Hardware and Software Configuration

[0754] Hardware

[0755] Terminal means: A device operated by a user, i.e., a worker, and includes small computers and smart devices. Examples include tablets and smartphones.

[0756] Server means: A server that manages and processes work history and feedback information.

[0757] Camera: Its role is to acquire image data to recognize the emotions of workers.

[0758] Robots: Provide feedback to workers and assist them in their work.

[0759] software

[0760] Content generation means: A program that generates optimal feedback and work instructions based on work history.

[0761] Content distribution means: A program that distributes generated feedback and work instructions to terminal means.

[0762] Feedback generation means: A program that generates appropriate feedback based on the user's operation and emotional state. Libraries used include DeepFace for emotion recognition and an SDK for robot operation (e.g., SoftBank Robotics' SDK).

[0763] Progress recording means: A program that records and manages work progress in a database.

[0764] Processing flow

[0765] The terminal means accepts operations by the worker. When the user starts operating the terminal, the information is sent to the server means, which then obtains the user's progress from the work history database and generates optimal feedback. At this time, the server means uses a camera to recognize the user's emotions in real time and uses the results to adjust the content of the feedback.

[0766] Specific examples

[0767] For example, a worker operates a tablet and the robot provides feedback such as "Keep working, you're almost there!" If the robot detects that the user looks discouraged from the camera's facial expression, it will provide an additional encouraging message (e.g., "You can do it! Do your best!").

[0768] Prompt Sentence Examples

[0769] The following is an example of a prompt provided to the generative AI model for emotion recognition in this system:

[0770] Provide a face image as input and analyze the dominant emotion of this face. The analysis result will include emotion categories such as "happy", "sad", "angry", and "neutral".

[0771] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0772] Step 1:

[0773] The user operates the terminal means to start a work session.

[0774] Input: User action (e.g. tapping the Start button)

[0775] Data processing: Receiving and recording operation events

[0776] Output: Sends a start work request to the server

[0777] Specific operation: The user taps the "Start" button displayed on the screen of the terminal means, and the operation event is sent by the terminal to the server.

[0778] Step 2:

[0779] The server receives the work start request and retrieves the user's work history from the database.

[0780] Input: Work start request, user ID

[0781] Data processing: Query the user's progress from the work history database

[0782] Output: User's work history data

[0783] Specific operation: When the server receives a request to start a task, it retrieves the past task history from the database based on the user ID.

[0784] Step 3:

[0785] The server uses a camera to recognize the user's emotions in real time.

[0786] Input: Camera video data

[0787] Data processing: Sentiment analysis using the DeepFace library

[0788] Output: User's emotional state (e.g. "happy", "sad", "angry", "neutral")

[0789] Specific operation: The server acquires video from the device's camera, analyzes the video using the DeepFace library, and recognizes the user's main emotions.

[0790] Step 4:

[0791] The server generates optimal feedback based on the user's task history and emotional state.

[0792] Input: Work history data, emotional state data

[0793] Data processing: Applying feedback generation algorithms based on work history and emotions

[0794] Output: Generated feedback

[0795] Specific operation: The server analyzes the user's work history and emotional state, and generates an adapted feedback message.

[0796] Step 5:

[0797] The server distributes the generated feedback to the terminal means.

[0798] Input: Feedback

[0799] Data processing: Sending feedback messages to devices

[0800] Output: Feedback presented to the user on their device, either visually or audibly.

[0801] Specific operation: The server sends the generated feedback message to the terminal means and transmits it to the user through the screen or speaker of the terminal means.

[0802] Step 6:

[0803] Evaluate the effectiveness of the feedback based on the user's emotional state and adjust as needed.

[0804] Input: Additional emotional state data of the user

[0805] Data processing: Reassessing and adjusting emotional states after feedback

[0806] Output: Adjusted feedback content

[0807] Specific behavior: After the user receives feedback, the camera footage is analyzed again, and if the emotional state has not improved, further encouragement or adaptive feedback is provided.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] [Third embodiment]

[0812] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0813] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0814] 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).

[0815] 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.

[0816] 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.

[0817] 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).

[0818] 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.

[0819] 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.

[0820] 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.

[0821] 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.

[0822] 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.

[0823] 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."

[0824] The present invention is a system that supports English language learning for young children, allowing young children who are users to operate devices such as tablets and smartphones to effectively learn English using learning content provided by a server.

[0825] System configuration

[0826] The system includes the following main components:

[0827] User: A child who uses the system and operates the terminal to carry out learning activities.

[0828] Terminal: A device operated by a user, such as a tablet or smartphone, that communicates with the server.

[0829] Server: A system that generates and distributes learning content and manages user progress.

[0830] Program processing explanation

[0831] User operations and device roles

[0832] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This causes the device to send the user's operation to the server.

[0833] Server initialization

[0834] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past and information on whether those answers were correct or incorrect.

[0835] Learning content generation and delivery

[0836] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. For example, the question may include a question in which the user selects the correct image for the word "apple." The generated set of questions is sent to the device, which then displays it on the user's screen.

[0837] User responses and server analysis

[0838] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[0839] Track your progress and prepare for your next session

[0840] The server records the user's answers in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, allowing learning to be tailored to the user's level of understanding.

[0841] Specific examples

[0842] For example, consider a situation where a user must select an image of an apple in response to the word "apple." In this case, the user must choose the correct image from among several images of fruits displayed on the tablet screen. If the user selects the image correctly, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects the image incorrectly, the server displays "Try again!" or a simple hint (e.g., "Red fruit").

[0843] This system allows young children to learn English in a fun way without getting bored. In addition, learning progress is recorded in real time and reflected in the next question setting, enabling effective learning.

[0844] The processing flow will be explained below.

[0845] Step 1:

[0846] User: Operate the tablet and tap the "Start" button.

[0847] Step 2:

[0848] Terminal: Detects the start operation and sends a "game start request" to the server.

[0849] Step 3:

[0850] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[0851] Step 4:

[0852] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[0853] Step 5:

[0854] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[0855] Step 6:

[0856] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[0857] Step 7:

[0858] User: Select the correct image from the options displayed on the screen and tap it.

[0859] Step 8:

[0860] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[0861] Step 9:

[0862] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[0863] Step 10:

[0864] Server: If the answer is correct, generate a positive feedback message such as "Great job!". If the answer is incorrect, generate a "Try again!" or a hint. For example, give a simple hint such as "Red fruit."

[0865] Step 11:

[0866] Server: Sends the generated feedback message to the terminal.

[0867] Step 12:

[0868] Terminal: Displays feedback messages received from the server to the user.

[0869] Step 13:

[0870] Server: Records the user's answer results and progress information in a database, including correct / incorrect answers and answer times.

[0871] Step 14:

[0872] Server: Analyzes the user's understanding for the next study session and prepares data to adjust the difficulty of the next set of questions.

[0873] This allows content to be dynamically adjusted according to the child's learning progress, ensuring continuous and effective English learning.

[0874] Example 1

[0875] 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."

[0876] Conventional English learning systems for young children have limited learning effectiveness due to insufficient dynamic content generation based on the user's level of understanding and appropriate management of individual learning progress. In addition, the lack of immediate and appropriate feedback on the user's answers often reduces motivation to learn.

[0877] 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.

[0878] In this invention, the server includes a user operation detection means by a terminal means that sends a request for the user to start a study session, a content generation means that generates optimal study content based on the user's study history, and a progress recording means that stores progress data in a database and uses it to generate questions for the next study session. This makes it possible to accurately manage each user's study progress and provide appropriate feedback and content.

[0879] A "terminal means" is a device that can be operated by a user and is a means for communicating with a server.

[0880] The "server means" is a central management system for receiving requests from the terminal means and executing various processes.

[0881] The "content generation means" is a means for generating optimal study content based on the user's study history.

[0882] The "content distribution means" is a means for distributing study content generated by the server means to the terminal means.

[0883] The "feedback generating means" is a means for analyzing the user's answer and generating appropriate feedback according to whether the answer is correct or incorrect.

[0884] The "progress recording means" is a means for recording the user's learning progress and for use in the next learning session.

[0885] The "user operation detection means" is a means for detecting an operation on the terminal means by the user to send a request to start a learning session.

[0886] The "means for saving progress data in a database" refers to means for recording and saving the learning progress data collected by the progress recording means in a database.

[0887] The present invention is a system for supporting English language learning for young children, in which the user, a young child, can effectively learn English by operating a terminal means such as a tablet or smartphone and using learning content provided by a server means.

[0888] System configuration

[0889] The system includes the following main components:

[0890] 1. User: A child who uses the system and operates the terminal means to carry out learning activities.

[0891] 2. Terminal means: A device operated by the user, such as a tablet or smartphone, that communicates with the server means.

[0892] 3. Server: A system that generates and distributes learning content, manages user progress, etc. It uses Python and machine learning libraries (e.g., scikit-learn).

[0893] Processing flow and specific system operation

[0894] User operations and the role of terminal means

[0895] The user operates the terminal means to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This operation information is transmitted from the terminal means to the server means.

[0896] Initial processing of the server means

[0897] The server analyzes the "game start request" received from the terminal and retrieves the user's learning history from the database. This learning history includes information on the questions the user has previously tackled and whether they were correct or incorrect.

