System

Abstract

A system is provided.SOLUTION: A system comprising means for a user to select a language to learn, means for a server to generate learning content in the language of interest using a generated AI model, means for the server to provide the generated learning content to the user, means for the server to track the user's learning progress and provide individual feedback, and means for the server to obtain and provide cultural background information related to the language selected by 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] In recent years, while there has been an emphasis on the preservation of unique languages ​​and cultures that have been passed down orally, these languages ​​are rapidly disappearing. As a result, their cultures and histories are in danger of being lost along with the languages. To solve this problem, new learning methods and systems are needed to effectively and smoothly learn these languages. However, existing learning methods are specialized for general language learning, and there is a problem in that they lack means for comprehensively learning disappearing languages ​​and their cultural backgrounds. [Means for solving the problem]

[0005] The present invention proposes a means for solving the aforementioned problems by providing a system in which a user selects a language they wish to learn and a server generates and provides learning content for that language using a generative AI model. Specifically, the system provides an environment for effectively learning a language that is being lost by including a means for the user to select the language they wish to learn, a means for the server to generate language learning content using a generative AI model, a means for providing the generated content to the user, a means for tracking the user's learning progress and providing individualized feedback, and a means for acquiring and providing cultural background information to the user. The system also further improves the user's learning experience by including a user account creation and authentication process, and a means for providing practice content corresponding to the user's selected practice module and evaluation feedback.

[0006] "User" refers to an individual or organization that uses the system, selects the language they wish to learn, and receives learning content.

[0007] "Server" refers to a computer system that processes data and generates and provides learning content using generative AI models in response to user requests.

[0008] "Generative AI models" refer to algorithms and machine learning models that use artificial intelligence to automatically generate learning content for specific languages.

[0009] "Learning content" refers to text, audio, video, and other learning materials related to the user's language of choice, created by a generative AI model.

[0010] "Cultural background information" is information about history, folklore, important events, etc. related to the selected language that is provided to users as part of their language learning.

[0011] "Study progress" refers to data that indicates the user's learning status and progress as they progress through language learning.

[0012] "Feedback" refers to evaluations and advice for improvement provided by the server based on the user's learning progress and practice results.

[0013] A "practice module" is a unit that includes practice content such as quizzes, audio practice, and translation practice, and is used by users to confirm and review what they have learned.

[0014] The "authentication process" refers to the procedure by which the server verifies the user's ID and grants access using the authentication information (email address and password) entered by the user when logging in. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

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

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

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

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0030] 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.

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

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. A specific embodiment of this system will be described below.

[0037] First, a user registers using their own device. The device receives the user's name, email address, password, and the language they want to learn, and sends it to the server. The server receives this information and stores it in a database. If registration is successful, the server sends the user a confirmation email. The user clicks the link in this confirmation email to activate their account, completing the registration process.

[0038] Next, the user enters their email address and password on the login screen and sends an authentication request to the server, which checks the database and, if successful, starts a login session, allowing the user to access the system.

[0039] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, and the server uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, etc., and the server provides it to the device. The device displays the learning content to the user, who then uses it to advance their language learning.

[0040] Furthermore, if the user wants to know about the cultural background related to the language as they learn it, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide it to the user through the device.

[0041] The system also constantly tracks the user's learning progress and provides personalized feedback. For example, the server analyzes whether the user has mastered a certain language skill or where they are struggling, and provides appropriate advice on improvement and additional learning resources.

[0042] When a user uses the practice module, the user similarly sends information to the server via the terminal, and the server provides the user with practice content such as quizzes, audio practice, translation practice, etc. When the user performs the practice and sends the results to the server, the server evaluates the practice results, generates feedback, and provides it to the user via the terminal.

[0043] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[0044] The processing flow will be explained below.

[0045] Step 1:

[0046] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[0047] Step 2:

[0048] The terminal acquires the input information and sends it to the server.

[0049] Step 3:

[0050] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[0051] Step 4:

[0052] The user receives a confirmation email and clicks the link in the email to activate their account.

[0053] Step 5:

[0054] The user enters their email address and password on the login page and submits the login request.

[0055] Step 6:

[0056] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[0057] Step 7:

[0058] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[0059] Step 8:

[0060] The terminal transmits the selected language information to the server.

[0061] Step 9:

[0062] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[0063] Step 10:

[0064] The server transmits the generated learning content to the terminal.

[0065] Step 11:

[0066] The terminal displays the received study content to the user, and the user begins studying.

[0067] Step 12:

[0068] While studying, users search for specific cultural background information (e.g., the history of Ainu culture).

[0069] Step 13:

[0070] The terminal sends the user's search query to the server.

[0071] Step 14:

[0072] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[0073] Step 15:

[0074] The terminal displays the received cultural background information to the user.

[0075] Step 16:

[0076] The user selects a practice module such as a quiz, audio practice, or translation practice.

[0077] Step 17:

[0078] The terminal transmits the selected practice module information to the server.

[0079] Step 18:

[0080] The server generates practice content according to the practice module and transmits it to the terminal.

[0081] Step 19:

[0082] The terminal displays the received practice content to the user, and the user starts practicing.

[0083] Step 20:

[0084] The user sends the practice results to the server.

[0085] Step 21:

[0086] The server evaluates the practice results and generates feedback.

[0087] Step 22:

[0088] The server transmits the generated feedback to the terminal.

[0089] Step 23:

[0090] The terminal displays the received feedback to the user.

[0091] This allows users to effectively learn a language that is being lost.

[0092] Example 1

[0093] 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."

[0094] To build a system for effectively learning a language that is being lost, it is necessary to provide an environment where users can easily start learning the language, as well as functions for providing individual feedback and cultural background information. Conventional language learning systems have limited functionality, making it difficult for learners to progress efficiently. Furthermore, implementing a wide range of functions, such as creating and managing user accounts, tracking learning progress, and providing practice content, is complicated.

[0095] 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.

[0096] In this invention, the server includes: means for a user to select a language they wish to learn; means for the server to generate learning content for the language using a generative AI model; means for the server to provide the generated learning content to the user; means for tracking the user's learning progress and providing individual feedback; means for acquiring and providing cultural background information related to the language selected by the user; means for receiving information required for the user to register and storing it in a database; means for the server to generate and send a confirmation email to the user; and means for the user to activate their account by clicking a link in the email. This enables a series of processes, from starting language learning to tracking progress, providing feedback, and learning cultural background, to be carried out in an integrated and efficient manner.

[0097] "User" refers to an individual or organization that uses the System to learn a language.

[0098] "Server" refers to a computer system that processes data and provides learning content and feedback in response to user requests.

[0099] "Generative AI model" refers to artificial intelligence technology that generates learning content based on specific data inputs.

[0100] "Learning Content" refers to materials such as text, audio, and video provided to support language learning.

[0101] "New registration" refers to the process by which a user registers the account information required to access the system for the first time.

[0102] "Verification Email" means the email containing the link a User needs to complete the registration process.

[0103] A "login session" refers to a series of access privilege management states that are initiated when a user accesses a system.

[0104] "Dashboard" refers to the interface that is displayed when a user accesses the system and is used to manage various operations and information.

[0105] "Cultural background information" refers to information about history, folklore, important events, customs, etc. related to the language being studied.

[0106] "Progress information" refers to data relating to progress and achievement recorded as a user progresses in language learning.

[0107] "Feedback" refers to evaluations and advice provided based on the user's learning progress.

[0108] "Practice Module" refers to practical learning features such as quizzes, audio practice, and translation practice provided for language learning.

[0109] The present invention is a system for effectively learning a language that is being lost, and is realized through cooperation between users, terminals, and a server. Specific embodiments will be described below.

[0110] First, a user registers using their own device. The device receives the user's name, email address, password, and desired language and sends this information to the server. The server receives this information and stores it in a database (e.g., MySQL (registered trademark) or PostgreSQL). Once the information is saved, the server generates a confirmation email and sends it to the user via the SMTP server. The user clicks the link in the confirmation email to activate their account, completing the registration process.

[0111] Next, the user enters their email address and password on the login screen and sends an authentication request to the server via their device. The server authenticates them against a database. If authentication is successful, the server starts a login session and the user is granted access to the dashboard.

[0112] After logging in, the user selects the language they want to learn on the dashboard. For example, if the user wants to learn "Ainu," the device sends that selection information to the server. The server then uses a generative AI model (e.g., GPT-4 (registered trademark)) to generate learning content for "Ainu." An example of a prompt used at this time is as follows:

[0113] Example prompt: "The user wants to learn Ainu. Please generate text, audio, and video learning content for beginners."

[0114] The generated learning content includes text, audio, video, etc., and is sent from the server to the device, which then displays the learning content to the user, allowing the user to progress with their language learning.

[0115] Furthermore, as the user progresses with their studies, progress information is periodically sent from the device to the server. The server receives this progress information and analyzes the user's learning progress and achievement. Based on the results of this analysis, the server generates personalized feedback and suggests appropriate advice for improvement and additional learning resources. This feedback is also provided to the user via the device.

[0116] If the user also wants to know about the cultural background related to the language selected, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide them to the user through the device.

[0117] Furthermore, when a user uses a practice module (such as a quiz, audio practice, or translation practice), the user sends information to the server via the device, and the server generates practice content. When the user performs the practice and sends the results to the server, the server evaluates the practice results and generates feedback to provide to the user via the device.

[0118] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[0119] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0120] Step 1:

[0121] A user visits the sign-up page.

[0122] A user enters the system's URL using a web browser to access the new registration page. Input: User's access information. Output: Display of the registration page.

[0123] Step 2:

[0124] The user enters the required information.

[0125] The user enters their name, email address, password, and the language they want to learn. Input: User information. Output: Registration information entered by the user.

[0126] Step 3:

[0127] The terminal sends the input information to the server.

[0128] The terminal sends the information entered by the user to the server via a POST request. Input: Registration information entered by the user. Output: Request to the server.

[0129] Step 4:

[0130] The server receives the information and stores it in a database.

[0131] The server receives the information, validates it, and then stores it in a database such as MySQL or PostgreSQL. Input: Submitted user information. Output: User information stored in the database.

[0132] Step 5:

[0133] The server generates a confirmation email and sends it to the user.

[0134] The server generates and sends a confirmation email using an SMTP server. Input: User information (email address). Output: The confirmation email sent.

[0135] Step 6:

[0136] The user clicks on the link in the confirmation email.

[0137] The user clicks on the link in the email to access the page where they can activate their account. Input: Link in the confirmation email. Output: Activation page displayed.

[0138] Step 7:

[0139] The server activates the account.

[0140] After the server verifies the link, it updates the user's account to "enabled." Input: Link access information. Output: Activated account information.

[0141] Step 8:

[0142] The user visits the login page.

[0143] The user accesses the system's login page. Input: User's access information. Output: Login page is displayed.

[0144] Step 9:

[0145] The user enters their email address and password.

[0146] The user enters their email address and password. Input: Login information. Output: Credentials entered by the user.

[0147] Step 10:

[0148] The terminal sends the input information to the server.

[0149] The terminal sends the entered information to the server via a POST request. Input: Authentication information. Output: Request to the server.

[0150] Step 11:

[0151] The server authenticates against a database.

[0152] The server checks the entered information against the database to verify that it is correct. Input: Authentication information. Output: Authentication result.

[0153] Step 12:

[0154] The server returns the authentication result.

[0155] If authentication is successful, the server starts a login session and allows the user to access the dashboard. Input: Authentication result. Output: Dashboard access permission.

[0156] Step 13:

[0157] A user visits a dashboard.

[0158] Authenticated users log in to the dashboard and configure various settings. Input: Dashboard access. Output: Dashboard display.

[0159] Step 14:

[0160] The user selects the language they want to learn.

[0161] The user selects the language they want to learn, such as "Ainu." Input: Selection of the language they want to learn. Output: Selection information.

[0162] Step 15:

[0163] The terminal transmits the selection information to the server.

[0164] The terminal sends the selected information to the server via a POST request. Input: Selected information. Output: Request to the server.

[0165] Step 16:

[0166] The server sends a prompt to the generative AI model.

[0167] The server sends a prompt to the generative AI model (e.g., GPT-4) requesting it to generate learning content for "Ainu." Input: Selection information and prompt. Output: Learning content generation request.

[0168] Step 17:

[0169] Generative AI models generate learning content.

[0170] The generative AI model generates appropriate learning content based on the prompt. Input: Prompt. Output: Generated learning content.

[0171] Step 18:

[0172] The server transmits the generated learning content to the terminal.

[0173] The server sends the generated learning content, such as text, audio, and video, back to the device. Input: Generated learning content. Output: Transmission to the device.

[0174] Step 19:

[0175] The terminal displays the learning content to the user.

[0176] The terminal displays the received learning content to the user. Input: Learning content. Output: Display to user.

[0177] Step 20:

[0178] Update progress information as users learn.

[0179] As users progress through their studies, progress information is automatically sent from their devices to the server. Input: User's study progress. Output: Progress information sent to the server.

[0180] Step 21:

[0181] The server analyzes the progress data and generates feedback.

[0182] The server analyzes the submitted progress data and generates feedback based on the learning progress and achievement level. Input: Progress data. Output: Feedback generation.

[0183] Step 22:

[0184] The server generates feedback and sends it to the device.

[0185] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[0186] Step 23:

[0187] The device displays the feedback to the user.

[0188] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[0189] Step 24:

[0190] The user requests cultural context information.

[0191] When a user wants to know cultural background information, he enters a query. Input: Request for cultural background information. Output: Query information.

[0192] Step 25:

[0193] The device sends a query to the server.

[0194] The terminal sends the user's query to the server. Input: Query information. Output: Request to the server.

[0195] Step 26:

[0196] The server retrieves the relevant information from the database.

[0197] The server retrieves relevant historical information, folklore, important events, etc. from the database. Input: Query information. Output: Related information.

[0198] Step 27:

[0199] The server sends the relevant information to the terminal.

[0200] The server sends the relevant information to the terminal. Input: relevant information. Output: transmission to the terminal.

[0201] Step 28:

[0202] The terminal displays the relevant information to the user.

[0203] The terminal displays the received relevant information to the user. Input: relevant information. Output: display to the user.

[0204] Step 29:

[0205] The user selects a practice module.

[0206] The user selects a practice module such as quiz, audio practice, translation practice, etc. Input: Selection of practice module. Output: Selection information.

[0207] Step 30:

[0208] The terminal transmits the selection information to the server.

[0209] The terminal sends the selection information to the server. Input: Selection information. Output: Request to the server.

[0210] Step 31:

[0211] The server generates the practice content.

[0212] The server generates appropriate practice content based on the selection. Input: Selection information. Output: Generated practice content.

[0213] Step 32:

[0214] The server transmits the practice content to the terminal.

[0215] The server sends the generated practice content to the terminal. Input: Generated practice content. Output: Transmission to the terminal.

[0216] Step 33:

[0217] The terminal displays the practice content to the user.

[0218] The device displays the practice content to the user. Input: Practice content. Output: Display to user.

[0219] Step 34:

[0220] The user performs the exercise.

[0221] The user performs the presented practice content. Input: Presented practice content. Output: User's practice results.

[0222] Step 35:

[0223] The device sends the practice results to the server.

[0224] The device sends the user's practice results to the server. Input: User's practice results. Output: Transmission to the server.

[0225] Step 36:

[0226] The server evaluates the practice results and generates feedback.

[0227] The server evaluates the received practice results and generates feedback based on them. Input: Practice results. Output: Generated feedback.

[0228] Step 37:

[0229] The server generates feedback and sends it to the device.

[0230] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[0231] Step 38:

[0232] The device displays the feedback to the user.

[0233] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[0234] (Application example 1)

[0235] 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."

[0236] Conventional language learning systems have the problem that it is difficult for users to take advantage of language learning experiences in a specific location, and they also have the problem that the effectiveness of learning is limited because they do not provide localized content or feedback according to the user's learning progress.

[0237] 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.

[0238] In this invention, the server includes a means for allowing a user to access content and information for language learning in a specific location, a means for providing learning content localized according to the user's location, a means for the user to check their learning progress in the specific location and receive appropriate feedback, and a means for providing content according to local events based on the results of the user's practice in the specific location, thereby enabling users to learn languages ​​more effectively and in accordance with the context in a physical store or other specific location.

[0239] - "User" means a person who uses the system to learn a language.

[0240] "Language to learn" refers to the specific language that the user wishes to learn through the system.

[0241] "Means" refers to the methods or devices that a user uses to achieve a goal.

[0242] A "server" is a computer system that generates and distributes learning content and stores and processes data.

[0243] A "generative AI model" is an artificial intelligence algorithm that automatically generates learning content based on the language of your choice.

[0244] "Learning content" refers to learning materials such as text, audio, and video provided to support language learning.

[0245] "Personalized feedback" refers to specific advice and assessments provided based on a user's learning progress and performance.

[0246] "Cultural background information" refers to data about the culture, history, folklore, etc. of the selected language.

[0247] "Specific location" refers to a brick-and-mortar store or other physical learning environment.

[0248] "Localized learning content" refers to learning materials that are customized for the location or region from which the user accesses them.

[0249] "User Account" means a unique identification created by a User to access the System.

[0250] "Authentication" refers to the verification procedures required for a user to log into a system.

[0251] A "practice module" is a set of practice materials or exercises designed to improve a particular language skill selected by the user.

[0252] A "local event" is a learning activity or event that a user conducts in a specific location.

[0253] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. Specifically, users use their smartphones to register and select the languages ​​they want to learn.

[0254] The server uses a generative AI model to generate learning content for the selected language and provides it to the user. The learning content includes text, audio, and video, which the user can view on their device to progress through the learning process. The server tracks learning progress and provides personalized feedback. It also provides cultural background information related to the language the user is learning.

[0255] Additionally, the system includes functionality to support learning in specific locations, such as brick-and-mortar stores. When users are in a specific location, they can access localized learning content tailored to that location. This enhances the brick-and-mortar learning experience and provides real-life knowledge and experiences related to the learning content.

