System

Abstract

To provide a system capable of continuously supporting target achievement of a user and improving a self-affirmation feeling.SOLUTION: A system, comprising: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting learning materials from the collected content; means for generating a learning plan and schedule based on the learning materials; means for applying the learning plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the learning plan; means for the virtual clone to undergo periodic tests; means for feeding back the test results to the user; and means for adjusting the learning plan based on the feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional self-improvement and goal-achievement systems help users achieve their goals independently, and often lack ongoing support. As a result, plans set at the beginning of the year are often not achieved by the end of the year. This lack of ongoing support for achieving goals leads to a decline in self-esteem and a sense of fulfillment. Furthermore, systems lack a mechanism for quantitatively evaluating and providing feedback on users' progress, making it difficult for users to realize their own progress. Therefore, a system that provides ongoing support and feedback for goal achievement and improves users' self-esteem is needed. [Means for solving the problem]

[0005] This invention provides a system that collects online content based on user-set goals, selects appropriate study materials using artificial intelligence, and generates a study plan and schedule. It also provides a mechanism for generating a virtual clone of the user in a virtual space, having the virtual clone implement the selected study plan and take periodic tests, and providing feedback on the results to the user. Specifically, the system includes: a means for the user to set goals; a means for collecting online content; a means for selecting study materials using artificial intelligence; a means for generating a study plan and schedule; a means for generating a virtual clone in a virtual space; a means for the virtual clone to implement the study plan; a means for the virtual clone to take periodic tests; a means for providing feedback on test results to the user; a means for adjusting the study plan based on the feedback; and a means for monitoring the user's goal achievement and suggesting updates to the study plan. In this way, the system provides continuous support for the user's goal achievement and improves their self-esteem.

[0006] "User" refers to an individual who uses the system to set goals, study towards those goals, and track their progress.

[0007] A "goal" is a specific outcome or state that a user wants to achieve.

[0008] "Content" refers to learning materials and information provided in the form of images, videos, text, etc.

[0009] "Artificial intelligence" refers to algorithms and systems that extract specific patterns from large amounts of data and select appropriate learning materials.

[0010] A "study plan" refers to specific study content and schedule created based on the goals set by the user.

[0011] "Schedule" refers to the specific study time and frequency set based on the study plan.

[0012] "Virtual space" refers to a digital environment in which user-generated virtual clones exist and where learning and testing can take place.

[0013] A "virtual clone" is a virtual entity that mimics the user's appearance and voice, and is a character that carries out a learning plan in a virtual space.

[0014] "Regular tests" are evaluation tests that are administered periodically to virtual clones, and refer to a means of checking their learning progress.

[0015] "Feedback" refers to reporting the results of regular tests to users and providing information about their learning status.

[0016] "Monitoring" refers to the process of continually observing a user's learning progress and adjusting or updating the learning plan as needed.

[0017] "Plan update" refers to revising an existing study plan and proposing new content and schedules based on the user's learning situation. [Brief explanation of the drawings]

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

[0019] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0022] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0023] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

[0025] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0026] [First embodiment]

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

[0028] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0030] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

[0033] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0035] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0036] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0039] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support goal achievement.

[0040] User Actions

[0041] User:

[0042] First, the user logs in to the system. After logging in, a goal setting screen appears. On this screen, the user can enter specific goals. For example, a goal could be set such as "I want to acquire everyday conversational English skills in one year."

[0043] Content collection and learning plan generation

[0044] server:

[0045] After a user sets a goal, the server collects content related to the goal from online sources. The collected content is provided in various formats, including images, videos, and text. This content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[0046] Based on the selected learning materials, the server generates a study plan and schedule that is optimal for the user, such as shadowing practice three times a week or a study plan for specific grammar points.

[0047] Virtual clone generation and learning

[0048] Device:

[0049] Next, the device generates a virtual clone in the virtual space based on the user's personal information (such as appearance and voice), which is a virtual entity that carries out the learning plan set by the user.

[0050] server:

[0051] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone may review English vocabulary or practice pronunciation at a set time each day.

[0052] Regular testing and feedback

[0053] server:

[0054] The server periodically tests the virtual clones, for example, conducting a listening test every Saturday, and collects the results. The server then provides the test results to the user as feedback.

[0055] User:

[0056] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[0057] Continuous monitoring and plan updates

[0058] server:

[0059] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and proposes updates to the learning plan as needed. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, a system is provided that helps the user achieve their goals and improves their self-esteem.

[0060] Specific examples

[0061] The following is a specific example of use. When User A sets the goal of "I want to acquire everyday conversational English in one year," the server collects and selects the latest video learning materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone performs 20 minutes of listening practice every day in a virtual space. The server conducts a listening test every Saturday and notifies User A of the results. User A receives feedback, and if progress is slower than expected, adjusts the study plan, such as increasing the time for listening practice. By repeating this process, User A can effectively progress in their studies toward achieving their goal.

[0062] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[0063] The processing flow will be explained below.

[0064] Step 1:

[0065] User:

[0066] The user logs into the system and enters specific goals on the goal setting screen.

[0067] Example: Set "I want to acquire everyday conversation level English in one year."

[0068] Step 2:

[0069] server:

[0070] Receive goals set by the user.

[0071] Step 3:

[0072] server:

[0073] The server collects content (images, videos, text) related to the goal from online sources.

[0074] Step 4:

[0075] server:

[0076] The collected content is passed to AI, which analyzes and evaluates it to select the most suitable learning material for the user.

[0077] Step 5:

[0078] server:

[0079] Generate a learning plan and schedule based on the learning materials.

[0080] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[0081] Step 6:

[0082] User:

[0083] The user reviews the proposed study schedule and adjusts it as needed.

[0084] Example: Approve the schedule.

[0085] Step 7:

[0086] Device:

[0087] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[0088] Step 8:

[0089] server:

[0090] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[0091] Step 9:

[0092] server:

[0093] The virtual clone implements the study plan at a set time each day.

[0094] For example: 20 minutes of shadowing practice every day.

[0095] Step 10:

[0096] server:

[0097] The server periodically runs tests on the virtual clones and collects the results.

[0098] Example: Listening tests are conducted every Saturday.

[0099] Step 11:

[0100] server:

[0101] Provide collected test results as feedback to users.

[0102] Step 12:

[0103] User:

[0104] Users receive feedback and see their progress.

[0105] Step 13:

[0106] User:

[0107] Adjust your study plan and schedule as needed.

[0108] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[0109] Step 14:

[0110] server:

[0111] The system continuously monitors the user's learning progress.

[0112] Step 15:

[0113] server:

[0114] Analyze learning data and suggest updates to your learning plan as needed.

[0115] Example: Propose a new plan that focuses on listening.

[0116] Step 16:

[0117] User:

[0118] The user accepts the proposed update plan and continues learning based on the new learning plan.

[0119] The above is the specific processing flow of this system. Through this series of steps, users can effectively progress through their studies toward achieving their goals.

[0120] Example 1

[0121] 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."

[0122] Conventional learning support systems have difficulty providing individualized learning plans, making it difficult to maintain learners' motivation. Furthermore, because learning progress is not monitored or feedback is not provided in real time, users often have difficulty grasping their own progress. This creates the problem of insufficient learning effectiveness.

[0123] 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.

[0124] In this invention, the server includes a means for allowing a user to set a goal, a means for collecting online content based on the goal, and an artificial intelligence means for selecting learning materials from the collected content, thereby enabling the provision of an individualized and effective learning plan.

[0125] The server also includes a means for the user to generate his or her own virtual agent in the virtual environment, a means for applying the learning plan and schedule to the virtual agent in the virtual space, and a means for the virtual agent to implement the learning plan, which allows learning to be carried out virtually and is expected to increase the user's motivation.

[0126] The server further includes a means for periodically evaluating the virtual agent, a means for providing feedback on the evaluation results to the user, and a means for adjusting the learning plan based on the feedback, thereby enabling real-time monitoring of the user's learning progress and prompt feedback and plan adjustment.

[0127] A "user" is an entity that uses the system to set goals and learn.

[0128] "Goals" are the specific learning content or skills that users aim to achieve through the system.

[0129] "Online content" refers to learning materials such as videos, text, and images that are accessible via the internet.

[0130] "Artificial intelligence means" is a general term for AI technologies that analyze collected content and appropriately select learning materials.

[0131] "Study plan and schedule" refers to specific learning content and its implementation plan designed to help users achieve their goals.

[0132] A "virtual space" is a digital environment created using computer graphics.

[0133] A "virtual agent" is a virtual learning entity that reflects the characteristics of the user and carries out a learning plan in a virtual space.

[0134] "Evaluation" refers to periodic testing and assessment of the learning that the virtual agent has performed.

[0135] "Feedback" means information and advice about learning progress provided to users based on assessment results.

[0136] "Plan adjustment" refers to reviewing and appropriately changing study plans and schedules based on feedback.

[0137] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support the user in achieving their goals.

[0138] Hardware and Software

[0139] User terminal (PC, smartphone): A device that allows users to access and operate the system.

[0140] Server: Responsible for data processing and storage. The server has the Django framework and AI libraries (e.g., TensorFlow, PyTorch) installed.

[0141] Web browser: Software that allows users to operate login screens and various interfaces.

[0142] Specific processing explanation

[0143] User login

[0144] User:

[0145] The user accesses the login screen through a web browser. The user enters their "user name" and "password" and clicks the "Login" button. The server compares the received authentication information with the database to verify whether the user is a legitimate user. Once the comparison is complete, the user's dashboard screen is displayed.

[0146] goal setting

[0147] User:

[0148] After logging in, users can enter specific learning goals on the goal setting screen. For example, they can set a goal such as "I want to acquire everyday conversational English skills in one year."

[0149] server:

[0150] The server receives the goal data entered by the user and stores it in the database, thereby registering the user's learning goals in the system.

[0151] Content collection and learning plan generation

[0152] server:

[0153] Based on the goals set by the user, the server collects online content. Content collection uses the YouTube API, Google Search API, etc. The collected content is analyzed using AI technology to select the most suitable learning materials for the user. Based on the selected learning materials, the server generates a learning plan and schedule.

[0154] Virtual clone generation and learning

[0155] Device:

[0156] The device generates a virtual clone in a virtual space based on the user's personal information (profile photo and voice sample), using image processing and voice recognition technology to create a realistic virtual clone.

[0157] server:

[0158] The learning plan is applied to the generated virtual clone, and the virtual clone carries out various learning activities according to the specified schedule.

[0159] Regular testing and feedback

[0160] server:

[0161] The server periodically tests the virtual clone, for example, conducting a listening test every Saturday, analyzes the results, and notifies the user of the analysis results as feedback.

[0162] User:

[0163] Users receive feedback and track their progress, adjusting their learning plans and schedules as needed through the server.

[0164] Continuous monitoring and plan updates

[0165] server:

[0166] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and suggests updating the learning plan as needed. For example, if the user's listening skills are lacking, the system will suggest a new learning plan focused on listening.

[0167] Prompt Sentence Examples

[0168] "The user has set the goal of 'I want to acquire conversational English skills in one year.' Please write a program that collects the latest English learning videos and exercises and suggests a study schedule of one hour, three times a week."

[0169] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[0170] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0171] Step 1: User Login

[0172] User:

[0173] Input: Enter your "Username" and "Password" into the login screen and click the [Login] button.

[0174] What happens: User enters login information and submits.

[0175] server:

[0176] Data processing: The entered authentication information is compared with a database.

[0177] Output: If the user is legitimate, the user's dashboard screen is displayed.

[0178] Specific operation: The server checks the user information against the database, and if it matches, the login process is successful.

[0179] Step 2: Goal Setting

[0180] User:

[0181] Input: Enter a specific learning goal, such as "I want to acquire everyday conversational English skills in one year," and click the "Set Goal" button.

[0182] Action: Enter your learning goals on the goal setting screen and submit.

[0183] server:

[0184] Data processing: Receive the entered target data and save it in the database.

[0185] Output: The user's learning goals are registered in the system.

[0186] Specific actions: The server saves this goal in the database and proceeds to the next step.

[0187] Step 3: Gather content and create a lesson plan

[0188] server:

[0189] Input: Goal data set by the user.

[0190] What it does: Collects relevant content online.

[0191] Data processing: We use APIs (e.g., YouTube API, Google Search API) to collect content information and analyze the data using artificial intelligence technology.

[0192] Output: Select appropriate learning materials from the collected content and generate an optimal learning plan and schedule for the user.

[0193] Specific operation: The server collects information from the YouTube API using keywords such as "daily conversation English learning videos" and categorizes them using natural language processing technology.

[0194] Step 4: Generating and training virtual clones

[0195] Device:

[0196] Input: User personal information (profile photo and voice sample).

[0197] Operation: Generates a virtual clone in virtual space.

[0198] Data processing: Using image processing and voice recognition technology to create a realistic virtual clone.

[0199] Output: Generate a virtual clone.

[0200] How it works: The device scans the user's photo and uses facial recognition technology to create a realistic virtual clone.

[0201] server:

[0202] Input: The generated virtual clone.

[0203] What it does: Apply a learning plan to a virtual clone.

[0204] Data processing: Using deep learning models, the virtual clone executes the learning plan.

[0205] Output: The virtual clone starts learning.

[0206] Specific operation: The server instructs the virtual clone to perform shadowing and listening practice.

[0207] Step 5: Regular testing and feedback

[0208] server:

[0209] Input: Training data for virtual clones.

[0210] Behavior: Periodically run tests on the virtual clone.

[0211] Data processing: Analyze the test results and provide feedback to the user.

[0212] Output: Notify the user of the evaluation results.

[0213] Specific operation: The server conducts a listening test every Saturday and notifies the user of the results as a rating such as "passed" or "needs improvement."

[0214] User:

[0215] Input: The notified feedback information.

[0216] What it does: Check your feedback and see your progress.

[0217] Data processing: Adjust your study plan and schedule based on the feedback.

[0218] Output: Updated learning plan.

[0219] What it does: Users review their assessments and adjust their study time based on their progress.

[0220] Step 6: Continuously monitor and update your plan

[0221] server:

[0222] Input: User learning data and progress information.

[0223] What it does: Continuously monitor learning progress.

[0224] Data processing: Analyze learning data and suggest updates to the learning plan as needed.

[0225] Output: Update suggestions.

[0226] How it works: The server analyzes the user's learning history and, if there are "lacks in listening skills," sends a notification suggesting a new listening-focused learning plan.

[0227] (Application example 1)

[0228] 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."

[0229] In today's online learning environment, providing content tailored to individual learning goals and managing progress are key challenges. Such systems must be able to select the most appropriate learning content from a wide variety of available content and provide an optimal learning plan for efficient learning. However, existing systems struggle to assess users' progress in real time and dynamically revise their learning plans as needed. Furthermore, technology for providing personalized learning experiences using virtual spaces is not yet fully developed. This leaves users without the support they need to effectively achieve their learning goals.

[0230] 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.

[0231] In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting learning materials from the collected content; means for generating a learning plan and schedule based on the learning materials; means for applying the learning plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the learning plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; means for adjusting the learning plan based on the feedback; means for monitoring the user's progress and updating and optimizing the learning plan in real time; and means for inputting prompts into a generating AI model and selecting optimal learning content. This allows the user to check their progress in real time and efficiently achieve their goals with an optimized learning plan.

[0232] "User" refers to an individual or corporation that uses the system to set learning goals and manage progress.

[0233] "Goals" are the specific learning or work objectives that users intend to achieve by using the system.

[0234] "Content" refers to learning materials such as images, videos, audio, and text that exist online.

[0235] "Artificial intelligence means" refers to processes and devices that use AI technology to analyze collected content and select the most appropriate learning materials.

[0236] A "learning plan" is a planned and scheduled combination of learning activities necessary to achieve a user's goals.

[0237] A "schedule" indicates the time frame and order of learning activities that should be carried out daily based on a learning plan.

[0238] A "virtual space" is a digital virtual environment constructed using technology such as computer graphics.

[0239] A "virtual clone" is a digital avatar generated in a virtual space based on the user's appearance and voice.

[0240] "Periodic tests" are tests that the virtual clone takes at regular intervals to evaluate its learning outcomes.

[0241] "Feedback" is information provided to users with periodic test results and progress data to help them adjust their study plans.

[0242] "Monitoring" means continuously observing and recording a user's learning progress.

[0243] "Real-time updating and optimization means" refers to the process and devices that instantly modify and improve the learning plan based on the user's learning progress and feedback.

[0244] A "generative AI model" is an artificial intelligence model that automatically generates optimal learning content based on the input prompt.

[0245] A "prompt" is an instruction entered to allow the generative AI model to select the most appropriate learning content.

[0246] To implement this invention, a system including the following elements is required. The system is composed of a server, a terminal, and a user. Specific processing procedures and how to execute them are explained below.

[0247] User Actions

[0248] The user first logs in to the system. After logging in, a goal setting screen appears, where the user can enter specific learning goals. For example, the user can set a goal such as "I want to acquire everyday conversational English skills in one year."

[0249] Content collection and learning plan generation

[0250] The server collects online content based on the user's set goals. The collected content exists in various formats, including images, videos, audio, and text. This content is analyzed and evaluated using artificial intelligence (AI) technology to select the most suitable learning materials for the user. Specific learning content is then selected by inputting a prompt into the generative AI model. For example, the prompt could be, "I want to learn business-level German in one year."

[0251] The server then generates an optimal study plan and schedule based on the selected study materials, including, for example, shadowing practice three times a week or a study plan for specific grammar points.

[0252] Virtual clone generation and learning

[0253] The device generates a virtual clone in the virtual space based on the user's personal information (appearance, voice, etc.), and the virtual clone is responsible for carrying out the learning plan set by the user.

[0254] The server applies the learning plan to the virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[0255] Regular testing and feedback

[0256] The server periodically tests the virtual clones, for example, by conducting a listening test every Saturday, and collects the results. The test results are then provided to the user as feedback.

[0257] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[0258] Continuous monitoring and plan updates

[0259] The server continuously monitors the user's learning progress. The server analyzes the user's learning data and has the function of proposing updates to the learning plan as necessary. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, the server can effectively support the user in achieving their goals.

[0260] This system enables efficient learning tailored to individual learning goals. In particular, the use of prompts by the generative AI model makes it possible to smoothly select the most suitable learning content for the user. For example, by using the prompt "I want to acquire business-level German in one year," the server can select learning materials specialized for German business conversation and generate the optimal learning plan.

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

[0262] Step 1:

[0263] The server receives the user's login information. It takes the user ID and password as input and performs authentication processing. If authentication is successful, a session is started, a session ID is generated and returned to the user. If authentication fails, an error message is returned.

[0264] Step 2:

[0265] After the user logs in, the goal setting screen is displayed. The user enters a specific learning goal (e.g., "I want to acquire daily conversation level English in one year"). The entered goal information is sent to the server.

[0266] Step 3:

[0267] The server receives the learning goals set by the user, analyzes the received data, and collects related online content, such as videos, texts, and audio files related to English conversation from the Internet. This collected data serves as input for the next processing step.

[0268] Step 4:

[0269] The server analyzes the collected content using artificial intelligence (AI) tools. An AI model (e.g., a natural language processing model) evaluates the content and selects the learning materials that best fit the user's learning goals. These selected learning materials serve as input for the next step.

[0270] Step 5:

[0271] The server generates a learning plan and schedule based on the selected learning materials. The generated learning plan includes a weekly schedule and daily learning tasks. These plans and schedules are used in the next step.

[0272] Step 6:

[0273] The device generates a virtual clone based on the user's personal information (face photo, voice, etc.). Using the data obtained from the user as input, the virtual clone is created using 3D modeling software and voice synthesis technology. The generated virtual clone is used in the next step.

[0274] Step 7:

[0275] The server applies the learning plan to the virtual clone, and the virtual clone begins learning based on a schedule set in the virtual space. For example, the virtual clone can practice listening and pronunciation for 20 minutes every day.

[0276] Step 8:

[0277] The server periodically tests the virtual clone, collects the results of each test (e.g., a listening test every Saturday), analyzes the data, and generates feedback based on the test results to provide to the user.

[0278] Step 9:

[0279] The user receives feedback from the server, including test results and progress, and checks their own learning status. If necessary, the user can request adjustments to their learning plan or schedule from the server.

