system

Abstract

We provide the system. [Solution] A means of receiving information and generating a customized educational plan, A means of presenting problems based on the aforementioned educational plan, An analytical means for receiving user responses and evaluating thinking ability and explanatory ability, A means for generating feedback based on the evaluation results from the aforementioned analysis means, A means of providing the aforementioned feedback to the user and tracking the user's progress, Means of providing educational plans through home devices and mobile devices, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, with the development of artificial intelligence technology, the ability of users to effectively interact with machines and AI systems through natural language has been highly regarded. However, many users lack efficient training means to appropriately improve their thinking and explanatory abilities, and this lack of ability has become a factor hindering personal growth and business efficiency. The present invention aims to provide a system for effectively training users' thinking and explanatory abilities and assisting in improving such abilities.

Means for Solving the Problems

[0005] The present invention includes means for receiving user information and generating a customized training plan. Furthermore, it includes means for presenting tasks to the user based on the training plan, and analytical means for receiving the user's responses and evaluating their thinking and explanatory abilities. Based on the analysis results, feedback is generated and provided to the user to track their progress. In addition, the training plan is dynamically adjusted according to the user's skill level and goals to provide training that is optimal for individual needs. In this way, the user's abilities are improved.

[0006] "User information" refers to information about system users, such as their attributes, skill levels, and goals.

[0007] A "training plan" refers to a set of customized educational tasks and processes designed to improve a user's skills.

[0008] A "challenge" refers to a specific question or task presented to evaluate and improve the user's thinking and explanatory abilities.

[0009] "Analysis methods" refer to technical methods and processes for evaluating user responses and analyzing their thinking and explanatory abilities.

[0010] "Feedback" refers to advice and comments provided to users based on their evaluation of their responses, including areas for improvement and strengths.

[0011] "Tracking progress" refers to the act of continuously monitoring and recording how much a user's skills are improving through training.

[0012] "Skill level" is an indicator that represents the level of a user's specific abilities or knowledge.

[0013] "Goals" refer to the specific results or states of ability that a user hopes to achieve through training. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0015] Next, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides a system for effectively improving users' thinking and explanatory abilities. The system is primarily implemented through interactions between a server, a terminal, and the user.

[0036] Users first access the system via their device and log in. After logging in, users can enter their skill level and training goals. This information is sent to the server via the device.

[0037] The server receives information provided by the user and uses an AI algorithm to generate a customized training plan based on that information. This training plan is designed to improve the user's skills and includes specific tasks and simulated scenarios.

[0038] The generated training plan is presented to the user via a device. The user enters their answers to the presented tasks into the device and sends them to the server.

[0039] The server receives this response and uses AI to evaluate the user's thinking and explanatory abilities. The evaluation process uses indicators such as the logic, structure, and originality of the response. The server then generates feedback based on this evaluation result and provides it to the user through the terminal.

[0040] Users can improve their answers by referring to the feedback they receive. The server also tracks the progress of each training session and generates reports that evaluate the user's growth over time.

[0041] As a concrete example, a user might be given the task of "summarizing a new product's explanation to consumers in three points." By entering their answer and receiving feedback from the server, users can develop the ability to create more refined explanations. Through this process, users can gradually improve their skills.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses a device to access the system and perform a login operation. The device sends the user's authentication information to the server and attempts to log in.

[0045] Step 2:

[0046] The server verifies the received authentication information and authenticates the user. If authentication is successful, the server generates a user profile screen and sends it to the device.

[0047] Step 3:

[0048] The user enters their skill level and training goals on their device. The device then sends this information to the server.

[0049] Step 4:

[0050] The server uses an AI algorithm to generate a personalized training plan based on the user's skill level and goals. The generated plan is then sent to the device.

[0051] Step 5:

[0052] The terminal displays the training plan sent from the server to the user and presents the first assignment.

[0053] Step 6:

[0054] The user enters their answer to the task into their device and sends it to the server.

[0055] Step 7:

[0056] The server receives user responses and analyzes them using AI. It evaluates the content, logic, structure, and originality of the responses.

[0057] Step 8:

[0058] The server generates feedback based on the analysis results. This feedback includes areas for improvement and strengths in the response.

[0059] Step 9:

[0060] The server sends the generated feedback to the terminal, and the terminal displays the feedback to the user.

[0061] Step 10:

[0062] Users can review feedback, revise their responses as needed, and repeat the process.

[0063] Step 11:

[0064] The server stores the results of each user's training session and tracks their progress. It periodically generates reports evaluating the user's performance and provides them to the user via their terminal.

[0065] (Example 1)

[0066] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0067] To effectively improve users' thinking and communication skills, customized training tailored to individual skill levels and goals is necessary. However, conventional systems have limitations in adequately supporting user growth because they do not adequately present or evaluate appropriate tasks, and the quality of feedback is inconsistent.

[0068] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0069] In this invention, the server includes means for receiving user information and creating individually optimized training plans using a generation algorithm, means for presenting tasks based on the training plan, and means for receiving user responses and evaluating thinking and explanatory abilities using a generation model. This enables the provision of appropriate training content according to the user's skill level and detailed, personalized feedback.

[0070] "User information" refers to individual data such as the user's skill level and training goals, and a customized training plan is generated based on this information.

[0071] A "generative algorithm" refers to a set of methods and processes for creating an optimized training plan based on user information.

[0072] A "training plan" refers to a plan that includes specific tasks and simulated scenarios, designed to improve the user's skills.

[0073] "Presentation means" refers to a method or apparatus for the server to display training tasks on the user's connected device.

[0074] "Analysis means" refers to methods or processes used to analyze a user's responses and evaluate their thinking and explanatory abilities.

[0075] A "generative model" refers to an artificial intelligence algorithm or computational model used to evaluate user responses.