[0256] The system's programs are built using frameworks such as Python and Flask, and databases such as SQLite are used to store data. The system's hardware includes smartphones and Wi-Fi access points.

[0257] For example, if a user wants to learn the Ainu language at a physical store, they can scan a QR code (registered trademark) to access the store and receive specific learning content related to Ainu culture. Additionally, cultural events and special experience content will also be provided based on the knowledge and skills the user has gained while studying at a specific location.

[0258] Below are some example input prompts for a generative AI model:

[0259] "Generate learning content about cultural background, history, and important events based on the user's language of choice. For example, create text, audio, and video that includes the following information: 'History of the Ainu Language,' 'Ainu Folklore,' and 'Modern Ainu Culture.'"

[0260] In this way, the system of the present invention provides localized content when users study in a specific location, enabling more effective language learning and cultural understanding.

[0261] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0262] Step 1:

[0263] A user accesses the system using a smartphone and enters their name, email address, password, and desired language into the new registration form. The entered data is sent from the device to the server. The server receives this information and stores it in an SQLite database. The server then sends a confirmation email to the user, and the user clicks on the link in the email to activate their account. This registers the user's basic information in the system. The input data is the name, email address, password, and desired language, and the output data is the activation of the user account.

[0264] Step 2:

[0265] The user enters an email address and password on the login screen and sends an authentication request. The device sends this information to the server, which checks the authentication information against a database. If authentication is successful, the server starts a login session and provides the user with a dashboard screen. The input data is the email address and password, and the output data is the start of the login session.

[0266] Step 3:

[0267] The user selects the language they want to learn from the dashboard screen. The selection information is sent from the device to the server. The server uses a generative AI model to generate learning content based on that language. The content includes text, audio, video, etc. The generated learning content is sent from the server to the device and provided to the user. The input data is the selected language, and the output data is the generated learning content.

[0268] Step 4:

[0269] The user uses the learning content and sends their progress to the server. The server receives the user's progress data and stores it in a database. The server then analyzes the user's progress, generates individual feedback, and sends it to the device. The input data is the user's learning progress, and the output data is individual feedback.

[0270] Step 5:

[0271] When a user wants to know the cultural background information related to the language they are learning, they send a query from their device to the server. The server retrieves the relevant cultural background information from the database and provides it to the user. The input data is the cultural background information query, and the output data is the relevant cultural background information.

[0272] Step 6:

[0273] When a user studies a language at a physical store, they use their smartphone to scan a QR code placed at a specific location. The scanned information is sent from the device to a server, which retrieves the specific location information. The server then generates localized learning content based on that information and provides it to the user. The input data is the scanned QR code information, and the output data is the localized learning content.

[0274] Step 7:

[0275] The user sends the results of practice performed at a specific location from the terminal to a server. The server receives the practice results and generates an evaluation and feedback. In addition, content related to the local event based on the results is also generated and provided to the user. The input data are the practice results, and the output data are the evaluation, feedback, and content related to the local event.

[0276] 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.

[0277] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[0278] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[0279] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[0280] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[0281] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[0282] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[0283] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[0284] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[0285] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[0286] The processing flow will be explained below.

[0287] Step 1:

[0288] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[0289] Step 2:

[0290] The terminal acquires the input information and sends it to the server.

[0291] Step 3:

[0292] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[0293] Step 4:

[0294] The user receives a confirmation email and clicks the link in the email to activate their account.

[0295] Step 5:

[0296] The user enters their email address and password on the login page and submits the login request.

[0297] Step 6:

[0298] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[0299] Step 7:

[0300] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[0301] Step 8:

[0302] The terminal transmits the selected language information to the server.

[0303] Step 9:

[0304] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[0305] Step 10:

[0306] The server transmits the generated learning content to the terminal.

[0307] Step 11:

[0308] The terminal displays the received study content to the user, and the user begins studying.

[0309] Step 12:

[0310] The device collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[0311] Step 13:

[0312] The server uses an emotion engine to analyze the user's emotion information and grasp the user's emotional state in real time.

[0313] Step 14:

[0314] The server adaptively changes learning content and feedback messages based on the user's emotional state. For example, if the user is experiencing difficulty, it adjusts the difficulty level or sends encouraging messages.

[0315] Step 15:

[0316] When a user wants to search for cultural background information while studying, they enter a search query.

[0317] Step 16:

[0318] The terminal sends the user's search query to the server.

[0319] Step 17:

[0320] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[0321] Step 18:

[0322] The device displays the received cultural background information to the user, and the emotion engine analyzes the user's emotions and provides additional information according to the user's level of interest and emotion.

[0323] Step 19:

[0324] The user selects a practice module.

[0325] Step 20:

[0326] The terminal transmits the selected practice module information to the server.

[0327] Step 21:

[0328] The server generates practice content according to the practice module and transmits it to the terminal.

[0329] Step 22:

[0330] The terminal displays the received practice content to the user, and the user starts practicing.

[0331] Step 23:

[0332] The user sends the results of their practice to the server. At the same time, the device collects the user's facial expressions and voice and sends them to the emotion engine.

[0333] Step 24:

[0334] The server evaluates the practice results and emotional data and generates feedback.

[0335] Step 25:

[0336] The server transmits the generated feedback to the terminal.

[0337] Step 26:

[0338] The device displays the received feedback to the user, which includes advice and suggestions for improvement based on the analysis results of the emotion engine.

[0339] This allows users to have an optimal learning experience that is tailored to their emotional state, enabling them to effectively acquire a language that is being lost and gain a deeper understanding of its cultural background.

[0340] Example 2

[0341] 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."

[0342] Conventional language learning systems provide learning content without considering the user's emotional state, which has the problem of not improving the learning effect and user experience. In particular, the lack of appropriate feedback and information when users are experiencing difficulty or are interested in the subject can lead to a decrease in users' motivation to learn.

[0343] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the terminal to collect user emotional data using a camera and microphone, a means for the server to analyze the collected emotional data using an emotion engine and adaptively change the learning content and feedback based on the user's emotional state, and a means for tracking the user's learning progress and providing individualized feedback. This enables adaptive learning support that takes the user's emotional state into consideration.

[0344] A "user" is a person who uses the system to select the language they want to learn and receives learning content.

[0345] A "server" is a device that processes information sent by users, generates learning content and feedback using generative AI models and emotion engines, and provides them to terminals.

[0346] A "terminal" is a device used by a user that transmits input from the user to a server and displays learning content and feedback sent from the server. It also has the function of collecting emotional data using a camera and microphone.

[0347] A "generative AI model" is an artificial intelligence model used by the server, and is a technology that generates learning content (text, audio, video, etc.) related to the language the user wants to learn.

[0348] The "emotion engine" is an analytical engine used by the server, which analyzes emotional data such as the user's facial expressions and tone of voice collected from the terminal to determine the user's emotional state.

[0349] "Learning Content" means educational material in the form of text, audio, video, etc., generated by a generative AI model and related to the language the user wishes to learn.

[0350] "Feedback" refers to adaptive messages and suggestions generated by the server based on the user's learning progress and emotional state, and is provided to support the user's learning.

[0351] "Emotional data" refers to information collected through the device's camera and microphone, such as the user's facial expressions and tone of voice, and is data that indicates the user's emotional state.

[0352] "Adaptive change" means that the content and difficulty of the learning content and feedback are dynamically adjusted based on the user's emotional state analyzed by the server using an emotion engine.

[0353] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[0354] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[0355] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[0356] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[0357] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[0358] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[0359] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[0360] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[0361] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[0362] Prompt Sentence Examples

[0363] "Generate detailed learning content about the Ainu language and send simple questions and encouraging messages to frustrated users."

[0364] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0365] Step 1:

[0366] (input)

[0367] Users enter their name, email address, password, and the language they want to learn into the terminal.

[0368] (process)

[0369] The terminal packages the input information in JSON format and sends it to the server.

[0370] (output)

[0371] The server parses the received JSON data and saves it in the database. If the registration is successful, the server generates a confirmation email and sends it to the user's email address.

[0372] (Specific actions)

[0373] The server analyzes the submitted user information, performs the registration process in the database, and sends a confirmation email to the user using the SMTP protocol.

[0374] Step 2:

[0375] (input)

[0376] The user clicks on the link in the confirmation email.

[0377] (process)

[0378] The device sends a request through this link to the server, which then activates the account.

[0379] (output)

[0380] The server validates the user's account and returns the result to the terminal.

[0381] (Specific actions)

[0382] The server validates the authentication token and updates the account status in the database to "valid."

[0383] Step 3:

[0384] (input)

[0385] The user enters their email address and password on the login screen.

[0386] (process)

[0387] The terminal transmits this authentication information to the server.

[0388] (output)

[0389] The server receives the information, checks it against a database, and if authentication is successful, starts a login session and returns the session ID to the terminal.

[0390] (Specific actions)

[0391] The server checks the received authentication information against the user information in its database, and if it is correct, it calls an API to start a session.

[0392] Step 4:

[0393] (input)

[0394] Users select the language they want to learn (e.g. Ainu) from the dashboard.

[0395] (process)

[0396] The terminal transmits the selected language information to the server.

[0397] (output)

[0398] The server sends prompts to the generative AI model to generate learning content in the corresponding language, which is then stored in a database and sent to the device.

[0399] (Specific actions)

[0400] The server uses an API to send prompt sentences to the generative AI model, stores the returned learning content in a database, and sends it to the user's device.

[0401] Step 5:

[0402] (input)

[0403] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time.

[0404] (process)

[0405] The collected data is formatted to be sent to the emotion engine.

[0406] (output)

[0407] The server uses an emotion engine to analyze the collected data and determine the user's emotional state.

[0408] (Specific actions)

[0409] The device digitizes the data acquired from the camera and microphone, and uploads it using an API that sends it to the emotion engine. The server then receives the analysis results.

[0410] Step 6:

[0411] (input)

[0412] Users become frustrated or disinterested while learning.

[0413] (process)

[0414] Based on the analysis results, the server generates learning content and feedback that is adapted to the user's emotional state.

[0415] (output)

[0416] New content and feedback will be sent to your device.

[0417] (Specific actions)

[0418] Based on the results of the emotion engine, the server reuses the generative AI model to dynamically generate content and feedback and send it to the device.

[0419] Step 7:

[0420] (input)

[0421] A user searches for specific cultural context information.

[0422] (process)

[0423] The server analyzes the user's level of interest using an emotion engine.

[0424] (output)

[0425] Highly relevant information and documentary videos are given priority.

[0426] (Specific actions)

[0427] The server analyzes the search query and sentiment data entered by the user, filters out highly relevant information resources, and sends them to the terminal.

[0428] Step 8:

[0429] (input)

[0430] The user completes the practice module and sends the results to the server.

[0431] (process)

[0432] The server also takes into account emotional data and generates positive feedback and specific suggestions for improvement, which it then sends to the device.

[0433] (output)

[0434] Feedback and suggestions for improvement are displayed on the user's device.

[0435] (Specific actions)

[0436] The server analyzes the user's practice results and emotional data, generates appropriate feedback and improvement suggestions, and sends them to the device.

[0437] (Application example 2)

[0438] 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."

[0439] In the process of learning a language that is disappearing, maintaining learners' motivation and providing them with an effective and deep understanding of the cultural background is a challenge. In particular, adaptive feedback and content adjustment based on the learner's emotional state are necessary. Furthermore, maximizing learning effectiveness in real time based on individual progress is required.

[0440] 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.

[0441] In this invention, the server includes means for allowing a user to select a language they wish to learn, means for generating learning content for the language using a generative AI model, means for providing the generated learning content to the user, means for analyzing the user's emotional state in real time using an emotion analysis engine, means for adaptively changing the difficulty level and feedback of the learning content based on the user's emotional state, means for tracking the user's learning progress and providing individualized feedback, and means for acquiring and providing cultural background information related to the language selected by the user. This allows the server to provide optimal content according to the user's emotional state and learning progress, improving the personalized learning experience and enabling effective language and cultural acquisition.

[0442] "User" refers to an individual or group of people who use this system to learn a language.

[0443] A "language to learn" is a particular language that a user wishes to learn.

[0444] "Server" refers to a computer system for storing and processing user data and providing learning content.

[0445] A "generative AI model" refers to a device or program that uses artificial intelligence algorithms to automatically generate learning content optimized for specific tasks.

[0446] "Learning Content" refers to learning materials and resources for learning a language of a user's choice, including text, audio, video, etc.

[0447] An "emotion analysis engine" refers to a device or program that analyzes data such as a user's facial expressions and tone of voice to identify their emotional state in real time.

[0448] "Emotional state" refers to the mental and emotional state a user exhibits while learning, including, for example, frustration or interest.

[0449] "Adaptive change" refers to dynamically changing the difficulty and type of learning content, taking into account the user's current situation and reactions.

[0450] "Learning progress" refers to the progress a user makes in the process of learning a language.

[0451] "Personalized feedback" refers to providing personalized advice and assessments based on a user's learning progress and emotional state.

[0452] "Cultural background information" refers to information such as history, customs, and culture related to the language the user is learning.

[0453] "Real-time" refers to the fact that user input and emotional state are analyzed in real time, and feedback and content are provided instantly.

[0454] This invention combines a sentiment analysis engine with a system for learning a language that is being lost to improve the effectiveness and experience of learning. This system is configured according to the roles of a user, a terminal, and a server. A specific embodiment of this system will be described in detail below.

[0455] Hardware and Software Use

[0456] The server stores and processes data and uses a generative AI model (e.g., GPT-3 (registered trademark) powered API) to provide appropriate learning content. The user device is a smartphone or a head-mounted display (e.g., Oculus Quest 2), which displays the learning content and analyzes the user's emotions.

[0457] The server also has installed an emotion analysis engine (e.g., OpenCV, DeepFace) to analyze the user's facial expressions and voice data, which allows it to identify the user's emotional state in real time and provide adaptive feedback.

[0458] System processing flow

[0459] 1. User Registration and Login

[0460] Users register using their own devices. The devices send the user's name, email address, password, and the language they wish to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[0461] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[0462] 2. Providing learning content

[0463] After logging in, users access a dashboard and select the language they want to learn. Once the user selects this language, the device sends the selection to the server, which uses a generative AI model to generate learning content (text, audio, video) and delivers it to the user.

[0464] 3. Sentiment Analysis and Adaptive Learning Support

[0465] The system incorporates an emotion analysis engine that recognizes the user's emotional state in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the analysis engine. The server then uses the emotion analysis engine to analyze this data and understand the user's emotional state.

[0466] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server will recognize this through its emotion analysis engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[0467] If a user expresses interest, the server can provide more detailed cultural background information and historical content. For example, if a user expresses interest in the history of Ainu culture, the server can provide a relevant documentary video.

[0468] Examples of prompt statements

[0469] 1. "Generate simple Ainu language exercises."

[0470] 2. "Please provide detailed content about Ainu culture."

[0471] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining a sentiment analysis engine with a generative AI model, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[0472] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0473] Step 1: User Registration

[0474] Input: Name, email address, password, and language you want to learn entered by the user via the device.

[0475] How it works: The device sends the entered information to the server, which stores it in a database and sends a confirmation email if registration is successful.

[0476] Output: Sends a confirmation email to the user and saves the user information in the database.

[0477] Step 2: Activate your account

[0478] Input: The user clicks on the link in the confirmation email they received.

[0479] What happens: The server verifies that the link was clicked and changes the account status to active.

[0480] Output: The user account is enabled.

[0481] Step 3: Log in

[0482] Input: The user enters their email address and password into the device.

[0483] Operation: The terminal sends a login request to the server, which checks it against a database and authenticates it.

[0484] Output: A login session is initiated and the user can access the system.

[0485] Step 4: Dashboard Access

[0486] Input: User accesses the dashboard after logging in.

[0487] How it works: The server collects the user's language history and progress data, generates a dashboard, and sends it to the device.

[0488] Output: A screen for selecting the language to learn and progress will be displayed.

[0489] Step 5: Select language and generate learning content

[0490] Input: The user selects the language they want to learn and sends that information to the server.

[0491] How it works: The server uses the generative AI model to generate learning content (text, audio, video).

[0492] Output: The generated learning content is provided to the user's device.

[0493] Step 6: Sentiment Analysis

[0494] Input: The user's facial expressions and tone of voice are input to the device via the camera and microphone.

[0495] How it works: The device sends this data to a server, which uses an emotion analysis engine to analyze the user's emotional state in real time.

[0496] Output: Data about the user's emotional state is sent to the server.

[0497] Step 7: Adaptive learning support

[0498] Input: User emotion data output by the emotion analysis engine.

[0499] How it works: The server adaptively changes the difficulty level and feedback of learning content based on the user's emotional state.

[0500] For example, if a frustrated emotional state is detected, the server generates less challenging exercises and encouraging messages.

[0501] Conversely, if they express interest, we generate content with detailed cultural background and history.

[0502] Output: Adaptively adjusted learning content and feedback are provided to the user.

[0503] Step 8: Evaluation and feedback of practice results

[0504] Input: The results of the practice module selected by the user.

[0505] How it works: The device sends the practice results to the server, which evaluates them and generates feedback, taking into account the emotional data.

[0506] Provide positive feedback and specific suggestions for improvement.

[0507] Output: Rating and feedback is provided to the user.

[0508] 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.

[0509] 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.

[0510] 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.

[0511] [Second embodiment]

[0512] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0513] 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.

[0514] 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).

[0515] 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.

[0516] 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.

[0517] 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).

[0518] 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. 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.

[0519] 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.

[0520] 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.

[0521] 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.

[0522] 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.

[0523] 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."

[0524] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. A specific embodiment of this system will be described below.

[0525] First, a user registers using their own device. The device receives the user's name, email address, password, and the language they want to learn, and sends it to the server. The server receives this information and stores it in a database. If registration is successful, the server sends the user a confirmation email. The user clicks the link in this confirmation email to activate their account, completing the registration process.

[0526] Next, the user enters their email address and password on the login screen and sends an authentication request to the server, which checks the database and, if successful, starts a login session, allowing the user to access the system.