[0280] Step 10:

[0281] The server monitors the user's progress and suggests updates to the learning plan. It analyzes the progress data and generates a new learning plan that focuses on skills that are lacking (e.g., listening skills), thereby continuously optimizing the user's learning experience.

[0282] Step 11:

[0283] A prompt sentence is input into the generative AI model to select the most suitable learning content. By providing a prompt sentence (e.g., "I want to acquire business-level German in one year") as input, the AI ​​model outputs learning materials that are best suited to the user's goals. These materials are used in Step 4.

[0284] 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.

[0285] This invention is a system that supports users in self-development and goal achievement, and is characterized by generating a study plan based on the goals set by the user, conducting study in a virtual space, and recognizing the user's emotions to provide feedback and adjust the plan.

[0286] User Actions

[0287] User:

[0288] First, the user logs into the system and enters specific goals on the goal setting screen.

[0289] Example: Set "I want to acquire everyday conversation level English in one year."

[0290] Content collection and learning plan generation

[0291] server:

[0292] The system receives the goals set by the user and collects content related to the goals (images, videos, text, etc.) from online sources. The collected content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[0293] Based on the selected learning materials, the server generates a study plan and schedule, which may include, for example, shadowing practice three times a week or a study plan for specific grammar points.

[0294] Virtual clone generation and learning

[0295] Device:

[0296] Based on the user's personal information (appearance, voice, etc.), the device generates a virtual clone in the virtual space, which is a virtual entity that carries out the learning plan set by the user.

[0297] server:

[0298] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[0299] Regular testing and feedback

[0300] server:

[0301] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided to the user as feedback.

[0302] User:

[0303] Users receive this feedback, see their progress, and adjust their learning plan or schedule as needed, which can be flexibly changed based on the user's needs and progress.

[0304] Supported by an emotional engine

[0305] server:

[0306] The server is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's emotions based on the user's tone of voice, facial expressions, and character input patterns.

[0307] server:

[0308] The emotion engine recognizes the user's emotions and adjusts the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan will be lightened slightly and an encouraging message will be sent.

[0309] Continuous monitoring and plan updates

[0310] server:

[0311] The system continuously monitors the user's learning progress and emotional state. The server analyzes the user's learning and emotional data and proposes updates to the learning plan as needed.

[0312] Specific examples

[0313] The following is a specific example of use. When User A sets the goal of "learning English at a conversational level in one year," the server collects and selects the latest video learning materials and practice questions related to English language learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, collects the results, and notifies User A.

[0314] Furthermore, the emotion engine recognizes User A's emotions and, if User A is feeling stressed, adjusts the study plan to help them continue studying without straining themselves. This allows User A to receive feedback and check their progress while studying effectively.

[0315] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and achieve goals while taking into consideration their emotions.

[0316] The processing flow will be explained below.

[0317] Step 1:

[0318] User:

[0319] The user logs into the system and enters specific goals on the goal setting screen.

[0320] Example: Set "I want to acquire everyday conversation level English in one year."

[0321] Step 2:

[0322] server:

[0323] Receive goals set by the user.

[0324] Step 3:

[0325] server:

[0326] The server collects content (images, videos, text) related to the goal from online sources.

[0327] Step 4:

[0328] server:

[0329] The collected content is passed to artificial intelligence (AI), which analyzes and evaluates it to select the most suitable learning material for the user.

[0330] Step 5:

[0331] server:

[0332] Generate a learning plan and schedule based on the learning materials.

[0333] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[0334] Step 6:

[0335] User:

[0336] The user reviews the proposed study schedule and adjusts it as needed.

[0337] Example: Approve the schedule.

[0338] Step 7:

[0339] Device:

[0340] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[0341] Step 8:

[0342] server:

[0343] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[0344] Step 9:

[0345] server:

[0346] The virtual clone implements the study plan at a set time each day.

[0347] For example: 20 minutes of shadowing practice every day.

[0348] Step 10:

[0349] server:

[0350] The server periodically runs tests on the virtual clones and collects the results.

[0351] Example: Listening tests are conducted every Saturday.

[0352] Step 11:

[0353] server:

[0354] Provide collected test results as feedback to users.

[0355] Step 12:

[0356] User:

[0357] Users receive feedback and see their progress.

[0358] Step 13:

[0359] User:

[0360] Adjust your study plan and schedule as needed.

[0361] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[0362] Step 14:

[0363] server:

[0364] The system continuously monitors the user's learning progress.

[0365] Step 15:

[0366] server:

[0367] Analyze learning data and suggest updates to your learning plan as needed.

[0368] Example: Propose a new plan that focuses on listening.

[0369] Step 16:

[0370] User:

[0371] The user accepts the proposed update plan and continues learning based on the new learning plan.

[0372] Step 17:

[0373] server:

[0374] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's tone of voice, facial expressions, and text input patterns.

[0375] Step 18:

[0376] server:

[0377] The emotion engine recognizes the user's emotions and adjusts the feedback and learning plan accordingly.

[0378] Example: If the user is feeling impatient, ease up on their study plan and send them an encouraging message.

[0379] Step 19:

[0380] server:

[0381] The results of emotion recognition and feedback adjustment are also monitored to optimize the entire system.

[0382] Step 20:

[0383] User:

[0384] Users receive emotional feedback, allowing them to continue their learning effortlessly.

[0385] The above is the specific processing flow of this system. Through this series of steps, users can study effectively and continuously toward achieving their goals.

[0386] Example 2

[0387] 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."

[0388] Conventional educational support systems lack sufficient ability to generate plans to help users achieve their goals or monitor their learning progress, making it difficult to flexibly adjust to each user's emotions and learning pace. This makes it difficult for users to maintain their motivation over the long term, often making it difficult to achieve their final goals. Furthermore, feedback based on learning progress is limited to simple results, and specific suggestions for improving learning are lacking.

[0389] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting study materials from the collected content; means for generating a study plan and schedule based on the study materials; means for applying the study plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the study plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; emotion engine means for recognizing the user's emotions and adjusting the study plan; means for adjusting the study plan based on the feedback and emotions; and means for continuously monitoring the user's study progress and emotion data and suggesting updates to the study plan. This enables effective study support for the user to achieve their goal and enables flexible adjustment of the study plan based on the user's study progress and emotional state.

[0390] "Means for users to set goals" refers to a function that provides an interface for users to specifically input and set their own learning goals and the results they wish to achieve.

[0391] "Means for collecting online content" refers to a function that automatically collects relevant educational materials and information from databases and websites on the Internet.

[0392] "Artificial intelligence means" refers to the algorithms and models that analyze collected data and identify and select the most appropriate learning materials for users.

[0393] "Means for generating study plans and schedules" refers to a function that designs a plan and time allocation that allows the user to study efficiently based on the selected study materials.

[0394] The "means for applying to a virtual clone in a virtual space" is a function for applying the generated learning plan and schedule to a virtual clone of the user existing in a digital space.

[0395] The "means by which the virtual clone implements the learning plan" refers to the function by which the virtual clone specifically implements the learning plan set in the virtual space.

[0396] The "means for undergoing periodic testing" is a function that allows the virtual clone to undergo periodic set tests and evaluations.

[0397] "Means for providing feedback on test results to the user" is a function that conveys the results of tests taken by the virtual clone to the user, informing them of the progress and results of their learning.

[0398] "Emotion engine means" refers to technology that recognizes and analyzes emotions from the user's tone of voice, facial expressions, input patterns, etc., and adjusts the learning plan appropriately based on that.

[0399] "Means to adjust study plans" refers to a function that optimizes and modifies existing study plans based on the user's condition, based on feedback and emotional data.

[0400] "Means for continuous monitoring and suggesting updates to the study plan" refers to a function that constantly monitors the user's study progress and emotional state, and suggests new study plans or adjusts the current plan as needed.

[0401] The present invention relates to a system for supporting users' self-development and goal achievement. This system generates a study plan based on the goals set by the user, conducts the study in a virtual space, and recognizes the user's emotions to provide feedback and adjust the plan. Each component and process of this system will be described in detail below.

[0402] User goal setting

[0403] User:

[0404] Users log in to the system and enter specific goals on the goal setting screen. The system's goal setting screen is implemented as a web application and is built using technologies such as HTML5 and React. Users enter specific information such as the skills they want to learn, the results they want to achieve, and the desired study period. For example, they could set the goal as "I want to acquire everyday conversational English skills in one year."

[0405] Content collection and lesson plan generation

[0406] server:

[0407] The system receives the goals set by the user and collects content related to the goal (images, videos, text, etc.) from the Internet. This content collection is done using scraping technology to obtain data, which is then analyzed by an AI engine (e.g., TensorFlow, PyTorch). Effective learning materials are selected as a result of the analysis.

[0408] server:

[0409] Based on the selected learning materials, the server generates a study plan and schedule that can include, for example, shadowing practice three times a week or a study plan for specific grammar points, and is presented in a format that fits the user's schedule.

[0410] Generation of virtual clones and execution of learning

[0411] Device:

[0412] The device generates a virtual clone in a virtual space based on the user's personal information (appearance, voice, etc.). The device is installed with virtual space generation software such as Unity or Unreal Engine, and a 3D model is created based on the user's facial photograph and voice data. This virtual clone is a virtual entity that carries out the learning plan set by the user.

[0413] server:

[0414] The server applies the learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. This virtual clone performs specific actions, such as reviewing English vocabulary and practicing pronunciation, at a set time each day.

[0415] Regular testing and feedback

[0416] server:

[0417] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided as feedback to the user, allowing them to track their progress.

[0418] User:

[0419] Users receive feedback and can adjust their learning plan and schedule as needed, which can be flexibly changed based on their needs and progress.

[0420] Supported by an emotional engine

[0421] server:

[0422] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from the user's tone of voice, facial expressions, and text input patterns. The emotion engine uses, for example, voice recognition technology and facial recognition technology.

[0423] server:

[0424] The emotion engine can recognize the user's emotions and adjust the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan can be lightened slightly and an encouraging message can be sent.

[0425] Continuous monitoring and plan updates

[0426] server:

[0427] The system continuously monitors the user's learning progress and emotional state. The server constantly monitors the user's learning data and emotional data and suggests updating the learning plan as needed. This monitoring and updating is performed using a database management system (e.g., PostgreSQL, MySQL).

[0428] Specific examples

[0429] As a concrete example, consider the case where User A sets the goal of "learning English conversation at a daily conversation level in one year." The server collects and selects the latest video materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone of User A that mimics his / her voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, and the results are collected and notified to User A. Furthermore, the emotion engine recognizes User A's emotions, and if User A is feeling stressed, it adjusts the study plan to help him / her continue studying without straining himself / herself.

[0430] Example prompts for generative AI models

[0431] Below are some example prompts for the generative AI model associated with this system:

[0432] Example prompt 1:

[0433] "Generate an effective study plan to help you master conversational English in one year. Schedule three times a week for one hour and combine shadowing, listening practice, and grammar study."

[0434] Example prompt 2:

[0435] "Please create questions for users to use in a listening test to gauge their level of mastery. The questions should be simple English conversation scenarios based on everyday conversations."

[0436] The system of the present invention provides comprehensive support necessary for users to achieve their goals, and unifies management of everything from creating study plans to providing emotional feedback, allowing users to effectively study while checking their own progress and emotional state and responding appropriately.

[0437] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0438] Step 1:

[0439] User:

[0440] The user logs into the system and enters specific goals on the goal setting screen. The input includes the skill they want to learn (e.g., English conversation) and the deadline for achieving the goal (e.g., one year). Based on this, the server receives the user's goal data. The input data might be goal: "everyday conversation level English conversation" and period: "one year." The server saves this data in a database.

[0441] Step 2:

[0442] server:

[0443] Based on the goal data set by the user, relevant learning content (images, videos, text, etc.) is collected from online sources. Web scraping technology is used to collect the data, and the collected data is temporarily stored in a database on the server. Next, an AI engine (e.g., TensorFlow, PyTorch) is used to analyze and evaluate the collected data and select the most suitable learning materials for the user. The input data are the goal data and collected content, and based on this, the most suitable learning materials (e.g., a list of video URLs or text materials) are output as the evaluation results.

[0444] Step 3:

[0445] server:

[0446] A learning plan and schedule is generated based on the selected learning materials. An AI engine is used to create a plan that matches the user's goals and learning pace. For example, a specific learning plan such as "one hour of shadowing practice three times a week, and 30 minutes of listening practice twice a week" is generated. The input data is the optimal learning materials and the user's goals, and the output data is the learning plan and schedule.

[0447] Step 4:

[0448] Device:

[0449] Based on the user's personal information (e.g., face photo and voice data), the device generates a virtual clone in a virtual space. This virtual clone is generated using Unity or Unreal Engine. The input data is the user's face photo and voice data, and the output data is a 3D model of the virtual clone. The generated virtual clone is then ready to execute the learning plan set by the user.

[0450] Step 5:

[0451] server:

[0452] The learning plan and schedule are applied to the virtual clone, and learning begins in the virtual space. The virtual clone begins learning at a fixed time each day and performs specific learning activities (e.g., reviewing English vocabulary and practicing pronunciation) in the virtual space. The input data are the learning plan and schedule and a 3D model of the virtual clone, and the output data is a learning execution log.

[0453] Step 6:

[0454] server:

[0455] Tests are periodically conducted on the virtual clones and the results are collected. For example, a listening test is conducted every Saturday to collect performance data on the virtual clones. The input data are the learning execution log and test data, and the output data are the test results.

[0456] Step 7:

[0457] server:

[0458] The collected test results are fed back to the user. This feedback includes the user's learning progress, achievement level, and specific advice on what to do next. The input data is the test results, and the output data is the feedback message. The feedback is provided to the user via email or in-app notification.

[0459] Step 8:

[0460] server:

[0461] An emotion engine is used to recognize the user's emotions and adjust the study plan. The emotion engine analyzes the user's tone of voice, facial expressions, and text input patterns to evaluate their emotional state. The input data is the user's voice and facial expression data, and the output data is the emotion evaluation result. For example, if the user is feeling stressed, the study plan is adjusted (e.g., reducing the study load or sending an encouraging message).

[0462] Step 9:

[0463] server:

[0464] The system continuously monitors the user's learning progress and emotional data and suggests updating the learning plan as necessary. The input data is learning progress data and emotional data, and the output data is a new learning plan. The updated plan is periodically applied to the virtual clone to optimize learning.

[0465] (Application example 2)

[0466] 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."

[0467] Conventional learning systems using virtual spaces have limited functionality in supporting users in setting and achieving specific goals in their daily lives. Furthermore, they are unable to provide appropriate feedback or adjust plans based on the user's emotional state, resulting in reduced learning efficiency. The present invention aims to solve these problems and provide a system that more effectively supports users' self-development and goal achievement. Furthermore, to enhance the shopping experience in virtual spaces, the present invention aims to more effectively support users in achieving their goals by providing a shopping plan generation function using a virtual assistant and a feedback function based on the user's emotions.

[0468] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a shopping plan based on purchasing goals set by the user, means for a virtual assistant to provide and explain optimal products to the user in a virtual space, and means for recognizing the user's emotions and providing feedback and adjusting the plan. This effectively supports the user in achieving their purchasing goals and enables appropriate feedback and plan adjustments to match the user's emotional state.

[0469] "User" refers to any individual or entity that uses the System and sets its own goals.

[0470] A "goal" is a specific objective or task that a user sets out to achieve.

[0471] "Content" refers to information such as images, videos, and text collected online, and is material related to learning and purchasing.

[0472] "Artificial intelligence means" refers to machine learning algorithms or systems that analyze collected content and select the most suitable learning materials or products for users.

[0473] A "study plan" refers to the learning content and schedule created based on the goals the user wants to achieve.

[0474] A "schedule" refers to a timetable or timetable for carrying out study and purchasing activities according to a study plan.

[0475] "Virtual space" refers to a three-dimensional virtual environment generated by computer simulation.

[0476] A "virtual clone" refers to a virtual being generated in a virtual space based on information such as the user's appearance and voice.

[0477] A "virtual assistant" is a virtual entity that provides and explains products based on the purchasing goals set by the user in a virtual space.

[0478] "Emotion" refers to a user's psychological state, which can be identified by tone of voice, facial expression, text input patterns, etc.

[0479] "Feedback" refers to information that provides users with results or recommendations regarding their learning or purchasing activities.

[0480] This invention is a system that allows users to effectively improve themselves and achieve their goals in a virtual space. The system generates shopping plans and study plans based on the goals set by the user, and then implements these plans in the virtual space using a virtual clone or virtual assistant. The system also recognizes the user's emotions and provides feedback and adjusts the plans accordingly.

[0481] Hardware and Software

[0482] The system is implemented using the following hardware and software.

[0483] Hardware: Computers, servers, and user devices (smartphones, head-mounted displays, etc.).

[0484] Software: Generative AI models (e.g., GPT), emotion engines (e.g., Emotion AI SDK), databases, and virtual world simulation software.

[0485] Specific processing steps

[0486] 1. User goal setting

[0487] The user logs in to the system and enters a specific goal on the goal setting screen. For example, they might set "I want to find and purchase new spring fashion items." This goal is then sent to the server.

[0488] 2. Content collection and plan generation

[0489] The server collects relevant content (images, videos, text, etc.) from online sources based on the user's set goals, analyzes and evaluates this content, and generates optimal shopping and learning plans.

[0490] 3. Creation and Implementation of Virtual Clone

[0491] The device generates a virtual clone and virtual assistant in the virtual space based on the user's profile. This virtual clone is a virtual presence that puts into practice the plan set by the user. The virtual assistant is responsible for providing and explaining the most suitable products to the user.

[0492] 4. Emotion Recognition and Feedback

[0493] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, typing patterns, etc. If the user is feeling stressed, the system will support them by adjusting their study or shopping plans and sending encouraging messages.

[0494] 5. Regular monitoring and plan updates

[0495] The server continuously monitors the user's progress in achieving their goals and their emotional state, and suggests updating the plan as necessary, thereby enabling continuous support for the user's goal achievement.

[0496] Specific examples

[0497] If a user selects "I want to find new spring fashion items," the server will collect relevant content and generate an appropriate shopping plan. The virtual assistant will provide and explain the best products to the user in the virtual space, and the emotion engine will monitor the user's emotions and provide feedback to help the user relax.

[0498] Prompt Sentence Examples

[0499] Next, generate a shopping plan to find the perfect fashion items for the user's set goals. The user's profile is as follows:

[0500] Age: 30

[0501] Gender: Female

[0502] Favorite style: Casual

[0503] User goal: Find new spring fashion items.

[0504] Generate the best plan to achieve your goals.

[0505] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0506] Step 1:

[0507] A user logs in to the system and enters a specific goal on the goal setting screen. This goal is sent to the server. The input is the user's goal (e.g., "I want to find and purchase new spring fashion items"), and the output is the goal data sent to the server.

[0508] Step 2:

[0509] The server collects relevant content (images, videos, text, etc.) from online sources based on the goals set by the user. The collected content is analyzed and evaluated using a generative AI model. The input is the user's goal data, and the output is the collected relevant content data.

[0510] Step 3:

[0511] The server selects the most suitable learning materials and products for the user from the analyzed and evaluated content, and generates a shopping plan or learning plan. The input is the related content data, and the output is the generated plan and schedule.

[0512] Step 4:

[0513] The device generates a virtual clone or virtual assistant in the virtual space based on the user's profile (appearance, voice, etc.). The input is the user's profile information, and the output is the generated virtual clone or virtual assistant.

[0514] Step 5:

[0515] The device's virtual clone or virtual assistant executes the learning plan or shopping plan generated by the server. Specific actions include introducing products or advancing learning content in the virtual space. The input is the generated plan, and the output is progress data of the plan as it is implemented.

[0516] Step 6:

[0517] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, and text input patterns. The input is the user's emotional data, and the output is the analyzed emotional state.

[0518] Step 7:

[0519] The server then provides feedback and adjusts the plan based on the analyzed emotional data. For example, if the user is feeling stressed, it can reduce the learning or shopping plan and send an encouraging message. The input is the emotional state and plan data, and the output is the adjusted plan and feedback message.

[0520] Step 8:

[0521] The server continuously monitors the user's progress toward their goals and emotional state, and suggests updating the plan as necessary. For example, if the user changes their set goals or if they experience prolonged stress, the server will suggest a major revision of the plan. The input is continuously acquired progress data and emotional data, and the output is a notification of the proposed update.