[0076] "Feedback" refers to evaluation results and reports of areas for improvement generated based on user responses, providing information to promote user growth.

[0077] "Logical structure" refers to the clarity of the logical framework and thought process in the user's response, and serves as a criterion for evaluation.

[0078] "Information structure" refers to the method of organizing and arranging the information contained in the user's response, and is an element that is paid attention to during evaluation.

[0079] "Original expression" refers to the creativity and unique perspective expressed by the user in their own words, and is considered as part of the evaluation.

[0080] The implementation of this system begins with the user accessing the server via a connecting device (e.g., personal computer, smartphone). The user logs into the system using the connecting device and enters user information such as skill level and training goals. The connecting device uses either a web browser or a dedicated application as its interface.

[0081] The server uses a generative AI model to analyze user information and generate a customized training plan. This generation process utilizes prompts to construct specific tasks and simulated scenarios. The generated training plan is then presented to the user via a connected device.

[0082] Upon receiving a user's input, the server analyzes the response using a generation AI model. The analysis evaluates the response's logical structure, information organization, and unique expression. Based on the analysis results, the server generates feedback for the user and provides improvement suggestions. This feedback is delivered to the user through a connected device.

[0083] As a concrete example, if a user submits an answer to the task "Summarize the explanation of a new product to consumers in three points," the server evaluates the answer and provides feedback from the perspectives of logic and originality. An example of a prompt to be input to the generative AI model is "Evaluate the input explanation and generate feedback including strengths and areas for improvement." Through this system, users can continuously improve their explanatory skills.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The user accesses the system and logs in using a terminal. They enter their username and password on the login screen, and this information is sent to the server. The server performs authentication and initiates a user session. The input is user information, and the output is a notification of successful authentication.

[0087] Step 2:

[0088] Users input their skill level and training goals via a terminal. This information is sent from the terminal to the server. The input is skill level and goal information, and the output is the result of registration in the database.

[0089] Step 3:

[0090] The server creates a training plan using a generated AI model based on the received user information. It inputs user data as prompts into the AI ​​model to generate an optimized training plan. The input consists of user information and prompts, and the output is a custom training plan.

[0091] Step 4:

[0092] The server sends the training plan to the terminal and presents it to the user. The terminal displays the received training plan in a format that is easy for the user to understand. The input is the training plan data, and the output is the screen display content.

[0093] Step 5:

[0094] The user inputs their answer to the presented task into a terminal and sends it to the server. The input is the user's answer, and the output is the data sent to the server.

[0095] Step 6:

[0096] The server evaluates the user's response using a generation AI model. It analyzes the logic, information structure, and originality of the response and generates results. The input is the user's response, and the output is the evaluation result.

[0097] Step 7:

[0098] The server generates feedback based on the evaluation results and provides it to the user via the terminal. The feedback points out specific areas for improvement. The input is the evaluation result, and the output is the feedback data.

[0099] Step 8:

[0100] The server records data from each training session and tracks user progress. This allows it to generate reports that evaluate user growth. The input is session data, and the output is the progress report.

[0101] (Application Example 1)

[0102] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0103] Traditional education systems suffer from a lack of customized programs to effectively improve users' critical thinking and communication skills, as well as insufficient provision of feedback utilizing the results of these assessments. Furthermore, the limited means users have for honing these skills on a daily basis leads to inefficiencies in skill development.

[0104] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0105] In this invention, the server includes means for receiving information and generating a customized educational plan; means for presenting problems based on the educational plan; means for receiving user responses and analyzing them to evaluate thinking and explanatory abilities; means for generating feedback; means for providing feedback to the user and tracking the user's progress; and means for providing the educational plan through home devices or mobile terminals. This enables users to receive training on a daily basis to improve their thinking and explanatory abilities.

[0106] "Means for receiving information and generating customized educational plans" refers to devices or programs that have the function of creating individual educational plans based on data provided by users.

[0107] "Means for presenting problems based on an educational plan" refers to devices or programs that have the function of presenting appropriate tasks or problems to the user in accordance with a generated educational plan.

[0108] "An analytical means for receiving user responses and evaluating thinking and explanatory abilities" refers to a device or program that has the function of measuring a person's thinking and explanatory abilities by acquiring the answers submitted by the user, classifying them, and evaluating them.

[0109] "Means for generating feedback" refers to devices or programs that have the function of generating information and advice to encourage user skill improvement based on analysis results.

[0110] "Means of providing feedback to users and tracking user progress" refers to devices or programs that have the function of sending evaluation results to users and recording and analyzing their subsequent progress.

[0111] "Means of providing educational plans through home devices and mobile terminals" refers to devices or programs that have the function of distributing generated educational plans to users via home electronic devices or portable information terminals.

[0112] The system implementing this invention mainly consists of a server, terminals, and users, and has functions for realizing educational plans on various platforms.

[0113] The server receives information provided by the user and generates a customized educational plan using a generative AI model based on that information. The generated educational plan includes specific tasks designed to improve the user's thinking and explanation abilities. The server can deliver this educational plan to the user via home electronic devices or mobile devices.

[0114] The terminal is responsible for presenting problems to the user based on the received educational plan. The user inputs a response to the presented task. The user's response, received through the terminal, is sent to the server for analysis. Based on this response, the server evaluates the user's thinking and explanatory abilities and generates feedback.

[0115] The server uses AI to analyze the logic, structure, and uniqueness of responses, generating precise feedback. This feedback is provided to the user via their device, allowing them to attempt further improvements based on this information. The user's progress is continuously tracked, and this data is stored in a database on the server.

[0116] For example, when a user addresses a problem such as "I want to improve my skills in explaining new technologies," continuous training is possible using home devices or mobile devices. The server provides appropriate tasks using prompts. In this example, a prompt might be, "The user wants to improve their skills in explaining new technologies. Please suggest three pieces of advice to help them improve their explanatory skills." This allows users to improve their thinking and explanatory abilities in their daily lives without being limited by physical constraints.