[0898] Learning content generation and delivery

[0899] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. During this process, it uses Python and machine learning libraries (e.g., scikit-learn) to select appropriate questions. The generated set of questions is sent to the terminal and displayed on the user's screen.

[0900] Obtaining user answers and sending them to the server

[0901] The user answers the displayed questions. For example, when the word "apple" is displayed, the user taps on the image of an apple. The terminal means transmits this answer information to the server means. The answer data includes the user ID, question ID, and answer content.

[0902] Server-based response analysis and feedback generation

[0903] The server analyzes the received answer data and determines whether it is correct. If the answer is correct, it generates a positive feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a simple hint such as "Try again!" or "Red fruit" or an instruction to try again.

[0904] Track your progress and prepare for your next session

[0905] The server means records the user's answers in a database, which is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session.

[0906] Specific examples

[0907] For example, consider a situation where a user selects an image of an apple for the word "apple." In this case, the user selects the correct one from multiple images displayed on the tablet screen. If the user selects the image correctly, the server means generates a message saying "Great job!" and sends it to the terminal means to display it to the user. If the user selects an incorrect image, a hint such as "Try again!" or "Red fruit" is displayed on the terminal means.

[0908] Example prompts for generative AI models

[0909] "I'm thinking of a system that allows young children to learn English using a tablet. In this system, a server receives requests from the device operated by the user, generates content based on the user's learning history, and delivers it to the device. The server analyzes the user's responses and provides feedback to the device. Please tell me the specific processing flow of this system."

[0910] In this way, the present invention realizes optimal content generation and feedback provision to improve the user's learning ability throughout the entire system.

[0911] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0912] Step 1:

[0913] The user taps the "Start" button displayed on the screen of the terminal means. The terminal means detects the user's tapping action, generates a "game start request" and sends it to the server means. The input is the user's tapping action, and the output is the "game start request." This request data includes the user ID.

[0914] Step 2:

[0915] The server analyzes the received "game start request" and retrieves the user's learning history from the database. The input is the "game start request" and the output is the user's learning history. The learning history (past questions, correct / incorrect data) is retrieved through an SQL query to the database.

[0916] Step 3:

[0917] The server generates a problem set appropriate to the user's level of understanding based on their learning history. The input is the learning history, and the output is the generated problem set. Using Python scripts and machine learning libraries (e.g., scikit-learn), the server extracts optimal problems and generates the set.

[0918] Step 4:

[0919] The server means distributes the generated problem set to the terminal means. The input is the generated problem set, and the output is the display of the problem set by the terminal means. The problem set is sent as an HTTP response and displayed on the screen of the terminal means.

[0920] Step 5:

[0921] The user answers the question. For example, by tapping on an image of an apple. The terminal means transmits this answer information to the server means. The input is the user's answer operation, and the output is answer data (user ID, question ID, answer content). This includes the operation of obtaining the tap position on the touch screen and creating a data package.

[0922] Step 6:

[0923] The server analyzes the received response data and determines whether it is correct or incorrect. The input is the response data, and the output is a feedback message. The analysis uses conditional branching to determine whether it is correct or incorrect, and generates feedback such as "Great job!" or "Try again!"

[0924] Step 7:

[0925] The server means sends the generated feedback message to the terminal means. The input is the feedback message and the output is a screen display for the user. An HTTP response is created and the feedback message is sent to the terminal means for display.

[0926] Step 8:

[0927] The server means records the answer results in a database and updates the progress information for the next learning session. The input is the answer results and the output is the updated progress data. The progress information is saved in the database using an SQL query.

[0928] In this way, each processing step proceeds with specific inputs and outputs, and the server means and terminal means work together to provide the user with an optimal learning experience.

[0929] (Application example 1)

[0930] 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."

[0931] Conventional English learning systems for young children are limited to learning content provided on devices, and have little connection to the real world, making it difficult to provide real-life English usage experiences. Furthermore, they lack interactive elements to maintain users' motivation to learn, and there has been a demand for educational entertainment, especially in brick-and-mortar stores.

[0932] 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.

[0933] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for the server means to generate optimal study content based on the user's study history, content distribution means for distributing study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's study progress, and means for providing interactive study activities using codes installed in physical stores. This enables English learning to be linked to the real world, increasing the user's motivation to study.

[0934] The "terminal means for accepting user operations" is a device such as a tablet or smartphone that is operated by the user to input data, and is a means for communicating with the server.

[0935] The "server means for receiving a request from the terminal means" refers to a server and its system for receiving a request based on a user's operation via a network and performing appropriate processing in response to the request.

[0936] "Content generation means by which the server means generates optimal learning content based on the user's learning history" refers to a function or module within the server that generates the most suitable learning material for a user based on the user's past learning history.

[0937] The "content distribution means for distributing learning content to the terminal means" is a function for transmitting the generated learning materials to the user's terminal device via a network.

[0938] The "feedback generating means for analyzing the user's answer and generating feedback" is a function for analyzing the answer given by the user and providing an appropriate response or advice in response to the answer.

[0939] The "progress recording means for recording the user's learning progress" is a function for storing the user's learning content and progress status in a database for future reference or for adjusting the learning plan.

[0940] "Means for providing interactive learning activities using codes installed in physical stores" refers to a means for unlocking specific learning content by scanning QR codes or other codes installed in the physical world, enabling interactive English learning.

[0941] This invention is a system for supporting English learning for young children, providing advanced data processing and interactive learning experiences similar to technologies such as modules installed in self-driving vehicles, logistics management systems in distribution centers, and control platforms for factory robots. The main components are terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal learning content based on the user's learning history, content distribution means for distributing the generated learning content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's learning progress, and means for providing interactive learning activities using codes installed in physical stores.

[0942] Specific examples of programs

[0943] 1. Server Role

[0944] Receiving a request from a terminal means: The server receives a learning content start request sent from the user's tablet or smartphone.

[0945] Content generation: Refer to the user's past learning history database to generate learning content of appropriate difficulty.

[0946] Delivery and feedback: The generated learning content is delivered to the user's terminal means, and after receiving the user's answer, the correctness of the answer is judged and appropriate feedback is generated.

[0947] Progress Record: The user's answers are recorded in a database to allow for continuation of learning and progress management.

[0948] 2. Role of the terminal

[0949] User interface: The user initiates the learning game via a terminal device such as a tablet or smartphone, for example by tapping the "Start" button on the screen.

[0950] Content display: The learning content received from the server is displayed to the user, allowing the user to answer questions.

[0951] Sending and receiving: Sending the user's answers to the server and displaying the feedback provided by the server.

[0952] 3. Interactive learning

[0953] In-store code redemption: Scanning QR codes in physical stores unlocks specific learning activities, allowing users to learn English through real-world experiences.

[0954] Interactive learning activities: Questions are presented that utilize in-store products and displays, and users can earn points and badges if they answer correctly.

[0955] Hardware and software used

[0956] Hardware

[0957] Terminal means such as tablets and smartphones

[0958] QR code installed in a physical store

[0959] software

[0960] Backend: Ruby on Rails

[0961] Frontend: React Native

[0962] Specific examples

[0963] For example, a user scans a QR code in a store and the following question appears on the tablet: "Find the red fruit in the store and tap its image." In this case, the user goes to the fruit section of the store and taps the image of an apple. The app checks the answer and displays the message "Great job!" The next question may also be an English question about a different product.

[0964] Prompt Sentence Examples

[0965] "Please provide an English learning question where the user scans a QR code with a tablet and finds an image of a fruit. Please also include a simple hint in the question."

[0966] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0967] Step 1:

[0968] The user operates the terminal device, such as a tablet or smartphone, and taps the "Start" button to start the learning game. This operation causes the terminal device to send a "game start request" to the server.

[0969] Input: User taps

[0970] Output: Sending "Game Start Request"

[0971] Specific operation: When the user taps the "Start" button on the screen, the corresponding request generation function runs within the terminal means, and a request is sent to the server.

[0972] Step 2:

[0973] The server receives the "game start request" and retrieves the user's learning history from the database. The server analyzes the user's answer data and progress information to determine the user's level of understanding.

[0974] Input: "Game start request", learning history data

[0975] Output: Analysis results based on user's learning comprehension information

[0976] Specific operation: The server analyzes the request received, reads past learning history from the database based on the user ID, analyzes progress information, and evaluates the level of understanding.

[0977] Step 3:

[0978] The server generates optimal learning content based on the user's learning history and comprehension level. Specifically, it creates problem sets for words that the user has often made mistakes on in the past and new words that the user should learn.

[0979] Input: learning history data, comprehension information

[0980] Output: Generated learning content (problem set)

[0981] Specific operation: Based on the information obtained from the database, the content generation algorithm in the server works to automatically generate an appropriate problem set.

[0982] Step 4:

[0983] The server distributes the generated learning content to the terminal means, and at this time, data is transferred to the terminal through the network.

[0984] Input: Generated learning content

[0985] Output: Data delivered to the terminal

[0986] Specific operation: The server sends the generated content to the terminal means as an HTTP response, and the terminal means receives this data.

[0987] Step 5:

[0988] The terminal means displays the received learning content on the user's screen, and the user answers the questions by, for example, tapping on the correct image for the displayed word.