[0522] 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.

[0523] 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.

[0524] 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.

[0525] [Second embodiment]

[0526] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0527] 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.

[0528] 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).

[0529] 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.

[0530] 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.

[0531] 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).

[0532] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0533] 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.

[0534] 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.

[0535] 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.

[0536] 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.

[0537] 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."

[0538] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support goal achievement.

[0539] User Actions

[0540] User:

[0541] First, the user logs in to the system. After logging in, a goal setting screen appears. On this screen, the user can enter specific goals. For example, a goal could be set such as "I want to acquire everyday conversational English skills in one year."

[0542] Content collection and learning plan generation

[0543] server:

[0544] After a user sets a goal, the server collects content related to the goal from online sources. The collected content is provided in various formats, including images, videos, and text. This content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[0545] Based on the selected learning materials, the server generates a study plan and schedule that is optimal for the user, such as shadowing practice three times a week or a study plan for specific grammar points.

[0546] Virtual clone generation and learning

[0547] Device:

[0548] Next, the device generates a virtual clone in the virtual space based on the user's personal information (such as appearance and voice), which is a virtual entity that carries out the learning plan set by the user.

[0549] server:

[0550] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone may review English vocabulary or practice pronunciation at a set time each day.

[0551] Regular testing and feedback

[0552] server:

[0553] The server periodically tests the virtual clones, for example, conducting a listening test every Saturday, and collects the results. The server then provides the test results to the user as feedback.

[0554] User:

[0555] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[0556] Continuous monitoring and plan updates

[0557] server:

[0558] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and proposes updates to the learning plan as needed. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, a system is provided that helps the user achieve their goals and improves their self-esteem.

[0559] Specific examples

[0560] The following is a specific example of use. When User A sets the goal of "I want to acquire everyday conversational English in one year," the server collects and selects the latest video learning materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone performs 20 minutes of listening practice every day in a virtual space. The server conducts a listening test every Saturday and notifies User A of the results. User A receives feedback, and if progress is slower than expected, adjusts the study plan, such as increasing the time for listening practice. By repeating this process, User A can effectively progress in their studies toward achieving their goal.

[0561] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] User:

[0565] The user logs into the system and enters specific goals on the goal setting screen.

[0566] Example: Set "I want to acquire everyday conversation level English in one year."

[0567] Step 2:

[0568] server:

[0569] Receive goals set by the user.

[0570] Step 3:

[0571] server:

[0572] The server collects content (images, videos, text) related to the goal from online sources.

[0573] Step 4:

[0574] server:

[0575] The collected content is passed to AI, which analyzes and evaluates it to select the most suitable learning material for the user.

[0576] Step 5:

[0577] server:

[0578] Generate a learning plan and schedule based on the learning materials.

[0579] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[0580] Step 6:

[0581] User:

[0582] The user reviews the proposed study schedule and adjusts it as needed.

[0583] Example: Approve the schedule.

[0584] Step 7:

[0585] Device:

[0586] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[0587] Step 8:

[0588] server:

[0589] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[0590] Step 9:

[0591] server:

[0592] The virtual clone implements the study plan at a set time each day.

[0593] For example: 20 minutes of shadowing practice every day.

[0594] Step 10:

[0595] server:

[0596] The server periodically runs tests on the virtual clones and collects the results.

[0597] Example: Listening tests are conducted every Saturday.

[0598] Step 11:

[0599] server:

[0600] Provide collected test results as feedback to users.

[0601] Step 12:

[0602] User:

[0603] Users receive feedback and see their progress.

[0604] Step 13:

[0605] User:

[0606] Adjust your study plan and schedule as needed.

[0607] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[0608] Step 14:

[0609] server:

[0610] The system continuously monitors the user's learning progress.

[0611] Step 15:

[0612] server:

[0613] Analyze learning data and suggest updates to your learning plan as needed.

[0614] Example: Propose a new plan that focuses on listening.

[0615] Step 16:

[0616] User:

[0617] The user accepts the proposed update plan and continues learning based on the new learning plan.

[0618] The above is the specific processing flow of this system. Through this series of steps, users can effectively progress through their studies toward achieving their goals.

[0619] Example 1

[0620] 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."

[0621] Conventional learning support systems have difficulty providing individualized learning plans, making it difficult to maintain learners' motivation. Furthermore, because learning progress is not monitored or feedback is not provided in real time, users often have difficulty grasping their own progress. This creates the problem of insufficient learning effectiveness.

[0622] 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.

[0623] In this invention, the server includes a means for allowing a user to set a goal, a means for collecting online content based on the goal, and an artificial intelligence means for selecting learning materials from the collected content, thereby enabling the provision of an individualized and effective learning plan.

[0624] The server also includes a means for the user to generate his or her own virtual agent in the virtual environment, a means for applying the learning plan and schedule to the virtual agent in the virtual space, and a means for the virtual agent to implement the learning plan, which allows learning to be carried out virtually and is expected to increase the user's motivation.

[0625] The server further includes a means for periodically evaluating the virtual agent, a means for providing feedback on the evaluation results to the user, and a means for adjusting the learning plan based on the feedback, thereby enabling real-time monitoring of the user's learning progress and prompt feedback and plan adjustment.

[0626] A "user" is an entity that uses the system to set goals and learn.

[0627] "Goals" are the specific learning content or skills that users aim to achieve through the system.

[0628] "Online content" refers to learning materials such as videos, text, and images that are accessible via the internet.

[0629] "Artificial intelligence means" is a general term for AI technologies that analyze collected content and appropriately select learning materials.

[0630] "Study plan and schedule" refers to specific learning content and its implementation plan designed to help users achieve their goals.

[0631] A "virtual space" is a digital environment created using computer graphics.

[0632] A "virtual agent" is a virtual learning entity that reflects the characteristics of the user and carries out a learning plan in a virtual space.

[0633] "Evaluation" refers to periodic testing and assessment of the learning that the virtual agent has performed.

[0634] "Feedback" means information and advice about learning progress provided to users based on assessment results.

[0635] "Plan adjustment" refers to reviewing and appropriately changing study plans and schedules based on feedback.

[0636] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support the user in achieving their goals.

[0637] Hardware and Software

[0638] User terminal (PC, smartphone): A device that allows users to access and operate the system.

[0639] Server: Responsible for data processing and storage. The server has the Django framework and AI libraries (e.g., TensorFlow, PyTorch) installed.

[0640] Web browser: Software that allows users to operate login screens and various interfaces.

[0641] Specific processing explanation

[0642] User login

[0643] User:

[0644] The user accesses the login screen through a web browser. The user enters their "user name" and "password" and clicks the "Login" button. The server compares the received authentication information with the database to verify whether the user is a legitimate user. Once the comparison is complete, the user's dashboard screen is displayed.

[0645] goal setting

[0646] User:

[0647] After logging in, users can enter specific learning goals on the goal setting screen. For example, they can set a goal such as "I want to acquire everyday conversational English skills in one year."

[0648] server:

[0649] The server receives the goal data entered by the user and stores it in the database, thereby registering the user's learning goals in the system.

[0650] Content collection and learning plan generation

[0651] server:

[0652] Based on the goals set by the user, the server collects online content. Content collection uses the YouTube API, Google Search API, etc. The collected content is analyzed using AI technology to select the most suitable learning materials for the user. Based on the selected learning materials, the server generates a learning plan and schedule.

[0653] Virtual clone generation and learning

[0654] Device:

[0655] The device generates a virtual clone in a virtual space based on the user's personal information (profile photo and voice sample), using image processing and voice recognition technology to create a realistic virtual clone.

[0656] server:

[0657] The learning plan is applied to the generated virtual clone, and the virtual clone carries out various learning activities according to the specified schedule.

[0658] Regular testing and feedback

[0659] server:

[0660] The server periodically tests the virtual clone, for example, conducting a listening test every Saturday, analyzes the results, and notifies the user of the analysis results as feedback.

[0661] User:

[0662] Users receive feedback and track their progress, adjusting their learning plans and schedules as needed through the server.

[0663] Continuous monitoring and plan updates

[0664] server:

[0665] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and suggests updating the learning plan as needed. For example, if the user's listening skills are lacking, the system will suggest a new learning plan focused on listening.

[0666] Prompt Sentence Examples

[0667] "The user has set the goal of 'I want to acquire conversational English skills in one year.' Please write a program that collects the latest English learning videos and exercises and suggests a study schedule of one hour, three times a week."

[0668] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[0669] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0670] Step 1: User Login

[0671] User:

[0672] Input: Enter your "Username" and "Password" into the login screen and click the [Login] button.

[0673] What happens: User enters login information and submits.

[0674] server:

[0675] Data processing: The entered authentication information is compared with a database.

[0676] Output: If the user is legitimate, the user's dashboard screen is displayed.

[0677] Specific operation: The server checks the user information against the database, and if it matches, the login process is successful.

[0678] Step 2: Goal Setting

[0679] User:

[0680] Input: Enter a specific learning goal, such as "I want to acquire everyday conversational English skills in one year," and click the "Set Goal" button.

[0681] Action: Enter your learning goals on the goal setting screen and submit.

[0682] server:

[0683] Data processing: Receive the entered target data and save it in the database.

[0684] Output: The user's learning goals are registered in the system.

[0685] Specific actions: The server saves this goal in the database and proceeds to the next step.

[0686] Step 3: Gather content and create a lesson plan

[0687] server:

[0688] Input: Goal data set by the user.

[0689] What it does: Collects relevant content online.

[0690] Data processing: We use APIs (e.g., YouTube API, Google Search API) to collect content information and analyze the data using artificial intelligence technology.

[0691] Output: Select appropriate learning materials from the collected content and generate an optimal learning plan and schedule for the user.

[0692] Specific operation: The server collects information from the YouTube API using keywords such as "daily conversation English learning videos" and categorizes them using natural language processing technology.

[0693] Step 4: Generating and training virtual clones

[0694] Device:

[0695] Input: User personal information (profile photo and voice sample).

[0696] Operation: Generates a virtual clone in virtual space.

[0697] Data processing: Using image processing and voice recognition technology to create a realistic virtual clone.

[0698] Output: Generate a virtual clone.

[0699] How it works: The device scans the user's photo and uses facial recognition technology to create a realistic virtual clone.

[0700] server:

[0701] Input: The generated virtual clone.

[0702] What it does: Apply a learning plan to a virtual clone.

[0703] Data processing: Using deep learning models, the virtual clone executes the learning plan.

[0704] Output: The virtual clone starts learning.

[0705] Specific operation: The server instructs the virtual clone to perform shadowing and listening practice.

[0706] Step 5: Regular testing and feedback

[0707] server:

[0708] Input: Training data for virtual clones.

[0709] Behavior: Periodically run tests on the virtual clone.

[0710] Data processing: Analyze the test results and provide feedback to the user.

[0711] Output: Notify the user of the evaluation results.

[0712] Specific operation: The server conducts a listening test every Saturday and notifies the user of the results as a rating such as "passed" or "needs improvement."

[0713] User:

[0714] Input: The notified feedback information.

[0715] Behavior: See feedback and track progress.

[0716] Data processing: Adjust your study plan and schedule based on the feedback.

[0717] Output: Updated learning plan.

[0718] What it does: Users review their assessments and adjust their study time based on their progress.

[0719] Step 6: Continuously monitor and update your plan

[0720] server:

[0721] Input: User learning data and progress information.

[0722] What it does: Continuously monitor learning progress.

[0723] Data processing: Analyze learning data and suggest updates to the learning plan as needed.

[0724] Output: Update suggestions.

[0725] How it works: The server analyzes the user's learning history and, if there are "lacks in listening skills," sends a notification suggesting a new listening-focused learning plan.

[0726] (Application example 1)

[0727] 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."

[0728] In today's online learning environment, providing content tailored to individual learning goals and managing progress are key challenges. Such systems must be able to select the most appropriate learning content from a wide variety of available content and provide an optimal learning plan for efficient learning. However, existing systems struggle to assess users' progress in real time and dynamically revise their learning plans as needed. Furthermore, technology for providing personalized learning experiences using virtual spaces is not yet fully developed. This leaves users without the support they need to effectively achieve their learning goals.

[0729] 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.

[0730] In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting learning materials from the collected content; means for generating a learning plan and schedule based on the learning materials; means for applying the learning plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the learning plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; means for adjusting the learning plan based on the feedback; means for monitoring the user's progress and updating and optimizing the learning plan in real time; and means for inputting prompts into a generating AI model and selecting optimal learning content. This allows the user to check their progress in real time and efficiently achieve their goals with an optimized learning plan.

[0731] "User" refers to an individual or corporation that uses the system to set learning goals and manage progress.

[0732] "Goals" are the specific learning or work objectives that users intend to achieve by using the system.

[0733] "Content" refers to learning materials such as images, videos, audio, and text that exist online.

[0734] "Artificial intelligence means" refers to processes and devices that use AI technology to analyze collected content and select the most appropriate learning materials.

[0735] A "learning plan" is a planned and scheduled combination of learning activities necessary to achieve a user's goals.

[0736] A "schedule" indicates the time frame and order of learning activities that should be carried out daily based on a learning plan.

[0737] A "virtual space" is a digital virtual environment constructed using technology such as computer graphics.

[0738] A "virtual clone" is a digital avatar generated in a virtual space based on the user's appearance and voice.

[0739] "Periodic tests" are tests that the virtual clone takes at regular intervals to evaluate its learning outcomes.

[0740] "Feedback" is information provided to users with periodic test results and progress data to help them adjust their study plans.

[0741] "Monitoring" means continuously observing and recording a user's learning progress.

[0742] "Real-time updating and optimization means" refers to the process and devices that instantly modify and improve the learning plan based on the user's learning progress and feedback.

[0743] A "generative AI model" is an artificial intelligence model that automatically generates optimal learning content based on the input prompt.

[0744] A "prompt" is an instruction entered to allow the generative AI model to select the most appropriate learning content.

[0745] To implement this invention, a system including the following elements is required. The system is composed of a server, a terminal, and a user. Specific processing procedures and how to execute them are explained below.

[0746] User Actions

[0747] The user first logs in to the system. After logging in, a goal setting screen appears, where the user can enter specific learning goals. For example, the user can set a goal such as "I want to acquire everyday conversational English skills in one year."

[0748] Content collection and learning plan generation

[0749] The server collects online content based on the user's set goals. The collected content exists in various formats, including images, videos, audio, and text. This content is analyzed and evaluated using artificial intelligence (AI) technology to select the most suitable learning materials for the user. Specific learning content is then selected by inputting a prompt into the generative AI model. For example, the prompt could be, "I want to learn business-level German in one year."

[0750] The server then generates an optimal study plan and schedule based on the selected study materials, including, for example, shadowing practice three times a week or a study plan for specific grammar points.

[0751] Virtual clone generation and learning

[0752] The device generates a virtual clone in the virtual space based on the user's personal information (appearance, voice, etc.), and the virtual clone is responsible for carrying out the learning plan set by the user.

[0753] The server applies the learning plan to the virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[0754] Regular testing and feedback

[0755] The server periodically tests the virtual clones, for example, by conducting a listening test every Saturday, and collects the results. The test results are then provided to the user as feedback.

[0756] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[0757] Continuous monitoring and plan updates

[0758] The server continuously monitors the user's learning progress. The server analyzes the user's learning data and has the function of proposing updates to the learning plan as necessary. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, the server can effectively support the user in achieving their goals.

[0759] This system enables efficient learning tailored to individual learning goals. In particular, the use of prompts by the generative AI model makes it possible to smoothly select the most suitable learning content for the user. For example, by using the prompt "I want to acquire business-level German in one year," the server can select learning materials specialized for German business conversation and generate the optimal learning plan.

[0760] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0761] Step 1:

[0762] The server receives the user's login information. It takes the user ID and password as input and performs authentication processing. If authentication is successful, a session is started, a session ID is generated and returned to the user. If authentication fails, an error message is returned.

[0763] Step 2:

[0764] After the user logs in, the goal setting screen is displayed. The user enters a specific learning goal (e.g., "I want to acquire daily conversation level English in one year"). The entered goal information is sent to the server.

[0765] Step 3:

[0766] The server receives the learning goals set by the user, analyzes the received data, and collects related online content, such as videos, texts, and audio files related to English conversation from the Internet. This collected data serves as input for the next processing step.

[0767] Step 4:

[0768] The server analyzes the collected content using artificial intelligence (AI) tools. An AI model (e.g., a natural language processing model) evaluates the content and selects learning materials that best fit the user's learning goals. These selected learning materials serve as input for the next step.

[0769] Step 5:

[0770] The server generates a learning plan and schedule based on the selected learning materials. The generated learning plan includes a weekly schedule and daily learning tasks. These plans and schedules are used in the next step.

[0771] Step 6:

[0772] The device generates a virtual clone based on the user's personal information (such as a photo of the face and voice). Using the data obtained from the user as input, the virtual clone is created using 3D modeling software and voice synthesis technology. The generated virtual clone is then used in the next step.

[0773] Step 7:

[0774] The server applies the learning plan to the virtual clone, and the virtual clone begins learning based on a schedule set in the virtual space. For example, the virtual clone can practice listening and pronunciation for 20 minutes every day.

[0775] Step 8:

[0776] The server periodically tests the virtual clone, collects the results of each test (e.g., a listening test every Saturday), analyzes the data, and generates feedback based on the test results to provide to the user.

[0777] Step 9:

[0778] The user receives feedback from the server, including test results and progress, and checks their own learning status. If necessary, the user can request adjustments to their learning plan or schedule from the server.

[0779] Step 10:

[0780] The server monitors the user's progress and suggests updates to the learning plan. It analyzes the progress data and generates a new learning plan that focuses on skills that are lacking (e.g., listening skills), thereby continuously optimizing the user's learning experience.

[0781] Step 11:

[0782] A prompt sentence is input into the generative AI model to select the most suitable learning content. By providing a prompt sentence (e.g., "I want to acquire business-level German in one year") as input, the AI ​​model outputs learning materials that are best suited to the user's goals. These materials are used in Step 4.

[0783] 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.

[0784] This invention is a system that supports users in self-development and goal achievement, and is characterized by generating a study plan based on the goals set by the user, conducting study in a virtual space, and recognizing the user's emotions to provide feedback and adjust the plan.

[0785] User Actions

[0786] User:

[0787] First, the user logs into the system and enters specific goals on the goal setting screen.

[0788] Example: Set "I want to acquire everyday conversation level English in one year."

[0789] Content collection and learning plan generation

[0790] server:

[0791] The system receives the goals set by the user and collects content related to the goals (images, videos, text, etc.) from online sources. The collected content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[0792] Based on the selected learning materials, the server generates a study plan and schedule, which may include, for example, shadowing practice three times a week or a study plan for specific grammar points.

[0793] Virtual clone generation and learning

[0794] Device:

[0795] Based on the user's personal information (appearance, voice, etc.), the device generates a virtual clone in the virtual space, which is a virtual entity that carries out the learning plan set by the user.

[0796] server:

[0797] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[0798] Regular testing and feedback

[0799] server:

[0800] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided to the user as feedback.

[0801] User:

[0802] Users receive this feedback, see their progress, and adjust their learning plan or schedule as needed, which can be flexibly changed based on the user's needs and progress.

[0803] Supported by an emotional engine

[0804] server:

[0805] The server is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's emotions based on the user's tone of voice, facial expressions, and character input patterns.

[0806] server:

[0807] The emotion engine recognizes the user's emotions and adjusts the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan will be lightened slightly and an encouraging message will be sent.

[0808] Continuous monitoring and plan updates

[0809] server:

[0810] The system continuously monitors the user's learning progress and emotional state. The server analyzes the user's learning and emotional data and proposes updates to the learning plan as needed.

[0811] Specific examples

[0812] The following is a specific example of use. When User A sets the goal of "learning English at a conversational level in one year," the server collects and selects the latest video learning materials and practice questions related to English language learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, collects the results, and notifies User A.

[0813] Furthermore, the emotion engine recognizes User A's emotions and, if User A is feeling stressed, adjusts the study plan to help them continue studying without straining themselves. This allows User A to receive feedback and check their progress while studying effectively.

[0814] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and achieve goals while taking into consideration their emotions.

[0815] The processing flow will be explained below.

[0816] Step 1:

[0817] User:

[0818] The user logs into the system and enters specific goals on the goal setting screen.