[0117] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0118] Step 1:

[0119] The server receives login information from a user and authenticates the user's account. The input is the user's ID and password, and the output is the authentication success or failure status. Upon successful authentication, the user's skill level and goals are retrieved from the database.

[0120] Step 2:

[0121] The server uses a generative AI model to generate a customized educational plan based on the acquired user information. The input is the user's skill level and goals, and the output is a list of tasks tailored to the user's needs. This list of tasks is individually optimized for each user.

[0122] Step 3:

[0123] The terminal presents the user with tasks based on the educational plan received from the server. The input is a list of tasks included in the educational plan, and the output is the specific problem content displayed on the screen. The user then considers the answer.

[0124] Step 4:

[0125] The user inputs their response to a task via a terminal. The input is the user's thought-out response, and the output is the transmission of that response to the server. After the response is sent, the server is ready for analysis.

[0126] Step 5:

[0127] The server receives user responses and analyzes them using a generative AI model. The input is the user's response, and the output is an evaluation of the response's logic, structure, and originality. In this process, the AI ​​model precisely analyzes the response.

[0128] Step 6:

[0129] The server generates feedback based on the analysis results. The input is the analysis results, and the output is a feedback message to be returned to the user. The feedback includes areas for improvement and points of success.

[0130] Step 7:

[0131] The terminal receives feedback from the server and presents it to the user. The input is the feedback message, and the output is the screen display to the user. The user then reviews this and uses it to make improvements.

[0132] Step 8:

[0133] The server stores the user's learning history in a database and tracks their progress. The input is the details of the learning session, and the output is the updated user learning profile. The progress data is used to optimize future educational plans.

[0134] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0135] This invention combines an emotion engine with a system that enhances the user's thinking and explanatory abilities. This system recognizes the user's emotional state and optimizes the training plan based on that state.

[0136] The user accesses the system through a terminal and performs a login operation. After successful login, the terminal provides an interface for the user to input their current skill level and training goals. This information is sent to the server.

[0137] The server uses an emotion engine to analyze data from the user's camera and microphone to identify the user's emotional state in real time. The emotion engine estimates the user's emotions by combining facial expression analysis, voice tone analysis, and other methods.

[0138] The server takes this emotional data, along with the user's skill level and goals, into consideration to generate a customized training plan. This training plan includes tasks and scenarios optimized for the user's emotions.

[0139] The terminal presents the user with a training plan sent from the server. The user works on the tasks and enters their answers, which are then sent to the server via the terminal. The server analyzes the received answers and evaluates the user's thinking and explanatory abilities. Furthermore, an emotion engine monitors the user's emotional changes and adjusts the training content and difficulty level accordingly.

[0140] For example, if a user is feeling frustrated with an assignment, the server can adjust the difficulty level of the assignment, making it more manageable for the user. In this way, the user load can be appropriately controlled, and an optimal learning environment can be provided.

[0141] The feedback includes not only the results analyzed by the server, but also advice and encouragement tailored to the user's emotional state. This allows users to improve their skills while receiving psychological support.

[0142] As described above, the present invention provides a system that supports effective skill improvement by understanding the user's emotional state in real time and providing individually optimized training.

[0143] The following describes the processing flow.

[0144] Step 1:

[0145] The user accesses the system using a device and enters their login information. The device sends this information to the server to attempt authentication.

[0146] Step 2:

[0147] The server verifies the user's authentication information, and if authentication is successful, it generates a user profile screen and sends it to the device.

[0148] Step 3:

[0149] The user enters information into a form on their device indicating their skill level and training goals, and the device then sends this data to the server.

[0150] Step 4:

[0151] The server activates the emotion engine and acquires emotion data in real time from the user's face and voice. The emotion engine uses image analysis and voice analysis to identify the user's emotional state.

[0152] Step 5:

[0153] The server comprehensively evaluates the user's skill information, emotional data, and goals, and uses an AI algorithm to generate a customized training plan.

[0154] Step 6:

[0155] The device displays a training plan retrieved from the server to the user. The plan includes tasks and learning content tailored to the user's emotional state.

[0156] Step 7:

[0157] Users work on the assigned tasks, enter their answers into their device, and send them to the server. Answers include both text input and multiple-choice questions.

[0158] Step 8:

[0159] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. It also monitors changes in the user's emotions in real time using an emotion engine.

[0160] Step 9:

[0161] The server generates feedback based on collected sentiment data and responses. This feedback includes specific areas for improvement and advice tailored to the user's emotions.

[0162] Step 10:

[0163] The server sends the generated feedback to the terminal, and the terminal displays that feedback to the user.

[0164] Step 11:

[0165] Users can refer to feedback, revise their answers if necessary, and resubmit them. This allows users to iteratively improve their skills.

[0166] Step 12:

[0167] The server integrates data from all training sessions to track user progress. The server periodically generates and provides progress reports to the user.

[0168] (Example 2)

[0169] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0170] Traditional educational systems have a problem in that they cannot be customized to take into account the emotional state of users when aiming to improve their skills, and therefore cannot reduce the psychological burden on users. In addition, the provision of fixed assignments made it difficult to respond flexibly to the individual learning pace and emotional changes of users.

[0171] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0172] In this invention, the server includes means for receiving user information and generating a customized training plan, means for acquiring the user's emotional state and analyzing it in real time using an emotion engine, and means for dynamically adjusting the content and difficulty level of the tasks in accordance with the user's emotional changes. This enables efficient skill development while providing a training environment optimized for each individual user and offering psychological support to the user.

[0173] "User information" refers to the individual skill level, training goals, and other relevant data of a user that the system receives.

[0174] A "training plan" refers to a set of tasks and scenarios customized based on user information, designed to improve the user's skills.