[0989] Input: Learning content, user actions

[0990] Output: User response data

[0991] Specific operation: The terminal means displays the content received from the server on the screen, and the user responds by touch operation.

[0992] Step 6:

[0993] The user's answer is transmitted from the terminal means to the server, which analyzes the answer, determines whether the answer is correct, and generates appropriate feedback.

[0994] Input: User response data

[0995] Output: Feedback message

[0996] Specific operation: The server analyzes the received answer and determines whether it is correct. If it is correct, it generates a feedback message such as "Great job!", if it is incorrect, it generates a feedback message such as "Try again!"

[0997] Step 7:

[0998] The server sends the generated feedback message to the terminal means, which displays it to the user, and the server records the user's response results in a database and prepares for the next session.

[0999] Input: Feedback message, response result data

[1000] Output: Show feedback, update progress record

[1001] Specific operation: The server sends the generated feedback message to the terminal means, which displays it on the screen, and at the same time, the server records the user's answer results in the database.

[1002] Step 8:

[1003] When a user scans a QR code installed in a physical store, a specific learning activity is unlocked. The server receives this request, generates the corresponding learning content, and delivers it to the terminal device.

[1004] Input: QR code scan request

[1005] Output: Unlocked learning content

[1006] Specific operation: A user scans a QR code in a store, and the terminal device sends the information to the server. The server receives this request, generates new learning content, and delivers it to the terminal device.

[1007] 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.

[1008] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. The user, a young child, operates a device such as a tablet or smartphone, making use of learning content provided by a server, and by combining this with an emotion engine, more effective and adaptable learning is possible.

[1009] System configuration

[1010] The system includes the following main components:

[1011] User: A young child operating a learning tool.

[1012] Device: A device operated by a user, such as a tablet or smartphone.

[1013] Server: A system that generates and distributes learning content, manages user progress, and recognizes emotions.

[1014] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[1015] Program processing explanation

[1016] User operations and device roles

[1017] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session.

[1018] Server initialization

[1019] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether those answers were correct or incorrect.

[1020] The role of the emotional engine

[1021] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state, which is classified into multiple categories such as "happiness," "sadness," and "surprise."

[1022] Learning content generation and delivery

[1023] The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question where the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[1024] User responses and server analysis

[1025] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[1026] Adjusting feedback based on emotional state

[1027] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, the server will reinforce the message of encouragement to motivate the user to try again.

[1028] Track your progress and prepare for your next session

[1029] The server records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, thereby providing optimal learning based on the user's level of understanding and emotional state.

[1030] Specific examples

[1031] For example, consider a scenario where a user selects an image of an apple in response to the word "apple." In this case, the user selects the correct image from multiple fruit images displayed on the tablet screen. If the user selects the correct image, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects an incorrect image, the server displays "Try again!" or a simple hint (e.g., "Red fruit"). If the emotion engine recognizes that the user's facial expression indicates disappointment, it adds an encouraging message.

[1032] This system allows young children to learn English in a fun and engaging way, and it also records their progress in real time and adjusts feedback based on their emotional state, making learning more effective.

[1033] The processing flow will be explained below.

[1034] Step 1:

[1035] User: Operate the tablet and tap the "Start" button.

[1036] Step 2:

[1037] Terminal: Detects the start operation and sends a "game start request" to the server.

[1038] Step 3:

[1039] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[1040] Step 4:

[1041] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[1042] Step 5:

[1043] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[1044] Step 6:

[1045] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[1046] Step 7:

[1047] Emotion engine: The device's camera and microphone are used to collect the user's facial expressions and voice data in real time, and the emotional state is analyzed. The analysis results are stored internally on the device.

[1048] Step 8:

[1049] User: Select the correct image from the options displayed on the screen and tap it.

[1050] Step 9:

[1051] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[1052] Step 10:

[1053] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[1054] Step 11:

[1055] Emotion engine: Sends the analyzed emotional state to the server, for example, whether the user is disappointed or happy.

[1056] Step 12:

[1057] Server: Generates a positive feedback message such as "Great job!" if the answer is correct, or "Try again!" or a hint if the answer is incorrect.

[1058] Step 13:

[1059] Server: Adjust the content and tone of the feedback based on the received emotional state. For example, if the user is sad, add an additional encouraging message.

[1060] Step 14:

[1061] Server: Sends the generated feedback message to the terminal.

[1062] Step 15:

[1063] Terminal: Displays feedback messages received from the server to the user.

[1064] Step 16:

[1065] Server: Records the user's answers, emotional state, and progress information in a database. This includes correct / incorrect answers, response time, emotional state, etc.

[1066] Step 17:

[1067] Server: Analyzes the user's comprehension and emotional state for the next learning session and prepares data to adjust the difficulty of the next set of questions.

[1068] This allows content and feedback to be dynamically adjusted according to the child's learning progress and emotional state, ensuring continuous and effective English learning.

[1069] Example 2

[1070] 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."

[1071] While conventional English learning systems have the ability to generate learning content and provide feedback based on a user's learning history, they lack the ability to adjust the feedback and learning content based on the user's emotional state. As a result, they are unable to address the stress and loss of motivation that users experience while studying, making it difficult for them to continue studying effectively. The objective of this invention is to provide an English learning system that recognizes a user's emotional state in real time and provides appropriate feedback and learning content based on that.

[1072] 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.

[1073] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal study content based on the user's learning history and emotional state, content distribution means for distributing the study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, emotion recognition means for recognizing the user's emotional state and adjusting the content and intensity of the feedback, and progress recording means for recording the user's study progress. This makes it possible to provide appropriate feedback and study content according to the user's emotional state, enabling effective and continuous learning.

[1074] The "terminal means for accepting user operations" refers to a device operated by a user, specifically a tablet or smartphone, which has an input device and an interface.

[1075] A "server means for receiving requests from a terminal means" is a computing device that is responsible for receiving and processing information or requests sent from a terminal means.

[1076] The "content generation means for generating optimal learning content based on the user's learning history and emotional state" is an algorithm or program that selects and generates appropriate learning materials based on the user's past learning history and emotional state recognized in real time.

[1077] The "content distribution means for distributing study content to terminal means" has the function of transferring the generated study content to terminal means and displaying it on a device operated by the user.

[1078] The "feedback generation means for analyzing the user's answers and generating feedback" has the function of analyzing the results of the user's answers to the study questions and generating an appropriate feedback message based on the results.

[1079] "Emotion recognition means that recognizes the user's emotional state and adjusts the content and intensity of feedback" refers to an algorithm or program that identifies the user's emotional state by analyzing their facial expressions and voice, and appropriately adjusts the content and intensity of feedback according to that state.

[1080] The "progress recording means for recording the user's learning progress" refers to a database or program for storing a record of the user's learning activities and managing the learning progress.

[1081] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. This system allows the user, a small child, to operate a device such as a tablet or smartphone, access learning content provided by a server, and, by combining this with an emotion engine, enables more effective and adaptive learning.

[1082] The system consists of the following main components:

[1083] User: A young child operating a learning tool.

[1084] Device: A device operated by a user, such as a tablet or smartphone.

[1085] Server: A central system that generates and distributes learning content, manages user progress, and recognizes emotions.

[1086] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[1087] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session. The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether they answered correctly or incorrectly.

[1088] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state. These emotional states are classified into multiple categories, such as "happiness," "sadness," and "surprise." The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question in which the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[1089] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[1090] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, it will reinforce the encouraging messages to motivate them to try again. The server also records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session. This allows the server to provide optimal learning based on the user's level of understanding and emotional state.

[1091] Below is an example of a prompt sentence to input to the generative AI model.

[1092] markdown

[1093] Describe a scenario in which a user is asked to select the correct image (an apple) for the word "apple."

[1094] Specific steps:

[1095] 1. A user sees the word "apple" displayed on a tablet screen and selects an apple from a selection of fruit images.

[1096] 2. The server verifies the answer and displays a positive message: "Great job!"

[1097] If you answer incorrectly, the server will display the message "Try again!" and a simple hint: "Red fruit."

[1098] The role of the Emotion Engine:

[1099] It recognizes the user's emotions and adds an encouraging message if, for example, they are feeling down.

[1100] The user's learning history and emotional state are recorded in a database and the questions presented in the next session are adjusted accordingly.

[1101] In this way, the present invention is a system that recognizes a user's emotions in real time and optimizes learning content and feedback, thereby enabling more effective and sustainable learning.

[1102] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1103] Step 1:

[1104] User operation start

[1105] The user taps the "Start" button displayed on the device screen. The user's tapping operation is the input, which sends a "game start request" to the server. The device detects this operation and sends the request to the server.

[1106] Step 2:

[1107] Server initialization

[1108] The server receives the "game start request" and retrieves past learning history from the database based on the user ID. The input data is the user ID and past session history, and the output is the user's learning history. The server uses this to evaluate the user's current learning situation.

[1109] Step 3:

[1110] Emotion Engine Operation

[1111] The emotion engine uses the device's camera and microphone to capture the user's facial expressions and voice data. The input data is image data from the camera and voice data from the microphone, and the output is the user's emotional state. The emotion engine analyzes this and classifies it into categories such as "joy," "sadness," and "surprise."