[0819] Example: Set "I want to acquire everyday conversation level English in one year."

[0820] Step 2:

[0821] server:

[0822] Receive goals set by the user.

[0823] Step 3:

[0824] server:

[0825] The server collects content (images, videos, text) related to the goal from online sources.

[0826] Step 4:

[0827] server:

[0828] The collected content is passed to artificial intelligence (AI), which analyzes and evaluates it to select the most suitable learning material for the user.

[0829] Step 5:

[0830] server:

[0831] Generate a learning plan and schedule based on the learning materials.

[0832] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[0833] Step 6:

[0834] User:

[0835] The user reviews the proposed study schedule and adjusts it as needed.

[0836] Example: Approve the schedule.

[0837] Step 7:

[0838] Device:

[0839] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[0840] Step 8:

[0841] server:

[0842] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[0843] Step 9:

[0844] server:

[0845] The virtual clone implements the study plan at a set time each day.

[0846] For example: 20 minutes of shadowing practice every day.

[0847] Step 10:

[0848] server:

[0849] The server periodically runs tests on the virtual clones and collects the results.

[0850] Example: Listening tests are conducted every Saturday.

[0851] Step 11:

[0852] server:

[0853] Provide collected test results as feedback to users.

[0854] Step 12:

[0855] User:

[0856] Users receive feedback and see their progress.

[0857] Step 13:

[0858] User:

[0859] Adjust your study plan and schedule as needed.

[0860] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[0861] Step 14:

[0862] server:

[0863] The system continuously monitors the user's learning progress.

[0864] Step 15:

[0865] server:

[0866] Analyze learning data and suggest updates to your learning plan as needed.

[0867] Example: Propose a new plan that focuses on listening.

[0868] Step 16:

[0869] User:

[0870] The user accepts the proposed update plan and continues learning based on the new learning plan.

[0871] Step 17:

[0872] server:

[0873] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's tone of voice, facial expressions, and text input patterns.

[0874] Step 18:

[0875] server:

[0876] The emotion engine recognizes the user's emotions and adjusts the feedback and learning plan accordingly.

[0877] Example: If the user is feeling impatient, ease up on their study plan and send them an encouraging message.

[0878] Step 19:

[0879] server:

[0880] The results of emotion recognition and feedback adjustment are also monitored to optimize the entire system.

[0881] Step 20:

[0882] User:

[0883] Users receive emotional feedback, allowing them to continue their learning effortlessly.

[0884] The above is the specific processing flow of this system. Through this series of steps, users can study effectively and continuously toward achieving their goals.

[0885] Example 2

[0886] 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."

[0887] Conventional educational support systems lack sufficient ability to generate plans to help users achieve their goals or monitor their learning progress, making it difficult to flexibly adjust to each user's emotions and learning pace. This makes it difficult for users to maintain their motivation over the long term, often making it difficult to achieve their final goals. Furthermore, feedback based on learning progress is limited to simple results, and specific suggestions for improving learning are lacking.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting study materials from the collected content; means for generating a study plan and schedule based on the study materials; means for applying the study plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the study plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; emotion engine means for recognizing the user's emotions and adjusting the study plan; means for adjusting the study plan based on the feedback and emotions; and means for continuously monitoring the user's study progress and emotion data and suggesting updates to the study plan. This enables effective study support for the user to achieve their goal and enables flexible adjustment of the study plan based on the user's study progress and emotional state.

[0889] "Means for users to set goals" refers to a function that provides an interface for users to specifically input and set their own learning goals and the results they wish to achieve.

[0890] "Means for collecting online content" refers to a function that automatically collects relevant educational materials and information from databases and websites on the Internet.

[0891] "Artificial intelligence means" refers to the algorithms and models that analyze collected data and identify and select the most appropriate learning materials for users.

[0892] "Means for generating study plans and schedules" refers to a function that designs a plan and time allocation that allows the user to study efficiently based on the selected study materials.

[0893] The "means for applying to a virtual clone in a virtual space" is a function for applying the generated learning plan and schedule to a virtual clone of the user existing in a digital space.

[0894] The "means by which the virtual clone implements the learning plan" refers to the function by which the virtual clone specifically implements the learning plan set in the virtual space.

[0895] The "means for undergoing periodic testing" is a function that allows the virtual clone to undergo periodic set tests and evaluations.

[0896] "Means for providing feedback on test results to the user" is a function that conveys the results of tests taken by the virtual clone to the user, informing them of the progress and results of their learning.

[0897] "Emotion engine means" refers to technology that recognizes and analyzes emotions from the user's tone of voice, facial expressions, input patterns, etc., and adjusts the learning plan appropriately based on that.

[0898] "Means to adjust study plans" refers to a function that optimizes and modifies existing study plans based on the user's condition, based on feedback and emotional data.

[0899] "Means for continuous monitoring and suggesting updates to the study plan" refers to a function that constantly monitors the user's study progress and emotional state, and suggests new study plans or adjusts the current plan as needed.

[0900] The present invention relates to a system for supporting users' self-development and goal achievement. This system generates a study plan based on the goals set by the user, conducts the study in a virtual space, and recognizes the user's emotions to provide feedback and adjust the plan. Each component and process of this system will be described in detail below.

[0901] User goal setting

[0902] User:

[0903] Users log in to the system and enter specific goals on the goal setting screen. The system's goal setting screen is implemented as a web application and is built using technologies such as HTML5 and React. Users enter specific information such as the skills they want to learn, the results they want to achieve, and the desired study period. For example, they could set the goal as "I want to acquire everyday conversational English skills in one year."

[0904] Content collection and lesson plan generation

[0905] server:

[0906] The system receives the goals set by the user and collects content related to the goal (images, videos, text, etc.) from the Internet. This content collection is done using scraping technology to obtain data, which is then analyzed by an AI engine (e.g., TensorFlow, PyTorch). Effective learning materials are selected as a result of the analysis.

[0907] server:

[0908] Based on the selected learning materials, the server generates a study plan and schedule that can include, for example, shadowing practice three times a week or a study plan for specific grammar points, and is presented in a format that fits the user's schedule.

[0909] Generation of virtual clones and execution of learning

[0910] Device:

[0911] The device generates a virtual clone in a virtual space based on the user's personal information (appearance, voice, etc.). The device is installed with virtual space generation software such as Unity or Unreal Engine, and a 3D model is created based on the user's facial photograph and voice data. This virtual clone is a virtual entity that carries out the learning plan set by the user.

[0912] server:

[0913] The server applies the learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. This virtual clone performs specific actions, such as reviewing English vocabulary and practicing pronunciation, at a set time each day.

[0914] Regular testing and feedback

[0915] server:

[0916] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided as feedback to the user, allowing them to track their progress.

[0917] User:

[0918] Users receive feedback and can adjust their learning plan and schedule as needed, which can be flexibly changed based on their needs and progress.

[0919] Supported by an emotional engine

[0920] server:

[0921] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from the user's tone of voice, facial expressions, and text input patterns. The emotion engine uses, for example, voice recognition technology and facial recognition technology.

[0922] server:

[0923] The emotion engine can recognize the user's emotions and adjust the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan can be lightened slightly and an encouraging message can be sent.

[0924] Continuous monitoring and plan updates

[0925] server:

[0926] The system continuously monitors the user's learning progress and emotional state. The server constantly monitors the user's learning data and emotional data and suggests updating the learning plan as needed. This monitoring and updating is performed using a database management system (e.g., PostgreSQL, MySQL).

[0927] Specific examples

[0928] As a concrete example, consider the case where User A sets the goal of "learning English conversation at a daily conversation level in one year." The server collects and selects the latest video materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone of User A that mimics his / her voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, and the results are collected and notified to User A. Furthermore, the emotion engine recognizes User A's emotions, and if User A is feeling stressed, it adjusts the study plan to help him / her continue studying without straining himself / herself.

[0929] Example prompts for generative AI models

[0930] Below are some example prompts for the generative AI model associated with this system:

[0931] Example prompt 1:

[0932] "Generate an effective study plan to help you master conversational English in one year. Schedule three times a week for one hour and combine shadowing, listening practice, and grammar study."

[0933] Example prompt 2:

[0934] "Please create questions for users to use in a listening test to gauge their level of mastery. The questions should be simple English conversation scenarios based on everyday conversations."

[0935] The system of the present invention provides comprehensive support necessary for users to achieve their goals, and unifies management of everything from creating study plans to providing emotional feedback, allowing users to effectively study while checking their own progress and emotional state and responding appropriately.

[0936] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0937] Step 1:

[0938] User:

[0939] The user logs into the system and enters specific goals on the goal setting screen. The input includes the skill they want to learn (e.g., English conversation) and the deadline for achieving the goal (e.g., one year). Based on this, the server receives the user's goal data. The input data might be goal: "everyday conversation level English conversation" and period: "one year." The server saves this data in a database.

[0940] Step 2:

[0941] server:

[0942] Based on the goal data set by the user, relevant learning content (images, videos, text, etc.) is collected from online sources. Web scraping technology is used to collect the data, and the collected data is temporarily stored in a database on the server. Next, an AI engine (e.g., TensorFlow, PyTorch) is used to analyze and evaluate the collected data and select the most suitable learning materials for the user. The input data are the goal data and collected content, and based on this, the most suitable learning materials (e.g., a list of video URLs or text materials) are output as the evaluation results.

[0943] Step 3:

[0944] server:

[0945] A learning plan and schedule is generated based on the selected learning materials. An AI engine is used to create a plan that matches the user's goals and learning pace. For example, a specific learning plan such as "one hour of shadowing practice three times a week, and 30 minutes of listening practice twice a week" is generated. The input data is the optimal learning materials and the user's goals, and the output data is the learning plan and schedule.

[0946] Step 4:

[0947] Device:

[0948] Based on the user's personal information (e.g., face photo and voice data), the device generates a virtual clone in a virtual space. This virtual clone is generated using Unity or Unreal Engine. The input data is the user's face photo and voice data, and the output data is a 3D model of the virtual clone. The generated virtual clone is then ready to execute the learning plan set by the user.

[0949] Step 5:

[0950] server:

[0951] The learning plan and schedule are applied to the virtual clone, and learning begins in the virtual space. The virtual clone begins learning at a fixed time each day and performs specific learning activities (e.g., reviewing English vocabulary and practicing pronunciation) in the virtual space. The input data are the learning plan and schedule and a 3D model of the virtual clone, and the output data is a learning execution log.

[0952] Step 6:

[0953] server:

[0954] Tests are periodically conducted on the virtual clones and the results are collected. For example, a listening test is conducted every Saturday to collect performance data on the virtual clones. The input data are the learning execution log and test data, and the output data are the test results.

[0955] Step 7:

[0956] server:

[0957] The collected test results are fed back to the user. This feedback includes the user's learning progress, achievement level, and specific advice on what to do next. The input data is the test results, and the output data is the feedback message. The feedback is provided to the user via email or in-app notification.

[0958] Step 8:

[0959] server:

[0960] An emotion engine is used to recognize the user's emotions and adjust the study plan. The emotion engine analyzes the user's tone of voice, facial expressions, and text input patterns to evaluate their emotional state. The input data is the user's voice and facial expression data, and the output data is the emotion evaluation result. For example, if the user is feeling stressed, the study plan is adjusted (e.g., reducing the study load or sending an encouraging message).

[0961] Step 9:

[0962] server:

[0963] The system continuously monitors the user's learning progress and emotional data and suggests updating the learning plan as necessary. The input data is learning progress data and emotional data, and the output data is a new learning plan. The updated plan is periodically applied to the virtual clone to optimize learning.

[0964] (Application example 2)

[0965] 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."

[0966] Conventional learning systems using virtual spaces have limited functionality in supporting users in setting and achieving specific goals in their daily lives. Furthermore, they are unable to provide appropriate feedback or adjust plans based on the user's emotional state, resulting in reduced learning efficiency. The present invention aims to solve these problems and provide a system that more effectively supports users' self-development and goal achievement. Furthermore, to enhance the shopping experience in virtual spaces, the present invention aims to more effectively support users in achieving their goals by providing a shopping plan generation function using a virtual assistant and a feedback function based on the user's emotions.

[0967] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a shopping plan based on purchasing goals set by the user, means for a virtual assistant to provide and explain optimal products to the user in a virtual space, and means for recognizing the user's emotions and providing feedback and adjusting the plan. This effectively supports the user in achieving their purchasing goals and enables appropriate feedback and plan adjustments to match the user's emotional state.

[0968] "User" refers to any individual or entity that uses the System and sets its own goals.

[0969] A "goal" is a specific objective or task that a user sets out to achieve.

[0970] "Content" refers to information such as images, videos, and text collected online, and is material related to learning and purchasing.

[0971] "Artificial intelligence means" refers to machine learning algorithms or systems that analyze collected content and select the most suitable learning materials or products for users.

[0972] A "study plan" refers to the learning content and schedule created based on the goals the user wants to achieve.

[0973] A "schedule" refers to a timetable or timetable for carrying out study and purchasing activities according to a study plan.

[0974] "Virtual space" refers to a three-dimensional virtual environment generated by computer simulation.

[0975] A "virtual clone" refers to a virtual being generated in a virtual space based on information such as the user's appearance and voice.

[0976] A "virtual assistant" is a virtual entity that provides and explains products based on the purchasing goals set by the user in a virtual space.

[0977] "Emotion" refers to a user's psychological state, which can be identified by tone of voice, facial expression, text input patterns, etc.

[0978] "Feedback" refers to information that provides users with results or recommendations regarding their learning or purchasing activities.

[0979] This invention is a system that allows users to effectively improve themselves and achieve their goals in a virtual space. The system generates shopping plans and study plans based on the goals set by the user, and then implements these plans in the virtual space using a virtual clone or virtual assistant. The system also recognizes the user's emotions and provides feedback and adjusts the plans accordingly.

[0980] Hardware and Software

[0981] The system is implemented using the following hardware and software.

[0982] Hardware: Computers, servers, and user devices (smartphones, head-mounted displays, etc.).

[0983] Software: Generative AI models (e.g., GPT), emotion engines (e.g., Emotion AI SDK), databases, and virtual world simulation software.

[0984] Specific processing steps

[0985] 1. User goal setting

[0986] The user logs in to the system and enters a specific goal on the goal setting screen. For example, they might set "I want to find and purchase new spring fashion items." This goal is then sent to the server.

[0987] 2. Content collection and plan generation

[0988] The server collects relevant content (images, videos, text, etc.) from online sources based on the user's set goals, analyzes and evaluates this content, and generates optimal shopping and learning plans.

[0989] 3. Creation and Implementation of Virtual Clone

[0990] The device generates a virtual clone and virtual assistant in the virtual space based on the user's profile. This virtual clone is a virtual presence that puts into practice the plan set by the user. The virtual assistant is responsible for providing and explaining the most suitable products to the user.

[0991] 4. Emotion Recognition and Feedback

[0992] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, typing patterns, etc. If the user is feeling stressed, the system will support them by adjusting their study or shopping plans and sending encouraging messages.

[0993] 5. Regular monitoring and plan updates

[0994] The server continuously monitors the user's progress in achieving their goals and their emotional state, and suggests updating the plan as necessary, thereby enabling continuous support for the user's goal achievement.

[0995] Specific examples

[0996] If a user selects "I want to find new spring fashion items," the server will collect relevant content and generate an appropriate shopping plan. The virtual assistant will provide and explain the best products to the user in the virtual space, and the emotion engine will monitor the user's emotions and provide feedback to help the user relax.

[0997] Prompt Sentence Examples

[0998] Next, generate a shopping plan to find the perfect fashion items for the user's set goals. The user's profile is as follows:

[0999] Age: 30

[1000] Gender: Female

[1001] Favorite style: Casual

[1002] User goal: Find new spring fashion items.

[1003] Generate the best plan to achieve your goals.

[1004] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1005] Step 1:

[1006] A user logs in to the system and enters a specific goal on the goal setting screen. This goal is sent to the server. The input is the user's goal (e.g., "I want to find and purchase new spring fashion items"), and the output is the goal data sent to the server.

[1007] Step 2:

[1008] The server collects relevant content (images, videos, text, etc.) from online sources based on the goals set by the user. The collected content is analyzed and evaluated using a generative AI model. The input is the user's goal data, and the output is the collected relevant content data.

[1009] Step 3:

[1010] The server selects the most suitable learning materials and products for the user from the analyzed and evaluated content, and generates a shopping plan or learning plan. The input is the related content data, and the output is the generated plan and schedule.

[1011] Step 4:

[1012] The device generates a virtual clone or virtual assistant in the virtual space based on the user's profile (appearance, voice, etc.). The input is the user's profile information, and the output is the generated virtual clone or virtual assistant.

[1013] Step 5:

[1014] The device's virtual clone or virtual assistant executes the learning plan or shopping plan generated by the server. Specific actions include introducing products or advancing learning content in the virtual space. The input is the generated plan, and the output is progress data of the plan as it is implemented.

[1015] Step 6:

[1016] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, and text input patterns. The input is the user's emotional data, and the output is the analyzed emotional state.

[1017] Step 7:

[1018] The server then provides feedback and adjusts the plan based on the analyzed emotional data. For example, if the user is feeling stressed, it can reduce the learning or shopping plan and send an encouraging message. The input is the emotional state and plan data, and the output is the adjusted plan and feedback message.

[1019] Step 8:

[1020] The server continuously monitors the user's progress toward their goals and emotional state, and suggests updating the plan as necessary. For example, if the user changes their set goals or if they experience prolonged stress, the server will suggest a major revision of the plan. The input is continuously acquired progress data and emotional data, and the output is a notification of the proposed update.

[1021] 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.

[1022] 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.

[1023] 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.

[1024] [Third embodiment]

[1025] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1026] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1027] 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).

[1028] 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.

[1029] 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.

[1030] 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).

[1031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1032] 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.

[1033] 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.

[1034] 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.

[1035] 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.

[1036] 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."

[1037] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support goal achievement.

[1038] User Actions

[1039] User:

[1040] First, the user logs in to the system. After logging in, a goal setting screen appears. On this screen, the user can enter specific goals. For example, a goal could be set such as "I want to acquire everyday conversational English skills in one year."

[1041] Content collection and learning plan generation

[1042] server:

[1043] After a user sets a goal, the server collects content related to the goal from online sources. The collected content is provided in various formats, including images, videos, and text. This content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[1044] Based on the selected learning materials, the server generates a study plan and schedule that is optimal for the user, such as shadowing practice three times a week or a study plan for specific grammar points.

[1045] Virtual clone generation and learning

[1046] Device:

[1047] Next, the device generates a virtual clone in the virtual space based on the user's personal information (such as appearance and voice), which is a virtual entity that carries out the learning plan set by the user.

[1048] server:

[1049] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone may review English vocabulary or practice pronunciation at a set time each day.

[1050] Regular testing and feedback

[1051] server:

[1052] The server periodically tests the virtual clones, for example, conducting a listening test every Saturday, and collects the results. The server then provides the test results to the user as feedback.

[1053] User:

[1054] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[1055] Continuous monitoring and plan updates

[1056] server:

[1057] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and proposes updates to the learning plan as needed. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, a system is provided that helps the user achieve their goals and improves their self-esteem.

[1058] Specific examples

[1059] The following is a specific example of use. When User A sets the goal of "I want to acquire everyday conversational English in one year," the server collects and selects the latest video learning materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone performs 20 minutes of listening practice every day in a virtual space. The server conducts a listening test every Saturday and notifies User A of the results. User A receives feedback, and if progress is slower than expected, adjusts the study plan, such as increasing the time for listening practice. By repeating this process, User A can effectively progress in their studies toward achieving their goal.

[1060] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[1061] The processing flow will be explained below.

[1062] Step 1:

[1063] User:

[1064] The user logs into the system and enters specific goals on the goal setting screen.

[1065] Example: Set "I want to acquire everyday conversation level English in one year."

[1066] Step 2:

[1067] server:

[1068] Receive goals set by the user.

[1069] Step 3:

[1070] server:

[1071] The server collects content (images, videos, text) related to the goal from online sources.

[1072] Step 4:

[1073] server:

[1074] The collected content is passed to AI, which analyzes and evaluates it to select the most suitable learning material for the user.

[1075] Step 5:

[1076] server:

[1077] Generate a learning plan and schedule based on the learning materials.