[0175] "Response" refers to the data of answers or responses that users submit to a task.

[0176] "Cognitive ability" refers to a user's capacity to understand, judge, and interpret information.

[0177] "Explanatory ability" refers to a user's ability to clearly communicate their thoughts and logic to others.

[0178] "Analysis tools" refer to the in-system methods used to evaluate user responses and measure thinking and explanatory abilities.

[0179] "Emotional state" refers to a state based on facial expressions, voice tone, and other factors that reflect the user's psychological and emotional position.

[0180] An "emotion engine" refers to software or hardware that detects and evaluates a user's emotions based on data obtained from cameras and microphones.

[0181] "Feedback" refers to information that includes evaluation results from analytical methods, as well as advice and suggestions for improvement based on the user's emotional state.

[0182] "Psychological support" refers to the act of providing encouragement and advice tailored to the user's emotional state, thereby assisting in the learning process.

[0183] "Dynamic adjustment" refers to changing the content and difficulty level of the task in real time in response to changes in the user's emotions.

[0184] This invention is a system for providing training tailored to the individual learning needs of users. Users log in using an interface provided via a terminal and input their skill level and training goals. The terminal sends this information to a server, and processing begins once the server receives it.

[0185] The server, equipped with an emotion engine, acquires emotional data in real time from the user's camera and microphone. This data is processed through facial expression analysis and voice tone analysis to identify the user's emotional state. This emotional data is combined with user input to create a customized training plan using a generative AI model. The training plan includes tasks and scenarios optimized according to the user's emotional state.

[0186] The terminal presents the user with a training plan from the server. The user uses the terminal to complete tasks, inputs their results, and sends them to the server. The server analyzes the responses and evaluates cognitive and explanatory abilities. Furthermore, the emotion engine continuously monitors the user's emotional changes and can dynamically adjust the content and difficulty of the tasks as needed.

[0187] For example, if a user wants training to improve their presentation skills, the server will provide relaxing tasks when the user is nervous, and conversely, actively incorporate challenging tasks when the user is relaxed, thereby maximizing the learning effect.

[0188] A concrete example of a prompt message would be, "Please suggest an appropriate training plan so that the user can improve their skills while taking their emotions into consideration." In this way, the objective of the present invention is to provide a learning environment in which the user can efficiently improve their skills while being emotionally supported.

[0189] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0190] Step 1:

[0191] Users log in to the system via a terminal and input their skill level and training goals. The terminal sends the entered information to the server. This information is used as data necessary for creating subsequent training plans.

[0192] Step 2:

[0193] The server uses an emotion engine to acquire video and audio data from the user's camera and microphone. Based on this input data, it performs facial expression analysis and voice tone analysis to identify the user's emotional state in real time. Emotional data is generated and used, along with the user's skill level information, to create a training plan.

[0194] Step 3:

[0195] The server generates training plans using a generative AI model. It incorporates optimized tasks and scenarios, taking into account the input skill level, goals, and emotional data. The generated training plans are individually customized to suit the user's needs.

[0196] Step 4:

[0197] The terminal receives the training plan sent from the server and presents it to the user. The user works on the presented tasks and inputs their responses into the terminal. These responses are sent to the server as output data.

[0198] Step 5:

[0199] The server analyzes the user's response data. Using analytical tools, it evaluates the user's cognitive and explanatory abilities. Based on this evaluation, it generates feedback and provides information to encourage further user growth.

[0200] Step 6:

[0201] The emotion engine continuously monitors the user's emotional changes and dynamically adjusts the content and difficulty of the training plan as needed. This process enables real-time responses to the user, allowing for efficient learning while reducing stress.

[0202] (Application Example 2)

[0203] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0204] Conventional individualized learning systems present fixed tasks without considering the user's emotional state, leading to problems such as decreased learning efficiency and increased user stress. This invention aims to solve these problems by recognizing the user's emotional state in real time and providing an optimal learning environment tailored to individual needs.

[0205] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0206] In this invention, the server includes means for receiving user information and generating a customized training plan, means for detecting the user's emotional state in real time and optimizing and adjusting the training plan based on the detected emotions, and means for generating feedback that takes into account the evaluation results from the analysis means and the emotional state. This makes it possible to provide a flexible learning plan that meets the individual needs of the user.

[0207] "User information" refers to data that includes a user's individual attributes, skill level, and learning goals.

[0208] A "customized training plan" is a learning plan optimized based on the user's individual needs and skills.

[0209] A "task" is a problem, exercise, or activity that a user must work on, and it forms part of the learning process.

[0210] "Analytical methods for evaluating thinking and explanatory abilities" refer to methods that measure a user's level of logical understanding and explanatory ability based on their responses and reactions.

[0211] "Emotional state" refers to a user's psychological emotional state and reactions, and is a factor that affects learning efficiency.

[0212] "Means of optimization and adjustment" refers to methods that change the learning content and difficulty level in real time according to the user's emotional state and skill level.

[0213] "Feedback" refers to information that provides evaluation results and instructions for improvement regarding the user's learning progress.

[0214] "Means for tracking and adjusting progress" refers to methods for continuously monitoring the user's learning progress and dynamically updating the learning plan based on that.

[0215] The system that implements this application primarily consists of a server and a terminal. First, the user accesses the system through the terminal and logs in. After logging in, the terminal provides the user with an interface for generating a personalized training plan. The user inputs their skill level and learning goals, and this information is sent to the server.

[0216] The server generates a customized training plan based on this user information. Furthermore, the server acquires data from the device's camera and microphone to understand the user's emotional state in real time. This utilizes facial expression and voice analysis libraries such as OpenCV. The server analyzes the user's facial expressions and voice tone to estimate their psychological state.

[0217] Based on the generated training plan and emotional data, the server can adjust the difficulty and content of the tasks. If the user's emotional state changes, the server immediately optimizes the learning tasks. This adjustment reduces the load and helps the user learn comfortably. Feedback includes advice and encouragement tailored to the user's emotions and learning content.