[1112] Step 4:

[1113] Generating learning content

[1114] The server generates an optimal problem set based on the user's learning history and emotional state. The input data are the learning history and emotional state, and the output is the generated problem set. For example, a problem set containing questions about selecting the correct image for the word "apple" is generated.

[1115] Step 5:

[1116] Delivery of learning content

[1117] The generated problem set is sent to the terminal, which displays it on the user's screen. The input data is the generated problem set, and the output is the displayed learning content.

[1118] Step 6:

[1119] User Answers

[1120] The user answers questions displayed on the screen by tapping on an image of an apple when asked the word "apple." The input data is the user's response, and the device sends this information to the server.

[1121] Step 7:

[1122] Server response analysis

[1123] The server analyzes the received user answer data and determines whether it is correct or incorrect. The input data is the user's answer, and the output is the result of the correct answer. If the answer is correct, the server generates a feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a hint such as "Try again!" or "Red fruit."

[1124] Step 8:

[1125] Feedback adjustment based on emotional state

[1126] The server adjusts the content and intensity of the feedback based on the user's emotional state obtained from the emotion engine. The input data is the emotional state and the result of the correct / incorrect judgment, and the output is the adjusted feedback message. For example, if the user is feeling depressed, the message of encouragement is strengthened.

[1127] Step 9:

[1128] Record your learning progress

[1129] The server records the user's answers and emotional state in a database. The input data are the answers and emotional state, and the output is an updated learning history. Based on this, the server adjusts the content of the next learning session and provides optimal learning according to the user's level of understanding and emotional state.

[1130] (Application example 2)

[1131] 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."

[1132] Improving worker productivity and motivation in factories is essential for maintaining an efficient work environment. However, conventional systems lack the means to properly grasp workers' emotional states and adjust feedback accordingly, which means they are unable to provide adaptive support according to their emotions. Therefore, there is a need for a system that can improve work efficiency and motivation by recognizing workers' emotional states in real time and adjusting feedback based on that information.

[1133] 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.

[1134] In this invention, the server includes a terminal means for accepting user operations, a server means for receiving requests from the terminal means, and a feedback generation means for the server means to generate optimal feedback based on the user's work history. This makes it possible to recognize the emotional state of the worker in real time and adjust the feedback based on that, thereby maintaining the motivation of the worker and providing an efficient work environment.

[1135] The "terminal means" is a device that accepts user operations, and includes, for example, a tablet or a smartphone.

[1136] The "server means" is a server system having the function of receiving requests from the terminal means and performing various processes.

[1137] The "content generation means" has the function of generating optimal learning content and feedback based on the user's learning history and work history.

[1138] The "content distribution means" has the function of distributing the generated learning content and feedback to the terminal means.

[1139] The "feedback generation means" has a function of analyzing the user's answers and operations and generating feedback based on them.

[1140] The "progress recording means" has the function of recording the user's learning progress and work progress.

[1141] The "emotion recognition means" has a function of recognizing the user's emotional state in real time using the terminal means.

[1142] The "feedback adjustment means" has a function of adjusting the content of the feedback based on the emotional state acquired from the emotion recognition means.

[1143] Overall system picture

[1144] The present invention is a system for improving the productivity and motivation of workers in a factory, and includes a terminal means, a server means, an emotion recognition means, a feedback generation means, and a feedback adjustment means.

[1145] Hardware and Software Configuration

[1146] Hardware

[1147] Terminal means: A device operated by a user, i.e., a worker, and includes small computers and smart devices. Examples include tablets and smartphones.

[1148] Server means: A server that manages and processes work history and feedback information.

[1149] Camera: Its role is to acquire image data to recognize the emotions of workers.

[1150] Robots: Provide feedback to workers and assist them in their work.

[1151] software

[1152] Content generation means: A program that generates optimal feedback and work instructions based on work history.

[1153] Content distribution means: A program that distributes generated feedback and work instructions to terminal means.

[1154] Feedback generation means: A program that generates appropriate feedback based on the user's operation and emotional state. Libraries used include DeepFace for emotion recognition and an SDK for robot operation (e.g., SoftBank Robotics' SDK).

[1155] Progress recording means: A program that records and manages work progress in a database.

[1156] Processing flow

[1157] The terminal means accepts operations by the worker. When the user starts operating the terminal, the information is sent to the server means, which then obtains the user's progress from the work history database and generates optimal feedback. At this time, the server means uses a camera to recognize the user's emotions in real time and uses the results to adjust the content of the feedback.

[1158] Specific examples

[1159] For example, a worker operates a tablet and the robot provides feedback such as "Keep working, you're almost there!" If the robot detects that the user looks discouraged from the camera's facial expression, it will provide an additional encouraging message (e.g., "You can do it! Do your best!").

[1160] Prompt Sentence Examples

[1161] The following is an example of a prompt provided to the generative AI model for emotion recognition in this system:

[1162] Provide a face image as input and analyze the dominant emotion of this face. The analysis result will include emotion categories such as "happy", "sad", "angry", and "neutral".

[1163] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1164] Step 1:

[1165] The user operates the terminal means to start a work session.

[1166] Input: User action (e.g. tapping the Start button)

[1167] Data processing: Receiving and recording operation events

[1168] Output: Sends a start work request to the server

[1169] Specific operation: The user taps the "Start" button displayed on the screen of the terminal means, and the operation event is sent by the terminal to the server.

[1170] Step 2:

[1171] The server receives the work start request and retrieves the user's work history from the database.

[1172] Input: Work start request, user ID

[1173] Data processing: Query the user's progress from the work history database

[1174] Output: User's work history data

[1175] Specific operation: When the server receives a request to start a task, it retrieves the past task history from the database based on the user ID.

[1176] Step 3:

[1177] The server uses a camera to recognize the user's emotions in real time.

[1178] Input: Camera video data

[1179] Data processing: Sentiment analysis using the DeepFace library

[1180] Output: User's emotional state (e.g. "happy", "sad", "angry", "neutral")

[1181] Specific operation: The server acquires video from the device's camera, analyzes the video using the DeepFace library, and recognizes the user's main emotions.

[1182] Step 4:

[1183] The server generates optimal feedback based on the user's task history and emotional state.

[1184] Input: Work history data, emotional state data

[1185] Data processing: Applying feedback generation algorithms based on work history and emotions

[1186] Output: Generated feedback

[1187] Specific operation: The server analyzes the user's work history and emotional state, and generates an adapted feedback message.

[1188] Step 5:

[1189] The server distributes the generated feedback to the terminal means.

[1190] Input: Feedback

[1191] Data processing: Sending feedback messages to devices

[1192] Output: Feedback presented to the user on their device, either visually or audibly.

[1193] Specific operation: The server sends the generated feedback message to the terminal means and transmits it to the user through the screen or speaker of the terminal means.

[1194] Step 6:

[1195] Evaluate the effectiveness of the feedback based on the user's emotional state and adjust as needed.

[1196] Input: Additional emotional state data of the user

[1197] Data processing: Reassessing and adjusting emotional states after feedback

[1198] Output: Adjusted feedback content

[1199] Specific behavior: After the user receives feedback, the camera footage is analyzed again, and if the emotional state has not improved, further encouragement or adaptive feedback is provided.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] [Fourth embodiment]

[1204] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1205] 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.

[1206] 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).

[1207] 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.

[1208] 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.

[1209] 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).

[1210] 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.

[1211] 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.

[1212] 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.

[1213] 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.

[1214] 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.

[1215] 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.

[1216] 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."

[1217] The present invention is a system that supports English language learning for young children, allowing young children who are users to operate devices such as tablets and smartphones to effectively learn English using learning content provided by a server.

[1218] System configuration

[1219] The system includes the following main components:

[1220] User: A child who uses the system and operates the terminal to carry out learning activities.

[1221] Terminal: A device operated by a user, such as a tablet or smartphone, that communicates with the server.

[1222] Server: A system that generates and distributes learning content and manages user progress.

[1223] Program processing explanation

[1224] User operations and device roles

[1225] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This causes the device to send the user's operation to the server.

[1226] Server initialization

[1227] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past and information on whether those answers were correct or incorrect.

[1228] Learning content generation and delivery

[1229] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. For example, the question may include a question in which the user selects the correct image for the word "apple." The generated set of questions is sent to the device, which then displays it on the user's screen.

[1230] User responses and server analysis

[1231] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[1232] Track your progress and prepare for your next session

[1233] The server records the user's answers in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, allowing learning to be tailored to the user's level of understanding.

[1234] Specific examples

[1235] For example, consider a situation where a user must select an image of an apple in response to the word "apple." In this case, the user must choose the correct image from among several images of fruits displayed on the tablet screen. If the user selects the image correctly, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects the image incorrectly, the server displays "Try again!" or a simple hint (e.g., "Red fruit").

[1236] This system allows young children to learn English in a fun way without getting bored. In addition, learning progress is recorded in real time and reflected in the next question setting, enabling effective learning.

[1237] The processing flow will be explained below.

[1238] Step 1:

[1239] User: Operate the tablet and tap the "Start" button.

[1240] Step 2:

[1241] Terminal: Detects the start operation and sends a "game start request" to the server.