[1078] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[1079] Step 6:

[1080] User:

[1081] The user reviews the proposed study schedule and adjusts it as needed.

[1082] Example: Approve the schedule.

[1083] Step 7:

[1084] Device:

[1085] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[1086] Step 8:

[1087] server:

[1088] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[1089] Step 9:

[1090] server:

[1091] The virtual clone implements the study plan at a set time each day.

[1092] For example: 20 minutes of shadowing practice every day.

[1093] Step 10:

[1094] server:

[1095] The server periodically runs tests on the virtual clones and collects the results.

[1096] Example: Listening tests are conducted every Saturday.

[1097] Step 11:

[1098] server:

[1099] Provide collected test results as feedback to users.

[1100] Step 12:

[1101] User:

[1102] Users receive feedback and see their progress.

[1103] Step 13:

[1104] User:

[1105] Adjust your study plan and schedule as needed.

[1106] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[1107] Step 14:

[1108] server:

[1109] The system continuously monitors the user's learning progress.

[1110] Step 15:

[1111] server:

[1112] Analyze learning data and suggest updates to your learning plan as needed.

[1113] Example: Propose a new plan that focuses on listening.

[1114] Step 16:

[1115] User:

[1116] The user accepts the proposed update plan and continues learning based on the new learning plan.

[1117] The above is the specific processing flow of this system. Through this series of steps, users can effectively progress through their studies toward achieving their goals.

[1118] Example 1

[1119] 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."

[1120] Conventional learning support systems have difficulty providing individualized learning plans, making it difficult to maintain learners' motivation. Furthermore, because learning progress is not monitored or feedback is not provided in real time, users often have difficulty grasping their own progress. This creates the problem of insufficient learning effectiveness.

[1121] 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.

[1122] In this invention, the server includes a means for allowing a user to set a goal, a means for collecting online content based on the goal, and an artificial intelligence means for selecting learning materials from the collected content, thereby enabling the provision of an individualized and effective learning plan.

[1123] The server also includes a means for the user to generate his or her own virtual agent in the virtual environment, a means for applying the learning plan and schedule to the virtual agent in the virtual space, and a means for the virtual agent to implement the learning plan, which allows learning to be carried out virtually and is expected to increase the user's motivation.

[1124] The server further includes a means for periodically evaluating the virtual agent, a means for providing feedback on the evaluation results to the user, and a means for adjusting the learning plan based on the feedback, thereby enabling real-time monitoring of the user's learning progress and prompt feedback and plan adjustment.

[1125] A "user" is an entity that uses the system to set goals and learn.

[1126] "Goals" are the specific learning content or skills that users aim to achieve through the system.

[1127] "Online content" refers to learning materials such as videos, text, and images that are accessible via the internet.

[1128] "Artificial intelligence means" is a general term for AI technologies that analyze collected content and appropriately select learning materials.

[1129] "Study plan and schedule" refers to specific learning content and its implementation plan designed to help users achieve their goals.

[1130] A "virtual space" is a digital environment created using computer graphics.

[1131] A "virtual agent" is a virtual learning entity that reflects the characteristics of the user and carries out a learning plan in a virtual space.

[1132] "Evaluation" refers to periodic testing and assessment of the learning that the virtual agent has performed.

[1133] "Feedback" means information and advice about learning progress provided to users based on assessment results.

[1134] "Plan adjustment" refers to reviewing and appropriately changing study plans and schedules based on feedback.

[1135] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support the user in achieving their goals.

[1136] Hardware and Software

[1137] User terminal (PC, smartphone): A device that allows users to access and operate the system.

[1138] Server: Responsible for data processing and storage. The server has the Django framework and AI libraries (e.g., TensorFlow, PyTorch) installed.

[1139] Web browser: Software that allows users to operate login screens and various interfaces.

[1140] Specific processing explanation

[1141] User login

[1142] User:

[1143] The user accesses the login screen through a web browser. The user enters their "user name" and "password" and clicks the "Login" button. The server compares the received authentication information with the database to verify whether the user is a legitimate user. Once the comparison is complete, the user's dashboard screen is displayed.

[1144] goal setting

[1145] User:

[1146] After logging in, users can enter specific learning goals on the goal setting screen. For example, they can set a goal such as "I want to acquire everyday conversational English skills in one year."

[1147] server:

[1148] The server receives the goal data entered by the user and stores it in the database, thereby registering the user's learning goals in the system.

[1149] Content collection and learning plan generation

[1150] server:

[1151] Based on the goals set by the user, the server collects online content. Content collection uses the YouTube API, Google Search API, etc. The collected content is analyzed using AI technology to select the most suitable learning materials for the user. Based on the selected learning materials, the server generates a learning plan and schedule.

[1152] Virtual clone generation and learning

[1153] Device:

[1154] The device generates a virtual clone in a virtual space based on the user's personal information (profile photo and voice sample), using image processing and voice recognition technology to create a realistic virtual clone.

[1155] server:

[1156] The learning plan is applied to the generated virtual clone, and the virtual clone carries out various learning activities according to the specified schedule.

[1157] Regular testing and feedback

[1158] server:

[1159] The server periodically tests the virtual clone, for example, conducting a listening test every Saturday, analyzes the results, and notifies the user of the analysis results as feedback.

[1160] User:

[1161] Users receive feedback and track their progress, adjusting their learning plans and schedules as needed through the server.

[1162] Continuous monitoring and plan updates

[1163] server:

[1164] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and suggests updating the learning plan as needed. For example, if the user's listening skills are lacking, the system will suggest a new learning plan focused on listening.

[1165] Prompt Sentence Examples

[1166] "The user has set the goal of 'I want to acquire conversational English skills in one year.' Please write a program that collects the latest English learning videos and exercises and suggests a study schedule of one hour, three times a week."

[1167] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[1168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1169] Step 1: User Login

[1170] User:

[1171] Input: Enter your "Username" and "Password" into the login screen and click the [Login] button.

[1172] What happens: User enters login information and submits.

[1173] server:

[1174] Data processing: The entered authentication information is compared with a database.

[1175] Output: If the user is legitimate, the user's dashboard screen is displayed.

[1176] Specific operation: The server checks the user information against the database, and if it matches, the login process is successful.

[1177] Step 2: Goal Setting

[1178] User:

[1179] Input: Enter a specific learning goal, such as "I want to acquire everyday conversational English skills in one year," and click the "Set Goal" button.

[1180] Action: Enter your learning goals on the goal setting screen and submit.

[1181] server:

[1182] Data processing: Receive the entered target data and save it in the database.

[1183] Output: The user's learning goals are registered in the system.

[1184] Specific actions: The server saves this goal in the database and proceeds to the next step.

[1185] Step 3: Gather content and create a lesson plan

[1186] server:

[1187] Input: Goal data set by the user.

[1188] What it does: Collects relevant content online.

[1189] Data processing: We use APIs (e.g., YouTube API, Google Search API) to collect content information and analyze the data using artificial intelligence technology.

[1190] Output: Select appropriate learning materials from the collected content and generate an optimal learning plan and schedule for the user.

[1191] Specific operation: The server collects information from the YouTube API using keywords such as "daily conversation English learning videos" and categorizes them using natural language processing technology.

[1192] Step 4: Generating and training virtual clones

[1193] Device:

[1194] Input: User personal information (profile photo and voice sample).

[1195] Operation: Generates a virtual clone in virtual space.

[1196] Data processing: Using image processing and voice recognition technology to create a realistic virtual clone.

[1197] Output: Generate a virtual clone.

[1198] How it works: The device scans the user's photo and uses facial recognition technology to create a realistic virtual clone.

[1199] server:

[1200] Input: The generated virtual clone.

[1201] What it does: Apply a learning plan to a virtual clone.

[1202] Data processing: Using deep learning models, the virtual clone executes the learning plan.

[1203] Output: The virtual clone starts learning.

[1204] Specific operation: The server instructs the virtual clone to perform shadowing and listening practice.

[1205] Step 5: Regular testing and feedback

[1206] server:

[1207] Input: Training data for virtual clones.

[1208] Behavior: Periodically run tests on the virtual clone.

[1209] Data processing: Analyze the test results and provide feedback to the user.

[1210] Output: Notify the user of the evaluation results.

[1211] Specific operation: The server conducts a listening test every Saturday and notifies the user of the results as a rating such as "passed" or "needs improvement."

[1212] User:

[1213] Input: The notified feedback information.

[1214] Behavior: See feedback and track progress.

[1215] Data processing: Adjust your study plan and schedule based on the feedback.

[1216] Output: Updated learning plan.

[1217] What it does: Users review their assessments and adjust their study time based on their progress.

[1218] Step 6: Continuously monitor and update your plan

[1219] server:

[1220] Input: User learning data and progress information.

[1221] What it does: Continuously monitor learning progress.

[1222] Data processing: Analyze learning data and suggest updates to the learning plan as needed.

[1223] Output: Update suggestions.

[1224] How it works: The server analyzes the user's learning history and, if there are "lacks in listening skills," sends a notification suggesting a new listening-focused learning plan.

[1225] (Application example 1)

[1226] 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."

[1227] In today's online learning environment, providing content tailored to individual learning goals and managing progress are key challenges. Such systems must be able to select the most appropriate learning content from a wide variety of available content and provide an optimal learning plan for efficient learning. However, existing systems struggle to assess users' progress in real time and dynamically revise their learning plans as needed. Furthermore, technology for providing personalized learning experiences using virtual spaces is not yet fully developed. This leaves users without the support they need to effectively achieve their learning goals.

[1228] 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.

[1229] In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting learning materials from the collected content; means for generating a learning plan and schedule based on the learning materials; means for applying the learning plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the learning plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; means for adjusting the learning plan based on the feedback; means for monitoring the user's progress and updating and optimizing the learning plan in real time; and means for inputting prompts into a generating AI model and selecting optimal learning content. This allows the user to check their progress in real time and efficiently achieve their goals with an optimized learning plan.

[1230] "User" refers to an individual or corporation that uses the system to set learning goals and manage progress.

[1231] "Goals" are the specific learning or work objectives that users intend to achieve by using the system.

[1232] "Content" refers to learning materials such as images, videos, audio, and text that exist online.

[1233] "Artificial intelligence means" refers to processes and devices that use AI technology to analyze collected content and select the most appropriate learning materials.

[1234] A "learning plan" is a planned and scheduled combination of learning activities necessary to achieve a user's goals.

[1235] A "schedule" indicates the time frame and order of learning activities that should be carried out daily based on a learning plan.

[1236] A "virtual space" is a digital virtual environment constructed using technology such as computer graphics.

[1237] A "virtual clone" is a digital avatar generated in a virtual space based on the user's appearance and voice.

[1238] "Periodic tests" are tests that the virtual clone takes at regular intervals to evaluate its learning outcomes.

[1239] "Feedback" is information provided to users with periodic test results and progress data to help them adjust their study plans.

[1240] "Monitoring" means continuously observing and recording a user's learning progress.

[1241] "Real-time updating and optimization means" refers to the process and devices that instantly modify and improve the learning plan based on the user's learning progress and feedback.

[1242] A "generative AI model" is an artificial intelligence model that automatically generates optimal learning content based on the input prompt.

[1243] A "prompt" is an instruction entered to allow the generative AI model to select the most appropriate learning content.

[1244] To implement this invention, a system including the following elements is required. The system is composed of a server, a terminal, and a user. Specific processing procedures and how to execute them are explained below.

[1245] User Actions

[1246] The user first logs in to the system. After logging in, a goal setting screen appears, where the user can enter specific learning goals. For example, the user can set a goal such as "I want to acquire everyday conversational English skills in one year."

[1247] Content collection and learning plan generation

[1248] The server collects online content based on the user's set goals. The collected content exists in various formats, including images, videos, audio, and text. This content is analyzed and evaluated using artificial intelligence (AI) technology to select the most suitable learning materials for the user. Specific learning content is then selected by inputting a prompt into the generative AI model. For example, the prompt could be, "I want to learn business-level German in one year."

[1249] The server then generates an optimal study plan and schedule based on the selected study materials, including, for example, shadowing practice three times a week or a study plan for specific grammar points.

[1250] Virtual clone generation and learning

[1251] The device generates a virtual clone in the virtual space based on the user's personal information (appearance, voice, etc.), and the virtual clone is responsible for carrying out the learning plan set by the user.

[1252] The server applies the learning plan to the virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[1253] Regular testing and feedback

[1254] The server periodically tests the virtual clones, for example, by conducting a listening test every Saturday, and collects the results. The test results are then provided to the user as feedback.

[1255] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[1256] Continuous monitoring and plan updates

[1257] The server continuously monitors the user's learning progress. The server analyzes the user's learning data and has the function of proposing updates to the learning plan as necessary. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, the server can effectively support the user in achieving their goals.

[1258] This system enables efficient learning tailored to individual learning goals. In particular, the use of prompts by the generative AI model makes it possible to smoothly select the most suitable learning content for the user. For example, by using the prompt "I want to acquire business-level German in one year," the server can select learning materials specialized for German business conversation and generate the optimal learning plan.

[1259] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1260] Step 1:

[1261] The server receives the user's login information. It takes the user ID and password as input and performs authentication processing. If authentication is successful, a session is started, a session ID is generated and returned to the user. If authentication fails, an error message is returned.

[1262] Step 2:

[1263] After the user logs in, the goal setting screen is displayed. The user enters a specific learning goal (e.g., "I want to acquire daily conversation level English in one year"). The entered goal information is sent to the server.

[1264] Step 3:

[1265] The server receives the learning goals set by the user, analyzes the received data, and collects related online content, such as videos, texts, and audio files related to English conversation from the Internet. This collected data serves as input for the next processing step.

[1266] Step 4:

[1267] The server analyzes the collected content using artificial intelligence (AI) tools. An AI model (e.g., a natural language processing model) evaluates the content and selects learning materials that best fit the user's learning goals. These selected learning materials serve as input for the next step.

[1268] Step 5:

[1269] The server generates a learning plan and schedule based on the selected learning materials. The generated learning plan includes a weekly schedule and daily learning tasks. These plans and schedules are used in the next step.

[1270] Step 6:

[1271] The device generates a virtual clone based on the user's personal information (such as a photo of the face and voice). Using the data obtained from the user as input, the virtual clone is created using 3D modeling software and voice synthesis technology. The generated virtual clone is then used in the next step.

[1272] Step 7:

[1273] The server applies the learning plan to the virtual clone, and the virtual clone begins learning based on a schedule set in the virtual space. For example, the virtual clone can practice listening and pronunciation for 20 minutes every day.

[1274] Step 8:

[1275] The server periodically tests the virtual clone, collects the results of each test (e.g., a listening test every Saturday), analyzes the data, and generates feedback based on the test results to provide to the user.

[1276] Step 9:

[1277] The user receives feedback from the server, including test results and progress, and checks their own learning status. If necessary, the user can request adjustments to their learning plan or schedule from the server.

[1278] Step 10:

[1279] The server monitors the user's progress and suggests updates to the learning plan. It analyzes the progress data and generates a new learning plan that focuses on skills that are lacking (e.g., listening skills), thereby continuously optimizing the user's learning experience.

[1280] Step 11:

[1281] A prompt sentence is input into the generative AI model to select the most suitable learning content. By providing a prompt sentence (e.g., "I want to acquire business-level German in one year") as input, the AI ​​model outputs learning materials that are best suited to the user's goals. These materials are used in Step 4.

[1282] 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.

[1283] This invention is a system that supports users in self-development and goal achievement, and is characterized by generating a study plan based on the goals set by the user, conducting study in a virtual space, and recognizing the user's emotions to provide feedback and adjust the plan.

[1284] User Actions

[1285] User:

[1286] First, the user logs into the system and enters specific goals on the goal setting screen.

[1287] Example: Set "I want to acquire everyday conversation level English in one year."

[1288] Content collection and learning plan generation

[1289] server:

[1290] The system receives the goals set by the user and collects content related to the goals (images, videos, text, etc.) from online sources. The collected content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[1291] Based on the selected learning materials, the server generates a study plan and schedule, which may include, for example, shadowing practice three times a week or a study plan for specific grammar points.

[1292] Virtual clone generation and learning

[1293] Device:

[1294] Based on the user's personal information (appearance, voice, etc.), the device generates a virtual clone in the virtual space, which is a virtual entity that carries out the learning plan set by the user.

[1295] server:

[1296] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[1297] Regular testing and feedback

[1298] server:

[1299] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided to the user as feedback.

[1300] User:

[1301] Users receive this feedback, see their progress, and adjust their learning plan or schedule as needed, which can be flexibly changed based on the user's needs and progress.

[1302] Supported by an emotional engine

[1303] server:

[1304] The server is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's emotions based on the user's tone of voice, facial expressions, and character input patterns.

[1305] server:

[1306] The emotion engine recognizes the user's emotions and adjusts the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan will be lightened slightly and an encouraging message will be sent.

[1307] Continuous monitoring and plan updates

[1308] server:

[1309] The system continuously monitors the user's learning progress and emotional state. The server analyzes the user's learning and emotional data and proposes updates to the learning plan as needed.

[1310] Specific examples

[1311] The following is a specific example of use. When User A sets the goal of "learning English at a conversational level in one year," the server collects and selects the latest video learning materials and practice questions related to English language learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, collects the results, and notifies User A.

[1312] Furthermore, the emotion engine recognizes User A's emotions and, if User A is feeling stressed, adjusts the study plan to help them continue studying without straining themselves. This allows User A to receive feedback and check their progress while studying effectively.

[1313] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and achieve goals while taking into consideration their emotions.

[1314] The processing flow will be explained below.

[1315] Step 1:

[1316] User:

[1317] The user logs into the system and enters specific goals on the goal setting screen.

[1318] Example: Set "I want to acquire everyday conversation level English in one year."

[1319] Step 2:

[1320] server:

[1321] Receive goals set by the user.

[1322] Step 3:

[1323] server:

[1324] The server collects content (images, videos, text) related to the goal from online sources.

[1325] Step 4:

[1326] server:

[1327] The collected content is passed to artificial intelligence (AI), which analyzes and evaluates it to select the most suitable learning material for the user.

[1328] Step 5:

[1329] server:

[1330] Generate a learning plan and schedule based on the learning materials.

[1331] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[1332] Step 6:

[1333] User:

[1334] The user reviews the proposed study schedule and adjusts it as needed.

[1335] Example: Approve the schedule.

[1336] Step 7:

[1337] Device:

[1338] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[1339] Step 8:

[1340] server:

[1341] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[1342] Step 9:

[1343] server:

[1344] The virtual clone implements the study plan at a set time each day.

[1345] For example: 20 minutes of shadowing practice every day.

[1346] Step 10:

[1347] server:

[1348] The server periodically runs tests on the virtual clones and collects the results.

[1349] Example: Listening tests are conducted every Saturday.

[1350] Step 11:

[1351] server:

[1352] Provide collected test results as feedback to users.

[1353] Step 12:

[1354] User:

[1355] Users receive feedback and see their progress.

[1356] Step 13:

[1357] User:

[1358] Adjust your study plan and schedule as needed.

[1359] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[1360] Step 14:

[1361] server:

[1362] The system continuously monitors the user's learning progress.

[1363] Step 15:

[1364] server:

[1365] Analyze learning data and suggest updates to your learning plan as needed.

[1366] Example: Propose a new plan that focuses on listening.

[1367] Step 16:

[1368] User:

[1369] The user accepts the proposed update plan and continues learning based on the new learning plan.

[1370] Step 17:

[1371] server:

[1372] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's tone of voice, facial expressions, and text input patterns.

[1373] Step 18:

[1374] server:

[1375] The emotion engine recognizes the user's emotions and adjusts the feedback and learning plan accordingly.

[1376] Example: If the user is feeling impatient, ease up on their study plan and send them an encouraging message.

[1377] Step 19:

[1378] server:

[1379] The results of emotion recognition and feedback adjustment are also monitored to optimize the entire system.

[1380] Step 20:

[1381] User:

[1382] Users receive emotional feedback, allowing them to continue their learning effortlessly.

[1383] The above is the specific processing flow of this system. Through this series of steps, users can effectively and sustainably advance their learning toward achieving their goals.

[1384] Example 2

[1385] 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."