[0218] As a concrete example, if a user becomes frustrated while working on a math problem, the server will immediately lower the difficulty level of the problem and re-present it to the user. Another example of a prompt generated by the AI ​​model is, "How should the difficulty level of the problem be adjusted if the user is feeling frustrated?" This will further enhance the user's learning experience.

[0219] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0220] Step 1:

[0221] Users access the system and log in using a terminal. The terminal displays an interface for entering the user's skill level and learning goals. After the user enters this information, it is sent to the server. This information is the input and the user information sent to the server as output.

[0222] Step 2:

[0223] The server generates a customized training plan based on information received from the user. This process involves referencing past learning history and statistics from a database on the server to select appropriate tasks and content. User information is used as input, and a training plan is generated as output.

[0224] Step 3:

[0225] The server uses an emotion recognition algorithm to analyze camera and microphone data sent from the terminal. Specifically, it utilizes libraries such as OpenCV to analyze facial expressions and estimate the user's emotional state. The input is audio and video data, and the output is the analyzed emotional state.

[0226] Step 4:

[0227] The server combines the generated training plan with emotional state data to present the user with the most suitable task. At this stage, the difficulty level of the task is adjusted according to the emotional state. The training plan and emotional state are used as input, and the optimized task is output.

[0228] Step 5:

[0229] The user works on the presented task and sends their answer to the server via their device. The device acts as an input, forwarding the user's answer to the server. The output is the answer data.

[0230] Step 6:

[0231] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. Logical reasoning and originality are used as evaluation criteria. Evaluation data is generated and output using the user's responses as input.

[0232] Step 7:

[0233] The server generates appropriate feedback based on the evaluation results and the user's emotional state. This feedback includes specific areas for improvement and emotionally responsive advice. The input is evaluation data and emotional state, and the output is feedback information.

[0234] Step 8:

[0235] The device displays the generated feedback to the user and also tracks the user's progress. An example of this prompt is, "How should the difficulty of the task be adjusted if the user is feeling frustrated?" The input is feedback information, and the output is presented to the user.

[0236] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0237] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0238] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0239] [Second Embodiment]

[0240] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0241] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0242] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0243] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0244] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0245] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0246] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0247] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0248] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0249] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0250] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0251] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0252] This invention provides a system for effectively improving users' thinking and explanatory abilities. The system is primarily implemented through interactions between a server, a terminal, and the user.

[0253] Users first access the system via their device and log in. After logging in, users can enter their skill level and training goals. This information is sent to the server via the device.

[0254] The server receives information provided by the user and uses an AI algorithm to generate a customized training plan based on that information. This training plan is designed to improve the user's skills and includes specific tasks and simulated scenarios.

[0255] The generated training plan is presented to the user via a device. The user enters their answers to the presented tasks into the device and sends them to the server.

[0256] The server receives this response and uses AI to evaluate the user's thinking and explanatory abilities. The evaluation process uses indicators such as the logic, structure, and originality of the response. The server then generates feedback based on this evaluation result and provides it to the user through the terminal.

[0257] Users can improve their answers by referring to the feedback they receive. The server also tracks the progress of each training session and generates reports that evaluate the user's growth over time.

[0258] As a concrete example, a user might be given the task of "summarizing a new product's explanation to consumers in three points." By entering their answer and receiving feedback from the server, users can develop the ability to create more refined explanations. Through this process, users can gradually improve their skills.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The user uses a device to access the system and perform a login operation. The device sends the user's authentication information to the server and attempts to log in.

[0262] Step 2:

[0263] The server verifies the received authentication information and authenticates the user. If authentication is successful, the server generates a user profile screen and sends it to the device.

[0264] Step 3:

[0265] The user enters their skill level and training goals on their device. The device then sends this information to the server.

[0266] Step 4:

[0267] The server uses an AI algorithm to generate a personalized training plan based on the user's skill level and goals. The generated plan is then sent to the device.

[0268] Step 5:

[0269] The terminal displays the training plan sent from the server to the user and presents the first assignment.

[0270] Step 6:

[0271] The user enters their answer to the task into their device and sends it to the server.

[0272] Step 7:

[0273] The server receives user responses and analyzes them using AI. It evaluates the content, logic, structure, and originality of the responses.

[0274] Step 8:

[0275] The server generates feedback based on the analysis results. This feedback includes areas for improvement and strengths in the response.

[0276] Step 9:

[0277] The server sends the generated feedback to the terminal, and the terminal displays the feedback to the user.

[0278] Step 10:

[0279] The process can be repeated where the user checks the feedback, modifies the answer if necessary, and resubmits it.

[0280] Step 11:

[0281] The server accumulates the results of each training session of the user and tracks the progress. Periodically generate a report evaluating the user's achievements and provide it to the user through the terminal.

[0282] (Example 1)

[0283] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0284] In order to effectively improve the user's thinking ability and explanatory ability, customized training according to individual skill levels and goals is necessary. However, in the conventional system, appropriate problem presentation and evaluation are not sufficiently performed, and since the quality of feedback is constant, there is a problem that the growth of the user cannot be sufficiently supported. [[ID=二十九]]

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following respective means.

[0286] In this invention, the server includes means for receiving user information and creating a training plan individually optimized by a generation algorithm, presenting means for presenting problems based on the training plan, and analysis means for receiving the user's answer and evaluating the thinking ability and explanatory ability using a generation model. As a result, it becomes possible to provide accurate training content according to the user's skill level and detailed and individualized feedback.

[0287] "User information" refers to individual data such as the user's skill level and training goals, and a customized training plan is generated based on this information.

[0288] A "generative algorithm" refers to a set of methods and processes for creating an optimized training plan based on user information.