[1242] Step 3:

[1243] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[1244] Step 4:

[1245] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[1246] Step 5:

[1247] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[1248] Step 6:

[1249] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[1250] Step 7:

[1251] User: Select the correct image from the options displayed on the screen and tap it.

[1252] Step 8:

[1253] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[1254] Step 9:

[1255] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[1256] Step 10:

[1257] Server: If the answer is correct, generate a positive feedback message such as "Great job!". If the answer is incorrect, generate a "Try again!" or a hint. For example, give a simple hint such as "Red fruit."

[1258] Step 11:

[1259] Server: Sends the generated feedback message to the terminal.

[1260] Step 12:

[1261] Terminal: Displays feedback messages received from the server to the user.

[1262] Step 13:

[1263] Server: Records the user's answer results and progress information in a database, including correct / incorrect answers and answer times.

[1264] Step 14:

[1265] Server: Analyzes the user's understanding for the next study session and prepares data to adjust the difficulty of the next set of questions.

[1266] This allows content to be dynamically adjusted according to the child's learning progress, ensuring continuous and effective English learning.

[1267] Example 1

[1268] 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."

[1269] Conventional English learning systems for young children have limited learning effectiveness due to insufficient dynamic content generation based on the user's level of understanding and appropriate management of individual learning progress. In addition, the lack of immediate and appropriate feedback on the user's answers often reduces motivation to learn.

[1270] 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.

[1271] In this invention, the server includes a user operation detection means by a terminal means that sends a request for the user to start a study session, a content generation means that generates optimal study content based on the user's study history, and a progress recording means that stores progress data in a database and uses it to generate questions for the next study session. This makes it possible to accurately manage each user's study progress and provide appropriate feedback and content.

[1272] A "terminal means" is a device that can be operated by a user and is a means for communicating with a server.

[1273] The "server means" is a central management system for receiving requests from the terminal means and executing various processes.

[1274] The "content generation means" is a means for generating optimal study content based on the user's study history.

[1275] The "content distribution means" is a means for distributing study content generated by the server means to the terminal means.

[1276] The "feedback generating means" is a means for analyzing the user's answer and generating appropriate feedback according to whether the answer is correct or incorrect.

[1277] The "progress recording means" is a means for recording the user's learning progress and for use in the next learning session.

[1278] The "user operation detection means" is a means for detecting an operation on the terminal means by the user to send a request to start a learning session.

[1279] The "means for saving progress data in a database" refers to means for recording and saving the learning progress data collected by the progress recording means in a database.

[1280] The present invention is a system for supporting English language learning for young children, in which the user, a young child, can effectively learn English by operating a terminal means such as a tablet or smartphone and using learning content provided by a server means.

[1281] System configuration

[1282] The system includes the following main components:

[1283] 1. User: A child who uses the system and operates the terminal means to carry out learning activities.

[1284] 2. Terminal means: A device operated by the user, such as a tablet or smartphone, that communicates with the server means.

[1285] 3. Server: A system that generates and distributes learning content, manages user progress, etc. It uses Python and machine learning libraries (e.g., scikit-learn).

[1286] Processing flow and specific system operation

[1287] User operations and the role of terminal means

[1288] The user operates the terminal means to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to start the learning session. This operation information is transmitted from the terminal means to the server means.

[1289] Initial processing of the server means

[1290] The server analyzes the "game start request" received from the terminal and retrieves the user's learning history from the database. This learning history includes information on the questions the user has previously tackled and whether they were correct or incorrect.

[1291] Learning content generation and delivery

[1292] The server generates a set of questions appropriate to the user's level of understanding based on the user's learning history. During this process, it uses Python and machine learning libraries (e.g., scikit-learn) to select appropriate questions. The generated set of questions is sent to the terminal and displayed on the user's screen.

[1293] Obtaining user answers and sending them to the server

[1294] The user answers the displayed questions. For example, when the word "apple" is displayed, the user taps on the image of an apple. The terminal means transmits this answer information to the server means. The answer data includes the user ID, question ID, and answer content.

[1295] Server-based response analysis and feedback generation

[1296] The server analyzes the received answer data and determines whether it is correct. If the answer is correct, it generates a positive feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a simple hint such as "Try again!" or "Red fruit" or an instruction to try again.

[1297] Track your progress and prepare for your next session

[1298] The server means records the user's answers in a database, which is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session.

[1299] Specific examples

[1300] For example, consider a situation where a user selects an image of an apple for the word "apple." In this case, the user selects the correct one from multiple images displayed on the tablet screen. If the user selects the image correctly, the server means generates a message saying "Great job!" and sends it to the terminal means to display it to the user. If the user selects an incorrect image, a hint such as "Try again!" or "Red fruit" is displayed on the terminal means.

[1301] Example prompts for generative AI models

[1302] "I'm thinking of a system that allows young children to learn English using a tablet. In this system, a server receives requests from the device operated by the user, generates content based on the user's learning history, and delivers it to the device. The server analyzes the user's responses and provides feedback to the device. Please tell me the specific processing flow of this system."

[1303] In this way, the present invention realizes optimal content generation and feedback provision to improve the user's learning ability throughout the entire system.

[1304] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1305] Step 1:

[1306] The user taps the "Start" button displayed on the screen of the terminal means. The terminal means detects the user's tapping action, generates a "game start request" and sends it to the server means. The input is the user's tapping action, and the output is the "game start request." This request data includes the user ID.

[1307] Step 2:

[1308] The server analyzes the received "game start request" and retrieves the user's learning history from the database. The input is the "game start request" and the output is the user's learning history. The learning history (past questions, correct / incorrect data) is retrieved through an SQL query to the database.

[1309] Step 3:

[1310] The server generates a problem set appropriate to the user's level of understanding based on their learning history. The input is the learning history, and the output is the generated problem set. Using Python scripts and machine learning libraries (e.g., scikit-learn), the server extracts optimal problems and generates the set.

[1311] Step 4:

[1312] The server means distributes the generated problem set to the terminal means. The input is the generated problem set, and the output is the display of the problem set by the terminal means. The problem set is sent as an HTTP response and displayed on the screen of the terminal means.

[1313] Step 5:

[1314] The user answers the question. For example, by tapping on an image of an apple. The terminal means transmits this answer information to the server means. The input is the user's answer operation, and the output is answer data (user ID, question ID, answer content). This includes the operation of obtaining the tap position on the touch screen and creating a data package.

[1315] Step 6:

[1316] The server analyzes the received response data and determines whether it is correct or incorrect. The input is the response data, and the output is a feedback message. The analysis uses conditional branching to determine whether it is correct or incorrect, and generates feedback such as "Great job!" or "Try again!"

[1317] Step 7:

[1318] The server means sends the generated feedback message to the terminal means. The input is the feedback message and the output is a screen display for the user. An HTTP response is created and the feedback message is sent to the terminal means for display.

[1319] Step 8:

[1320] The server means records the answer results in a database and updates the progress information for the next learning session. The input is the answer results and the output is the updated progress data. The progress information is saved in the database using an SQL query.

[1321] In this way, each processing step proceeds with specific inputs and outputs, and the server means and terminal means work together to provide the user with an optimal learning experience.

[1322] (Application example 1)

[1323] 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."

[1324] Conventional English learning systems for young children are limited to learning content provided on devices, and have little connection to the real world, making it difficult to provide real-life English usage experiences. Furthermore, they lack interactive elements to maintain users' motivation to learn, and there has been a demand for educational entertainment, especially in brick-and-mortar stores.

[1325] 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.

[1326] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for the server means to generate optimal study content based on the user's study history, content distribution means for distributing study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's study progress, and means for providing interactive study activities using codes installed in physical stores. This enables English learning to be linked to the real world, increasing the user's motivation to study.

[1327] The "terminal means for accepting user operations" is a device such as a tablet or smartphone that is operated by the user to input data, and is a means for communicating with the server.

[1328] The "server means for receiving a request from the terminal means" refers to a server and its system for receiving a request based on a user's operation via a network and performing appropriate processing in response to the request.

[1329] "Content generation means by which the server means generates optimal learning content based on the user's learning history" refers to a function or module within the server that generates the most suitable learning material for a user based on the user's past learning history.

[1330] The "content distribution means for distributing learning content to the terminal means" is a function for transmitting the generated learning materials to the user's terminal device via a network.

[1331] The "feedback generating means for analyzing the user's answer and generating feedback" is a function for analyzing the answer given by the user and providing an appropriate response or advice in response to the answer.

[1332] The "progress recording means for recording the user's learning progress" is a function for storing the user's learning content and progress status in a database for future reference or for adjusting the learning plan.

[1333] "Means for providing interactive learning activities using codes installed in physical stores" refers to a means for unlocking specific learning content by scanning QR codes or other codes installed in the physical world, enabling interactive English learning.

[1334] This invention is a system for supporting English learning for young children, providing advanced data processing and interactive learning experiences similar to technologies such as modules installed in self-driving vehicles, logistics management systems in distribution centers, and control platforms for factory robots. The main components are terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal learning content based on the user's learning history, content distribution means for distributing the generated learning content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, progress recording means for recording the user's learning progress, and means for providing interactive learning activities using codes installed in physical stores.

[1335] Specific examples of programs

[1336] 1. Server Role

[1337] Receiving a request from a terminal means: The server receives a learning content start request sent from the user's tablet or smartphone.