[1386] Conventional educational support systems lack sufficient ability to generate plans to help users achieve their goals or monitor their learning progress, making it difficult to flexibly adjust to each user's emotions and learning pace. This makes it difficult for users to maintain their motivation over the long term, often making it difficult to achieve their final goals. Furthermore, feedback based on learning progress is limited to simple results, and specific suggestions for improving learning are lacking.

[1387] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting study materials from the collected content; means for generating a study plan and schedule based on the study materials; means for applying the study plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the study plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; emotion engine means for recognizing the user's emotions and adjusting the study plan; means for adjusting the study plan based on the feedback and emotions; and means for continuously monitoring the user's study progress and emotion data and suggesting updates to the study plan. This enables effective study support for the user to achieve their goal and enables flexible adjustment of the study plan based on the user's study progress and emotional state.

[1388] "Means for users to set goals" refers to a function that provides an interface for users to specifically input and set their own learning goals and the results they wish to achieve.

[1389] "Means for collecting online content" refers to a function that automatically collects relevant educational materials and information from databases and websites on the Internet.

[1390] "Artificial intelligence means" refers to the algorithms and models that analyze collected data and identify and select the most appropriate learning materials for users.

[1391] "Means for generating study plans and schedules" refers to a function that designs a plan and time allocation that allows the user to study efficiently based on the selected study materials.

[1392] The "means for applying to a virtual clone in a virtual space" is a function for applying the generated learning plan and schedule to a virtual clone of the user existing in a digital space.

[1393] The "means by which the virtual clone implements the learning plan" refers to the function by which the virtual clone specifically implements the learning plan set in the virtual space.

[1394] The "means for undergoing periodic testing" is a function that allows the virtual clone to undergo periodic set tests and evaluations.

[1395] "Means for providing feedback on test results to the user" is a function that conveys the results of tests taken by the virtual clone to the user, informing them of the progress and results of their learning.

[1396] "Emotion engine means" refers to technology that recognizes and analyzes emotions from the user's tone of voice, facial expressions, input patterns, etc., and adjusts the learning plan appropriately based on that.

[1397] "Means to adjust study plans" refers to a function that optimizes and modifies existing study plans based on the user's condition, based on feedback and emotional data.

[1398] "Means for continuous monitoring and suggesting updates to the study plan" refers to a function that constantly monitors the user's study progress and emotional state, and suggests new study plans or adjusts the current plan as needed.

[1399] The present invention relates to a system for supporting users' self-development and goal achievement. This system generates a study plan based on the goals set by the user, conducts the study in a virtual space, and recognizes the user's emotions to provide feedback and adjust the plan. Each component and process of this system will be described in detail below.

[1400] User goal setting

[1401] User:

[1402] Users log in to the system and enter specific goals on the goal setting screen. The system's goal setting screen is implemented as a web application and is built using technologies such as HTML5 and React. Users enter specific information such as the skills they want to learn, the results they want to achieve, and the desired study period. For example, they could set the goal as "I want to acquire everyday conversational English skills in one year."

[1403] Content collection and lesson plan generation

[1404] server:

[1405] The system receives the goals set by the user and collects content related to the goal (images, videos, text, etc.) from the Internet. This content collection is done using scraping technology to obtain data, which is then analyzed by an AI engine (e.g., TensorFlow, PyTorch). Effective learning materials are selected as a result of the analysis.

[1406] server:

[1407] Based on the selected learning materials, the server generates a study plan and schedule that can include, for example, shadowing practice three times a week or a study plan for specific grammar points, and is presented in a format that fits the user's schedule.

[1408] Generation of virtual clones and training execution

[1409] Device:

[1410] The device generates a virtual clone in a virtual space based on the user's personal information (appearance, voice, etc.). The device is installed with virtual space generation software such as Unity or Unreal Engine, and a 3D model is created based on the user's facial photograph and voice data. This virtual clone is a virtual entity that carries out the learning plan set by the user.

[1411] server:

[1412] The server applies the learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. This virtual clone performs specific actions, such as reviewing English vocabulary and practicing pronunciation, at a set time each day.

[1413] Regular testing and feedback

[1414] server:

[1415] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided as feedback to the user, allowing them to track their progress.

[1416] User:

[1417] Users receive feedback and can adjust their learning plan or schedule as needed, which can be flexibly changed based on their needs and progress.

[1418] Supported by an emotional engine

[1419] server:

[1420] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from the user's tone of voice, facial expressions, and text input patterns. The emotion engine uses, for example, voice recognition technology and facial recognition technology.

[1421] server:

[1422] The emotion engine can recognize the user's emotions and adjust the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan can be lightened slightly and an encouraging message can be sent.

[1423] Continuous monitoring and plan updates

[1424] server:

[1425] The system continuously monitors the user's learning progress and emotional state. The server constantly monitors the user's learning data and emotional data and suggests updating the learning plan as needed. This monitoring and updating is performed using a database management system (e.g., PostgreSQL, MySQL).

[1426] Specific examples

[1427] As a concrete example, consider the case where User A sets the goal of "learning English conversation at a daily conversation level in one year." The server collects and selects the latest video materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone of User A that mimics his / her voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, and the results are collected and notified to User A. Furthermore, the emotion engine recognizes User A's emotions, and if User A is feeling stressed, it adjusts the study plan to help him / her continue studying without straining himself / herself.

[1428] Example prompts for generative AI models

[1429] Below are some example prompts for the generative AI model associated with this system:

[1430] Example prompt 1:

[1431] "Generate an effective study plan to help you master conversational English in one year. Schedule three times a week for one hour and combine shadowing, listening practice, and grammar study."

[1432] Example prompt 2:

[1433] "Please create questions for users to use in a listening test to gauge their level of mastery. The questions should be simple English conversation scenarios based on everyday conversations."

[1434] The system of the present invention provides comprehensive support necessary for users to achieve their goals, and unifies management of everything from creating study plans to providing emotional feedback, allowing users to effectively study while checking their own progress and emotional state and responding appropriately.

[1435] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1436] Step 1:

[1437] User:

[1438] The user logs into the system and enters specific goals on the goal setting screen. The input includes the skill they want to learn (e.g., English conversation) and the deadline for achieving the goal (e.g., one year). Based on this, the server receives the user's goal data. The input data might be goal: "everyday conversation level English conversation" and period: "one year." The server saves this data in a database.

[1439] Step 2:

[1440] server:

[1441] Based on the goal data set by the user, relevant learning content (images, videos, text, etc.) is collected from online sources. Web scraping technology is used to collect the data, and the collected data is temporarily stored in a database on the server. Next, an AI engine (e.g., TensorFlow, PyTorch) is used to analyze and evaluate the collected data and select the most suitable learning materials for the user. The input data are the goal data and collected content, and based on this, the most suitable learning materials (e.g., a list of video URLs or text materials) are output as the evaluation results.

[1442] Step 3:

[1443] server:

[1444] A learning plan and schedule is generated based on the selected learning materials. An AI engine is used to create a plan that matches the user's goals and learning pace. For example, a specific learning plan such as "one hour of shadowing practice three times a week, and 30 minutes of listening practice twice a week" is generated. The input data is the optimal learning materials and the user's goals, and the output data is the learning plan and schedule.

[1445] Step 4:

[1446] Device:

[1447] Based on the user's personal information (e.g., face photo and voice data), the device generates a virtual clone in a virtual space. This virtual clone is generated using Unity or Unreal Engine. The input data is the user's face photo and voice data, and the output data is a 3D model of the virtual clone. The generated virtual clone is then ready to execute the learning plan set by the user.

[1448] Step 5:

[1449] server:

[1450] The learning plan and schedule are applied to the virtual clone, and learning begins in the virtual space. The virtual clone begins learning at a fixed time each day and performs specific learning activities (e.g., reviewing English vocabulary and practicing pronunciation) in the virtual space. The input data are the learning plan and schedule and a 3D model of the virtual clone, and the output data is a learning execution log.

[1451] Step 6:

[1452] server:

[1453] Tests are periodically conducted on the virtual clones and the results are collected. For example, a listening test is conducted every Saturday to collect performance data on the virtual clones. The input data are the learning execution log and test data, and the output data are the test results.

[1454] Step 7:

[1455] server:

[1456] The collected test results are fed back to the user. This feedback includes the user's learning progress, achievement level, and specific advice on what to do next. The input data is the test results, and the output data is the feedback message. The feedback is provided to the user via email or in-app notification.

[1457] Step 8:

[1458] server:

[1459] An emotion engine is used to recognize the user's emotions and adjust the study plan. The emotion engine analyzes the user's tone of voice, facial expressions, and text input patterns to evaluate their emotional state. The input data is the user's voice and facial expression data, and the output data is the emotion evaluation result. For example, if the user is feeling stressed, the study plan is adjusted (e.g., reducing the study load or sending an encouraging message).

[1460] Step 9:

[1461] server:

[1462] The system continuously monitors the user's learning progress and emotional data and suggests updating the learning plan as necessary. The input data is learning progress data and emotional data, and the output data is a new learning plan. The updated plan is periodically applied to the virtual clone to optimize learning.

[1463] (Application example 2)

[1464] 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."

[1465] Conventional learning systems using virtual spaces have limited functionality in supporting users in setting and achieving specific goals in their daily lives. Furthermore, they are unable to provide appropriate feedback or adjust plans based on the user's emotional state, resulting in reduced learning efficiency. The present invention aims to solve these problems and provide a system that more effectively supports users' self-development and goal achievement. Furthermore, to enhance the shopping experience in virtual spaces, the present invention aims to more effectively support users in achieving their goals by providing a shopping plan generation function using a virtual assistant and a feedback function based on the user's emotions.

[1466] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a shopping plan based on purchasing goals set by the user, means for a virtual assistant to provide and explain optimal products to the user in a virtual space, and means for recognizing the user's emotions and providing feedback and adjusting the plan. This effectively supports the user in achieving their purchasing goals and enables appropriate feedback and plan adjustments to match the user's emotional state.

[1467] "User" refers to any individual or entity that uses the System and sets its own goals.

[1468] A "goal" is a specific objective or task that a user sets out to achieve.

[1469] "Content" refers to information such as images, videos, and text collected online, and is material related to learning and purchasing.

[1470] "Artificial intelligence means" refers to machine learning algorithms or systems that analyze collected content and select the most suitable learning materials or products for users.

[1471] A "study plan" refers to the learning content and schedule created based on the goals the user wants to achieve.

[1472] A "schedule" refers to a timetable or timetable for carrying out study and purchasing activities according to a study plan.

[1473] "Virtual space" refers to a three-dimensional virtual environment generated by computer simulation.

[1474] A "virtual clone" refers to a virtual being generated in a virtual space based on information such as the user's appearance and voice.

[1475] A "virtual assistant" is a virtual entity that provides and explains products based on the purchasing goals set by the user in a virtual space.

[1476] "Emotion" refers to a user's psychological state, which can be identified by tone of voice, facial expression, text input patterns, etc.

[1477] "Feedback" refers to information that provides users with results or recommendations regarding their learning or purchasing activities.

[1478] This invention is a system that allows users to effectively improve themselves and achieve their goals in a virtual space. The system generates shopping plans and study plans based on the goals set by the user, and then implements these plans in the virtual space using a virtual clone or virtual assistant. The system also recognizes the user's emotions and provides feedback and adjusts the plans accordingly.

[1479] Hardware and Software

[1480] The system is implemented using the following hardware and software.

[1481] Hardware: Computers, servers, and user devices (smartphones, head-mounted displays, etc.).

[1482] Software: Generative AI models (e.g., GPT), emotion engines (e.g., Emotion AI SDK), databases, and virtual world simulation software.

[1483] Specific processing steps

[1484] 1. User goal setting

[1485] The user logs in to the system and enters a specific goal on the goal setting screen. For example, they might set, "I want to find and purchase new spring fashion items." This goal is then sent to the server.

[1486] 2. Content collection and plan generation

[1487] The server collects relevant content (images, videos, text, etc.) from online sources based on the user's set goals, analyzes and evaluates this content, and generates optimal shopping and learning plans.

[1488] 3. Creation and Implementation of Virtual Clone

[1489] The device generates a virtual clone and virtual assistant in the virtual space based on the user's profile. This virtual clone is a virtual presence that puts into practice the plan set by the user. The virtual assistant is responsible for providing and explaining the most suitable products to the user.

[1490] 4. Emotion Recognition and Feedback

[1491] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, typing patterns, etc. If the user is feeling stressed, the system will support them by adjusting their study or shopping plans and sending encouraging messages.

[1492] 5. Regular monitoring and plan updates

[1493] The server continuously monitors the user's progress in achieving their goals and their emotional state, and suggests updating the plan as necessary, thereby enabling continuous support for the user's goal achievement.

[1494] Specific examples

[1495] If a user selects "I want to find new spring fashion items," the server will collect relevant content and generate an appropriate shopping plan. The virtual assistant will provide and explain the best products to the user in the virtual space, and the emotion engine will monitor the user's emotions and provide feedback to help the user relax.

[1496] Prompt Sentence Examples

[1497] Next, generate a shopping plan to find the perfect fashion items for the user's set goals. The user's profile is as follows:

[1498] Age: 30

[1499] Gender: Female

[1500] Favorite style: Casual

[1501] User goal: Find new spring fashion items.

[1502] Generate the best plan to achieve your goals.

[1503] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1504] Step 1:

[1505] A user logs in to the system and enters a specific goal on the goal setting screen. This goal is sent to the server. The input is the user's goal (e.g., "I want to find and purchase new spring fashion items"), and the output is the goal data sent to the server.

[1506] Step 2:

[1507] The server collects relevant content (images, videos, text, etc.) from online sources based on the goals set by the user. The collected content is analyzed and evaluated using a generative AI model. The input is the user's goal data, and the output is the collected relevant content data.

[1508] Step 3:

[1509] The server selects the most suitable learning materials and products for the user from the analyzed and evaluated content, and generates a shopping plan or learning plan. The input is the related content data, and the output is the generated plan and schedule.

[1510] Step 4:

[1511] The device generates a virtual clone or virtual assistant in the virtual space based on the user's profile (appearance, voice, etc.). The input is the user's profile information, and the output is the generated virtual clone or virtual assistant.

[1512] Step 5:

[1513] The device's virtual clone or virtual assistant executes the learning plan or shopping plan generated by the server. Specific actions include introducing products or advancing learning content in the virtual space. The input is the generated plan, and the output is progress data of the plan as it is implemented.

[1514] Step 6:

[1515] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, and text input patterns. The input is the user's emotional data, and the output is the analyzed emotional state.

[1516] Step 7:

[1517] The server then provides feedback and adjusts the plan based on the analyzed emotional data. For example, if the user is feeling stressed, it can reduce the learning or shopping plan and send an encouraging message. The input is the emotional state and plan data, and the output is the adjusted plan and feedback message.

[1518] Step 8:

[1519] The server continuously monitors the user's progress toward their goals and emotional state, and suggests updating the plan as necessary. For example, if the user changes their set goals or if they experience prolonged stress, the server will suggest a major revision of the plan. The input is continuously acquired progress data and emotional data, and the output is a notification of the proposed update.

[1520] 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.

[1521] 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.

[1522] 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.

[1523] [Fourth embodiment]

[1524] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1525] 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.

[1526] 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).

[1527] 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.

[1528] 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.

[1529] 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).

[1530] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1531] 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.

[1532] 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.

[1533] 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.

[1534] 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.

[1535] 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.

[1536] 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."

[1537] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support goal achievement.

[1538] User Actions

[1539] User:

[1540] First, the user logs in to the system. After logging in, a goal setting screen appears. On this screen, the user can enter specific goals. For example, a goal could be set such as "I want to acquire everyday conversational English skills in one year."

[1541] Content collection and learning plan generation

[1542] server:

[1543] After a user sets a goal, the server collects content related to the goal from online sources. The collected content is provided in various formats, including images, videos, and text. This content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[1544] Based on the selected learning materials, the server generates a study plan and schedule that is optimal for the user, such as shadowing practice three times a week or a study plan for specific grammar points.

[1545] Virtual clone generation and learning

[1546] Device:

[1547] Next, the device generates a virtual clone in the virtual space based on the user's personal information (such as appearance and voice), which is a virtual entity that carries out the learning plan set by the user.

[1548] server:

[1549] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone may review English vocabulary or practice pronunciation at a set time each day.

[1550] Regular testing and feedback

[1551] server:

[1552] The server periodically tests the virtual clones, for example, conducting a listening test every Saturday, and collects the results. The server then provides the test results to the user as feedback.

[1553] User:

[1554] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[1555] Continuous monitoring and plan updates

[1556] server:

[1557] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and proposes updates to the learning plan as needed. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, a system is provided that helps the user achieve their goals and improves their self-esteem.

[1558] Specific examples

[1559] The following is a specific example of use. When User A sets the goal of "I want to acquire everyday conversational English in one year," the server collects and selects the latest video learning materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone performs 20 minutes of listening practice every day in a virtual space. The server conducts a listening test every Saturday and notifies User A of the results. User A receives feedback, and if progress is slower than expected, adjusts the study plan, such as increasing the time for listening practice. By repeating this process, User A can effectively progress in their studies toward achieving their goal.

[1560] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[1561] The processing flow will be explained below.

[1562] Step 1:

[1563] User:

[1564] The user logs into the system and enters specific goals on the goal setting screen.

[1565] Example: Set "I want to acquire everyday conversation level English in one year."

[1566] Step 2:

[1567] server:

[1568] Receive goals set by the user.

[1569] Step 3:

[1570] server:

[1571] The server collects content (images, videos, text) related to the goal from online sources.

[1572] Step 4:

[1573] server:

[1574] The collected content is passed to AI, which analyzes and evaluates it to select the most suitable learning material for the user.

[1575] Step 5:

[1576] server:

[1577] Generate a learning plan and schedule based on the learning materials.

[1578] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[1579] Step 6:

[1580] User:

[1581] The user reviews the proposed study schedule and adjusts it as needed.

[1582] Example: Approve the schedule.

[1583] Step 7:

[1584] Device:

[1585] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[1586] Step 8:

[1587] server:

[1588] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[1589] Step 9:

[1590] server:

[1591] The virtual clone implements the study plan at a set time each day.

[1592] For example: 20 minutes of shadowing practice every day.

[1593] Step 10:

[1594] server:

[1595] The server periodically runs tests on the virtual clones and collects the results.

[1596] Example: Listening tests are conducted every Saturday.

[1597] Step 11:

[1598] server:

[1599] Provide collected test results as feedback to users.

[1600] Step 12:

[1601] User:

[1602] Users receive feedback and see their progress.

[1603] Step 13:

[1604] User:

[1605] Adjust your study plan and schedule as needed.

[1606] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[1607] Step 14:

[1608] server:

[1609] The system continuously monitors the user's learning progress.

[1610] Step 15:

[1611] server:

[1612] Analyze learning data and suggest updates to your learning plan as needed.

[1613] Example: Propose a new plan that focuses on listening.

[1614] Step 16:

[1615] User:

[1616] The user accepts the proposed update plan and continues learning based on the new learning plan.

[1617] The above is the specific processing flow of this system. Through this series of steps, users can effectively progress through their studies toward achieving their goals.

[1618] Example 1

[1619] 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."

[1620] Conventional learning support systems have difficulty providing individualized learning plans, making it difficult to maintain learners' motivation. Furthermore, because learning progress is not monitored or feedback is not provided in real time, users often have difficulty grasping their own progress. This creates the problem of insufficient learning effectiveness.

[1621] 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.

[1622] In this invention, the server includes a means for allowing a user to set a goal, a means for collecting online content based on the goal, and an artificial intelligence means for selecting learning materials from the collected content, thereby enabling the provision of an individualized and effective learning plan.

[1623] The server also includes a means for the user to generate his or her own virtual agent in the virtual environment, a means for applying the learning plan and schedule to the virtual agent in the virtual space, and a means for the virtual agent to implement the learning plan, which allows learning to be carried out virtually and is expected to increase the user's motivation.

[1624] The server further includes a means for periodically evaluating the virtual agent, a means for providing feedback on the evaluation results to the user, and a means for adjusting the learning plan based on the feedback, thereby enabling real-time monitoring of the user's learning progress and prompt feedback and plan adjustment.

[1625] A "user" is an entity that uses the system to set goals and learn.

[1626] "Goals" are the specific learning content or skills that users aim to achieve through the system.

[1627] "Online content" refers to learning materials such as videos, text, and images that are accessible via the internet.