[0289] A "training plan" refers to a plan that includes specific tasks and simulated scenarios, designed to improve the user's skills.

[0290] "Presentation means" refers to a method or apparatus for the server to display training tasks on the user's connected device.

[0291] "Analysis means" refers to methods or processes used to analyze a user's responses and evaluate their thinking and explanatory abilities.

[0292] A "generative model" refers to an artificial intelligence algorithm or computational model used to evaluate user responses.

[0293] "Feedback" refers to evaluation results and reports of areas for improvement generated based on user responses, providing information to promote user growth.

[0294] "Logical structure" refers to the clarity of the logical framework and thought process in the user's response, and serves as a criterion for evaluation.

[0295] "Information structure" refers to the method of organizing and arranging the information contained in the user's response, and is an element that is paid attention to during evaluation.

[0296] "Original expression" refers to the creativity and unique perspective expressed by the user in their own words, and is considered as part of the evaluation.

[0297] The implementation of this system begins with the user accessing the server via a connecting device (e.g., personal computer, smartphone). The user logs into the system using the connecting device and enters user information such as skill level and training goals. The connecting device uses either a web browser or a dedicated application as its interface.

[0298] The server uses a generative AI model to analyze user information and generate a customized training plan. This generation process utilizes prompts to construct specific tasks and simulated scenarios. The generated training plan is then presented to the user via a connected device.

[0299] Upon receiving a user's input, the server analyzes the response using a generation AI model. The analysis evaluates the response's logical structure, information organization, and unique expression. Based on the analysis results, the server generates feedback for the user and provides improvement suggestions. This feedback is delivered to the user through a connected device.

[0300] As a concrete example, if a user submits an answer to the task "Summarize the explanation of a new product to consumers in three points," the server evaluates the answer and provides feedback from the perspectives of logic and originality. An example of a prompt to be input to the generative AI model is "Evaluate the input explanation and generate feedback including strengths and areas for improvement." Through this system, users can continuously improve their explanatory skills.

[0301] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0302] Step 1:

[0303] The user accesses and logs in to the system using a terminal. The username and password are entered on the login screen, and this information is sent to the server. The server performs authentication and starts a user session. The input is user information, and the output is a notification of successful authentication.

[0304] Step 2:

[0305] The user enters their skill level and training goals via the terminal. This information is sent from the terminal to the server. The input is the skill level and goal information, and the output is the registration result in the database.

[0306] Step 3:

[0307] The server creates a training plan using an AI model generated based on the received user information. The user's input data is entered into the AI model as a prompt sentence to generate an optimized training plan. The input is user information and the prompt sentence, and the output is a custom training plan.

[0308] Step 4:

[0309] The server sends the training plan to the terminal and presents it to the user. The terminal displays the received training plan in a form that is easy for the user to understand. The input is the training plan data, and the output is the content displayed on the screen.

[0310] Step 5:

[0311] The user enters an answer to the presented task on the terminal and sends it to the server. The input is the user's answer, and the output is the data transmission to the server.

[0312] Step 6:

[0313] The server evaluates the user's answer using the generated AI model. It analyzes the logic, information structure, and originality of the answer and generates a result. The input is the user's answer, and the output is the evaluation result.

[0314] Step 7:

[0315] The server generates feedback based on the evaluation results and provides it to the user via the terminal. The feedback points out specific areas for improvement. The input is the evaluation result, and the output is the feedback data.

[0316] Step 8:

[0317] The server records data from each training session and tracks user progress. This allows it to generate reports that evaluate user growth. The input is session data, and the output is the progress report.

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0320] Traditional education systems suffer from a lack of customized programs to effectively improve users' critical thinking and communication skills, as well as insufficient provision of feedback utilizing the results of these assessments. Furthermore, the limited means users have for honing these skills on a daily basis leads to inefficiencies in skill development.

[0321] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0322] In this invention, the server includes means for receiving information and generating a customized educational plan; means for presenting problems based on the educational plan; means for receiving user responses and analyzing them to evaluate thinking and explanatory abilities; means for generating feedback; means for providing feedback to the user and tracking the user's progress; and means for providing the educational plan through home devices or mobile terminals. This enables users to receive training on a daily basis to improve their thinking and explanatory abilities.

[0323] "Means for receiving information and generating customized educational plans" refers to devices or programs that have the function of creating individual educational plans based on data provided by users.

[0324] "Means for presenting problems based on an educational plan" refers to devices or programs that have the function of presenting appropriate tasks or problems to the user in accordance with a generated educational plan.

[0325] "An analytical means for receiving user responses and evaluating thinking and explanatory abilities" refers to a device or program that has the function of measuring a person's thinking and explanatory abilities by acquiring the answers submitted by the user, classifying them, and evaluating them.

[0326] "Means for generating feedback" refers to devices or programs that have the function of generating information and advice to encourage user skill improvement based on analysis results.

[0327] "Means of providing feedback to users and tracking user progress" refers to devices or programs that have the function of sending evaluation results to users and recording and analyzing their subsequent progress.

[0328] "Means of providing educational plans through home devices and mobile terminals" refers to devices or programs that have the function of distributing generated educational plans to users via home electronic devices or portable information terminals.

[0329] The system implementing this invention mainly consists of a server, terminals, and users, and has functions for realizing educational plans on various platforms.

[0330] The server receives information provided by the user and generates a customized educational plan using a generative AI model based on that information. The generated educational plan includes specific tasks designed to improve the user's thinking and explanation abilities. The server can deliver this educational plan to the user via home electronic devices or mobile devices.

[0331] The terminal is responsible for presenting problems to the user based on the received educational plan. The user inputs a response to the presented task. The user's response, received through the terminal, is sent to the server for analysis. Based on this response, the server evaluates the user's thinking and explanatory abilities and generates feedback.