[0527] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, and the server uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, etc., and the server provides it to the device. The device displays the learning content to the user, who then uses it to advance their language learning.

[0528] Furthermore, if the user wants to know about the cultural background related to the language as they learn it, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide it to the user through the device.

[0529] The system also constantly tracks the user's learning progress and provides personalized feedback. For example, the server analyzes whether the user has mastered a certain language skill or where they are struggling, and provides appropriate advice on improvement and additional learning resources.

[0530] When a user uses the practice module, the user similarly sends information to the server via the terminal, and the server provides the user with practice content such as quizzes, audio practice, translation practice, etc. When the user performs the practice and sends the results to the server, the server evaluates the practice results, generates feedback, and provides it to the user via the terminal.

[0531] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[0532] The processing flow will be explained below.

[0533] Step 1:

[0534] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[0535] Step 2:

[0536] The terminal acquires the input information and sends it to the server.

[0537] Step 3:

[0538] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[0539] Step 4:

[0540] The user receives a confirmation email and clicks the link in the email to activate their account.

[0541] Step 5:

[0542] The user enters their email address and password on the login page and submits the login request.

[0543] Step 6:

[0544] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[0545] Step 7:

[0546] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[0547] Step 8:

[0548] The terminal transmits the selected language information to the server.

[0549] Step 9:

[0550] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[0551] Step 10:

[0552] The server transmits the generated learning content to the terminal.

[0553] Step 11:

[0554] The terminal displays the received study content to the user, and the user begins studying.

[0555] Step 12:

[0556] While studying, users search for specific cultural background information (e.g., the history of Ainu culture).

[0557] Step 13:

[0558] The terminal sends the user's search query to the server.

[0559] Step 14:

[0560] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[0561] Step 15:

[0562] The terminal displays the received cultural background information to the user.

[0563] Step 16:

[0564] The user selects a practice module such as a quiz, audio practice, or translation practice.

[0565] Step 17:

[0566] The terminal transmits the selected practice module information to the server.

[0567] Step 18:

[0568] The server generates practice content according to the practice module and transmits it to the terminal.

[0569] Step 19:

[0570] The terminal displays the received practice content to the user, and the user starts practicing.

[0571] Step 20:

[0572] The user sends the practice results to the server.

[0573] Step 21:

[0574] The server evaluates the practice results and generates feedback.

[0575] Step 22:

[0576] The server transmits the generated feedback to the terminal.

[0577] Step 23:

[0578] The terminal displays the received feedback to the user.

[0579] This allows users to effectively learn a language that is being lost.

[0580] Example 1

[0581] 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."

[0582] To build a system for effectively learning a language that is being lost, it is necessary to provide an environment where users can easily start learning the language, as well as functions for providing individual feedback and cultural background information. Conventional language learning systems have limited functionality, making it difficult for learners to progress efficiently. Furthermore, implementing a wide range of functions, such as creating and managing user accounts, tracking learning progress, and providing practice content, is complicated.

[0583] 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.

[0584] In this invention, the server includes: means for a user to select a language they wish to learn; means for the server to generate learning content for the language using a generative AI model; means for the server to provide the generated learning content to the user; means for tracking the user's learning progress and providing individual feedback; means for acquiring and providing cultural background information related to the language selected by the user; means for receiving information required for the user to register and storing it in a database; means for the server to generate and send a confirmation email to the user; and means for the user to activate their account by clicking a link in the email. This enables a series of processes, from starting language learning to tracking progress, providing feedback, and learning cultural background, to be carried out in an integrated and efficient manner.

[0585] "User" refers to an individual or organization that uses the System to learn a language.

[0586] "Server" refers to a computer system that processes data and provides learning content and feedback in response to user requests.

[0587] "Generative AI model" refers to artificial intelligence technology that generates learning content based on specific data inputs.

[0588] "Learning Content" refers to materials such as text, audio, and video provided to support language learning.

[0589] "New registration" refers to the process by which a user registers the account information required to access the system for the first time.

[0590] "Verification Email" means the email containing the link a User needs to complete the registration process.

[0591] A "login session" refers to a series of access privilege management states that are initiated when a user accesses a system.

[0592] "Dashboard" refers to the interface that is displayed when a user accesses the system and is used to manage various operations and information.

[0593] "Cultural background information" refers to information about history, folklore, important events, customs, etc. related to the language being studied.

[0594] "Progress information" refers to data relating to progress and achievement recorded as a user progresses in language learning.

[0595] "Feedback" refers to evaluations and advice provided based on the user's learning progress.

[0596] "Practice Module" refers to practical learning features such as quizzes, audio practice, and translation practice provided for language learning.

[0597] The present invention is a system for effectively learning a language that is being lost, and is realized through cooperation between users, terminals, and a server. Specific embodiments will be described below.

[0598] First, a user registers using their device. The device receives the user's name, email address, password, and desired language and sends this information to the server. The server receives this information and stores it in a database (e.g., MySQL or PostgreSQL). Once the information is saved, the server generates a confirmation email and sends it to the user via the SMTP server. The user clicks the link in the confirmation email to activate their account, completing the registration process.

[0599] Next, the user enters their email address and password on the login screen and sends an authentication request to the server via their device. The server authenticates them against a database. If authentication is successful, the server starts a login session and the user is granted access to the dashboard.

[0600] After logging in, the user selects the language they want to learn on the dashboard. For example, if the user wants to learn "Ainu," the device sends that selection information to the server. The server then uses a generative AI model (e.g., GPT-4) to generate learning content for "Ainu." An example of a prompt used at this time is as follows:

[0601] Example prompt: "The user wants to learn Ainu. Please generate text, audio, and video learning content for beginners."

[0602] The generated learning content includes text, audio, video, etc., and is sent from the server to the device, which then displays the learning content to the user, allowing the user to progress with their language learning.

[0603] Furthermore, as the user progresses with their studies, progress information is periodically sent from the device to the server. The server receives this progress information and analyzes the user's learning progress and achievement. Based on the results of this analysis, the server generates personalized feedback and suggests appropriate advice for improvement and additional learning resources. This feedback is also provided to the user via the device.

[0604] If the user also wants to know about the cultural background related to the language selected, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide them to the user through the device.

[0605] Furthermore, when a user uses a practice module (such as a quiz, audio practice, or translation practice), the user sends information to the server via the device, and the server generates practice content. When the user performs the practice and sends the results to the server, the server evaluates the practice results and generates feedback to provide to the user via the device.

[0606] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[0607] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0608] Step 1:

[0609] A user visits the sign-up page.

[0610] A user enters the system's URL using a web browser to access the new registration page. Input: User's access information. Output: Display of the registration page.

[0611] Step 2:

[0612] The user enters the required information.

[0613] The user enters their name, email address, password, and the language they want to learn. Input: User information. Output: Registration information entered by the user.

[0614] Step 3:

[0615] The terminal sends the input information to the server.

[0616] The terminal sends the information entered by the user to the server via a POST request. Input: Registration information entered by the user. Output: Request to the server.

[0617] Step 4:

[0618] The server receives the information and stores it in a database.

[0619] The server receives the information, validates it, and then stores it in a database such as MySQL or PostgreSQL. Input: Submitted user information. Output: User information stored in the database.

[0620] Step 5:

[0621] The server generates a confirmation email and sends it to the user.

[0622] The server generates and sends a confirmation email using an SMTP server. Input: User information (email address). Output: The confirmation email sent.

[0623] Step 6:

[0624] The user clicks on the link in the confirmation email.

[0625] The user clicks on the link in the email to access the page where they can activate their account. Input: Link in the confirmation email. Output: Activation page displayed.

[0626] Step 7:

[0627] The server activates the account.

[0628] After the server verifies the link, it updates the user's account to "enabled." Input: Link access information. Output: Activated account information.

[0629] Step 8:

[0630] The user visits the login page.

[0631] The user accesses the system's login page. Input: User's access information. Output: Login page is displayed.

[0632] Step 9:

[0633] The user enters their email address and password.

[0634] The user enters their email address and password. Input: Login information. Output: Credentials entered by the user.

[0635] Step 10:

[0636] The terminal sends the input information to the server.

[0637] The terminal sends the entered information to the server via a POST request. Input: Authentication information. Output: Request to the server.

[0638] Step 11:

[0639] The server authenticates against a database.

[0640] The server checks the entered information against the database to verify that it is correct. Input: Authentication information. Output: Authentication result.

[0641] Step 12:

[0642] The server returns the authentication result.

[0643] If authentication is successful, the server starts a login session and allows the user to access the dashboard. Input: Authentication result. Output: Dashboard access permission.

[0644] Step 13:

[0645] A user visits a dashboard.

[0646] Authenticated users log in to the dashboard and configure various settings. Input: Dashboard access. Output: Dashboard display.

[0647] Step 14:

[0648] The user selects the language they want to learn.

[0649] The user selects the language they want to learn, such as "Ainu." Input: Selection of the language they want to learn. Output: Selection information.

[0650] Step 15:

[0651] The terminal transmits the selection information to the server.

[0652] The terminal sends the selected information to the server via a POST request. Input: Selected information. Output: Request to the server.

[0653] Step 16:

[0654] The server sends a prompt to the generative AI model.

[0655] The server sends a prompt to the generative AI model (e.g., GPT-4) requesting it to generate learning content for "Ainu." Input: Selection information and prompt. Output: Learning content generation request.

[0656] Step 17:

[0657] Generative AI models generate learning content.

[0658] The generative AI model generates appropriate learning content based on the prompt. Input: Prompt. Output: Generated learning content.

[0659] Step 18:

[0660] The server transmits the generated learning content to the terminal.

[0661] The server sends the generated learning content, such as text, audio, and video, back to the device. Input: Generated learning content. Output: Transmission to the device.

[0662] Step 19:

[0663] The terminal displays the learning content to the user.

[0664] The terminal displays the received learning content to the user. Input: Learning content. Output: Display to user.

[0665] Step 20:

[0666] Update progress information as users learn.

[0667] As users progress through their studies, progress information is automatically sent from their devices to the server. Input: User's study progress. Output: Progress information sent to the server.

[0668] Step 21:

[0669] The server analyzes the progress data and generates feedback.

[0670] The server analyzes the submitted progress data and generates feedback based on the learning progress and achievement level. Input: Progress data. Output: Feedback generation.

[0671] Step 22:

[0672] The server generates feedback and sends it to the device.

[0673] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[0674] Step 23:

[0675] The device displays the feedback to the user.

[0676] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[0677] Step 24:

[0678] The user requests cultural context information.

[0679] When a user wants to know cultural background information, he enters a query. Input: Request for cultural background information. Output: Query information.

[0680] Step 25:

[0681] The device sends a query to the server.

[0682] The terminal sends the user's query to the server. Input: Query information. Output: Request to the server.

[0683] Step 26:

[0684] The server retrieves the relevant information from the database.

[0685] The server retrieves relevant historical information, folklore, important events, etc. from the database. Input: Query information. Output: Related information.

[0686] Step 27:

[0687] The server sends the relevant information to the terminal.

[0688] The server sends the relevant information to the terminal. Input: relevant information. Output: transmission to the terminal.

[0689] Step 28:

[0690] The terminal displays the relevant information to the user.

[0691] The terminal displays the received relevant information to the user. Input: relevant information. Output: display to the user.

[0692] Step 29:

[0693] The user selects a practice module.

[0694] The user selects a practice module such as quiz, audio practice, translation practice, etc. Input: Selection of practice module. Output: Selection information.

[0695] Step 30:

[0696] The terminal transmits the selection information to the server.

[0697] The terminal sends the selection information to the server. Input: Selection information. Output: Request to the server.

[0698] Step 31:

[0699] The server generates the practice content.

[0700] The server generates appropriate practice content based on the selection. Input: Selection information. Output: Generated practice content.

[0701] Step 32:

[0702] The server transmits the practice content to the terminal.

[0703] The server sends the generated practice content to the terminal. Input: Generated practice content. Output: Transmission to the terminal.

[0704] Step 33:

[0705] The terminal displays the practice content to the user.

[0706] The device displays the practice content to the user. Input: Practice content. Output: Display to user.

[0707] Step 34:

[0708] The user performs the exercise.

[0709] The user performs the presented practice content. Input: Presented practice content. Output: User's practice results.

[0710] Step 35:

[0711] The device sends the practice results to the server.

[0712] The device sends the user's practice results to the server. Input: User's practice results. Output: Transmission to the server.

[0713] Step 36:

[0714] The server evaluates the practice results and generates feedback.

[0715] The server evaluates the received practice results and generates feedback based on them. Input: Practice results. Output: Generated feedback.

[0716] Step 37:

[0717] The server generates feedback and sends it to the device.

[0718] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[0719] Step 38:

[0720] The device displays the feedback to the user.

[0721] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[0722] (Application example 1)

[0723] 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."

[0724] Conventional language learning systems have the problem that it is difficult for users to take advantage of language learning experiences in a specific location, and they also have the problem that the effectiveness of learning is limited because they do not provide localized content or feedback according to the user's learning progress.

[0725] 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.

[0726] In this invention, the server includes a means for allowing a user to access content and information for language learning in a specific location, a means for providing learning content localized according to the user's location, a means for the user to check their learning progress in the specific location and receive appropriate feedback, and a means for providing content according to local events based on the results of the user's practice in the specific location, thereby enabling users to learn languages ​​more effectively and in accordance with the context in a physical store or other specific location.

[0727] - "User" means a person who uses the system to learn a language.

[0728] "Language to learn" refers to the specific language that the user wishes to learn through the system.

[0729] "Means" refers to the methods or devices that a user uses to achieve a goal.

[0730] A "server" is a computer system that generates and distributes learning content and stores and processes data.

[0731] A "generative AI model" is an artificial intelligence algorithm that automatically generates learning content based on the language of your choice.

[0732] "Learning content" refers to learning materials such as text, audio, and video provided to support language learning.

[0733] "Personalized feedback" refers to specific advice and assessments provided based on a user's learning progress and performance.

[0734] "Cultural background information" refers to data about the culture, history, folklore, etc. of the selected language.

[0735] "Specific location" refers to a brick-and-mortar store or other physical learning environment.

[0736] "Localized learning content" refers to learning materials that are customized for the location or region from which the user accesses them.

[0737] "User Account" means a unique identification created by a User to access the System.

[0738] "Authentication" refers to the verification procedures required for a user to log into a system.

[0739] A "practice module" is a set of practice materials or exercises designed to improve a particular language skill selected by the user.

[0740] A "local event" is a learning activity or event that a user conducts in a specific location.

[0741] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. Specifically, users use their smartphones to register and select the languages ​​they want to learn.

[0742] The server uses a generative AI model to generate learning content for the selected language and provides it to the user. The learning content includes text, audio, and video, which the user can view on their device to progress through the learning process. The server tracks learning progress and provides personalized feedback. It also provides cultural background information related to the language the user is learning.

[0743] Additionally, the system includes functionality to support learning in specific locations, such as brick-and-mortar stores. When users are in a specific location, they can access localized learning content tailored to that location. This enhances the brick-and-mortar learning experience and provides real-life knowledge and experiences related to the learning content.

[0744] The system's programs are built using frameworks such as Python and Flask, and databases such as SQLite are used to store data. The system's hardware includes smartphones and Wi-Fi access points.

[0745] For example, if a user wants to learn the Ainu language at a physical store, they can scan a QR code to access the store and receive specific learning content related to Ainu culture. Additionally, cultural events and special experience content will be provided based on the knowledge and skills the user has gained while studying at a specific location.

[0746] Below are some example input prompts for a generative AI model:

[0747] "Generate learning content about cultural background, history, and important events based on the user's language of choice. For example, create text, audio, and video that includes the following information: 'History of the Ainu Language,' 'Ainu Folklore,' and 'Modern Ainu Culture.'"

[0748] In this way, the system of the present invention provides localized content when users study in a specific location, enabling more effective language learning and cultural understanding.

[0749] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0750] Step 1:

[0751] A user accesses the system using a smartphone and enters their name, email address, password, and desired language into the new registration form. The entered data is sent from the device to the server. The server receives this information and stores it in an SQLite database. The server then sends a confirmation email to the user, and the user clicks on the link in the email to activate their account. This registers the user's basic information in the system. The input data is the name, email address, password, and desired language, and the output data is the activation of the user account.

[0752] Step 2:

[0753] The user enters an email address and password on the login screen and sends an authentication request. The device sends this information to the server, which checks the authentication information against a database. If authentication is successful, the server starts a login session and provides the user with a dashboard screen. The input data is the email address and password, and the output data is the start of the login session.

[0754] Step 3:

[0755] The user selects the language they want to learn from the dashboard screen. The selection information is sent from the device to the server. The server uses a generative AI model to generate learning content based on that language. The content includes text, audio, video, etc. The generated learning content is sent from the server to the device and provided to the user. The input data is the selected language, and the output data is the generated learning content.

[0756] Step 4:

[0757] The user uses the learning content and sends their progress to the server. The server receives the user's progress data and stores it in a database. The server then analyzes the user's progress, generates individual feedback, and sends it to the device. The input data is the user's learning progress, and the output data is individual feedback.

[0758] Step 5:

[0759] When a user wants to know the cultural background information related to the language they are learning, they send a query from their device to the server. The server retrieves the relevant cultural background information from the database and provides it to the user. The input data is the cultural background information query, and the output data is the relevant cultural background information.

[0760] Step 6:

[0761] When a user studies a language at a physical store, they use their smartphone to scan a QR code placed at a specific location. The scanned information is sent from the device to a server, which retrieves the specific location information. The server then generates localized learning content based on that information and provides it to the user. The input data is the scanned QR code information, and the output data is the localized learning content.

[0762] Step 7:

[0763] The user sends the results of practice performed at a specific location from the terminal to a server. The server receives the practice results and generates an evaluation and feedback. In addition, content related to the local event based on the results is also generated and provided to the user. The input data are the practice results, and the output data are the evaluation, feedback, and content related to the local event.

[0764] 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.