[1628] "Artificial intelligence means" is a general term for AI technologies that analyze collected content and appropriately select learning materials.

[1629] "Study plan and schedule" refers to specific learning content and its implementation plan designed to help users achieve their goals.

[1630] A "virtual space" is a digital environment created using computer graphics.

[1631] A "virtual agent" is a virtual learning entity that reflects the characteristics of the user and carries out a learning plan in a virtual space.

[1632] "Evaluation" refers to periodic testing and assessment of the learning that the virtual agent has performed.

[1633] "Feedback" means information and advice about learning progress provided to users based on assessment results.

[1634] "Plan adjustment" refers to reviewing and appropriately changing study plans and schedules based on feedback.

[1635] A specific embodiment for carrying out this invention is shown below: This system is composed of three main elements: a user, a terminal, and a server, and provides a learning process to support the user in achieving their goals.

[1636] Hardware and Software

[1637] User terminal (PC, smartphone): A device that allows users to access and operate the system.

[1638] Server: Responsible for data processing and storage. The server has the Django framework and AI libraries (e.g., TensorFlow, PyTorch) installed.

[1639] Web browser: Software that allows users to operate login screens and various interfaces.

[1640] Specific processing explanation

[1641] User login

[1642] User:

[1643] The user accesses the login screen through a web browser. The user enters their "user name" and "password" and clicks the "Login" button. The server compares the received authentication information with the database to verify whether the user is a legitimate user. Once the comparison is complete, the user's dashboard screen is displayed.

[1644] goal setting

[1645] User:

[1646] After logging in, users can enter specific learning goals on the goal setting screen. For example, they can set a goal such as "I want to acquire everyday conversational English skills in one year."

[1647] server:

[1648] The server receives the goal data entered by the user and stores it in the database, thereby registering the user's learning goals in the system.

[1649] Content collection and learning plan generation

[1650] server:

[1651] Based on the goals set by the user, the server collects online content. Content collection uses the YouTube API, Google Search API, etc. The collected content is analyzed using AI technology to select the most suitable learning materials for the user. Based on the selected learning materials, the server generates a learning plan and schedule.

[1652] Virtual clone generation and learning

[1653] Device:

[1654] The device generates a virtual clone in a virtual space based on the user's personal information (profile photo and voice sample), using image processing and voice recognition technology to create a realistic virtual clone.

[1655] server:

[1656] The learning plan is applied to the generated virtual clone, and the virtual clone carries out various learning activities according to the specified schedule.

[1657] Regular testing and feedback

[1658] server:

[1659] The server periodically tests the virtual clone, for example, conducting a listening test every Saturday, analyzes the results, and notifies the user of the analysis results as feedback.

[1660] User:

[1661] Users receive feedback and track their progress, adjusting their learning plans and schedules as needed through the server.

[1662] Continuous monitoring and plan updates

[1663] server:

[1664] The system continuously monitors the user's learning progress. The server analyzes the user's learning data and suggests updating the learning plan as needed. For example, if the user's listening skills are lacking, the system will suggest a new learning plan focused on listening.

[1665] Prompt Sentence Examples

[1666] "The user has set the goal of 'I want to acquire conversational English skills in one year.' Please write a program that collects the latest English learning videos and exercises and suggests a study schedule of one hour, three times a week."

[1667] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and adjust as needed to achieve their goals.

[1668] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1669] Step 1: User Login

[1670] User:

[1671] Input: Enter your "Username" and "Password" into the login screen and click the [Login] button.

[1672] What happens: User enters login information and submits.

[1673] server:

[1674] Data processing: The entered authentication information is compared with a database.

[1675] Output: If the user is legitimate, the user's dashboard screen is displayed.

[1676] Specific operation: The server checks the user information against the database, and if it matches, the login process is successful.

[1677] Step 2: Goal Setting

[1678] User:

[1679] Input: Enter a specific learning goal, such as "I want to acquire everyday conversational English skills in one year," and click the "Set Goal" button.

[1680] Action: Enter your learning goals on the goal setting screen and submit.

[1681] server:

[1682] Data processing: Receive the entered target data and save it in the database.

[1683] Output: The user's learning goals are registered in the system.

[1684] Specific actions: The server saves this goal in the database and proceeds to the next step.

[1685] Step 3: Gather content and create a lesson plan

[1686] server:

[1687] Input: Goal data set by the user.

[1688] What it does: Collects relevant content online.

[1689] Data processing: We use APIs (e.g., YouTube API, Google Search API) to collect content information and analyze the data using artificial intelligence technology.

[1690] Output: Select appropriate learning materials from the collected content and generate an optimal learning plan and schedule for the user.

[1691] Specific operation: The server collects information from the YouTube API using keywords such as "daily conversation English learning videos" and categorizes them using natural language processing technology.

[1692] Step 4: Generating and training virtual clones

[1693] Device:

[1694] Input: User personal information (profile photo and voice sample).

[1695] Operation: Generates a virtual clone in virtual space.

[1696] Data processing: Using image processing and voice recognition technology to create a realistic virtual clone.

[1697] Output: Generate a virtual clone.

[1698] How it works: The device scans the user's photo and uses facial recognition technology to create a realistic virtual clone.

[1699] server:

[1700] Input: The generated virtual clone.

[1701] What it does: Apply a learning plan to a virtual clone.

[1702] Data processing: Using deep learning models, the virtual clone executes the learning plan.

[1703] Output: The virtual clone starts learning.

[1704] Specific operation: The server instructs the virtual clone to perform shadowing and listening practice.

[1705] Step 5: Regular testing and feedback

[1706] server:

[1707] Input: Training data for virtual clones.

[1708] Behavior: Periodically run tests on the virtual clone.

[1709] Data processing: Analyze the test results and provide feedback to the user.

[1710] Output: Notify the user of the evaluation results.

[1711] Specific operation: The server conducts a listening test every Saturday and notifies the user of the results as a rating such as "passed" or "needs improvement."

[1712] User:

[1713] Input: The notified feedback information.

[1714] Behavior: See feedback and track progress.

[1715] Data processing: Adjust your study plan and schedule based on the feedback.

[1716] Output: Updated learning plan.

[1717] What it does: Users review their assessments and adjust their study time based on their progress.

[1718] Step 6: Continuously monitor and update your plan

[1719] server:

[1720] Input: User learning data and progress information.

[1721] What it does: Continuously monitor learning progress.

[1722] Data processing: Analyze learning data and suggest updates to the learning plan as needed.

[1723] Output: Update suggestions.

[1724] How it works: The server analyzes the user's learning history and, if there are "lacks in listening skills," sends a notification suggesting a new listening-focused learning plan.

[1725] (Application example 1)

[1726] 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."

[1727] In today's online learning environment, providing content tailored to individual learning goals and managing progress are key challenges. Such systems must be able to select the most appropriate learning content from a wide variety of available content and provide an optimal learning plan for efficient learning. However, existing systems struggle to assess users' progress in real time and dynamically revise their learning plans as needed. Furthermore, technology for providing personalized learning experiences using virtual spaces is not yet fully developed. This leaves users without the support they need to effectively achieve their learning goals.

[1728] 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.

[1729] In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting learning materials from the collected content; means for generating a learning plan and schedule based on the learning materials; means for applying the learning plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the learning plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; means for adjusting the learning plan based on the feedback; means for monitoring the user's progress and updating and optimizing the learning plan in real time; and means for inputting prompts into a generating AI model and selecting optimal learning content. This allows the user to check their progress in real time and efficiently achieve their goals with an optimized learning plan.

[1730] "User" refers to an individual or corporation that uses the system to set learning goals and manage progress.

[1731] "Goals" are the specific learning or work objectives that users intend to achieve by using the system.

[1732] "Content" refers to learning materials such as images, videos, audio, and text that exist online.

[1733] "Artificial intelligence means" refers to processes and devices that use AI technology to analyze collected content and select the most appropriate learning materials.

[1734] A "learning plan" is a planned and scheduled combination of learning activities necessary to achieve a user's goals.

[1735] A "schedule" indicates the time frame and order of learning activities that should be carried out daily based on a learning plan.

[1736] A "virtual space" is a digital virtual environment constructed using technology such as computer graphics.

[1737] A "virtual clone" is a digital avatar generated in a virtual space based on the user's appearance and voice.

[1738] "Periodic tests" are tests that the virtual clone takes at regular intervals to evaluate its learning outcomes.

[1739] "Feedback" is information provided to users with periodic test results and progress data to help them adjust their study plans.

[1740] "Monitoring" means continuously observing and recording a user's learning progress.

[1741] "Real-time updating and optimization means" refers to the process and devices that instantly modify and improve the learning plan based on the user's learning progress and feedback.

[1742] A "generative AI model" is an artificial intelligence model that automatically generates optimal learning content based on the input prompt.

[1743] A "prompt" is an instruction entered to allow the generative AI model to select the most appropriate learning content.

[1744] To implement this invention, a system including the following elements is required. The system is composed of a server, a terminal, and a user. Specific processing procedures and how to execute them are explained below.

[1745] User Actions

[1746] The user first logs in to the system. After logging in, a goal setting screen appears, where the user can enter specific learning goals. For example, the user can set a goal such as "I want to acquire everyday conversational English skills in one year."

[1747] Content collection and learning plan generation

[1748] The server collects online content based on the user's set goals. The collected content exists in various formats, including images, videos, audio, and text. This content is analyzed and evaluated using artificial intelligence (AI) technology to select the most suitable learning materials for the user. Specific learning content is then selected by inputting a prompt into the generative AI model. For example, the prompt could be, "I want to learn business-level German in one year."

[1749] The server then generates an optimal study plan and schedule based on the selected study materials, including, for example, shadowing practice three times a week or a study plan for specific grammar points.

[1750] Virtual clone generation and learning

[1751] The device generates a virtual clone in the virtual space based on the user's personal information (appearance, voice, etc.), and the virtual clone is responsible for carrying out the learning plan set by the user.

[1752] The server applies the learning plan to the virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[1753] Regular testing and feedback

[1754] The server periodically tests the virtual clones, for example, by conducting a listening test every Saturday, and collects the results. The test results are then provided to the user as feedback.

[1755] Users can receive this feedback, see their progress, and make adjustments to their learning plan or schedule as needed. These adjustments are flexible and can be changed based on the user's needs and progress.

[1756] Continuous monitoring and plan updates

[1757] The server continuously monitors the user's learning progress. The server analyzes the user's learning data and has the function of proposing updates to the learning plan as necessary. For example, if the user's listening skills are lacking, the server will propose a new learning plan focused on listening. In this way, the server can effectively support the user in achieving their goals.

[1758] This system enables efficient learning tailored to individual learning goals. In particular, the use of prompts by the generative AI model makes it possible to smoothly select the most suitable learning content for the user. For example, by using the prompt "I want to acquire business-level German in one year," the server can select learning materials specialized for German business conversation and generate the optimal learning plan.

[1759] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1760] Step 1:

[1761] The server receives the user's login information. It takes the user ID and password as input and performs authentication processing. If authentication is successful, a session is started, a session ID is generated and returned to the user. If authentication fails, an error message is returned.

[1762] Step 2:

[1763] After the user logs in, the goal setting screen is displayed. The user enters a specific learning goal (e.g., "I want to acquire daily conversation level English in one year"). The entered goal information is sent to the server.

[1764] Step 3:

[1765] The server receives the learning goals set by the user, analyzes the received data, and collects related online content, such as videos, texts, and audio files related to English conversation from the Internet. This collected data serves as input for the next processing step.

[1766] Step 4:

[1767] The server analyzes the collected content using artificial intelligence (AI) tools. An AI model (e.g., a natural language processing model) evaluates the content and selects learning materials that best fit the user's learning goals. These selected learning materials serve as input for the next step.

[1768] Step 5:

[1769] The server generates a learning plan and schedule based on the selected learning materials. The generated learning plan includes a weekly schedule and daily learning tasks. These plans and schedules are used in the next step.

[1770] Step 6:

[1771] The device generates a virtual clone based on the user's personal information (such as a photo of the face and voice). Using the data obtained from the user as input, the virtual clone is created using 3D modeling software and voice synthesis technology. The generated virtual clone is then used in the next step.

[1772] Step 7:

[1773] The server applies the learning plan to the virtual clone, and the virtual clone begins learning based on a schedule set in the virtual space. For example, the virtual clone can practice listening and pronunciation for 20 minutes every day.

[1774] Step 8:

[1775] The server periodically tests the virtual clone, collects the results of each test (e.g., a listening test every Saturday), analyzes the data, and generates feedback based on the test results to provide to the user.

[1776] Step 9:

[1777] The user receives feedback from the server, including test results and progress, and checks their own learning status. If necessary, the user can request adjustments to their learning plan or schedule from the server.

[1778] Step 10:

[1779] The server monitors the user's progress and suggests updates to the learning plan. It analyzes the progress data and generates a new learning plan that focuses on skills that are lacking (e.g., listening skills), thereby continuously optimizing the user's learning experience.

[1780] Step 11:

[1781] A prompt sentence is input into the generative AI model to select the most suitable learning content. By providing a prompt sentence (e.g., "I want to acquire business-level German in one year") as input, the AI ​​model outputs learning materials that are best suited to the user's goals. These materials are used in Step 4.

[1782] 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.

[1783] This invention is a system that supports users in self-development and goal achievement, and is characterized by generating a study plan based on the goals set by the user, conducting study in a virtual space, and recognizing the user's emotions to provide feedback and adjust the plan.

[1784] User Actions

[1785] User:

[1786] First, the user logs into the system and enters specific goals on the goal setting screen.

[1787] Example: Set "I want to acquire everyday conversation level English in one year."

[1788] Content collection and learning plan generation

[1789] server:

[1790] The system receives the goals set by the user and collects content related to the goals (images, videos, text, etc.) from online sources. The collected content is analyzed and evaluated by artificial intelligence (AI) to select the most suitable learning materials for the user.

[1791] Based on the selected learning materials, the server generates a study plan and schedule, which may include, for example, shadowing practice three times a week or a study plan for specific grammar points.

[1792] Virtual clone generation and learning

[1793] Device:

[1794] Based on the user's personal information (appearance, voice, etc.), the device generates a virtual clone in the virtual space, which is a virtual entity that carries out the learning plan set by the user.

[1795] server:

[1796] The server applies a learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. For example, the virtual clone reviews English vocabulary and practices pronunciation at a set time every day.

[1797] Regular testing and feedback

[1798] server:

[1799] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided to the user as feedback.

[1800] User:

[1801] Users receive this feedback, see their progress, and adjust their learning plan or schedule as needed, which can be flexibly changed based on the user's needs and progress.

[1802] Supported by an emotional engine

[1803] server:

[1804] The server is equipped with an emotion engine that recognizes the user's emotions and analyzes the user's emotions based on the user's tone of voice, facial expressions, and character input patterns.

[1805] server:

[1806] The emotion engine recognizes the user's emotions and adjusts the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan will be lightened slightly and an encouraging message will be sent.

[1807] Continuous monitoring and plan updates

[1808] server:

[1809] The system continuously monitors the user's learning progress and emotional state. The server analyzes the user's learning and emotional data and proposes updates to the learning plan as needed.

[1810] Specific examples

[1811] The following is a specific example of use. When User A sets the goal of "learning English at a conversational level in one year," the server collects and selects the latest video learning materials and practice questions related to English language learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone that mimics User A's voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, collects the results, and notifies User A.

[1812] Furthermore, the emotion engine recognizes User A's emotions and, if User A is feeling stressed, adjusts the study plan to help them continue studying without straining themselves. This allows User A to receive feedback and check their progress while studying effectively.

[1813] In this way, the present invention is a system that provides comprehensive support for users to plan, execute, track progress, and achieve goals while taking into consideration their emotions.

[1814] The processing flow will be explained below.

[1815] Step 1:

[1816] User:

[1817] The user logs into the system and enters specific goals on the goal setting screen.

[1818] Example: Set "I want to acquire everyday conversation level English in one year."

[1819] Step 2:

[1820] server:

[1821] Receive goals set by the user.

[1822] Step 3:

[1823] server:

[1824] The server collects content (images, videos, text) related to the goal from online sources.

[1825] Step 4:

[1826] server:

[1827] The collected content is passed to artificial intelligence (AI), which analyzes and evaluates it to select the most suitable learning material for the user.

[1828] Step 5:

[1829] server:

[1830] Generate a learning plan and schedule based on the learning materials.

[1831] Example: Create a plan that includes shadowing practice and grammar study three times a week.

[1832] Step 6:

[1833] User:

[1834] The user reviews the proposed study schedule and adjusts it as needed.

[1835] Example: Approve the schedule.

[1836] Step 7:

[1837] Device:

[1838] A virtual clone is generated in a virtual space based on the user's appearance and voice.

[1839] Step 8:

[1840] server:

[1841] The learning plan is applied to the generated virtual clone, and the virtual clone begins learning.

[1842] Step 9:

[1843] server:

[1844] The virtual clone implements the study plan at a set time each day.

[1845] For example: 20 minutes of shadowing practice every day.

[1846] Step 10:

[1847] server:

[1848] The server periodically runs tests on the virtual clones and collects the results.

[1849] Example: Listening tests are conducted every Saturday.

[1850] Step 11:

[1851] server:

[1852] Provide collected test results as feedback to users.

[1853] Step 12:

[1854] User:

[1855] Users receive feedback and see their progress.

[1856] Step 13:

[1857] User:

[1858] Adjust your study plan and schedule as needed.

[1859] For example, if your listening skills are lacking, increase the amount of time you spend practicing listening.

[1860] Step 14:

[1861] server:

[1862] The system continuously monitors the user's learning progress.

[1863] Step 15:

[1864] server:

[1865] Analyze learning data and suggest updates to your learning plan as needed.

[1866] Example: Propose a new plan that focuses on listening.

[1867] Step 16:

[1868] User:

[1869] The user accepts the proposed update plan and continues learning based on the new learning plan.

[1870] Step 17:

[1871] server:

[1872] The server is equipped with an emotion engine that recognizes the user's emotions by analyzing the user's tone of voice, facial expressions, and text input patterns.

[1873] Step 18:

[1874] server:

[1875] The emotion engine recognizes the user's emotions and adjusts the feedback and learning plan accordingly.

[1876] Example: If the user is feeling impatient, ease up on their study plan and send them an encouraging message.

[1877] Step 19:

[1878] server:

[1879] The results of emotion recognition and feedback adjustment are also monitored to optimize the entire system.

[1880] Step 20:

[1881] User:

[1882] Users receive emotional feedback, allowing them to continue their learning effortlessly.

[1883] The above is the specific processing flow of this system. Through this series of steps, users can effectively and sustainably advance their learning toward achieving their goals.

[1884] Example 2

[1885] 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."

[1886] Conventional educational support systems lack sufficient ability to generate plans to help users achieve their goals or monitor their learning progress, making it difficult to flexibly adjust to each user's emotions and learning pace. This makes it difficult for users to maintain their motivation over the long term, often making it difficult to achieve their final goals. Furthermore, feedback based on learning progress is limited to simple results, and specific suggestions for improving learning are lacking.

[1887] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: means for a user to set a goal; means for collecting online content based on the goal; artificial intelligence means for selecting study materials from the collected content; means for generating a study plan and schedule based on the study materials; means for applying the study plan and schedule to a virtual clone in a virtual space; means for the virtual clone to implement the study plan; means for the virtual clone to take periodic tests; means for providing feedback on the test results to the user; emotion engine means for recognizing the user's emotions and adjusting the study plan; means for adjusting the study plan based on the feedback and emotions; and means for continuously monitoring the user's study progress and emotion data and suggesting updates to the study plan. This enables effective study support for the user to achieve their goal and enables flexible adjustment of the study plan based on the user's study progress and emotional state.

[1888] "Means for users to set goals" refers to a function that provides an interface for users to specifically input and set their own learning goals and the results they wish to achieve.

[1889] "Means for collecting online content" refers to a function that automatically collects relevant educational materials and information from databases and websites on the Internet.

[1890] "Artificial intelligence means" refers to the algorithms and models that analyze collected data and identify and select the most appropriate learning materials for users.

[1891] "Means for generating study plans and schedules" refers to a function that designs a plan and time allocation that allows the user to study efficiently based on the selected study materials.

[1892] The "means for applying to a virtual clone in a virtual space" is a function for applying the generated learning plan and schedule to a virtual clone of the user existing in a digital space.