[0332] The server uses AI to analyze the logic, structure, and uniqueness of responses, generating precise feedback. This feedback is provided to the user via their device, allowing them to attempt further improvements based on this information. The user's progress is continuously tracked, and this data is stored in a database on the server.

[0333] For example, when a user addresses a problem such as "I want to improve my skills in explaining new technologies," continuous training is possible using home devices or mobile devices. The server provides appropriate tasks using prompts. In this example, a prompt might be, "The user wants to improve their skills in explaining new technologies. Please suggest three pieces of advice to help them improve their explanatory skills." This allows users to improve their thinking and explanatory abilities in their daily lives without being limited by physical constraints.

[0334] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0335] Step 1:

[0336] The server receives login information from a user and authenticates the user's account. The input is the user's ID and password, and the output is the authentication success or failure status. Upon successful authentication, the user's skill level and goals are retrieved from the database.

[0337] Step 2:

[0338] The server uses a generative AI model to generate a customized educational plan based on the acquired user information. The input is the user's skill level and goals, and the output is a list of tasks tailored to the user's needs. This list of tasks is individually optimized for each user.

[0339] Step 3:

[0340] The terminal presents the user with tasks based on the educational plan received from the server. The input is a list of tasks included in the educational plan, and the output is the specific problem content displayed on the screen. The user then considers the answer.

[0341] Step 4:

[0342] The user inputs their response to a task via a terminal. The input is the user's thought-out response, and the output is the transmission of that response to the server. After the response is sent, the server is ready for analysis.

[0343] Step 5:

[0344] The server receives user responses and analyzes them using a generative AI model. The input is the user's response, and the output is an evaluation of the response's logic, structure, and originality. In this process, the AI ​​model precisely analyzes the response.

[0345] Step 6:

[0346] The server generates feedback based on the analysis results. The input is the analysis results, and the output is a feedback message to be returned to the user. The feedback includes areas for improvement and points of success.

[0347] Step 7:

[0348] The terminal receives feedback from the server and presents it to the user. The input is the feedback message, and the output is the screen display to the user. The user then reviews this and uses it to make improvements.

[0349] Step 8:

[0350] The server stores the user's learning history in a database and tracks their progress. The input is the details of the learning session, and the output is the updated user learning profile. The progress data is used to optimize future educational plans.

[0351] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0352] This invention combines an emotion engine with a system that enhances the user's thinking and explanatory abilities. This system recognizes the user's emotional state and optimizes the training plan based on that state.

[0353] The user accesses the system through a terminal and performs a login operation. After successful login, the terminal provides an interface for the user to input their current skill level and training goals. This information is sent to the server.

[0354] The server uses an emotion engine to analyze data from the user's camera and microphone to identify the user's emotional state in real time. The emotion engine estimates the user's emotions by combining facial expression analysis, voice tone analysis, and other methods.

[0355] The server takes this emotional data, along with the user's skill level and goals, into consideration to generate a customized training plan. This training plan includes tasks and scenarios optimized for the user's emotions.

[0356] The terminal presents the user with a training plan sent from the server. The user works on the tasks and enters their answers, which are then sent to the server via the terminal. The server analyzes the received answers and evaluates the user's thinking and explanatory abilities. Furthermore, an emotion engine monitors the user's emotional changes and adjusts the training content and difficulty level accordingly.

[0357] For example, if a user is feeling frustrated with an assignment, the server can adjust the difficulty level of the assignment, making it more manageable for the user. In this way, the user load can be appropriately controlled, and an optimal learning environment can be provided.

[0358] The feedback includes not only the results analyzed by the server, but also advice and encouragement tailored to the user's emotional state. This allows users to improve their skills while receiving psychological support.

[0359] As described above, the present invention provides a system that supports effective skill improvement by understanding the user's emotional state in real time and providing individually optimized training.

[0360] The following describes the processing flow.

[0361] Step 1:

[0362] The user accesses the system using a device and enters their login information. The device sends this information to the server to attempt authentication.

[0363] Step 2:

[0364] The server verifies the user's authentication information, and if authentication is successful, it generates a user profile screen and sends it to the device.

[0365] Step 3:

[0366] The user enters information into a form on their device indicating their skill level and training goals, and the device then sends this data to the server.

[0367] Step 4:

[0368] The server activates the emotion engine and acquires emotion data in real time from the user's face and voice. The emotion engine uses image analysis and voice analysis to identify the user's emotional state.

[0369] Step 5:

[0370] The server comprehensively evaluates the user's skill information, emotional data, and goals, and uses an AI algorithm to generate a customized training plan.

[0371] Step 6:

[0372] The device displays a training plan retrieved from the server to the user. The plan includes tasks and learning content tailored to the user's emotional state.

[0373] Step 7:

[0374] Users work on the assigned tasks, enter their answers into their device, and send them to the server. Answers include both text input and multiple-choice questions.

[0375] Step 8:

[0376] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. It also monitors changes in the user's emotions in real time using an emotion engine.

[0377] Step 9:

[0378] The server generates feedback based on collected sentiment data and responses. This feedback includes specific areas for improvement and advice tailored to the user's emotions.

[0379] Step 10:

[0380] The server sends the generated feedback to the terminal, and the terminal displays that feedback to the user.

[0381] Step 11:

[0382] Users can refer to feedback, revise their answers if necessary, and resubmit them. This allows users to iteratively improve their skills.

[0383] Step 12:

[0384] The server integrates data from all training sessions to track user progress. The server periodically generates and provides progress reports to the user.

[0385] (Example 2)

[0386] Next, we will describe Example 2. 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".

[0387] Traditional educational systems have a problem in that they cannot be customized to take into account the emotional state of users when aiming to improve their skills, and therefore cannot reduce the psychological burden on users. In addition, the provision of fixed assignments made it difficult to respond flexibly to the individual learning pace and emotional changes of users.