[0765] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[0766] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[0767] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[0768] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[0769] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[0770] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[0771] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[0772] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[0773] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[0774] The processing flow will be explained below.

[0775] Step 1:

[0776] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[0777] Step 2:

[0778] The terminal acquires the input information and sends it to the server.

[0779] Step 3:

[0780] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[0781] Step 4:

[0782] The user receives a confirmation email and clicks the link in the email to activate their account.

[0783] Step 5:

[0784] The user enters their email address and password on the login page and submits the login request.

[0785] Step 6:

[0786] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[0787] Step 7:

[0788] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[0789] Step 8:

[0790] The terminal transmits the selected language information to the server.

[0791] Step 9:

[0792] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[0793] Step 10:

[0794] The server transmits the generated learning content to the terminal.

[0795] Step 11:

[0796] The terminal displays the received study content to the user, and the user begins studying.

[0797] Step 12:

[0798] The device collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[0799] Step 13:

[0800] The server uses an emotion engine to analyze the user's emotion information and grasp the user's emotional state in real time.

[0801] Step 14:

[0802] The server adaptively changes learning content and feedback messages based on the user's emotional state. For example, if the user is experiencing difficulty, it adjusts the difficulty level or sends encouraging messages.

[0803] Step 15:

[0804] When a user wants to search for cultural background information while studying, they enter a search query.

[0805] Step 16:

[0806] The terminal sends the user's search query to the server.

[0807] Step 17:

[0808] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[0809] Step 18:

[0810] The device displays the received cultural background information to the user, and the emotion engine analyzes the user's emotions and provides additional information according to the user's level of interest and emotion.

[0811] Step 19:

[0812] The user selects a practice module.

[0813] Step 20:

[0814] The terminal transmits the selected practice module information to the server.

[0815] Step 21:

[0816] The server generates practice content according to the practice module and transmits it to the terminal.

[0817] Step 22:

[0818] The terminal displays the received practice content to the user, and the user starts practicing.

[0819] Step 23:

[0820] The user sends the results of their practice to the server. At the same time, the device collects the user's facial expressions and voice and sends them to the emotion engine.

[0821] Step 24:

[0822] The server evaluates the practice results and emotional data and generates feedback.

[0823] Step 25:

[0824] The server transmits the generated feedback to the terminal.

[0825] Step 26:

[0826] The device displays the received feedback to the user, which includes advice and suggestions for improvement based on the analysis results of the emotion engine.

[0827] This allows users to have an optimal learning experience that is tailored to their emotional state, enabling them to effectively acquire a language that is being lost and gain a deeper understanding of its cultural background.

[0828] Example 2

[0829] 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."

[0830] Conventional language learning systems provide learning content without considering the user's emotional state, which has the problem of not improving the learning effect and user experience. In particular, the lack of appropriate feedback and information when users are experiencing difficulty or are interested in the subject can lead to a decrease in users' motivation to learn.

[0831] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the terminal to collect user emotional data using a camera and microphone, a means for the server to analyze the collected emotional data using an emotion engine and adaptively change the learning content and feedback based on the user's emotional state, and a means for tracking the user's learning progress and providing individualized feedback. This enables adaptive learning support that takes the user's emotional state into consideration.

[0832] A "user" is a person who uses the system to select the language they want to learn and receives learning content.

[0833] A "server" is a device that processes information sent by users, generates learning content and feedback using generative AI models and emotion engines, and provides them to terminals.

[0834] A "terminal" is a device used by a user that transmits input from the user to a server and displays learning content and feedback sent from the server. It also has the function of collecting emotional data using a camera and microphone.

[0835] A "generative AI model" is an artificial intelligence model used by the server, and is a technology that generates learning content (text, audio, video, etc.) related to the language the user wants to learn.

[0836] The "emotion engine" is an analytical engine used by the server, which analyzes emotional data such as the user's facial expressions and tone of voice collected from the terminal to determine the user's emotional state.

[0837] "Learning Content" means educational material in the form of text, audio, video, etc., generated by a generative AI model and related to the language the user wishes to learn.

[0838] "Feedback" refers to adaptive messages and suggestions generated by the server based on the user's learning progress and emotional state, and is provided to support the user's learning.

[0839] "Emotional data" refers to information collected through the device's camera and microphone, such as the user's facial expressions and tone of voice, and is data that indicates the user's emotional state.

[0840] "Adaptive change" means that the content and difficulty of the learning content and feedback are dynamically adjusted based on the user's emotional state analyzed by the server using an emotion engine.

[0841] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[0842] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[0843] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[0844] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[0845] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[0846] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[0847] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[0848] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[0849] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[0850] Prompt Sentence Examples

[0851] "Generate detailed learning content about the Ainu language and send simple questions and encouraging messages to frustrated users."

[0852] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0853] Step 1:

[0854] (input)

[0855] Users enter their name, email address, password, and the language they want to learn into the terminal.

[0856] (process)

[0857] The terminal packages the input information in JSON format and sends it to the server.

[0858] (output)

[0859] The server parses the received JSON data and saves it in the database. If the registration is successful, the server generates a confirmation email and sends it to the user's email address.

[0860] (Specific actions)

[0861] The server analyzes the submitted user information, performs the registration process in the database, and sends a confirmation email to the user using the SMTP protocol.

[0862] Step 2:

[0863] (input)

[0864] The user clicks on the link in the confirmation email.

[0865] (process)

[0866] The device sends a request through this link to the server, which then activates the account.

[0867] (output)

[0868] The server validates the user's account and returns the result to the terminal.

[0869] (Specific actions)

[0870] The server validates the authentication token and updates the account status in the database to "valid."

[0871] Step 3:

[0872] (input)

[0873] The user enters their email address and password on the login screen.

[0874] (process)

[0875] The terminal transmits this authentication information to the server.

[0876] (output)

[0877] The server receives the information, checks it against a database, and if authentication is successful, starts a login session and returns the session ID to the terminal.

[0878] (Specific actions)

[0879] The server checks the received authentication information against the user information in its database, and if it is correct, it calls an API to start a session.

[0880] Step 4:

[0881] (input)

[0882] Users select the language they want to learn (e.g. Ainu) from the dashboard.

[0883] (process)

[0884] The terminal transmits the selected language information to the server.

[0885] (output)

[0886] The server sends prompts to the generative AI model to generate learning content in the corresponding language, which is then stored in a database and sent to the device.

[0887] (Specific actions)

[0888] The server uses an API to send prompt sentences to the generative AI model, stores the returned learning content in a database, and sends it to the user's device.

[0889] Step 5:

[0890] (input)

[0891] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time.

[0892] (process)

[0893] The collected data is formatted to be sent to the emotion engine.

[0894] (output)

[0895] The server uses an emotion engine to analyze the collected data and determine the user's emotional state.

[0896] (Specific actions)

[0897] The device digitizes the data acquired from the camera and microphone, and uploads it using an API that sends it to the emotion engine. The server then receives the analysis results.

[0898] Step 6:

[0899] (input)

[0900] Users become frustrated or disinterested while learning.

[0901] (process)

[0902] Based on the analysis results, the server generates learning content and feedback that is adapted to the user's emotional state.

[0903] (output)

[0904] New content and feedback will be sent to your device.

[0905] (Specific actions)

[0906] Based on the results of the emotion engine, the server reuses the generative AI model to dynamically generate content and feedback and send it to the device.

[0907] Step 7:

[0908] (input)

[0909] A user searches for specific cultural context information.

[0910] (process)

[0911] The server analyzes the user's level of interest using an emotion engine.

[0912] (output)

[0913] Highly relevant information and documentary videos are given priority.

[0914] (Specific actions)

[0915] The server analyzes the search query and sentiment data entered by the user, filters out highly relevant information resources, and sends them to the terminal.

[0916] Step 8:

[0917] (input)

[0918] The user completes the practice module and sends the results to the server.

[0919] (process)

[0920] The server also takes into account emotional data and generates positive feedback and specific suggestions for improvement, which it then sends to the device.

[0921] (output)

[0922] Feedback and suggestions for improvement are displayed on the user's device.

[0923] (Specific actions)

[0924] The server analyzes the user's practice results and emotional data, generates appropriate feedback and improvement suggestions, and sends them to the device.

[0925] (Application example 2)

[0926] 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."

[0927] In the process of learning a language that is disappearing, maintaining learners' motivation and providing them with an effective and deep understanding of the cultural background is a challenge. In particular, adaptive feedback and content adjustment based on the learner's emotional state are necessary. Furthermore, maximizing learning effectiveness in real time based on individual progress is required.

[0928] 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.

[0929] In this invention, the server includes means for allowing a user to select a language they wish to learn, means for generating learning content for the language using a generative AI model, means for providing the generated learning content to the user, means for analyzing the user's emotional state in real time using an emotion analysis engine, means for adaptively changing the difficulty level and feedback of the learning content based on the user's emotional state, means for tracking the user's learning progress and providing individualized feedback, and means for acquiring and providing cultural background information related to the language selected by the user. This allows the server to provide optimal content according to the user's emotional state and learning progress, improving the personalized learning experience and enabling effective language and cultural acquisition.

[0930] "User" refers to an individual or group of people who use this system to learn a language.

[0931] A "language to learn" is a particular language that a user wishes to learn.

[0932] "Server" refers to a computer system for storing and processing user data and providing learning content.

[0933] A "generative AI model" refers to a device or program that uses artificial intelligence algorithms to automatically generate learning content optimized for specific tasks.

[0934] "Learning Content" refers to learning materials and resources for learning a language of a user's choice, including text, audio, video, etc.

[0935] An "emotion analysis engine" refers to a device or program that analyzes data such as a user's facial expressions and tone of voice to identify their emotional state in real time.

[0936] "Emotional state" refers to the mental and emotional state a user exhibits while learning, including, for example, frustration or interest.

[0937] "Adaptive change" refers to dynamically changing the difficulty and type of learning content, taking into account the user's current situation and reactions.

[0938] "Learning progress" refers to the progress a user makes in the process of learning a language.

[0939] "Personalized feedback" refers to providing personalized advice and assessments based on a user's learning progress and emotional state.

[0940] "Cultural background information" refers to information such as history, customs, and culture related to the language the user is learning.

[0941] "Real-time" refers to the fact that user input and emotional state are analyzed in real time, and feedback and content are provided instantly.

[0942] This invention combines a sentiment analysis engine with a system for learning a language that is being lost to improve the effectiveness and experience of learning. This system is configured according to the roles of a user, a terminal, and a server. A specific embodiment of this system will be described in detail below.

[0943] Hardware and Software Use

[0944] The server stores and processes data and uses a generative AI model (e.g., GPT-3 powered API) to provide appropriate learning content. The user device is a smartphone or a head-mounted display (e.g., Oculus Quest 2), which displays the learning content and analyzes the user's emotions.

[0945] The server also has installed an emotion analysis engine (e.g., OpenCV, DeepFace) to analyze the user's facial expressions and voice data, which allows it to identify the user's emotional state in real time and provide adaptive feedback.

[0946] System processing flow

[0947] 1. User Registration and Login

[0948] Users register using their own devices. The devices send the user's name, email address, password, and the language they wish to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[0949] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[0950] 2. Providing learning content

[0951] After logging in, users access a dashboard and select the language they want to learn. Once the user selects this language, the device sends the selection to the server, which uses a generative AI model to generate learning content (text, audio, video) and delivers it to the user.

[0952] 3. Sentiment Analysis and Adaptive Learning Support

[0953] The system incorporates an emotion analysis engine that recognizes the user's emotional state in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the analysis engine. The server then uses the emotion analysis engine to analyze this data and understand the user's emotional state.

[0954] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server will recognize this through its emotion analysis engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[0955] If a user expresses interest, the server can provide more detailed cultural background information and historical content. For example, if a user expresses interest in the history of Ainu culture, the server can provide a relevant documentary video.

[0956] Examples of prompt statements

[0957] 1. "Generate simple Ainu language exercises."

[0958] 2. "Please provide detailed content about Ainu culture."

[0959] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining a sentiment analysis engine with a generative AI model, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[0960] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0961] Step 1: User Registration

[0962] Input: Name, email address, password, and language you want to learn entered by the user via the device.

[0963] How it works: The device sends the entered information to the server, which stores it in a database and sends a confirmation email if registration is successful.

[0964] Output: Sends a confirmation email to the user and saves the user information in the database.

[0965] Step 2: Activate your account

[0966] Input: The user clicks on the link in the confirmation email they received.

[0967] What happens: The server verifies that the link was clicked and changes the account status to active.

[0968] Output: The user account is enabled.

[0969] Step 3: Log in

[0970] Input: The user enters their email address and password into the device.

[0971] Operation: The terminal sends a login request to the server, which checks it against a database and authenticates it.

[0972] Output: A login session is initiated and the user can access the system.

[0973] Step 4: Dashboard Access

[0974] Input: User accesses the dashboard after logging in.

[0975] How it works: The server collects the user's language history and progress data, generates a dashboard, and sends it to the device.

[0976] Output: A screen for selecting the language to learn and progress will be displayed.

[0977] Step 5: Select language and generate learning content

[0978] Input: The user selects the language they want to learn and sends that information to the server.

[0979] How it works: The server uses the generative AI model to generate learning content (text, audio, video).

[0980] Output: The generated learning content is provided to the user's device.

[0981] Step 6: Sentiment Analysis

[0982] Input: The user's facial expressions and tone of voice are input to the device via the camera and microphone.

[0983] How it works: The device sends this data to a server, which uses an emotion analysis engine to analyze the user's emotional state in real time.

[0984] Output: Data about the user's emotional state is sent to the server.

[0985] Step 7: Adaptive learning support

[0986] Input: User emotion data output by the emotion analysis engine.

[0987] How it works: The server adaptively changes the difficulty level and feedback of learning content based on the user's emotional state.

[0988] For example, if a frustrated emotional state is detected, the server generates less challenging exercises and encouraging messages.

[0989] Conversely, if they express interest, we generate content with detailed cultural background and history.

[0990] Output: Adaptively adjusted learning content and feedback are provided to the user.

[0991] Step 8: Evaluation and feedback of practice results

[0992] Input: The results of the practice module selected by the user.

[0993] How it works: The device sends the practice results to the server, which evaluates them and generates feedback, taking into account the emotional data.

[0994] Provide positive feedback and specific suggestions for improvement.

[0995] Output: Rating and feedback is provided to the user.

[0996] 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.

[0997] 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.

[0998] 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.

[0999] [Third embodiment]

[1000] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1001] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1002] 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).

[1003] 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.

[1004] 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.

[1005] 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).

[1006] 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. 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.

[1007] 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.

[1008] 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.

[1009] 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.

[1010] 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.

[1011] 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."

[1012] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. A specific embodiment of this system will be described below.

[1013] First, a user registers using their own device. The device receives the user's name, email address, password, and the language they want to learn, and sends it to the server. The server receives this information and stores it in a database. If registration is successful, the server sends the user a confirmation email. The user clicks the link in this confirmation email to activate their account, completing the registration process.

[1014] Next, the user enters their email address and password on the login screen and sends an authentication request to the server, which checks the database and, if successful, starts a login session, allowing the user to access the system.

[1015] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, and the server uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, etc., and the server provides it to the device. The device displays the learning content to the user, who then uses it to advance their language learning.

[1016] Furthermore, if the user wants to know about the cultural background related to the language as they learn it, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide it to the user through the device.

[1017] The system also constantly tracks the user's learning progress and provides personalized feedback. For example, the server analyzes whether the user has mastered a certain language skill or where they are struggling, and provides appropriate advice on improvement and additional learning resources.

[1018] When a user uses the practice module, the user similarly sends information to the server via the terminal, and the server provides the user with practice content such as quizzes, audio practice, translation practice, etc. When the user performs the practice and sends the results to the server, the server evaluates the practice results, generates feedback, and provides it to the user via the terminal.

[1019] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[1020] The processing flow will be explained below.

[1021] Step 1:

[1022] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[1023] Step 2:

[1024] The terminal acquires the input information and sends it to the server.

[1025] Step 3:

[1026] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[1027] Step 4:

[1028] The user receives a confirmation email and clicks the link in the email to activate their account.

[1029] Step 5:

[1030] The user enters their email address and password on the login page and submits the login request.

[1031] Step 6:

[1032] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[1033] Step 7:

[1034] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[1035] Step 8:

[1036] The terminal transmits the selected language information to the server.

[1037] Step 9:

[1038] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[1039] Step 10:

[1040] The server transmits the generated learning content to the terminal.

[1041] Step 11:

[1042] The terminal displays the received study content to the user, and the user begins studying.

[1043] Step 12:

[1044] While studying, users search for specific cultural background information (e.g., the history of Ainu culture).

[1045] Step 13:

[1046] The terminal sends the user's search query to the server.

[1047] Step 14:

[1048] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[1049] Step 15:

[1050] The terminal displays the received cultural background information to the user.

[1051] Step 16:

[1052] The user selects a practice module such as a quiz, audio practice, or translation practice.

[1053] Step 17:

[1054] The terminal transmits the selected practice module information to the server.

[1055] Step 18:

[1056] The server generates practice content according to the practice module and transmits it to the terminal.

[1057] Step 19:

[1058] The terminal displays the received practice content to the user, and the user starts practicing.

[1059] Step 20:

[1060] The user sends the practice results to the server.

[1061] Step 21:

[1062] The server evaluates the practice results and generates feedback.

[1063] Step 22:

[1064] The server transmits the generated feedback to the terminal.

[1065] Step 23:

[1066] The terminal displays the received feedback to the user.

[1067] This allows users to effectively learn a language that is being lost.

[1068] Example 1

[1069] 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."

[1070] To build a system for effectively learning a language that is being lost, it is necessary to provide an environment where users can easily start learning the language, as well as functions for providing individual feedback and cultural background information. Conventional language learning systems have limited functionality, making it difficult for learners to progress efficiently. Furthermore, implementing a wide range of functions, such as creating and managing user accounts, tracking learning progress, and providing practice content, is complicated.

[1071] 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.