[1338] Content generation: Refer to the user's past learning history database to generate learning content of appropriate difficulty.

[1339] Delivery and feedback: The generated learning content is delivered to the user's terminal means, and after receiving the user's answer, the correctness of the answer is judged and appropriate feedback is generated.

[1340] Progress Record: The user's answers are recorded in a database to allow for continuation of learning and progress management.

[1341] 2. Role of the terminal

[1342] User interface: The user initiates the learning game via a terminal device such as a tablet or smartphone, for example by tapping the "Start" button on the screen.

[1343] Content display: The learning content received from the server is displayed to the user, allowing the user to answer questions.

[1344] Sending and receiving: Sending the user's answers to the server and displaying the feedback provided by the server.

[1345] 3. Interactive learning

[1346] In-store code redemption: Scanning QR codes in physical stores unlocks specific learning activities, allowing users to learn English through real-world experiences.

[1347] Interactive learning activities: Questions are presented that utilize in-store products and displays, and users can earn points and badges if they answer correctly.

[1348] Hardware and software used

[1349] Hardware

[1350] Terminal means such as tablets and smartphones

[1351] QR code installed in a physical store

[1352] software

[1353] Backend: Ruby on Rails

[1354] Frontend: React Native

[1355] Specific examples

[1356] For example, a user scans a QR code in a store and the following question appears on the tablet: "Find the red fruit in the store and tap its image." In this case, the user goes to the fruit section of the store and taps the image of an apple. The app checks the answer and displays the message "Great job!" The next question may also be an English question about a different product.

[1357] Prompt Sentence Examples

[1358] "Please provide an English learning question where the user scans a QR code with a tablet and finds an image of a fruit. Please also include a simple hint in the question."

[1359] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1360] Step 1:

[1361] The user operates the terminal device, such as a tablet or smartphone, and taps the "Start" button to start the learning game. This operation causes the terminal device to send a "game start request" to the server.

[1362] Input: User taps

[1363] Output: Sending "Game Start Request"

[1364] Specific operation: When the user taps the "Start" button on the screen, the corresponding request generation function runs within the terminal means, and a request is sent to the server.

[1365] Step 2:

[1366] The server receives the "game start request" and retrieves the user's learning history from the database. The server analyzes the user's answer data and progress information to determine the user's level of understanding.

[1367] Input: "Game start request", learning history data

[1368] Output: Analysis results based on user's learning comprehension information

[1369] Specific operation: The server analyzes the request received, reads past learning history from the database based on the user ID, analyzes progress information, and evaluates the level of understanding.

[1370] Step 3:

[1371] The server generates optimal learning content based on the user's learning history and comprehension level. Specifically, it creates problem sets for words that the user has often made mistakes on in the past and new words that the user should learn.

[1372] Input: learning history data, comprehension information

[1373] Output: Generated learning content (problem set)

[1374] Specific operation: Based on the information obtained from the database, the content generation algorithm in the server works to automatically generate an appropriate problem set.

[1375] Step 4:

[1376] The server distributes the generated learning content to the terminal means, and at this time, data is transferred to the terminal through the network.

[1377] Input: Generated learning content

[1378] Output: Data delivered to the terminal

[1379] Specific operation: The server sends the generated content to the terminal means as an HTTP response, and the terminal means receives this data.

[1380] Step 5:

[1381] The terminal means displays the received learning content on the user's screen, and the user answers the questions by, for example, tapping on the correct image for the displayed word.

[1382] Input: Learning content, user actions

[1383] Output: User response data

[1384] Specific operation: The terminal means displays the content received from the server on the screen, and the user responds by touch operation.

[1385] Step 6:

[1386] The user's answer is transmitted from the terminal means to the server, which analyzes the answer, determines whether the answer is correct, and generates appropriate feedback.

[1387] Input: User response data

[1388] Output: Feedback message

[1389] Specific operation: The server analyzes the received answer and determines whether it is correct. If it is correct, it generates a feedback message such as "Great job!", if it is incorrect, it generates a feedback message such as "Try again!"

[1390] Step 7:

[1391] The server sends the generated feedback message to the terminal means, which displays it to the user, and the server records the user's response results in a database and prepares for the next session.

[1392] Input: Feedback message, response result data

[1393] Output: Show feedback, update progress record

[1394] Specific operation: The server sends the generated feedback message to the terminal means, which displays it on the screen, and at the same time, the server records the user's answer results in the database.

[1395] Step 8:

[1396] When a user scans a QR code installed in a physical store, a specific learning activity is unlocked. The server receives this request, generates the corresponding learning content, and delivers it to the terminal device.

[1397] Input: QR code scan request

[1398] Output: Unlocked learning content

[1399] Specific operation: A user scans a QR code in a store, and the terminal device sends the information to the server. The server receives this request, generates new learning content, and delivers it to the terminal device.

[1400] 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.

[1401] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. The user, a young child, operates a device such as a tablet or smartphone, making use of learning content provided by a server, and by combining this with an emotion engine, more effective and adaptable learning is possible.

[1402] System configuration

[1403] The system includes the following main components:

[1404] User: A young child operating a learning tool.

[1405] Device: A device operated by a user, such as a tablet or smartphone.

[1406] Server: A system that generates and distributes learning content, manages user progress, and recognizes emotions.

[1407] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[1408] Program processing explanation

[1409] User operations and device roles

[1410] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session.

[1411] Server initialization

[1412] The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether those answers were correct or incorrect.

[1413] The role of the emotional engine

[1414] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state, which is classified into multiple categories such as "happiness," "sadness," and "surprise."

[1415] Learning content generation and delivery

[1416] The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question where the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[1417] User responses and server analysis

[1418] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[1419] Adjusting feedback based on emotional state

[1420] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, the server will reinforce the message of encouragement to motivate the user to try again.

[1421] Track your progress and prepare for your next session

[1422] The server records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session, thereby providing optimal learning based on the user's level of understanding and emotional state.

[1423] Specific examples

[1424] For example, consider a scenario where a user selects an image of an apple in response to the word "apple." In this case, the user selects the correct image from multiple fruit images displayed on the tablet screen. If the user selects the correct image, the server generates a message saying "Great job!" and sends it to the device to display to the user. If the user selects an incorrect image, the server displays "Try again!" or a simple hint (e.g., "Red fruit"). If the emotion engine recognizes that the user's facial expression indicates disappointment, it adds an encouraging message.

[1425] This system allows young children to learn English in a fun and engaging way, and it also records their progress in real time and adjusts feedback based on their emotional state, making learning more effective.

[1426] The processing flow will be explained below.

[1427] Step 1:

[1428] User: Operate the tablet and tap the "Start" button.

[1429] Step 2:

[1430] Terminal: Detects the start operation and sends a "game start request" to the server.

[1431] Step 3:

[1432] Server: Analyzes the received "game start request" and retrieves the user's learning history from the database.

[1433] Step 4:

[1434] Server: Generates a set of questions appropriate to the user's level based on their learning history and current progress. For example, if the user learned "fruit words" in the previous lesson, the next set will also select related topics.

[1435] Step 5:

[1436] Server: Converts the generated problem set into an appropriate data format such as JSON and sends it to the terminal.

[1437] Step 6:

[1438] Terminal: Analyzes the received problem set and displays it in a user interface, for example, displaying the word "apple" and images of several fruits.

[1439] Step 7:

[1440] Emotion engine: The device's camera and microphone are used to collect the user's facial expressions and voice data in real time, and the emotional state is analyzed. The analysis results are stored internally on the device.

[1441] Step 8:

[1442] User: Select the correct image from the options displayed on the screen and tap it.

[1443] Step 9:

[1444] Terminal: Sends data such as the ID and coordinates of the image selected by the user to the server as "answer data."

[1445] Step 10:

[1446] Server: Analyzes the received "answer data" and determines whether it is correct or incorrect.

[1447] Step 11:

[1448] Emotion engine: Sends the analyzed emotional state to the server, for example, whether the user is disappointed or happy.

[1449] Step 12:

[1450] Server: Generates a positive feedback message such as "Great job!" if the answer is correct, or "Try again!" or a hint if the answer is incorrect.

[1451] Step 13:

[1452] Server: Adjust the content and tone of the feedback based on the received emotional state. For example, if the user is sad, add an additional encouraging message.

[1453] Step 14:

[1454] Server: Sends the generated feedback message to the terminal.

[1455] Step 15:

[1456] Terminal: Displays feedback messages received from the server to the user.

[1457] Step 16:

[1458] Server: Records the user's answers, emotional state, and progress information in a database. This includes correct / incorrect answers, response time, emotional state, etc.

[1459] Step 17:

[1460] Server: Analyzes the user's comprehension and emotional state for the next learning session and prepares data to adjust the difficulty of the next set of questions.

[1461] This allows content and feedback to be dynamically adjusted according to the child's learning progress and emotional state, ensuring continuous and effective English learning.

[1462] Example 2

[1463] 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."

[1464] While conventional English learning systems have the ability to generate learning content and provide feedback based on a user's learning history, they lack the ability to adjust the feedback and learning content based on the user's emotional state. As a result, they are unable to address the stress and loss of motivation that users experience while studying, making it difficult for them to continue studying effectively. The objective of this invention is to provide an English learning system that recognizes a user's emotional state in real time and provides appropriate feedback and learning content based on that.