[1893] The "means by which the virtual clone implements the learning plan" refers to the function by which the virtual clone specifically implements the learning plan set in the virtual space.

[1894] The "means for undergoing periodic testing" is a function that allows the virtual clone to undergo periodic set tests and evaluations.

[1895] "Means for providing feedback on test results to the user" is a function that conveys the results of tests taken by the virtual clone to the user, informing them of the progress and results of their learning.

[1896] "Emotion engine means" refers to technology that recognizes and analyzes emotions from the user's tone of voice, facial expressions, input patterns, etc., and adjusts the learning plan appropriately based on that.

[1897] "Means to adjust study plans" refers to a function that optimizes and modifies existing study plans based on the user's condition, based on feedback and emotional data.

[1898] "Means for continuous monitoring and suggesting updates to the study plan" refers to a function that constantly monitors the user's study progress and emotional state, and suggests new study plans or adjusts the current plan as needed.

[1899] The present invention relates to a system for supporting users' self-development and goal achievement. This system generates a study plan based on the goals set by the user, conducts the study in a virtual space, and recognizes the user's emotions to provide feedback and adjust the plan. Each component and process of this system will be described in detail below.

[1900] User goal setting

[1901] User:

[1902] Users log in to the system and enter specific goals on the goal setting screen. The system's goal setting screen is implemented as a web application and is built using technologies such as HTML5 and React. Users enter specific information such as the skills they want to learn, the results they want to achieve, and the desired study period. For example, they could set the goal as "I want to acquire everyday conversational English skills in one year."

[1903] Content collection and lesson plan generation

[1904] server:

[1905] The system receives the goals set by the user and collects content related to the goal (images, videos, text, etc.) from the Internet. This content collection is done using scraping technology to obtain data, which is then analyzed by an AI engine (e.g., TensorFlow, PyTorch). Effective learning materials are selected as a result of the analysis.

[1906] server:

[1907] Based on the selected learning materials, the server generates a study plan and schedule that can include, for example, shadowing practice three times a week or a study plan for specific grammar points, and is presented in a format that fits the user's schedule.

[1908] Generation of virtual clones and training execution

[1909] Device:

[1910] The device generates a virtual clone in a virtual space based on the user's personal information (appearance, voice, etc.). The device is installed with virtual space generation software such as Unity or Unreal Engine, and a 3D model is created based on the user's facial photograph and voice data. This virtual clone is a virtual entity that carries out the learning plan set by the user.

[1911] server:

[1912] The server applies the learning plan to the created virtual clone, and the virtual clone begins learning in the virtual space. This virtual clone performs specific actions, such as reviewing English vocabulary and practicing pronunciation, at a set time each day.

[1913] Regular testing and feedback

[1914] server:

[1915] The server periodically tests the virtual clone and collects the results. For example, a listening test is conducted every Saturday, and the results are provided as feedback to the user, allowing them to track their progress.

[1916] User:

[1917] Users receive feedback and can adjust their learning plan or schedule as needed, which can be flexibly changed based on their needs and progress.

[1918] Supported by an emotional engine

[1919] server:

[1920] The server is equipped with an emotion engine that recognizes the user's emotions. The emotion engine analyzes emotions from the user's tone of voice, facial expressions, and text input patterns. The emotion engine uses, for example, voice recognition technology and facial recognition technology.

[1921] server:

[1922] The emotion engine can recognize the user's emotions and adjust the learning plan and feedback accordingly. For example, if the user is feeling impatient, the learning plan can be lightened slightly and an encouraging message can be sent.

[1923] Continuous monitoring and plan updates

[1924] server:

[1925] The system continuously monitors the user's learning progress and emotional state. The server constantly monitors the user's learning data and emotional data and suggests updating the learning plan as needed. This monitoring and updating is performed using a database management system (e.g., PostgreSQL, MySQL).

[1926] Specific examples

[1927] As a concrete example, consider the case where User A sets the goal of "learning English conversation at a daily conversation level in one year." The server collects and selects the latest video materials and practice questions related to English learning, and proposes a study schedule of one hour three times a week. The device generates a virtual clone of User A that mimics his / her voice and appearance, and this virtual clone practices listening for 20 minutes every day in a virtual space. The server conducts a listening test every Saturday, and the results are collected and notified to User A. Furthermore, the emotion engine recognizes User A's emotions, and if User A is feeling stressed, it adjusts the study plan to help him / her continue studying without straining himself / herself.

[1928] Example prompts for generative AI models

[1929] Below are some example prompts for the generative AI model associated with this system:

[1930] Example prompt 1:

[1931] "Generate an effective study plan to help you master conversational English in one year. Schedule three times a week for one hour and combine shadowing, listening practice, and grammar study."

[1932] Example prompt 2:

[1933] "Please create questions for users to use in a listening test to gauge their level of mastery. The questions should be simple English conversation scenarios based on everyday conversations."

[1934] The system of the present invention provides comprehensive support necessary for users to achieve their goals, and unifies management of everything from creating study plans to providing emotional feedback, allowing users to effectively study while checking their own progress and emotional state and responding appropriately.

[1935] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1936] Step 1:

[1937] User:

[1938] The user logs into the system and enters specific goals on the goal setting screen. The input includes the skill they want to learn (e.g., English conversation) and the deadline for achieving the goal (e.g., one year). Based on this, the server receives the user's goal data. The input data might be goal: "everyday conversation level English conversation" and period: "one year." The server saves this data in a database.

[1939] Step 2:

[1940] server:

[1941] Based on the goal data set by the user, relevant learning content (images, videos, text, etc.) is collected from online sources. Web scraping technology is used to collect the data, and the collected data is temporarily stored in a database on the server. Next, an AI engine (e.g., TensorFlow, PyTorch) is used to analyze and evaluate the collected data and select the most suitable learning materials for the user. The input data are the goal data and collected content, and based on this, the most suitable learning materials (e.g., a list of video URLs or text materials) are output as the evaluation results.

[1942] Step 3:

[1943] server:

[1944] A learning plan and schedule is generated based on the selected learning materials. An AI engine is used to create a plan that matches the user's goals and learning pace. For example, a specific learning plan such as "one hour of shadowing practice three times a week, and 30 minutes of listening practice twice a week" is generated. The input data is the optimal learning materials and the user's goals, and the output data is the learning plan and schedule.

[1945] Step 4:

[1946] Device:

[1947] Based on the user's personal information (e.g., face photo and voice data), the device generates a virtual clone in a virtual space. This virtual clone is generated using Unity or Unreal Engine. The input data is the user's face photo and voice data, and the output data is a 3D model of the virtual clone. The generated virtual clone is then ready to execute the learning plan set by the user.

[1948] Step 5:

[1949] server:

[1950] The learning plan and schedule are applied to the virtual clone, and learning begins in the virtual space. The virtual clone begins learning at a fixed time each day and performs specific learning activities (e.g., reviewing English vocabulary and practicing pronunciation) in the virtual space. The input data are the learning plan and schedule and a 3D model of the virtual clone, and the output data is a learning execution log.

[1951] Step 6:

[1952] server:

[1953] Tests are periodically conducted on the virtual clones and the results are collected. For example, a listening test is conducted every Saturday to collect performance data on the virtual clones. The input data are the learning execution log and test data, and the output data are the test results.

[1954] Step 7:

[1955] server:

[1956] The collected test results are fed back to the user. This feedback includes the user's learning progress, achievement level, and specific advice on what to do next. The input data is the test results, and the output data is the feedback message. The feedback is provided to the user via email or in-app notification.

[1957] Step 8:

[1958] server:

[1959] An emotion engine is used to recognize the user's emotions and adjust the study plan. The emotion engine analyzes the user's tone of voice, facial expressions, and text input patterns to evaluate their emotional state. The input data is the user's voice and facial expression data, and the output data is the emotion evaluation result. For example, if the user is feeling stressed, the study plan is adjusted (e.g., reducing the study load or sending an encouraging message).

[1960] Step 9:

[1961] server:

[1962] The system continuously monitors the user's learning progress and emotional data and suggests updating the learning plan as necessary. The input data is learning progress data and emotional data, and the output data is a new learning plan. The updated plan is periodically applied to the virtual clone to optimize learning.

[1963] (Application example 2)

[1964] 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."

[1965] Conventional learning systems using virtual spaces have limited functionality in supporting users in setting and achieving specific goals in their daily lives. Furthermore, they are unable to provide appropriate feedback or adjust plans based on the user's emotional state, resulting in reduced learning efficiency. The present invention aims to solve these problems and provide a system that more effectively supports users' self-development and goal achievement. Furthermore, to enhance the shopping experience in virtual spaces, the present invention aims to more effectively support users in achieving their goals by providing a shopping plan generation function using a virtual assistant and a feedback function based on the user's emotions.

[1966] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a shopping plan based on purchasing goals set by the user, means for a virtual assistant to provide and explain optimal products to the user in a virtual space, and means for recognizing the user's emotions and providing feedback and adjusting the plan. This effectively supports the user in achieving their purchasing goals and enables appropriate feedback and plan adjustments to match the user's emotional state.

[1967] "User" refers to any individual or entity that uses the System and sets its own goals.

[1968] A "goal" is a specific objective or task that a user sets out to achieve.

[1969] "Content" refers to information such as images, videos, and text collected online, and is material related to learning and purchasing.

[1970] "Artificial intelligence means" refers to machine learning algorithms or systems that analyze collected content and select the most suitable learning materials or products for users.

[1971] A "study plan" refers to the learning content and schedule created based on the goals the user wants to achieve.

[1972] A "schedule" refers to a timetable or timetable for carrying out study and purchasing activities according to a study plan.

[1973] "Virtual space" refers to a three-dimensional virtual environment generated by computer simulation.

[1974] A "virtual clone" refers to a virtual being generated in a virtual space based on information such as the user's appearance and voice.

[1975] A "virtual assistant" is a virtual entity that provides and explains products based on the purchasing goals set by the user in a virtual space.

[1976] "Emotion" refers to a user's psychological state, which can be identified by tone of voice, facial expression, text input patterns, etc.

[1977] "Feedback" refers to information that provides users with results or recommendations regarding their learning or purchasing activities.

[1978] This invention is a system that allows users to effectively improve themselves and achieve their goals in a virtual space. The system generates shopping plans and study plans based on the goals set by the user, and then implements these plans in the virtual space using a virtual clone or virtual assistant. The system also recognizes the user's emotions and provides feedback and adjusts the plans accordingly.

[1979] Hardware and Software

[1980] The system is implemented using the following hardware and software.

[1981] Hardware: Computers, servers, and user devices (smartphones, head-mounted displays, etc.).

[1982] Software: Generative AI models (e.g., GPT), emotion engines (e.g., Emotion AI SDK), databases, and virtual world simulation software.

[1983] Specific processing steps

[1984] 1. User goal setting

[1985] The user logs in to the system and enters a specific goal on the goal setting screen. For example, they might set, "I want to find and purchase new spring fashion items." This goal is then sent to the server.

[1986] 2. Content collection and plan generation

[1987] The server collects relevant content (images, videos, text, etc.) from online sources based on the user's set goals, analyzes and evaluates this content, and generates optimal shopping and learning plans.

[1988] 3. Creation and Implementation of Virtual Clone

[1989] The device generates a virtual clone and virtual assistant in the virtual space based on the user's profile. This virtual clone is a virtual presence that puts into practice the plan set by the user. The virtual assistant is responsible for providing and explaining the most suitable products to the user.

[1990] 4. Emotion Recognition and Feedback

[1991] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, typing patterns, etc. If the user is feeling stressed, the system will support them by adjusting their study or shopping plans and sending encouraging messages.

[1992] 5. Regular monitoring and plan updates

[1993] The server continuously monitors the user's progress in achieving their goals and their emotional state, and suggests updating the plan as necessary, thereby enabling continuous support for the user's goal achievement.

[1994] Specific examples

[1995] If a user selects "I want to find new spring fashion items," the server will collect relevant content and generate an appropriate shopping plan. The virtual assistant will provide and explain the best products to the user in the virtual space, and the emotion engine will monitor the user's emotions and provide feedback to help the user relax.

[1996] Prompt Sentence Examples

[1997] Next, generate a shopping plan to find the perfect fashion items for the user's set goals. The user's profile is as follows:

[1998] Age: 30

[1999] Gender: Female

[2000] Favorite style: Casual

[2001] User goal: Find new spring fashion items.

[2002] Generate the best plan to achieve your goals.

[2003] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2004] Step 1:

[2005] A user logs in to the system and enters a specific goal on the goal setting screen. This goal is sent to the server. The input is the user's goal (e.g., "I want to find and purchase new spring fashion items"), and the output is the goal data sent to the server.

[2006] Step 2:

[2007] The server collects relevant content (images, videos, text, etc.) from online sources based on the goals set by the user. The collected content is analyzed and evaluated using a generative AI model. The input is the user's goal data, and the output is the collected relevant content data.

[2008] Step 3:

[2009] The server selects the most suitable learning materials and products for the user from the analyzed and evaluated content, and generates a shopping plan or learning plan. The input is the related content data, and the output is the generated plan and schedule.

[2010] Step 4:

[2011] The device generates a virtual clone or virtual assistant in the virtual space based on the user's profile (appearance, voice, etc.). The input is the user's profile information, and the output is the generated virtual clone or virtual assistant.

[2012] Step 5:

[2013] The device's virtual clone or virtual assistant executes the learning plan or shopping plan generated by the server. Specific actions include introducing products or advancing learning content in the virtual space. The input is the generated plan, and the output is progress data of the plan as it is implemented.

[2014] Step 6:

[2015] The server is equipped with an emotion engine that analyzes the user's emotions from their tone of voice, facial expressions, and text input patterns. The input is the user's emotional data, and the output is the analyzed emotional state.

[2016] Step 7:

[2017] The server then provides feedback and adjusts the plan based on the analyzed emotional data. For example, if the user is feeling stressed, it can reduce the learning or shopping plan and send an encouraging message. The input is the emotional state and plan data, and the output is the adjusted plan and feedback message.

[2018] Step 8:

[2019] The server continuously monitors the user's progress toward their goals and emotional state, and suggests updating the plan as necessary. For example, if the user changes their set goals or if they experience prolonged stress, the server will suggest a major revision of the plan. The input is continuously acquired progress data and emotional data, and the output is a notification of the proposed update.

[2020] 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.

[2021] 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.

[2022] 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.

[2023] 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.

[2024] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2025] 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.

[2026] 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).

[2027] 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.

[2028] 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."

[2029] 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.

[2030] 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).

[2031] 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.

[2032] 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.

[2033] 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.

[2034] 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.

[2035] 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.

[2036] 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.

[2037] 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.

[2038] 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.

[2039] 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.

[2040] 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.

[2041] The following is further disclosed regarding the above embodiment.

[2042] (Claim 1)

[2043] a means for users to set goals;

[2044] means for collecting online content based on said goal;

[2045] artificial intelligence means for selecting learning materials from the collected content;

[2046] means for generating a learning plan and schedule based on the learning materials;

[2047] means for applying the learning plan and schedule to a virtual clone in a virtual space;

[2048] means for said virtual clone to implement a learning plan;

[2049] means for subjecting said virtual clone to periodic testing;

[2050] a means for providing feedback of the test results to a user;

[2051] means for adjusting a learning plan based on said feedback;

[2052] A system including:

[2053] (Claim 2)

[2054] 10. The system of claim 1, further comprising means for a user to create a virtual clone of himself or herself in the virtual environment.

[2055] (Claim 3)

[2056] 10. The system of claim 1, further comprising means for monitoring a user's progress and suggesting updates to the study plan.

[2057] "Example 1"

[2058] (Claim 1)

[2059] a means for users to set goals;

[2060] means for collecting online content based on said goal;

[2061] artificial intelligence means for selecting learning materials from the collected content;

[2062] means for generating a learning plan and schedule based on the learning materials;

[2063] means for applying the learning plan and schedule to a virtual agent in a virtual space;

[2064] means for said virtual agent to implement a learning plan;

[2065] means for subjecting said virtual agent to periodic evaluation;

[2066] a means for feeding back the evaluation results to a user;

[2067] means for adjusting a learning plan based on said feedback;

[2068] A system including:

[2069] (Claim 2)

[2070] 10. The system of claim 1, further comprising means for a user to generate their own virtual agent in the virtual environment.

[2071] (Claim 3)

[2072] 10. The system of claim 1, further comprising means for monitoring a user's progress and suggesting updates to the study plan.

[2073] "Application Example 1"

[2074] (Claim 1)

[2075] a means for users to set goals;

[2076] means for collecting online content based on said goal;

[2077] artificial intelligence means for selecting learning materials from the collected content;

[2078] means for generating a learning plan and schedule based on the learning materials;

[2079] means for applying the learning plan and schedule to a virtual clone in a virtual space;

[2080] means for said virtual clone to implement a learning plan;

[2081] means for subjecting said virtual clone to periodic testing;

[2082] a means for providing feedback of the test results to a user;

[2083] means for adjusting a learning plan based on said feedback;

[2084] A means to monitor user progress and update and optimize learning plans in real time;

[2085] A means to input prompt sentences into the generative AI model and select the optimal learning content;

[2086] A system including:

[2087] (Claim 2)

[2088] 10. The system of claim 1, further comprising means for a user to create a virtual clone of himself or herself in the virtual environment.

[2089] (Claim 3)

[2090] 10. The system of claim 1, further comprising means for monitoring a user's progress and suggesting updates to the study plan.

[2091] "Example 2: Combining Emotion Engines"

[2092] (Claim 1)

[2093] a means for users to set goals;

[2094] means for collecting online content based on said goal;

[2095] artificial intelligence means for selecting learning materials from the collected content;

[2096] means for generating a learning plan and schedule based on the learning materials;

[2097] means for applying the learning plan and schedule to a virtual clone in a virtual space;

[2098] means for said virtual clone to implement a learning plan;

[2099] means for subjecting said virtual clone to periodic testing;

[2100] a means for providing feedback of the test results to a user;

[2101] an emotion engine means for recognizing a user's emotions and adjusting a learning plan;

[2102] means for adjusting a learning plan based on said feedback and emotions;

[2103] A means of continuously monitoring the user's learning progress and emotional data and suggesting updates to the learning plan;

[2104] A system including:

[2105] (Claim 2)

[2106] 10. The system of claim 1, further comprising means for a user to create a virtual clone of himself or herself in the virtual environment.

[2107] (Claim 3)

[2108] 10. The system of claim 1, further comprising means for monitoring a user's progress and suggesting updates to the study plan.

[2109] "Application example 2 when combining emotion engines"

[2110] (Claim 1)

[2111] a means for users to set goals;

[2112] means for collecting online content based on said goal;

[2113] artificial intelligence means for selecting learning materials from the collected content;

[2114] means for generating a learning plan and schedule based on the learning materials;

[2115] means for applying the learning plan and schedule to a virtual clone in a virtual space;

[2116] means for said virtual clone to implement a learning plan;

[2117] means for subjecting said virtual clone to periodic testing;

[2118] a means for providing feedback of the test results to a user;

[2119] means for adjusting a learning plan based on said feedback;

[2120] means for generating a shopping plan based on the purchasing goals set by the user;

[2121] A means for a virtual assistant to provide and explain the best products to users in a virtual space,

[2122] A way to recognize user emotions and provide feedback and adjust plans.

[2123] A system including:

[2124] (Claim 2)

[2125] 10. The system of claim 1, further comprising means for a user to create a virtual clone of himself or herself in the virtual environment.

[2126] (Claim 3)

[2127] 10. The system of claim 1, further comprising means for monitoring a user's progress and suggesting updates to the study plan. [Explanation of symbols]

[2128] 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 users to set goals; means for collecting online content based on said goal; artificial intelligence means for selecting learning materials from the collected content; means for generating a learning plan and schedule based on the learning materials; means for applying the learning plan and schedule to a virtual clone in a virtual space; means for said virtual clone to implement a learning plan; means for subjecting said virtual clone to periodic testing; a means for providing feedback of the test results to a user; means for adjusting a learning plan based on said feedback; A system including:

2. 10. The system of claim 1, further comprising means for a user to create a virtual clone of himself or herself in the virtual environment.

3. 10. The system of claim 1, further comprising means for monitoring a user's progress and suggesting updates to the study plan.

Images