[1072] In this invention, the server includes: means for a user to select a language they wish to learn; means for the server to generate learning content for the language using a generative AI model; means for the server to provide the generated learning content to the user; means for tracking the user's learning progress and providing individual feedback; means for acquiring and providing cultural background information related to the language selected by the user; means for receiving information required for the user to register and storing it in a database; means for the server to generate and send a confirmation email to the user; and means for the user to activate their account by clicking a link in the email. This enables a series of processes, from starting language learning to tracking progress, providing feedback, and learning cultural background, to be carried out in an integrated and efficient manner.

[1073] "User" refers to an individual or organization that uses the System to learn a language.

[1074] "Server" refers to a computer system that processes data and provides learning content and feedback in response to user requests.

[1075] "Generative AI model" refers to artificial intelligence technology that generates learning content based on specific data inputs.

[1076] "Learning Content" refers to materials such as text, audio, and video provided to support language learning.

[1077] "New registration" refers to the process by which a user registers the account information required to access the system for the first time.

[1078] "Verification Email" means the email containing the link a User needs to complete the registration process.

[1079] A "login session" refers to a series of access privilege management states that are initiated when a user accesses a system.

[1080] "Dashboard" refers to the interface that is displayed when a user accesses the system and is used to manage various operations and information.

[1081] "Cultural background information" refers to information about history, folklore, important events, customs, etc. related to the language being studied.

[1082] "Progress information" refers to data relating to progress and achievement recorded as a user progresses in language learning.

[1083] "Feedback" refers to evaluations and advice provided based on the user's learning progress.

[1084] "Practice Module" refers to practical learning features such as quizzes, audio practice, and translation practice provided for language learning.

[1085] The present invention is a system for effectively learning a language that is being lost, and is realized through cooperation between users, terminals, and a server. Specific embodiments will be described below.

[1086] First, a user registers using their device. The device receives the user's name, email address, password, and desired language and sends this information to the server. The server receives this information and stores it in a database (e.g., MySQL or PostgreSQL). Once the information is saved, the server generates a confirmation email and sends it to the user via the SMTP server. The user clicks the link in the confirmation email to activate their account, completing the registration process.

[1087] Next, the user enters their email address and password on the login screen and sends an authentication request to the server via their device. The server authenticates them against a database. If authentication is successful, the server starts a login session and the user is granted access to the dashboard.

[1088] After logging in, the user selects the language they want to learn on the dashboard. For example, if the user wants to learn "Ainu," the device sends that selection information to the server. The server then uses a generative AI model (e.g., GPT-4) to generate learning content for "Ainu." An example of a prompt used at this time is as follows:

[1089] Example prompt: "The user wants to learn Ainu. Please generate text, audio, and video learning content for beginners."

[1090] The generated learning content includes text, audio, video, etc., and is sent from the server to the device, which then displays the learning content to the user, allowing the user to progress with their language learning.

[1091] Furthermore, as the user progresses with their studies, progress information is periodically sent from the device to the server. The server receives this progress information and analyzes the user's learning progress and achievement. Based on the results of this analysis, the server generates personalized feedback and suggests appropriate advice for improvement and additional learning resources. This feedback is also provided to the user via the device.

[1092] If the user also wants to know about the cultural background related to the language selected, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide them to the user through the device.

[1093] Furthermore, when a user uses a practice module (such as a quiz, audio practice, or translation practice), the user sends information to the server via the device, and the server generates practice content. When the user performs the practice and sends the results to the server, the server evaluates the practice results and generates feedback to provide to the user via the device.

[1094] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[1095] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1096] Step 1:

[1097] A user visits the sign-up page.

[1098] A user enters the system's URL using a web browser to access the new registration page. Input: User's access information. Output: Display of the registration page.

[1099] Step 2:

[1100] The user enters the required information.

[1101] The user enters their name, email address, password, and the language they want to learn. Input: User information. Output: Registration information entered by the user.

[1102] Step 3:

[1103] The terminal sends the input information to the server.

[1104] The terminal sends the information entered by the user to the server via a POST request. Input: Registration information entered by the user. Output: Request to the server.

[1105] Step 4:

[1106] The server receives the information and stores it in a database.

[1107] The server receives the information, validates it, and then stores it in a database such as MySQL or PostgreSQL. Input: Submitted user information. Output: User information stored in the database.

[1108] Step 5:

[1109] The server generates a confirmation email and sends it to the user.

[1110] The server generates and sends a confirmation email using an SMTP server. Input: User information (email address). Output: The confirmation email sent.

[1111] Step 6:

[1112] The user clicks on the link in the confirmation email.

[1113] The user clicks on the link in the email to access the page where they can activate their account. Input: Link in the confirmation email. Output: Activation page displayed.

[1114] Step 7:

[1115] The server activates the account.

[1116] After the server verifies the link, it updates the user's account to "enabled." Input: Link access information. Output: Activated account information.

[1117] Step 8:

[1118] The user visits the login page.

[1119] The user accesses the system's login page. Input: User's access information. Output: Login page is displayed.

[1120] Step 9:

[1121] The user enters their email address and password.

[1122] The user enters their email address and password. Input: Login information. Output: Credentials entered by the user.

[1123] Step 10:

[1124] The terminal sends the input information to the server.

[1125] The terminal sends the entered information to the server via a POST request. Input: Authentication information. Output: Request to the server.

[1126] Step 11:

[1127] The server authenticates against a database.

[1128] The server checks the entered information against the database to verify that it is correct. Input: Authentication information. Output: Authentication result.

[1129] Step 12:

[1130] The server returns the authentication result.

[1131] If authentication is successful, the server starts a login session and allows the user to access the dashboard. Input: Authentication result. Output: Dashboard access permission.

[1132] Step 13:

[1133] A user visits a dashboard.

[1134] Authenticated users log in to the dashboard and configure various settings. Input: Dashboard access. Output: Dashboard display.

[1135] Step 14:

[1136] The user selects the language they want to learn.

[1137] The user selects the language they want to learn, such as "Ainu." Input: Selection of the language they want to learn. Output: Selection information.

[1138] Step 15:

[1139] The terminal transmits the selection information to the server.

[1140] The terminal sends the selected information to the server via a POST request. Input: Selected information. Output: Request to the server.

[1141] Step 16:

[1142] The server sends a prompt to the generative AI model.

[1143] The server sends a prompt to the generative AI model (e.g., GPT-4) requesting it to generate learning content for "Ainu." Input: Selection information and prompt. Output: Learning content generation request.

[1144] Step 17:

[1145] Generative AI models generate learning content.

[1146] The generative AI model generates appropriate learning content based on the prompt. Input: Prompt. Output: Generated learning content.

[1147] Step 18:

[1148] The server transmits the generated learning content to the terminal.

[1149] The server sends the generated learning content, such as text, audio, and video, back to the device. Input: Generated learning content. Output: Transmission to the device.

[1150] Step 19:

[1151] The terminal displays the learning content to the user.

[1152] The terminal displays the received learning content to the user. Input: Learning content. Output: Display to user.

[1153] Step 20:

[1154] Update progress information as users learn.

[1155] As users progress through their studies, progress information is automatically sent from their devices to the server. Input: User's study progress. Output: Progress information sent to the server.

[1156] Step 21:

[1157] The server analyzes the progress data and generates feedback.

[1158] The server analyzes the submitted progress data and generates feedback based on the learning progress and achievement level. Input: Progress data. Output: Feedback generation.

[1159] Step 22:

[1160] The server generates feedback and sends it to the device.

[1161] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[1162] Step 23:

[1163] The device displays the feedback to the user.

[1164] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[1165] Step 24:

[1166] The user requests cultural context information.

[1167] When a user wants to know cultural background information, he enters a query. Input: Request for cultural background information. Output: Query information.

[1168] Step 25:

[1169] The device sends a query to the server.

[1170] The terminal sends the user's query to the server. Input: Query information. Output: Request to the server.

[1171] Step 26:

[1172] The server retrieves the relevant information from the database.

[1173] The server retrieves relevant historical information, folklore, important events, etc. from the database. Input: Query information. Output: Related information.

[1174] Step 27:

[1175] The server sends the relevant information to the terminal.

[1176] The server sends the relevant information to the terminal. Input: relevant information. Output: transmission to the terminal.

[1177] Step 28:

[1178] The terminal displays the relevant information to the user.

[1179] The terminal displays the received relevant information to the user. Input: relevant information. Output: display to the user.

[1180] Step 29:

[1181] The user selects a practice module.

[1182] The user selects a practice module such as quiz, audio practice, translation practice, etc. Input: Selection of practice module. Output: Selection information.

[1183] Step 30:

[1184] The terminal transmits the selection information to the server.

[1185] The terminal sends the selection information to the server. Input: Selection information. Output: Request to the server.

[1186] Step 31:

[1187] The server generates the practice content.

[1188] The server generates appropriate practice content based on the selection. Input: Selection information. Output: Generated practice content.

[1189] Step 32:

[1190] The server transmits the practice content to the terminal.

[1191] The server sends the generated practice content to the terminal. Input: Generated practice content. Output: Transmission to the terminal.

[1192] Step 33:

[1193] The terminal displays the practice content to the user.

[1194] The device displays the practice content to the user. Input: Practice content. Output: Display to user.

[1195] Step 34:

[1196] The user performs the exercise.

[1197] The user performs the presented practice content. Input: Presented practice content. Output: User's practice results.

[1198] Step 35:

[1199] The device sends the practice results to the server.

[1200] The device sends the user's practice results to the server. Input: User's practice results. Output: Transmission to the server.

[1201] Step 36:

[1202] The server evaluates the practice results and generates feedback.

[1203] The server evaluates the received practice results and generates feedback based on them. Input: Practice results. Output: Generated feedback.

[1204] Step 37:

[1205] The server generates feedback and sends it to the device.

[1206] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[1207] Step 38:

[1208] The device displays the feedback to the user.

[1209] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[1210] (Application example 1)

[1211] 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."

[1212] Conventional language learning systems have the problem that it is difficult for users to take advantage of language learning experiences in a specific location, and they also have the problem that the effectiveness of learning is limited because they do not provide localized content or feedback according to the user's learning progress.

[1213] 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.

[1214] In this invention, the server includes a means for allowing a user to access content and information for language learning in a specific location, a means for providing learning content localized according to the user's location, a means for the user to check their learning progress in the specific location and receive appropriate feedback, and a means for providing content according to local events based on the results of the user's practice in the specific location, thereby enabling users to learn languages ​​more effectively and in accordance with the context in a physical store or other specific location.

[1215] - "User" means a person who uses the system to learn a language.

[1216] "Language to learn" refers to the specific language that the user wishes to learn through the system.

[1217] "Means" refers to the methods or devices that a user uses to achieve a goal.

[1218] A "server" is a computer system that generates and distributes learning content and stores and processes data.

[1219] A "generative AI model" is an artificial intelligence algorithm that automatically generates learning content based on the language of your choice.

[1220] "Learning content" refers to learning materials such as text, audio, and video provided to support language learning.

[1221] "Personalized feedback" refers to specific advice and assessments provided based on a user's learning progress and performance.

[1222] "Cultural background information" refers to data about the culture, history, folklore, etc. of the selected language.

[1223] "Specific location" refers to a brick-and-mortar store or other physical learning environment.

[1224] "Localized learning content" refers to learning materials that are customized for the location or region from which the user accesses them.

[1225] "User Account" means a unique identification created by a User to access the System.

[1226] "Authentication" refers to the verification procedures required for a user to log into a system.

[1227] A "practice module" is a set of practice materials or exercises designed to improve a particular language skill selected by the user.

[1228] A "local event" is a learning activity or event that a user conducts in a specific location.

[1229] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. Specifically, users use their smartphones to register and select the languages ​​they want to learn.

[1230] The server uses a generative AI model to generate learning content for the selected language and provides it to the user. The learning content includes text, audio, and video, which the user can view on their device to progress through the learning process. The server tracks learning progress and provides personalized feedback. It also provides cultural background information related to the language the user is learning.

[1231] Additionally, the system includes functionality to support learning in specific locations, such as brick-and-mortar stores. When users are in a specific location, they can access localized learning content tailored to that location. This enhances the brick-and-mortar learning experience and provides real-life knowledge and experiences related to the learning content.

[1232] The system's programs are built using frameworks such as Python and Flask, and databases such as SQLite are used to store data. The system's hardware includes smartphones and Wi-Fi access points.

[1233] For example, if a user wants to learn the Ainu language at a physical store, they can scan a QR code to access the store and receive specific learning content related to Ainu culture. Additionally, cultural events and special experience content will be provided based on the knowledge and skills the user has gained while studying at a specific location.

[1234] Below are some example input prompts for a generative AI model:

[1235] "Generate learning content about cultural background, history, and important events based on the user's language of choice. For example, create text, audio, and video that includes the following information: 'History of the Ainu Language,' 'Ainu Folklore,' and 'Modern Ainu Culture.'"

[1236] In this way, the system of the present invention provides localized content when users study in a specific location, enabling more effective language learning and cultural understanding.

[1237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1238] Step 1:

[1239] A user accesses the system using a smartphone and enters their name, email address, password, and desired language into the new registration form. The entered data is sent from the device to the server. The server receives this information and stores it in an SQLite database. The server then sends a confirmation email to the user, and the user clicks on the link in the email to activate their account. This registers the user's basic information in the system. The input data is the name, email address, password, and desired language, and the output data is the activation of the user account.

[1240] Step 2:

[1241] The user enters an email address and password on the login screen and sends an authentication request. The device sends this information to the server, which checks the authentication information against a database. If authentication is successful, the server starts a login session and provides the user with a dashboard screen. The input data is the email address and password, and the output data is the start of the login session.

[1242] Step 3:

[1243] The user selects the language they want to learn from the dashboard screen. The selection information is sent from the device to the server. The server uses a generative AI model to generate learning content based on that language. The content includes text, audio, video, etc. The generated learning content is sent from the server to the device and provided to the user. The input data is the selected language, and the output data is the generated learning content.

[1244] Step 4:

[1245] The user uses the learning content and sends their progress to the server. The server receives the user's progress data and stores it in a database. The server then analyzes the user's progress, generates individual feedback, and sends it to the device. The input data is the user's learning progress, and the output data is individual feedback.

[1246] Step 5:

[1247] When a user wants to know the cultural background information related to the language they are learning, they send a query from their device to the server. The server retrieves the relevant cultural background information from the database and provides it to the user. The input data is the cultural background information query, and the output data is the relevant cultural background information.

[1248] Step 6:

[1249] When a user studies a language at a physical store, they use their smartphone to scan a QR code placed at a specific location. The scanned information is sent from the device to a server, which retrieves the specific location information. The server then generates localized learning content based on that information and provides it to the user. The input data is the scanned QR code information, and the output data is the localized learning content.

[1250] Step 7:

[1251] The user sends the results of practice performed at a specific location from the terminal to a server. The server receives the practice results and generates an evaluation and feedback. In addition, content related to the local event based on the results is also generated and provided to the user. The input data are the practice results, and the output data are the evaluation, feedback, and content related to the local event.

[1252] 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.

[1253] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[1254] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[1255] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[1256] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[1257] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[1258] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[1259] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[1260] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[1261] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[1262] The processing flow will be explained below.

[1263] Step 1:

[1264] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[1265] Step 2:

[1266] The terminal acquires the input information and sends it to the server.

[1267] Step 3:

[1268] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[1269] Step 4:

[1270] The user receives a confirmation email and clicks the link in the email to activate their account.

[1271] Step 5:

[1272] The user enters their email address and password on the login page and submits the login request.

[1273] Step 6:

[1274] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[1275] Step 7:

[1276] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[1277] Step 8:

[1278] The terminal transmits the selected language information to the server.

[1279] Step 9:

[1280] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[1281] Step 10:

[1282] The server transmits the generated learning content to the terminal.

[1283] Step 11:

[1284] The terminal displays the received study content to the user, and the user begins studying.

[1285] Step 12:

[1286] The device collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[1287] Step 13:

[1288] The server uses an emotion engine to analyze the user's emotion information and grasp the user's emotional state in real time.

[1289] Step 14:

[1290] The server adaptively changes learning content and feedback messages based on the user's emotional state. For example, if the user is experiencing difficulty, it adjusts the difficulty level or sends encouraging messages.

[1291] Step 15:

[1292] When a user wants to search for cultural background information while studying, they enter a search query.

[1293] Step 16:

[1294] The terminal sends the user's search query to the server.

[1295] Step 17:

[1296] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[1297] Step 18:

[1298] The device displays the received cultural background information to the user, and the emotion engine analyzes the user's emotions and provides additional information according to the user's level of interest and emotion.

[1299] Step 19:

[1300] The user selects a practice module.

[1301] Step 20:

[1302] The terminal transmits the selected practice module information to the server.

[1303] Step 21:

[1304] The server generates practice content according to the practice module and transmits it to the terminal.

[1305] Step 22:

[1306] The terminal displays the received practice content to the user, and the user starts practicing.

[1307] Step 23:

[1308] The user sends the results of their practice to the server. At the same time, the device collects the user's facial expressions and voice and sends them to the emotion engine.

[1309] Step 24:

[1310] The server evaluates the practice results and emotional data and generates feedback.

[1311] Step 25:

[1312] The server transmits the generated feedback to the terminal.

[1313] Step 26:

[1314] The device displays the received feedback to the user, which includes advice and suggestions for improvement based on the analysis results of the emotion engine.

[1315] This allows users to have an optimal learning experience that is tailored to their emotional state, enabling them to effectively acquire a language that is being lost and gain a deeper understanding of its cultural background.

[1316] Example 2

[1317] 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."

[1318] Conventional language learning systems provide learning content without considering the user's emotional state, which has the problem of not improving the learning effect and user experience. In particular, the lack of appropriate feedback and information when users are experiencing difficulty or are interested in the subject can lead to a decrease in users' motivation to learn.

[1319] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the terminal to collect user emotional data using a camera and microphone, a means for the server to analyze the collected emotional data using an emotion engine and adaptively change the learning content and feedback based on the user's emotional state, and a means for tracking the user's learning progress and providing individualized feedback. This enables adaptive learning support that takes the user's emotional state into consideration.