[1465] 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.

[1466] In this invention, the server includes terminal means for accepting user operations, server means for receiving requests from the terminal means, content generation means for generating optimal study content based on the user's learning history and emotional state, content distribution means for distributing the study content to the terminal means, feedback generation means for analyzing the user's answers and generating feedback, emotion recognition means for recognizing the user's emotional state and adjusting the content and intensity of the feedback, and progress recording means for recording the user's study progress. This makes it possible to provide appropriate feedback and study content according to the user's emotional state, enabling effective and continuous learning.

[1467] The "terminal means for accepting user operations" refers to a device operated by a user, specifically a tablet or smartphone, which has an input device and an interface.

[1468] A "server means for receiving requests from a terminal means" is a computing device that is responsible for receiving and processing information or requests sent from a terminal means.

[1469] The "content generation means for generating optimal learning content based on the user's learning history and emotional state" is an algorithm or program that selects and generates appropriate learning materials based on the user's past learning history and emotional state recognized in real time.

[1470] The "content distribution means for distributing study content to terminal means" has the function of transferring the generated study content to terminal means and displaying it on a device operated by the user.

[1471] The "feedback generation means for analyzing the user's answers and generating feedback" has the function of analyzing the results of the user's answers to the study questions and generating an appropriate feedback message based on the results.

[1472] "Emotion recognition means that recognizes the user's emotional state and adjusts the content and intensity of feedback" refers to an algorithm or program that identifies the user's emotional state by analyzing their facial expressions and voice, and appropriately adjusts the content and intensity of feedback according to that state.

[1473] The "progress recording means for recording the user's learning progress" refers to a database or program for storing a record of the user's learning activities and managing the learning progress.

[1474] The present invention relates to an English learning system that recognizes the user's emotions to improve the learning experience. This system allows the user, a small child, to operate a device such as a tablet or smartphone, access learning content provided by a server, and, by combining this with an emotion engine, enables more effective and adaptive learning.

[1475] The system consists of the following main components:

[1476] User: A young child operating a learning tool.

[1477] Device: A device operated by a user, such as a tablet or smartphone.

[1478] Server: A central system that generates and distributes learning content, manages user progress, and recognizes emotions.

[1479] Emotion engine: A function that recognizes the user's emotional state by analyzing their facial expressions and voice.

[1480] The user operates the device to start the learning game. Specifically, the user taps the "Start" button displayed on the screen to begin the learning session. The server analyzes the received "game start request" and retrieves the user's learning history from the database. This learning history includes questions the user has answered in the past, as well as information on whether they answered correctly or incorrectly.

[1481] The emotion engine uses the device's camera and microphone to analyze the user's facial expressions and voice in real time to recognize their emotional state. These emotional states are classified into multiple categories, such as "happiness," "sadness," and "surprise." The server generates an optimal problem set based on the user's learning history and emotional state. For example, the problem set may include a question in which the user selects the correct image for the word "apple." The generated problem set is sent to the device, which then displays it on the user's screen.

[1482] The user answers the displayed question. For example, they tap on the image of an apple when they see the word "apple." The device sends this answer information to the server. The server analyzes the received answer data and determines whether it is correct. If the answer is correct, the server generates a positive feedback message such as "Great job!", and if the answer is incorrect, it generates a hint or instructions to try again.

[1483] The server adjusts the content and intensity of the feedback appropriately based on the emotional state obtained from the emotion engine. For example, if the user is feeling down, it will reinforce the encouraging messages to motivate them to try again. The server also records the user's answers and emotional state in a database. This record is used to manage the user's learning progress and adjust the difficulty of the questions presented in the next learning session. This allows the server to provide optimal learning based on the user's level of understanding and emotional state.

[1484] Below is an example of a prompt sentence to input to the generative AI model.

[1485] markdown

[1486] Describe a scenario in which a user is asked to select the correct image (an apple) for the word "apple."

[1487] Specific steps:

[1488] 1. A user sees the word "apple" displayed on a tablet screen and selects an apple from a selection of fruit images.

[1489] 2. The server verifies the answer and displays a positive message: "Great job!"

[1490] If you answer incorrectly, the server will display the message "Try again!" and a simple hint: "Red fruit."

[1491] The role of the Emotion Engine:

[1492] It recognizes the user's emotions and adds an encouraging message if, for example, they are feeling down.

[1493] The user's learning history and emotional state are recorded in a database and the questions presented in the next session are adjusted accordingly.

[1494] In this way, the present invention is a system that recognizes a user's emotions in real time and optimizes learning content and feedback, thereby enabling more effective and sustainable learning.

[1495] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1496] Step 1:

[1497] User operation start

[1498] The user taps the "Start" button displayed on the device screen. The user's tapping operation is the input, which sends a "game start request" to the server. The device detects this operation and sends the request to the server.

[1499] Step 2:

[1500] Server initialization

[1501] The server receives the "game start request" and retrieves past learning history from the database based on the user ID. The input data is the user ID and past session history, and the output is the user's learning history. The server uses this to evaluate the user's current learning situation.

[1502] Step 3:

[1503] Emotion Engine Operation

[1504] The emotion engine uses the device's camera and microphone to capture the user's facial expressions and voice data. The input data is image data from the camera and voice data from the microphone, and the output is the user's emotional state. The emotion engine analyzes this and classifies it into categories such as "joy," "sadness," and "surprise."

[1505] Step 4:

[1506] Generating learning content

[1507] The server generates an optimal problem set based on the user's learning history and emotional state. The input data are the learning history and emotional state, and the output is the generated problem set. For example, a problem set containing questions about selecting the correct image for the word "apple" is generated.

[1508] Step 5:

[1509] Delivery of learning content

[1510] The generated problem set is sent to the terminal, which displays it on the user's screen. The input data is the generated problem set, and the output is the displayed learning content.

[1511] Step 6:

[1512] User Answers

[1513] The user answers questions displayed on the screen by tapping on an image of an apple when asked the word "apple." The input data is the user's response, and the device sends this information to the server.

[1514] Step 7:

[1515] Server response analysis

[1516] The server analyzes the received user answer data and determines whether it is correct or incorrect. The input data is the user's answer, and the output is the result of the correct answer. If the answer is correct, the server generates a feedback message such as "Great job!" and sends it to the terminal. If the answer is incorrect, it generates a hint such as "Try again!" or "Red fruit."

[1517] Step 8:

[1518] Feedback adjustment based on emotional state

[1519] The server adjusts the content and intensity of the feedback based on the user's emotional state obtained from the emotion engine. The input data is the emotional state and the result of the correct / incorrect judgment, and the output is the adjusted feedback message. For example, if the user is feeling depressed, the message of encouragement is strengthened.

[1520] Step 9:

[1521] Record your learning progress

[1522] The server records the user's answers and emotional state in a database. The input data are the answers and emotional state, and the output is an updated learning history. Based on this, the server adjusts the content of the next learning session and provides optimal learning according to the user's level of understanding and emotional state.

[1523] (Application example 2)

[1524] 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."

[1525] Improving worker productivity and motivation in factories is essential for maintaining an efficient work environment. However, conventional systems lack the means to properly grasp workers' emotional states and adjust feedback accordingly, which means they are unable to provide adaptive support according to their emotions. Therefore, there is a need for a system that can improve work efficiency and motivation by recognizing workers' emotional states in real time and adjusting feedback based on that information.

[1526] 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.

[1527] In this invention, the server includes a terminal means for accepting user operations, a server means for receiving requests from the terminal means, and a feedback generation means for the server means to generate optimal feedback based on the user's work history. This makes it possible to recognize the emotional state of the worker in real time and adjust the feedback based on that, thereby maintaining the motivation of the worker and providing an efficient work environment.

[1528] The "terminal means" is a device that accepts user operations, and includes, for example, a tablet or a smartphone.

[1529] The "server means" is a server system having the function of receiving requests from the terminal means and performing various processes.

[1530] The "content generation means" has the function of generating optimal learning content and feedback based on the user's learning history and work history.

[1531] The "content distribution means" has the function of distributing the generated learning content and feedback to the terminal means.

[1532] The "feedback generation means" has a function of analyzing the user's answers and operations and generating feedback based on them.

[1533] The "progress recording means" has the function of recording the user's learning progress and work progress.

[1534] The "emotion recognition means" has a function of recognizing the user's emotional state in real time using the terminal means.

[1535] The "feedback adjustment means" has a function of adjusting the content of the feedback based on the emotional state acquired from the emotion recognition means.

[1536] Overall system picture

[1537] The present invention is a system for improving the productivity and motivation of workers in a factory, and includes a terminal means, a server means, an emotion recognition means, a feedback generation means, and a feedback adjustment means.

[1538] Hardware and Software Configuration

[1539] Hardware

[1540] Terminal means: A device operated by a user, i.e., a worker, and includes small computers and smart devices. Examples include tablets and smartphones.

[1541] Server means: A server that manages and processes work history and feedback information.

[1542] Camera: Its role is to acquire image data to recognize the emotions of workers.

[1543] Robots: Provide feedback to workers and assist them in their work.

[1544] software

[1545] Content generation means: A program that generates optimal feedback and work instructions based on work history.