[1320] A "user" is a person who uses the system to select the language they want to learn and receives learning content.

[1321] A "server" is a device that processes information sent by users, generates learning content and feedback using generative AI models and emotion engines, and provides them to terminals.

[1322] A "terminal" is a device used by a user that transmits input from the user to a server and displays learning content and feedback sent from the server. It also has the function of collecting emotional data using a camera and microphone.

[1323] A "generative AI model" is an artificial intelligence model used by the server, and is a technology that generates learning content (text, audio, video, etc.) related to the language the user wants to learn.

[1324] The "emotion engine" is an analytical engine used by the server, which analyzes emotional data such as the user's facial expressions and tone of voice collected from the terminal to determine the user's emotional state.

[1325] "Learning Content" means educational material in the form of text, audio, video, etc., generated by a generative AI model and related to the language the user wishes to learn.

[1326] "Feedback" refers to adaptive messages and suggestions generated by the server based on the user's learning progress and emotional state, and is provided to support the user's learning.

[1327] "Emotional data" refers to information collected through the device's camera and microphone, such as the user's facial expressions and tone of voice, and is data that indicates the user's emotional state.

[1328] "Adaptive change" means that the content and difficulty of the learning content and feedback are dynamically adjusted based on the user's emotional state analyzed by the server using an emotion engine.

[1329] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[1330] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[1331] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[1332] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[1333] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[1334] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[1335] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[1336] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[1337] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[1338] Prompt Sentence Examples

[1339] "Generate detailed learning content about the Ainu language and send simple questions and encouraging messages to frustrated users."

[1340] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1341] Step 1:

[1342] (input)

[1343] Users enter their name, email address, password, and the language they want to learn into the terminal.

[1344] (process)

[1345] The terminal packages the input information in JSON format and sends it to the server.

[1346] (output)

[1347] The server parses the received JSON data and saves it in the database. If the registration is successful, the server generates a confirmation email and sends it to the user's email address.

[1348] (Specific actions)

[1349] The server analyzes the submitted user information, performs the registration process in the database, and sends a confirmation email to the user using the SMTP protocol.

[1350] Step 2:

[1351] (input)

[1352] The user clicks on the link in the confirmation email.

[1353] (process)

[1354] The device sends a request through this link to the server, which then activates the account.

[1355] (output)

[1356] The server validates the user's account and returns the result to the terminal.

[1357] (Specific actions)

[1358] The server validates the authentication token and updates the account status in the database to "valid."

[1359] Step 3:

[1360] (input)

[1361] The user enters their email address and password on the login screen.

[1362] (process)

[1363] The terminal transmits this authentication information to the server.

[1364] (output)

[1365] The server receives the information, checks it against a database, and if authentication is successful, starts a login session and returns the session ID to the terminal.

[1366] (Specific actions)

[1367] The server checks the received authentication information against the user information in its database, and if it is correct, it calls an API to start a session.

[1368] Step 4:

[1369] (input)

[1370] Users select the language they want to learn (e.g. Ainu) from the dashboard.

[1371] (process)

[1372] The terminal transmits the selected language information to the server.

[1373] (output)

[1374] The server sends prompts to the generative AI model to generate learning content in the corresponding language, which is then stored in a database and sent to the device.

[1375] (Specific actions)

[1376] The server uses an API to send prompt sentences to the generative AI model, stores the returned learning content in a database, and sends it to the user's device.

[1377] Step 5:

[1378] (input)

[1379] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time.

[1380] (process)

[1381] The collected data is formatted to be sent to the emotion engine.

[1382] (output)

[1383] The server uses an emotion engine to analyze the collected data and determine the user's emotional state.

[1384] (Specific actions)

[1385] The device digitizes the data acquired from the camera and microphone, and uploads it using an API that sends it to the emotion engine. The server then receives the analysis results.

[1386] Step 6:

[1387] (input)

[1388] Users become frustrated or disinterested while learning.

[1389] (process)

[1390] Based on the analysis results, the server generates learning content and feedback that is adapted to the user's emotional state.

[1391] (output)

[1392] New content and feedback will be sent to your device.

[1393] (Specific actions)

[1394] Based on the results of the emotion engine, the server reuses the generative AI model to dynamically generate content and feedback and send it to the device.

[1395] Step 7:

[1396] (input)

[1397] A user searches for specific cultural context information.

[1398] (process)

[1399] The server analyzes the user's level of interest using an emotion engine.

[1400] (output)

[1401] Highly relevant information and documentary videos are given priority.

[1402] (Specific actions)

[1403] The server analyzes the search query and sentiment data entered by the user, filters out highly relevant information resources, and sends them to the terminal.

[1404] Step 8:

[1405] (input)

[1406] The user completes the practice module and sends the results to the server.

[1407] (process)

[1408] The server also takes into account emotional data and generates positive feedback and specific suggestions for improvement, which it then sends to the device.

[1409] (output)

[1410] Feedback and suggestions for improvement are displayed on the user's device.

[1411] (Specific actions)

[1412] The server analyzes the user's practice results and emotional data, generates appropriate feedback and improvement suggestions, and sends them to the device.

[1413] (Application example 2)

[1414] 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."

[1415] In the process of learning a language that is disappearing, maintaining learners' motivation and providing them with an effective and deep understanding of the cultural background is a challenge. In particular, adaptive feedback and content adjustment based on the learner's emotional state are necessary. Furthermore, maximizing learning effectiveness in real time based on individual progress is required.

[1416] 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.

[1417] In this invention, the server includes means for allowing a user to select a language they wish to learn, means for generating learning content for the language using a generative AI model, means for providing the generated learning content to the user, means for analyzing the user's emotional state in real time using an emotion analysis engine, means for adaptively changing the difficulty level and feedback of the learning content based on the user's emotional state, means for tracking the user's learning progress and providing individualized feedback, and means for acquiring and providing cultural background information related to the language selected by the user. This allows the server to provide optimal content according to the user's emotional state and learning progress, improving the personalized learning experience and enabling effective language and cultural acquisition.

[1418] "User" refers to an individual or group of people who use this system to learn a language.

[1419] A "language to learn" is a particular language that a user wishes to learn.

[1420] "Server" refers to a computer system for storing and processing user data and providing learning content.

[1421] A "generative AI model" refers to a device or program that uses artificial intelligence algorithms to automatically generate learning content optimized for specific tasks.

[1422] "Learning Content" refers to learning materials and resources for learning a language of a user's choice, including text, audio, video, etc.

[1423] An "emotion analysis engine" refers to a device or program that analyzes data such as a user's facial expressions and tone of voice to identify their emotional state in real time.

[1424] "Emotional state" refers to the mental and emotional state a user exhibits while learning, including, for example, frustration or interest.

[1425] "Adaptive change" refers to dynamically changing the difficulty and type of learning content, taking into account the user's current situation and reactions.

[1426] "Learning progress" refers to the progress a user makes in the process of learning a language.

[1427] "Personalized feedback" refers to providing personalized advice and assessments based on a user's learning progress and emotional state.

[1428] "Cultural background information" refers to information such as history, customs, and culture related to the language the user is learning.

[1429] "Real-time" refers to the fact that user input and emotional state are analyzed in real time, and feedback and content are provided instantly.

[1430] This invention combines a sentiment analysis engine with a system for learning a language that is being lost to improve the effectiveness and experience of learning. This system is configured according to the roles of a user, a terminal, and a server. A specific embodiment of this system will be described in detail below.

[1431] Hardware and Software Use

[1432] The server stores and processes data and uses a generative AI model (e.g., GPT-3 powered API) to provide appropriate learning content. The user device is a smartphone or a head-mounted display (e.g., Oculus Quest 2), which displays the learning content and analyzes the user's emotions.

[1433] The server also has installed an emotion analysis engine (e.g., OpenCV, DeepFace) to analyze the user's facial expressions and voice data, which allows it to identify the user's emotional state in real time and provide adaptive feedback.

[1434] System processing flow

[1435] 1. User Registration and Login

[1436] Users register using their own devices. The devices send the user's name, email address, password, and the language they wish to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[1437] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[1438] 2. Providing learning content

[1439] After logging in, users access a dashboard and select the language they want to learn. Once the user selects this language, the device sends the selection to the server, which uses a generative AI model to generate learning content (text, audio, video) and delivers it to the user.

[1440] 3. Sentiment Analysis and Adaptive Learning Support

[1441] The system incorporates an emotion analysis engine that recognizes the user's emotional state in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the analysis engine. The server then uses the emotion analysis engine to analyze this data and understand the user's emotional state.

[1442] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server will recognize this through its emotion analysis engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[1443] If a user expresses interest, the server can provide more detailed cultural background information and historical content. For example, if a user expresses interest in the history of Ainu culture, the server can provide a relevant documentary video.

[1444] Examples of prompt statements

[1445] 1. "Generate simple Ainu language exercises."

[1446] 2. "Please provide detailed content about Ainu culture."

[1447] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining a sentiment analysis engine with a generative AI model, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[1448] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1449] Step 1: User Registration

[1450] Input: Name, email address, password, and language you want to learn entered by the user via the device.

[1451] How it works: The device sends the entered information to the server, which stores it in a database and sends a confirmation email if registration is successful.

[1452] Output: Sends a confirmation email to the user and saves the user information in the database.

[1453] Step 2: Activate your account

[1454] Input: The user clicks on the link in the confirmation email they received.

[1455] What happens: The server verifies that the link was clicked and changes the account status to active.

[1456] Output: The user account is enabled.

[1457] Step 3: Log in

[1458] Input: The user enters their email address and password into the device.

[1459] Operation: The terminal sends a login request to the server, which checks it against a database and authenticates it.

[1460] Output: A login session is initiated and the user can access the system.

[1461] Step 4: Dashboard Access

[1462] Input: User accesses the dashboard after logging in.

[1463] How it works: The server collects the user's language history and progress data, generates a dashboard, and sends it to the device.

[1464] Output: A screen for selecting the language to learn and progress will be displayed.

[1465] Step 5: Select language and generate learning content

[1466] Input: The user selects the language they want to learn and sends that information to the server.

[1467] How it works: The server uses the generative AI model to generate learning content (text, audio, video).

[1468] Output: The generated learning content is provided to the user's device.

[1469] Step 6: Sentiment Analysis

[1470] Input: The user's facial expressions and tone of voice are input to the device via the camera and microphone.

[1471] How it works: The device sends this data to a server, which uses an emotion analysis engine to analyze the user's emotional state in real time.

[1472] Output: Data about the user's emotional state is sent to the server.

[1473] Step 7: Adaptive learning support

[1474] Input: User emotion data output by the emotion analysis engine.

[1475] How it works: The server adaptively changes the difficulty level and feedback of learning content based on the user's emotional state.

[1476] For example, if a frustrated emotional state is detected, the server generates less challenging exercises and encouraging messages.

[1477] Conversely, if they express interest, we generate content with detailed cultural background and history.

[1478] Output: Adaptively adjusted learning content and feedback are provided to the user.

[1479] Step 8: Evaluation and feedback of practice results

[1480] Input: The results of the practice module selected by the user.

[1481] How it works: The device sends the practice results to the server, which evaluates them and generates feedback, taking into account the emotional data.

[1482] Provide positive feedback and specific suggestions for improvement.

[1483] Output: Rating and feedback is provided to the user.

[1484] 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.

[1485] 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.

[1486] 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.

[1487] [Fourth embodiment]

[1488] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1489] 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.

[1490] 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).

[1491] 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.

[1492] 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.

[1493] 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).

[1494] 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. 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.

[1495] 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.

[1496] 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.

[1497] 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.

[1498] 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.

[1499] 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.

[1500] 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."

[1501] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. A specific embodiment of this system will be described below.

[1502] First, a user registers using their own device. The device receives the user's name, email address, password, and the language they want to learn, and sends it to the server. The server receives this information and stores it in a database. If registration is successful, the server sends the user a confirmation email. The user clicks the link in this confirmation email to activate their account, completing the registration process.

[1503] Next, the user enters their email address and password on the login screen and sends an authentication request to the server, which checks the database and, if successful, starts a login session, allowing the user to access the system.

[1504] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, and the server uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, etc., and the server provides it to the device. The device displays the learning content to the user, who then uses it to advance their language learning.

[1505] Furthermore, if the user wants to know about the cultural background related to the language as they learn it, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide it to the user through the device.

[1506] The system also constantly tracks the user's learning progress and provides personalized feedback. For example, the server analyzes whether the user has mastered a certain language skill or where they are struggling, and provides appropriate advice on improvement and additional learning resources.

[1507] When a user uses the practice module, the user similarly sends information to the server via the terminal, and the server provides the user with practice content such as quizzes, audio practice, translation practice, etc. When the user performs the practice and sends the results to the server, the server evaluates the practice results, generates feedback, and provides it to the user via the terminal.

[1508] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[1509] The processing flow will be explained below.

[1510] Step 1:

[1511] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[1512] Step 2:

[1513] The terminal acquires the input information and sends it to the server.

[1514] Step 3:

[1515] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[1516] Step 4:

[1517] The user receives a confirmation email and clicks the link in the email to activate their account.

[1518] Step 5:

[1519] The user enters their email address and password on the login page and submits the login request.

[1520] Step 6:

[1521] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[1522] Step 7:

[1523] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[1524] Step 8:

[1525] The terminal transmits the selected language information to the server.

[1526] Step 9:

[1527] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[1528] Step 10:

[1529] The server transmits the generated learning content to the terminal.

[1530] Step 11:

[1531] The terminal displays the received study content to the user, and the user begins studying.

[1532] Step 12:

[1533] While studying, users search for specific cultural background information (e.g., the history of Ainu culture).

[1534] Step 13:

[1535] The terminal sends the user's search query to the server.

[1536] Step 14:

[1537] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[1538] Step 15:

[1539] The terminal displays the received cultural background information to the user.

[1540] Step 16:

[1541] The user selects a practice module such as a quiz, audio practice, or translation practice.

[1542] Step 17:

[1543] The terminal transmits the selected practice module information to the server.

[1544] Step 18:

[1545] The server generates practice content according to the practice module and transmits it to the terminal.

[1546] Step 19:

[1547] The terminal displays the received practice content to the user, and the user starts practicing.

[1548] Step 20:

[1549] The user sends the practice results to the server.

[1550] Step 21:

[1551] The server evaluates the practice results and generates feedback.

[1552] Step 22:

[1553] The server transmits the generated feedback to the terminal.

[1554] Step 23:

[1555] The terminal displays the received feedback to the user.

[1556] This allows users to effectively learn a language that is being lost.

[1557] Example 1

[1558] 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."

[1559] To build a system for effectively learning a language that is being lost, it is necessary to provide an environment where users can easily start learning the language, as well as functions for providing individual feedback and cultural background information. Conventional language learning systems have limited functionality, making it difficult for learners to progress efficiently. Furthermore, implementing a wide range of functions, such as creating and managing user accounts, tracking learning progress, and providing practice content, is complicated.

[1560] 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.

[1561] In this invention, the server includes: means for a user to select a language they wish to learn; means for the server to generate learning content for the language using a generative AI model; means for the server to provide the generated learning content to the user; means for tracking the user's learning progress and providing individual feedback; means for acquiring and providing cultural background information related to the language selected by the user; means for receiving information required for the user to register and storing it in a database; means for the server to generate and send a confirmation email to the user; and means for the user to activate their account by clicking a link in the email. This enables a series of processes, from starting language learning to tracking progress, providing feedback, and learning cultural background, to be carried out in an integrated and efficient manner.

[1562] "User" refers to an individual or organization that uses the System to learn a language.

[1563] "Server" refers to a computer system that processes data and provides learning content and feedback in response to user requests.

[1564] "Generative AI model" refers to artificial intelligence technology that generates learning content based on specific data inputs.

[1565] "Learning Content" refers to materials such as text, audio, and video provided to support language learning.

[1566] "New registration" refers to the process by which a user registers the account information required to access the system for the first time.

[1567] "Verification Email" means the email containing the link a User needs to complete the registration process.

[1568] A "login session" refers to a series of access privilege management states that are initiated when a user accesses a system.

[1569] "Dashboard" refers to the interface that is displayed when a user accesses the system and is used to manage various operations and information.

[1570] "Cultural background information" refers to information about history, folklore, important events, customs, etc. related to the language being studied.

[1571] "Progress information" refers to data relating to progress and achievement recorded as a user progresses in language learning.

[1572] "Feedback" refers to evaluations and advice provided based on the user's learning progress.

[1573] "Practice Module" refers to practical learning features such as quizzes, audio practice, and translation practice provided for language learning.

[1574] The present invention is a system for effectively learning a language that is being lost, and is realized through cooperation between users, terminals, and a server. Specific embodiments will be described below.

[1575] First, a user registers using their device. The device receives the user's name, email address, password, and desired language and sends this information to the server. The server receives this information and stores it in a database (e.g., MySQL or PostgreSQL). Once the information is saved, the server generates a confirmation email and sends it to the user via the SMTP server. The user clicks the link in the confirmation email to activate their account, completing the registration process.

[1576] Next, the user enters their email address and password on the login screen and sends an authentication request to the server via their device. The server authenticates them against a database. If authentication is successful, the server starts a login session and the user is granted access to the dashboard.

[1577] After logging in, the user selects the language they want to learn on the dashboard. For example, if the user wants to learn "Ainu," the device sends that selection information to the server. The server then uses a generative AI model (e.g., GPT-4) to generate learning content for "Ainu." An example of a prompt used at this time is as follows:

[1578] Example prompt: "The user wants to learn Ainu. Please generate text, audio, and video learning content for beginners."

[1579] The generated learning content includes text, audio, video, etc., and is sent from the server to the device, which then displays the learning content to the user, allowing the user to progress with their language learning.

[1580] Furthermore, as the user progresses with their studies, progress information is periodically sent from the device to the server. The server receives this progress information and analyzes the user's learning progress and achievement. Based on the results of this analysis, the server generates personalized feedback and suggests appropriate advice for improvement and additional learning resources. This feedback is also provided to the user via the device.

[1581] If the user also wants to know about the cultural background related to the language selected, the device will send a query to the server based on the user's request, and the server will retrieve relevant historical information, folklore, important events, etc. from the database and provide them to the user through the device.

[1582] Furthermore, when a user uses a practice module (such as a quiz, audio practice, or translation practice), the user sends information to the server via the device, and the server generates practice content. When the user performs the practice and sends the results to the server, the server evaluates the practice results and generates feedback to provide to the user via the device.

[1583] Through this series of processes, users can effectively learn languages ​​that are disappearing and understand their cultural background. This system aims to support the learning of languages ​​that are disappearing and the preservation of culture, thereby creating new value.

[1584] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1585] Step 1:

[1586] A user visits the sign-up page.

[1587] A user enters the system's URL using a web browser to access the new registration page. Input: User's access information. Output: Display of the registration page.

[1588] Step 2:

[1589] The user enters the required information.

[1590] The user enters their name, email address, password, and the language they want to learn. Input: User information. Output: Registration information entered by the user.

[1591] Step 3:

[1592] The terminal sends the input information to the server.

[1593] The terminal sends the information entered by the user to the server via a POST request. Input: Registration information entered by the user. Output: Request to the server.

[1594] Step 4:

[1595] The server receives the information and stores it in a database.

[1596] The server receives the information, validates it, and then stores it in a database such as MySQL or PostgreSQL. Input: Submitted user information. Output: User information stored in the database.

[1597] Step 5:

[1598] The server generates a confirmation email and sends it to the user.

[1599] The server generates and sends a confirmation email using an SMTP server. Input: User information (email address). Output: The confirmation email sent.

[1600] Step 6:

[1601] The user clicks on the link in the confirmation email.

[1602] The user clicks on the link in the email to access the page where they can activate their account. Input: Link in the confirmation email. Output: Activation page displayed.

[1603] Step 7:

[1604] The server activates the account.

[1605] After the server verifies the link, it updates the user's account to "enabled." Input: Link access information. Output: Activated account information.

[1606] Step 8:

[1607] The user visits the login page.

[1608] The user accesses the system's login page. Input: User's access information. Output: Login page is displayed.

[1609] Step 9:

[1610] The user enters their email address and password.

[1611] The user enters their email address and password. Input: Login information. Output: Credentials entered by the user.

[1612] Step 10:

[1613] The terminal sends the input information to the server.

[1614] The terminal sends the entered information to the server via a POST request. Input: Authentication information. Output: Request to the server.

[1615] Step 11:

[1616] The server authenticates against a database.

[1617] The server checks the entered information against the database to verify that it is correct. Input: Authentication information. Output: Authentication result.

[1618] Step 12:

[1619] The server returns the authentication result.

[1620] If authentication is successful, the server starts a login session and allows the user to access the dashboard. Input: Authentication result. Output: Dashboard access permission.

[1621] Step 13:

[1622] A user visits a dashboard.

[1623] Authenticated users log in to the dashboard and configure various settings. Input: Dashboard access. Output: Dashboard display.

[1624] Step 14:

[1625] The user selects the language they want to learn.

[1626] The user selects the language they want to learn, such as "Ainu." Input: Selection of the language they want to learn. Output: Selection information.

[1627] Step 15:

[1628] The terminal transmits the selection information to the server.

[1629] The terminal sends the selected information to the server via a POST request. Input: Selected information. Output: Request to the server.

[1630] Step 16:

[1631] The server sends a prompt to the generative AI model.

[1632] The server sends a prompt to the generative AI model (e.g., GPT-4) requesting it to generate learning content for "Ainu." Input: Selection information and prompt. Output: Learning content generation request.

[1633] Step 17:

[1634] Generative AI models generate learning content.

[1635] The generative AI model generates appropriate learning content based on the prompt. Input: Prompt. Output: Generated learning content.

[1636] Step 18:

[1637] The server transmits the generated learning content to the terminal.

[1638] The server sends the generated learning content, such as text, audio, and video, back to the device. Input: Generated learning content. Output: Transmission to the device.

[1639] Step 19:

[1640] The terminal displays the learning content to the user.

[1641] The terminal displays the received learning content to the user. Input: Learning content. Output: Display to user.

[1642] Step 20:

[1643] Update progress information as users learn.

[1644] As users progress through their studies, progress information is automatically sent from their devices to the server. Input: User's study progress. Output: Progress information sent to the server.

[1645] Step 21:

[1646] The server analyzes the progress data and generates feedback.

[1647] The server analyzes the submitted progress data and generates feedback based on the learning progress and achievement level. Input: Progress data. Output: Feedback generation.

[1648] Step 22:

[1649] The server generates feedback and sends it to the device.

[1650] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[1651] Step 23:

[1652] The device displays the feedback to the user.

[1653] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[1654] Step 24:

[1655] The user requests cultural context information.

[1656] When a user wants to know cultural background information, he enters a query. Input: Request for cultural background information. Output: Query information.

[1657] Step 25:

[1658] The device sends a query to the server.

[1659] The terminal sends the user's query to the server. Input: Query information. Output: Request to the server.

[1660] Step 26:

[1661] The server retrieves the relevant information from the database.

[1662] The server retrieves relevant historical information, folklore, important events, etc. from the database. Input: Query information. Output: Related information.

[1663] Step 27:

[1664] The server sends the relevant information to the terminal.

[1665] The server sends the relevant information to the terminal. Input: relevant information. Output: transmission to the terminal.

[1666] Step 28:

[1667] The terminal displays the relevant information to the user.

[1668] The terminal displays the received relevant information to the user. Input: relevant information. Output: display to the user.

[1669] Step 29:

[1670] The user selects a practice module.

[1671] The user selects a practice module such as quiz, audio practice, translation practice, etc. Input: Selection of practice module. Output: Selection information.

[1672] Step 30:

[1673] The terminal transmits the selection information to the server.

[1674] The terminal sends the selection information to the server. Input: Selection information. Output: Request to the server.

[1675] Step 31:

[1676] The server generates the practice content.

[1677] The server generates appropriate practice content based on the selection. Input: Selection information. Output: Generated practice content.

[1678] Step 32:

[1679] The server transmits the practice content to the terminal.

[1680] The server sends the generated practice content to the terminal. Input: Generated practice content. Output: Transmission to the terminal.

[1681] Step 33:

[1682] The terminal displays the practice content to the user.

[1683] The device displays the practice content to the user. Input: Practice content. Output: Display to user.

[1684] Step 34:

[1685] The user performs the exercise.

[1686] The user performs the presented practice content. Input: Presented practice content. Output: User's practice results.

[1687] Step 35:

[1688] The device sends the practice results to the server.

[1689] The device sends the user's practice results to the server. Input: User's practice results. Output: Transmission to the server.

[1690] Step 36:

[1691] The server evaluates the practice results and generates feedback.

[1692] The server evaluates the received practice results and generates feedback based on them. Input: Practice results. Output: Generated feedback.

[1693] Step 37:

[1694] The server generates feedback and sends it to the device.

[1695] The server sends the generated feedback to the terminal. Input: Generated feedback. Output: Send to terminal.

[1696] Step 38:

[1697] The device displays the feedback to the user.

[1698] The terminal displays the received feedback to the user. Input: Feedback. Output: Display to user.

[1699] (Application example 1)

[1700] 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."

[1701] Conventional language learning systems have the problem that it is difficult for users to take advantage of language learning experiences in a specific location, and they also have the problem that the effectiveness of learning is limited because they do not provide localized content or feedback according to the user's learning progress.

[1702] 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.

[1703] In this invention, the server includes a means for allowing a user to access content and information for language learning in a specific location, a means for providing learning content localized according to the user's location, a means for the user to check their learning progress in the specific location and receive appropriate feedback, and a means for providing content according to local events based on the results of the user's practice in the specific location, thereby enabling users to learn languages ​​more effectively and in accordance with the context in a physical store or other specific location.

[1704] - "User" means a person who uses the system to learn a language.

[1705] "Language to learn" refers to the specific language that the user wishes to learn through the system.

[1706] "Means" refers to the methods or devices that a user uses to achieve a goal.

[1707] A "server" is a computer system that generates and distributes learning content and stores and processes data.

[1708] A "generative AI model" is an artificial intelligence algorithm that automatically generates learning content based on the language of your choice.

[1709] "Learning content" refers to learning materials such as text, audio, and video provided to support language learning.

[1710] "Personalized feedback" refers to specific advice and assessments provided based on a user's learning progress and performance.

[1711] "Cultural background information" refers to data about the culture, history, folklore, etc. of the selected language.

[1712] "Specific location" refers to a brick-and-mortar store or other physical learning environment.

[1713] "Localized learning content" refers to learning materials that are customized for the location or region from which the user accesses them.

[1714] "User Account" means a unique identification created by a User to access the System.

[1715] "Authentication" refers to the verification procedures required for a user to log into a system.

[1716] A "practice module" is a set of practice materials or exercises designed to improve a particular language skill selected by the user.

[1717] A "local event" is a learning activity or event that a user conducts in a specific location.

[1718] This invention relates to a system for effectively learning languages ​​that are being lost, and is realized through cooperation between users, terminals, and servers. Specifically, users use their smartphones to register and select the languages ​​they want to learn.

[1719] The server uses a generative AI model to generate learning content for the selected language and provides it to the user. The learning content includes text, audio, and video, which the user can view on their device to progress through the learning process. The server tracks learning progress and provides personalized feedback. It also provides cultural background information related to the language the user is learning.

[1720] Additionally, the system includes functionality to support learning in specific locations, such as brick-and-mortar stores. When users are in a specific location, they can access localized learning content tailored to that location. This enhances the brick-and-mortar learning experience and provides real-life knowledge and experiences related to the learning content.

[1721] The system's programs are built using frameworks such as Python and Flask, and databases such as SQLite are used to store data. The system's hardware includes smartphones and Wi-Fi access points.

[1722] For example, if a user wants to learn the Ainu language at a physical store, they can scan a QR code to access the store and receive specific learning content related to Ainu culture. Additionally, cultural events and special experience content will be provided based on the knowledge and skills the user has gained while studying at a specific location.

[1723] Below are some example input prompts for a generative AI model:

[1724] "Generate learning content about cultural background, history, and important events based on the user's language of choice. For example, create text, audio, and video that includes the following information: 'History of the Ainu Language,' 'Ainu Folklore,' and 'Modern Ainu Culture.'"

[1725] In this way, the system of the present invention provides localized content when users study in a specific location, enabling more effective language learning and cultural understanding.

[1726] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1727] Step 1:

[1728] A user accesses the system using a smartphone and enters their name, email address, password, and desired language into the new registration form. The entered data is sent from the device to the server. The server receives this information and stores it in an SQLite database. The server then sends a confirmation email to the user, and the user clicks on the link in the email to activate their account. This registers the user's basic information in the system. The input data is the name, email address, password, and desired language, and the output data is the activation of the user account.

[1729] Step 2:

[1730] The user enters an email address and password on the login screen and sends an authentication request. The device sends this information to the server, which checks the authentication information against a database. If authentication is successful, the server starts a login session and provides the user with a dashboard screen. The input data is the email address and password, and the output data is the start of the login session.

[1731] Step 3:

[1732] The user selects the language they want to learn from the dashboard screen. The selection information is sent from the device to the server. The server uses a generative AI model to generate learning content based on that language. The content includes text, audio, video, etc. The generated learning content is sent from the server to the device and provided to the user. The input data is the selected language, and the output data is the generated learning content.

[1733] Step 4:

[1734] The user uses the learning content and sends their progress to the server. The server receives the user's progress data and stores it in a database. The server then analyzes the user's progress, generates individual feedback, and sends it to the device. The input data is the user's learning progress, and the output data is individual feedback.

[1735] Step 5:

[1736] When a user wants to know the cultural background information related to the language they are learning, they send a query from their device to the server. The server retrieves the relevant cultural background information from the database and provides it to the user. The input data is the cultural background information query, and the output data is the relevant cultural background information.

[1737] Step 6:

[1738] When a user studies a language at a physical store, they use their smartphone to scan a QR code placed at a specific location. The scanned information is sent from the device to a server, which retrieves the specific location information. The server then generates localized learning content based on that information and provides it to the user. The input data is the scanned QR code information, and the output data is the localized learning content.

[1739] Step 7:

[1740] The user sends the results of practice performed at a specific location from the terminal to a server. The server receives the practice results and generates an evaluation and feedback. In addition, content related to the local event based on the results is also generated and provided to the user. The input data are the practice results, and the output data are the evaluation, feedback, and content related to the local event.

[1741] 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.

[1742] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[1743] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[1744] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[1745] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[1746] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[1747] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[1748] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[1749] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[1750] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[1751] The processing flow will be explained below.

[1752] Step 1:

[1753] A user visits the sign-up page and enters their name, email address, password, and language preferences.

[1754] Step 2:

[1755] The terminal acquires the input information and sends it to the server.

[1756] Step 3:

[1757] The server stores the received information in a database and sends a confirmation email to the user to let them know that their registration was successful.

[1758] Step 4:

[1759] The user receives a confirmation email and clicks the link in the email to activate their account.

[1760] Step 5:

[1761] The user enters their email address and password on the login page and submits the login request.

[1762] Step 6:

[1763] The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session and notifies the user of the success.

[1764] Step 7:

[1765] The user accesses the dashboard and selects the language they want to learn (e.g., Ainu).

[1766] Step 8:

[1767] The terminal transmits the selected language information to the server.

[1768] Step 9:

[1769] The server uses the generative AI model to generate learning content (text, audio, video) for the specified language.

[1770] Step 10:

[1771] The server transmits the generated learning content to the terminal.

[1772] Step 11:

[1773] The terminal displays the received study content to the user, and the user begins studying.

[1774] Step 12:

[1775] The device collects the user's facial expressions and voice through the camera and microphone and sends them to the emotion engine.

[1776] Step 13:

[1777] The server uses an emotion engine to analyze the user's emotion information and grasp the user's emotional state in real time.

[1778] Step 14:

[1779] The server adaptively changes learning content and feedback messages based on the user's emotional state. For example, if the user is experiencing difficulty, it adjusts the difficulty level or sends encouraging messages.

[1780] Step 15:

[1781] When a user wants to search for cultural background information while studying, they enter a search query.

[1782] Step 16:

[1783] The terminal sends the user's search query to the server.

[1784] Step 17:

[1785] The server retrieves relevant cultural background information from a database and sends it to the terminal.

[1786] Step 18:

[1787] The device displays the received cultural background information to the user, and the emotion engine analyzes the user's emotions and provides additional information according to the user's level of interest and emotion.

[1788] Step 19:

[1789] The user selects a practice module.

[1790] Step 20:

[1791] The terminal transmits the selected practice module information to the server.

[1792] Step 21:

[1793] The server generates practice content according to the practice module and transmits it to the terminal.

[1794] Step 22:

[1795] The terminal displays the received practice content to the user, and the user starts practicing.

[1796] Step 23:

[1797] The user sends the results of their practice to the server. At the same time, the device collects the user's facial expressions and voice and sends them to the emotion engine.

[1798] Step 24:

[1799] The server evaluates the practice results and emotional data and generates feedback.

[1800] Step 25:

[1801] The server transmits the generated feedback to the terminal.

[1802] Step 26:

[1803] The device displays the received feedback to the user, which includes advice and suggestions for improvement based on the analysis results of the emotion engine.

[1804] This allows users to have an optimal learning experience that is tailored to their emotional state, enabling them to effectively acquire a language that is being lost and gain a deeper understanding of its cultural background.

[1805] Example 2

[1806] 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."

[1807] Conventional language learning systems provide learning content without considering the user's emotional state, which has the problem of not improving the learning effect and user experience. In particular, the lack of appropriate feedback and information when users are experiencing difficulty or are interested in the subject can lead to a decrease in users' motivation to learn.

[1808] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for the terminal to collect user emotional data using a camera and microphone, a means for the server to analyze the collected emotional data using an emotion engine and adaptively change the learning content and feedback based on the user's emotional state, and a means for tracking the user's learning progress and providing individualized feedback. This enables adaptive learning support that takes the user's emotional state into consideration.

[1809] A "user" is a person who uses the system to select the language they want to learn and receives learning content.

[1810] A "server" is a device that processes information sent by users, generates learning content and feedback using generative AI models and emotion engines, and provides them to terminals.

[1811] A "terminal" is a device used by a user that transmits input from the user to a server and displays learning content and feedback sent from the server. It also has the function of collecting emotional data using a camera and microphone.

[1812] A "generative AI model" is an artificial intelligence model used by the server, and is a technology that generates learning content (text, audio, video, etc.) related to the language the user wants to learn.

[1813] The "emotion engine" is an analytical engine used by the server, which analyzes emotional data such as the user's facial expressions and tone of voice collected from the terminal to determine the user's emotional state.

[1814] "Learning Content" means educational material in the form of text, audio, video, etc., generated by a generative AI model and related to the language the user wishes to learn.

[1815] "Feedback" refers to adaptive messages and suggestions generated by the server based on the user's learning progress and emotional state, and is provided to support the user's learning.

[1816] "Emotional data" refers to information collected through the device's camera and microphone, such as the user's facial expressions and tone of voice, and is data that indicates the user's emotional state.

[1817] "Adaptive change" means that the content and difficulty of the learning content and feedback are dynamically adjusted based on the user's emotional state analyzed by the server using an emotion engine.

[1818] This invention combines an emotion engine with a system for learning languages ​​that are disappearing, improving the effectiveness and experience of learning. A specific embodiment of this system will be described below, along with the roles of the user, terminal, and server.

[1819] First, a user registers using their device. The device sends the user's name, email address, password, and the language they want to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[1820] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[1821] After logging in, the user accesses the dashboard and selects the language they want to learn. For example, let's say the user wants to learn "Ainu." When the user selects this language, the device sends the selection information to the server, which then uses a generative AI model to generate learning content for "Ainu." This learning content includes text, audio, video, and more. The server provides the generated learning content to the device, which then displays it to the user.