[1546] Content distribution means: A program that distributes generated feedback and work instructions to terminal means.

[1547] Feedback generation means: A program that generates appropriate feedback based on the user's operation and emotional state. Libraries used include DeepFace for emotion recognition and an SDK for robot operation (e.g., SoftBank Robotics' SDK).

[1548] Progress recording means: A program that records and manages work progress in a database.

[1549] Processing flow

[1550] The terminal means accepts operations by the worker. When the user starts operating the terminal, the information is sent to the server means, which then obtains the user's progress from the work history database and generates optimal feedback. At this time, the server means uses a camera to recognize the user's emotions in real time and uses the results to adjust the content of the feedback.

[1551] Specific examples

[1552] For example, a worker operates a tablet and the robot provides feedback such as "Keep working, you're almost there!" If the robot detects that the user looks discouraged from the camera's facial expression, it will provide an additional encouraging message (e.g., "You can do it! Do your best!").

[1553] Prompt Sentence Examples

[1554] The following is an example of a prompt provided to the generative AI model for emotion recognition in this system:

[1555] Provide a face image as input and analyze the dominant emotion of this face. The analysis result will include emotion categories such as "happy", "sad", "angry", and "neutral".

[1556] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1557] Step 1:

[1558] The user operates the terminal means to start a work session.

[1559] Input: User action (e.g. tapping the Start button)

[1560] Data processing: Receiving and recording operation events

[1561] Output: Sends a start work request to the server

[1562] Specific operation: The user taps the "Start" button displayed on the screen of the terminal means, and the operation event is sent by the terminal to the server.

[1563] Step 2:

[1564] The server receives the work start request and retrieves the user's work history from the database.

[1565] Input: Work start request, user ID

[1566] Data processing: Query the user's progress from the work history database

[1567] Output: User's work history data

[1568] Specific operation: When the server receives a request to start a task, it retrieves the past task history from the database based on the user ID.

[1569] Step 3:

[1570] The server uses a camera to recognize the user's emotions in real time.

[1571] Input: Camera video data

[1572] Data processing: Sentiment analysis using the DeepFace library

[1573] Output: User's emotional state (e.g. "happy", "sad", "angry", "neutral")

[1574] Specific operation: The server acquires video from the device's camera, analyzes the video using the DeepFace library, and recognizes the user's main emotions.

[1575] Step 4:

[1576] The server generates optimal feedback based on the user's task history and emotional state.

[1577] Input: Work history data, emotional state data

[1578] Data processing: Applying feedback generation algorithms based on work history and emotions

[1579] Output: Generated feedback

[1580] Specific operation: The server analyzes the user's work history and emotional state, and generates an adapted feedback message.

[1581] Step 5:

[1582] The server distributes the generated feedback to the terminal means.

[1583] Input: Feedback

[1584] Data processing: Sending feedback messages to devices

[1585] Output: Feedback presented to the user on their device, either visually or audibly.

[1586] Specific operation: The server sends the generated feedback message to the terminal means and transmits it to the user through the screen or speaker of the terminal means.

[1587] Step 6:

[1588] Evaluate the effectiveness of the feedback based on the user's emotional state and adjust as needed.

[1589] Input: Additional emotional state data of the user

[1590] Data processing: Reassessing and adjusting emotional states after feedback

[1591] Output: Adjusted feedback content

[1592] Specific behavior: After the user receives feedback, the camera footage is analyzed again, and if the emotional state has not improved, further encouragement or adaptive feedback is provided.

[1593] 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.

[1594] 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.

[1595] 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.

[1596] 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.

[1597] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1598] 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.

[1599] 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).

[1600] 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.

[1601] 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."

[1602] 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.

[1603] 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).

[1604] 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.

[1605] 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.

[1606] 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.

[1607] 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.

[1608] 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.

[1609] 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.

[1610] 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.

[1611] 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.

[1612] 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.

[1613] 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.

[1614] The following is further disclosed regarding the above embodiment.

[1615] (Claim 1)

[1616] terminal means for accepting user operations;

[1617] server means for receiving requests from terminal means;

[1618] a content generation means for generating optimal study content based on the user's study history;

[1619] a content distribution means for distributing learning content to the terminal means;

[1620] a feedback generating means for analyzing the user's response and generating feedback;

[1621] a progress recording means for recording the user's learning progress;

[1622] A system including:

[1623] (Claim 2)

[1624] 2. The system according to claim 1, wherein the content generating means comprises means for dynamically changing the difficulty level of the problem set based on the user's learning history.

[1625] (Claim 3)

[1626] 2. The system of claim 1, wherein the feedback generating means comprises means for generating positive feedback when the user answers correctly and a hint or instructions to try again when the user answers incorrectly.

[1627] "Example 1"

[1628] (Claim 1)

[1629] terminal means for accepting user operations;

[1630] server means for receiving requests from terminal means;

[1631] a content generation means for generating optimal study content based on the user's study history;

[1632] a content distribution means for distributing learning content to the terminal means;

[1633] a feedback generating means for analyzing the user's response and generating feedback;

[1634] a progress recording means for recording the user's learning progress;

[1635] a user operation detection means for detecting a user operation by a terminal means, the user transmitting a request to start a learning session;

[1636] a progress recording means for storing progress data in a database and using the data to generate questions for the next learning session;

[1637] A system including:

[1638] (Claim 2)

[1639] 2. The system according to claim 1, wherein the content generating means comprises means for dynamically changing the difficulty level of the problem set based on the user's learning history.

[1640] (Claim 3)

[1641] 2. The system of claim 1, wherein the feedback generating means comprises means for generating positive feedback when the user answers correctly and a hint or instructions to try again when the user answers incorrectly.

[1642] "Application Example 1"

[1643] (Claim 1)

[1644] terminal means for accepting user operations;

[1645] server means for receiving requests from terminal means;

[1646] a content generation means for generating optimal study content based on the user's study history;

[1647] a content distribution means for distributing learning content to the terminal means;

[1648] a feedback generating means for analyzing the user's response and generating feedback;

[1649] a progress recording means for recording the user's learning progress;

[1650] a means to provide interactive learning activities using codes placed in physical stores;

[1651] A system including:

[1652] (Claim 2)

[1653] 2. The system according to claim 1, wherein the content generating means comprises means for dynamically changing the difficulty level of the problem set based on the user's learning history.

[1654] (Claim 3)

[1655] 2. The system of claim 1, wherein the feedback generating means comprises means for generating positive feedback when the user answers correctly and a hint or instructions to try again when the user answers incorrectly.

[1656] "Example 2: Combining Emotion Engines"

[1657] (Claim 1)

[1658] terminal means for accepting user operations;

[1659] server means for receiving requests from terminal means;

[1660] a content generation means for generating optimal study content based on the user's study history and emotional state;

[1661] a content distribution means for distributing learning content to the terminal means;

[1662] a feedback generating means for analyzing the user's response and generating feedback;

[1663] an emotion recognition means for recognizing the user's emotional state and adjusting the feedback content and intensity;

[1664] a progress recording means for recording the user's learning progress;

[1665] A system including:

[1666] (Claim 2)

[1667] 10. The system of claim 1, wherein the content generating means comprises means for dynamically changing the difficulty of the problem set based on the user's learning history and emotional state.

[1668] (Claim 3)

[1669] 2. The system of claim 1, wherein the feedback generating means includes means for generating positive feedback when the user answers correctly and a hint or instructions to try again when the user answers incorrectly, and means for adjusting the feedback content based on the user's emotional state.

[1670] "Application example 2 when combining emotion engines"

[1671] (Claim 1)

[1672] a terminal means for accepting user operations;

[1673] server means for receiving requests from terminal means;

[1674] a content generation means for generating optimal study content based on the user's study history;

[1675] a content distribution means for distributing learning content to the terminal means;

[1676] a feedback generating means for analyzing the user's response and generating feedback;

[1677] a progress recording means for recording the user's learning progress;

[1678] emotion recognition means for recognizing an emotional state of a user in real time by the terminal means;

[1679] a feedback adjusting means for adjusting the content of the feedback based on the emotional state acquired from the emotion recognition means;

[1680] A system including:

[1681] (Claim 2)

[1682] 2. The system according to claim 1, wherein the content generating means comprises means for dynamically changing the difficulty level of the problem set based on the user's learning history.

[1683] (Claim 3)

[1684] 2. The system of claim 1, wherein the feedback generating means comprises means for generating positive feedback when the user answers correctly and a hint or instructions to try again when the user answers incorrectly, and wherein the feedback adjusting means comprises means for adjusting the intensity of the feedback based on the user's emotional state. [Explanation of symbols]

[1685] 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. terminal means for accepting user operations; server means for receiving requests from terminal means; a content generation means for generating optimal study content based on the user's study history; a content distribution means for distributing learning content to the terminal means; a feedback generating means for analyzing the user's response and generating feedback; a progress recording means for recording the user's learning progress; A system including:

2. 2. The system according to claim 1, wherein the content generating means comprises means for dynamically changing the difficulty level of the problem set based on the user's learning history.

3. 2. The system of claim 1, wherein the feedback generating means comprises means for generating positive feedback when the user answers correctly and a hint or instruction to try again when the user answers incorrectly.

Images