[1822] Furthermore, the system incorporates an emotion engine that recognizes the user's emotions in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the emotion engine. The server then uses the emotion engine to analyze this data and grasp the user's emotional state in real time.

[1823] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server can recognize this through its emotion engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[1824] Furthermore, when a user searches for cultural background information, the emotion engine measures the user's level of interest and emotion and prioritizes providing more relevant information based on this. For example, if a user expresses interest in the history of Ainu culture, the engine will adapt by providing a more detailed documentary video.

[1825] From selecting a practice module to completing it and providing feedback, the emotion engine constantly monitors the user's emotional state to provide an optimal learning experience. For example, when a user completes a practice module and submits the results to the server, the server will take their emotional data into consideration and provide positive feedback and specific suggestions for improvement.

[1826] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining it with an emotion engine, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[1827] Prompt Sentence Examples

[1828] "Generate detailed learning content about the Ainu language and send simple questions and encouraging messages to frustrated users."

[1829] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1830] Step 1:

[1831] (input)

[1832] Users enter their name, email address, password, and the language they want to learn into the terminal.

[1833] (process)

[1834] The terminal packages the input information in JSON format and sends it to the server.

[1835] (output)

[1836] The server parses the received JSON data and saves it in the database. If the registration is successful, the server generates a confirmation email and sends it to the user's email address.

[1837] (Specific actions)

[1838] The server analyzes the submitted user information, performs the registration process in the database, and sends a confirmation email to the user using the SMTP protocol.

[1839] Step 2:

[1840] (input)

[1841] The user clicks on the link in the confirmation email.

[1842] (process)

[1843] The device sends a request through this link to the server, which then activates the account.

[1844] (output)

[1845] The server validates the user's account and returns the result to the terminal.

[1846] (Specific actions)

[1847] The server validates the authentication token and updates the account status in the database to "valid."

[1848] Step 3:

[1849] (input)

[1850] The user enters their email address and password on the login screen.

[1851] (process)

[1852] The terminal transmits this authentication information to the server.

[1853] (output)

[1854] The server receives the information, checks it against a database, and if authentication is successful, starts a login session and returns the session ID to the terminal.

[1855] (Specific actions)

[1856] The server checks the received authentication information against the user information in its database, and if it is correct, it calls an API to start a session.

[1857] Step 4:

[1858] (input)

[1859] Users select the language they want to learn (e.g. Ainu) from the dashboard.

[1860] (process)

[1861] The terminal transmits the selected language information to the server.

[1862] (output)

[1863] The server sends prompts to the generative AI model to generate learning content in the corresponding language, which is then stored in a database and sent to the device.

[1864] (Specific actions)

[1865] The server uses an API to send prompt sentences to the generative AI model, stores the returned learning content in a database, and sends it to the user's device.

[1866] Step 5:

[1867] (input)

[1868] The device uses a built-in camera and microphone to collect the user's facial expressions and tone of voice in real time.

[1869] (process)

[1870] The collected data is formatted to be sent to the emotion engine.

[1871] (output)

[1872] The server uses an emotion engine to analyze the collected data and determine the user's emotional state.

[1873] (Specific actions)

[1874] The device digitizes the data acquired from the camera and microphone, and uploads it using an API that sends it to the emotion engine. The server then receives the analysis results.

[1875] Step 6:

[1876] (input)

[1877] Users become frustrated or disinterested while learning.

[1878] (process)

[1879] Based on the analysis results, the server generates learning content and feedback that is adapted to the user's emotional state.

[1880] (output)

[1881] New content and feedback will be sent to your device.

[1882] (Specific actions)

[1883] Based on the results of the emotion engine, the server reuses the generative AI model to dynamically generate content and feedback and send it to the device.

[1884] Step 7:

[1885] (input)

[1886] A user searches for specific cultural context information.

[1887] (process)

[1888] The server analyzes the user's level of interest using an emotion engine.

[1889] (output)

[1890] Highly relevant information and documentary videos are given priority.

[1891] (Specific actions)

[1892] The server analyzes the search query and sentiment data entered by the user, filters out highly relevant information resources, and sends them to the terminal.

[1893] Step 8:

[1894] (input)

[1895] The user completes the practice module and sends the results to the server.

[1896] (process)

[1897] The server also takes into account emotional data and generates positive feedback and specific suggestions for improvement, which it then sends to the device.

[1898] (output)

[1899] Feedback and suggestions for improvement are displayed on the user's device.

[1900] (Specific actions)

[1901] The server analyzes the user's practice results and emotional data, generates appropriate feedback and improvement suggestions, and sends them to the device.

[1902] (Application example 2)

[1903] 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."

[1904] In the process of learning a language that is disappearing, maintaining learners' motivation and providing them with an effective and deep understanding of the cultural background is a challenge. In particular, adaptive feedback and content adjustment based on the learner's emotional state are necessary. Furthermore, maximizing learning effectiveness in real time based on individual progress is required.

[1905] 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.

[1906] In this invention, the server includes means for allowing a user to select a language they wish to learn, means for generating learning content for the language using a generative AI model, means for providing the generated learning content to the user, means for analyzing the user's emotional state in real time using an emotion analysis engine, means for adaptively changing the difficulty level and feedback of the learning content based on the user's emotional state, means for tracking the user's learning progress and providing individualized feedback, and means for acquiring and providing cultural background information related to the language selected by the user. This allows the server to provide optimal content according to the user's emotional state and learning progress, improving the personalized learning experience and enabling effective language and cultural acquisition.

[1907] "User" refers to an individual or group of people who use this system to learn a language.

[1908] A "language to learn" is a particular language that a user wishes to learn.

[1909] "Server" refers to a computer system for storing and processing user data and providing learning content.

[1910] A "generative AI model" refers to a device or program that uses artificial intelligence algorithms to automatically generate learning content optimized for specific tasks.

[1911] "Learning Content" refers to learning materials and resources for learning a language of a user's choice, including text, audio, video, etc.

[1912] An "emotion analysis engine" refers to a device or program that analyzes data such as a user's facial expressions and tone of voice to identify their emotional state in real time.

[1913] "Emotional state" refers to the mental and emotional state a user exhibits while learning, including, for example, frustration or interest.

[1914] "Adaptive change" refers to dynamically changing the difficulty and type of learning content, taking into account the user's current situation and reactions.

[1915] "Learning progress" refers to the progress a user makes in the process of learning a language.

[1916] "Personalized feedback" refers to providing personalized advice and assessments based on a user's learning progress and emotional state.

[1917] "Cultural background information" refers to information such as history, customs, and culture related to the language the user is learning.

[1918] "Real-time" refers to the fact that user input and emotional state are analyzed in real time, and feedback and content are provided instantly.

[1919] This invention combines a sentiment analysis engine with a system for learning a language that is being lost to improve the effectiveness and experience of learning. This system is configured according to the roles of a user, a terminal, and a server. A specific embodiment of this system will be described in detail below.

[1920] Hardware and Software Use

[1921] The server stores and processes data and uses a generative AI model (e.g., GPT-3 powered API) to provide appropriate learning content. The user device is a smartphone or a head-mounted display (e.g., Oculus Quest 2), which displays the learning content and analyzes the user's emotions.

[1922] The server also has installed an emotion analysis engine (e.g., OpenCV, DeepFace) to analyze the user's facial expressions and voice data, which allows it to identify the user's emotional state in real time and provide adaptive feedback.

[1923] System processing flow

[1924] 1. User Registration and Login

[1925] Users register using their own devices. The devices send the user's name, email address, password, and the language they wish to learn to the server. The server stores this information in a database and sends a confirmation email if registration is successful. The user clicks on a link in the confirmation email to activate their account.

[1926] Next, the user enters their email address and password on the login screen and submits a login request. The server receives the information, authenticates it against a database, and if authentication is successful, starts a login session, allowing the user to access the system.

[1927] 2. Providing learning content

[1928] After logging in, users access a dashboard and select the language they want to learn. Once the user selects this language, the device sends the selection to the server, which uses a generative AI model to generate learning content (text, audio, video) and delivers it to the user.

[1929] 3. Sentiment Analysis and Adaptive Learning Support

[1930] The system incorporates an emotion analysis engine that recognizes the user's emotional state in real time. The device collects the user's facial expressions and tone of voice through a camera and microphone and sends them to the analysis engine. The server then uses the emotion analysis engine to analyze this data and understand the user's emotional state.

[1931] For example, if a user experiences difficulty while studying and shows facial expressions that indicate frustration, the server will recognize this through its emotion analysis engine and adaptively change the learning content and feedback, such as providing easier practice questions or sending encouraging messages to motivate the user.

[1932] If a user expresses interest, the server can provide more detailed cultural background information and historical content. For example, if a user expresses interest in the history of Ainu culture, the server can provide a relevant documentary video.

[1933] Examples of prompt statements

[1934] 1. "Generate simple Ainu language exercises."

[1935] 2. "Please provide detailed content about Ainu culture."

[1936] In this way, the system helps users effectively learn languages ​​that are disappearing and gain a deeper understanding of their cultural background. In particular, by combining a sentiment analysis engine with a generative AI model, the system can provide an adaptive learning experience that responds to the user's emotional state, improving learning efficiency and user satisfaction.

[1937] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1938] Step 1: User Registration

[1939] Input: Name, email address, password, and language you want to learn entered by the user via the device.

[1940] How it works: The device sends the entered information to the server, which stores it in a database and sends a confirmation email if registration is successful.

[1941] Output: Sends a confirmation email to the user and saves the user information in the database.

[1942] Step 2: Activate your account

[1943] Input: The user clicks on the link in the confirmation email they received.

[1944] What happens: The server verifies that the link was clicked and changes the account status to active.

[1945] Output: The user account is enabled.

[1946] Step 3: Log in

[1947] Input: The user enters their email address and password into the device.

[1948] Operation: The terminal sends a login request to the server, which checks it against a database and authenticates it.

[1949] Output: A login session is initiated and the user can access the system.

[1950] Step 4: Dashboard Access

[1951] Input: User accesses the dashboard after logging in.

[1952] How it works: The server collects the user's language history and progress data, generates a dashboard, and sends it to the device.

[1953] Output: A screen for selecting the language to learn and progress will be displayed.

[1954] Step 5: Select language and generate learning content

[1955] Input: The user selects the language they want to learn and sends that information to the server.

[1956] How it works: The server uses the generative AI model to generate learning content (text, audio, video).

[1957] Output: The generated learning content is provided to the user's device.

[1958] Step 6: Sentiment Analysis

[1959] Input: The user's facial expressions and tone of voice are input to the device via the camera and microphone.

[1960] How it works: The device sends this data to a server, which uses an emotion analysis engine to analyze the user's emotional state in real time.

[1961] Output: Data about the user's emotional state is sent to the server.

[1962] Step 7: Adaptive learning support

[1963] Input: User emotion data output by the emotion analysis engine.

[1964] How it works: The server adaptively changes the difficulty level and feedback of learning content based on the user's emotional state.

[1965] For example, if a frustrated emotional state is detected, the server generates less challenging exercises and encouraging messages.

[1966] Conversely, if they express interest, we generate content with detailed cultural background and history.

[1967] Output: Adaptively adjusted learning content and feedback are provided to the user.

[1968] Step 8: Evaluation and feedback of practice results

[1969] Input: The results of the practice module selected by the user.

[1970] How it works: The device sends the practice results to the server, which evaluates them and generates feedback, taking into account the emotional data.

[1971] Provide positive feedback and specific suggestions for improvement.

[1972] Output: Rating and feedback is provided to the user.

[1973] 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.

[1974] 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.

[1975] 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 robot 414.

[1976] 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.

[1977] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1978] 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.

[1979] 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).

[1980] 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.

[1981] 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."

[1982] 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.

[1983] 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).

[1984] 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.

[1985] 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.

[1986] 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.

[1987] 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.

[1988] 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.

[1989] 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.

[1990] 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.

[1991] 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.

[1992] 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.

[1993] 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.

[1994] The following is further disclosed regarding the above embodiment.

[1995] (Claim 1)

[1996] a means for the user to select the language they wish to learn;

[1997] A means for the server to generate learning content for the corresponding language using the generative AI model;

[1998] a means for the server to provide the generated learning content to the user;

[1999] a means of tracking the user's learning progress and providing personalized feedback;

[2000] means for obtaining and providing cultural context information relevant to a user-selected language;

[2001] A system including:

[2002] (Claim 2)

[2003] means for receiving user-entered information and creating a user account;

[2004] a means for the user to enter login information and perform authentication;

[2005] The system of claim 1 further comprising:

[2006] (Claim 3)

[2007] means for providing practice content corresponding to a practice module selected by a user;

[2008] means for receiving the results of the exercises performed by the user and providing evaluation and feedback;

[2009] The system of claim 1 further comprising:

[2010] "Example 1"

[2011] (Claim 1)

[2012] a means for the user to select the language they wish to learn;

[2013] A means for the server to generate learning content for the corresponding language using the generative AI model;

[2014] a means for the server to provide the generated learning content to the user;

[2015] a means of tracking the user's learning progress and providing personalized feedback;

[2016] means for obtaining and providing cultural context information relevant to a user-selected language;

[2017] a means for receiving and storing in a database the information required for a user to register;

[2018] means for the server to generate and send a confirmation email to the user;

[2019] A way for users to activate their account by clicking a link in the email;

[2020] A system including:

[2021] (Claim 2)

[2022] means for receiving user-entered information and creating a user account;

[2023] a means for the user to enter login information and perform authentication;

[2024] A way for users to access their dashboard and select the language they want to learn;

[2025] a server that generates learning content by sending prompt sentences to a generative AI model;

[2026] a means for a user to request cultural background information and for a server to retrieve and provide relevant information from a database;

[2027] 10. The system of claim 1, comprising:

[2028] (Claim 3)

[2029] means for providing practice content corresponding to a practice module selected by a user;

[2030] means for receiving the results of the exercises performed by the user and providing evaluation and feedback;

[2031] means for transmitting the user's learning progress information to a server, and the server analyzing the progress data to generate feedback;

[2032] a means for transmitting the cultural background information requested by the user as a query to a server, and for the server to retrieve and provide related information from a database;

[2033] 10. The system of claim 1, comprising:

[2034] "Application Example 1"

[2035] (Claim 1)

[2036] a means for the user to select the language they wish to learn;

[2037] A means for the server to generate learning content for the corresponding language using the generative AI model;

[2038] a means for the server to provide the generated learning content to the user;

[2039] a means of tracking the user's learning progress and providing personalized feedback;

[2040] means for obtaining and providing cultural context information relevant to a user-selected language;

[2041] A means for users to access content and information for language learning in a specific location;

[2042] A means for providing localized learning content according to the location from which the user accesses the content;

[2043] A system including:

[2044] (Claim 2)

[2045] means for receiving user-entered information and creating a user account;

[2046] a means for the user to enter login information and perform authentication;

[2047] A means for users to check their learning progress in a specific location and receive appropriate feedback;

[2048] The system of claim 1 further comprising:

[2049] (Claim 3)

[2050] means for providing practice content corresponding to a practice module selected by a user;

[2051] means for receiving the results of the exercises performed by the user and providing evaluation and feedback;

[2052] A means for providing content according to a local event based on the results of a user's practice at a specific location;

[2053] The system of claim 1 further comprising:

[2054] "Example 2: Combining Emotion Engines"

[2055] (Claim 1)

[2056] a means for the user to select the language they wish to learn;

[2057] A means for the server to generate learning content for the corresponding language using the generative AI model;

[2058] a means for the server to provide the generated learning content to the user;

[2059] a means of tracking the user's learning progress and providing personalized feedback;

[2060] means for obtaining and providing cultural context information relevant to a user-selected language;

[2061] A means for the terminal to collect user emotion data using a camera and a microphone;

[2062] a means for the server to analyze the collected emotion data using an emotion engine and adaptively change learning content and feedback based on the user's emotional state;

[2063] A system including:

[2064] (Claim 2)

[2065] means for receiving user-entered information and creating a user account;

[2066] a means for the user to enter login information and perform authentication;

[2067] The system of claim 1 further comprising:

[2068] (Claim 3)

[2069] means for providing practice content corresponding to a practice module selected by a user;

[2070] means for receiving the results of the exercises performed by the user and providing evaluation and feedback;

[2071] The system of claim 1 further comprising:

[2072] "Application example 2 when combining emotion engines"

[2073] (Claim 1)

[2074] a means for the user to select the language they wish to learn;

[2075] A means for the server to generate learning content for the corresponding language using the generative AI model;

[2076] a means for the server to provide the generated learning content to the user;

[2077] a means of tracking the user's learning progress and providing personalized feedback;

[2078] means for obtaining and providing cultural context information relevant to a user-selected language;

[2079] means for analyzing the emotional state of a user in real time using an emotion analysis engine;

[2080] means for adaptively changing the difficulty level and feedback of learning content based on the user's emotional state;

[2081] A system including:

[2082] (Claim 2)

[2083] means for receiving user-entered information and creating a user account;

[2084] a means for the user to enter login information and perform authentication;

[2085] The system of claim 1 further comprising:

[2086] (Claim 3)

[2087] means for providing practice content corresponding to a practice module selected by a user;

[2088] means for receiving the results of the exercises performed by the user and providing evaluation and feedback;

[2089] a means for providing optimal exercises and encouraging messages according to the user's emotional state;

[2090] The system of claim 1 further comprising: [Explanation of symbols]

[2091] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for the user to select the language they wish to learn; A means for the server to generate learning content for the corresponding language using the generative AI model; a means for the server to provide the generated learning content to the user; a means of tracking the user's learning progress and providing personalized feedback; means for obtaining and providing cultural context information relevant to a user-selected language; A system including:

2. means for receiving user-entered information and creating a user account; a means for the user to enter login information and perform authentication; The system of claim 1 further comprising:

3. means for providing practice content corresponding to a practice module selected by a user; means for receiving the results of the exercises performed by the user and providing evaluation and feedback; The system of claim 1 further comprising:

Images