[0388] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0389] In this invention, the server includes means for receiving user information and generating a customized training plan, means for acquiring the user's emotional state and analyzing it in real time using an emotion engine, and means for dynamically adjusting the content and difficulty level of the tasks in accordance with the user's emotional changes. This enables efficient skill development while providing a training environment optimized for each individual user and offering psychological support to the user.

[0390] "User information" refers to the individual skill level, training goals, and other relevant data of a user that the system receives.

[0391] A "training plan" refers to a set of tasks and scenarios customized based on user information, designed to improve the user's skills.

[0392] "Response" refers to the data of answers or responses that users submit to a task.

[0393] "Cognitive ability" refers to a user's capacity to understand, judge, and interpret information.

[0394] "Explanatory ability" refers to a user's ability to clearly communicate their thoughts and logic to others.

[0395] "Analysis tools" refer to the in-system methods used to evaluate user responses and measure thinking and explanatory abilities.

[0396] "Emotional state" refers to a state based on facial expressions, voice tone, and other factors that reflect the user's psychological and emotional position.

[0397] An "emotion engine" refers to software or hardware that detects and evaluates a user's emotions based on data obtained from cameras and microphones.

[0398] "Feedback" refers to information that includes evaluation results from analytical methods, as well as advice and suggestions for improvement based on the user's emotional state.

[0399] "Psychological support" refers to the act of providing encouragement and advice tailored to the user's emotional state, thereby assisting in the learning process.

[0400] "Dynamic adjustment" refers to changing the content and difficulty level of the task in real time in response to changes in the user's emotions.

[0401] This invention is a system for providing training tailored to the individual learning needs of users. Users log in using an interface provided via a terminal and input their skill level and training goals. The terminal sends this information to a server, and processing begins once the server receives it.

[0402] The server, equipped with an emotion engine, acquires emotional data in real time from the user's camera and microphone. This data is processed through facial expression analysis and voice tone analysis to identify the user's emotional state. This emotional data is combined with user input to create a customized training plan using a generative AI model. The training plan includes tasks and scenarios optimized according to the user's emotional state.

[0403] The terminal presents the user with a training plan from the server. The user uses the terminal to complete tasks, inputs their results, and sends them to the server. The server analyzes the responses and evaluates cognitive and explanatory abilities. Furthermore, the emotion engine continuously monitors the user's emotional changes and can dynamically adjust the content and difficulty of the tasks as needed.

[0404] For example, if a user wants training to improve their presentation skills, the server will provide relaxing tasks when the user is nervous, and conversely, actively incorporate challenging tasks when the user is relaxed, thereby maximizing the learning effect.

[0405] A concrete example of a prompt message would be, "Please suggest an appropriate training plan so that the user can improve their skills while taking their emotions into consideration." In this way, the objective of the present invention is to provide a learning environment in which the user can efficiently improve their skills while being emotionally supported.

[0406] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0407] Step 1:

[0408] Users log in to the system via a terminal and input their skill level and training goals. The terminal sends the entered information to the server. This information is used as data necessary for creating subsequent training plans.

[0409] Step 2:

[0410] The server uses an emotion engine to acquire video and audio data from the user's camera and microphone. Based on this input data, it performs facial expression analysis and voice tone analysis to identify the user's emotional state in real time. Emotional data is generated and used, along with the user's skill level information, to create a training plan.

[0411] Step 3:

[0412] The server generates training plans using a generative AI model. It incorporates optimized tasks and scenarios, taking into account the input skill level, goals, and emotional data. The generated training plans are individually customized to suit the user's needs.

[0413] Step 4:

[0414] The terminal receives the training plan sent from the server and presents it to the user. The user works on the presented tasks and inputs their responses into the terminal. These responses are sent to the server as output data.

[0415] Step 5:

[0416] The server analyzes the user's response data. Using analytical tools, it evaluates the user's cognitive and explanatory abilities. Based on this evaluation, it generates feedback and provides information to encourage further user growth.

[0417] Step 6:

[0418] The emotion engine continuously monitors the user's emotional changes and dynamically adjusts the content and difficulty of the training plan as needed. This process enables real-time responses to the user, allowing for efficient learning while reducing stress.

[0419] (Application Example 2)

[0420] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0421] Conventional individualized learning systems present fixed tasks without considering the user's emotional state, leading to problems such as decreased learning efficiency and increased user stress. This invention aims to solve these problems by recognizing the user's emotional state in real time and providing an optimal learning environment tailored to individual needs.

[0422] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0423] In this invention, the server includes means for receiving user information and generating a customized training plan, means for detecting the user's emotional state in real time and optimizing and adjusting the training plan based on the detected emotions, and means for generating feedback that takes into account the evaluation results from the analysis means and the emotional state. This makes it possible to provide a flexible learning plan that meets the individual needs of the user.

[0424] "User information" refers to data that includes a user's individual attributes, skill level, and learning goals.

[0425] A "customized training plan" is a learning plan optimized based on the user's individual needs and skills.

[0426] A "task" is a problem, exercise, or activity that a user must work on, and it forms part of the learning process.

[0427] "Analytical methods for evaluating thinking and explanatory abilities" refer to methods that measure a user's level of logical understanding and explanatory ability based on their responses and reactions.

[0428] "Emotional state" refers to a user's psychological emotional state and reactions, and is a factor that affects learning efficiency.

[0429] "Means of optimization and adjustment" refers to methods that change the learning content and difficulty level in real time according to the user's emotional state and skill level.

[0430] "Feedback" refers to information that provides evaluation results and instructions for improvement regarding the user's learning progress.

[0431] "Means for tracking and adjusting progress" refers to methods for continuously monitoring the user's learning progress and dynamically updating the learning plan based on that.

[0432] The system that implements this application primarily consists of a server and a terminal. First, the user accesses the system through the terminal and logs in. After logging in, the terminal provides the user with an interface for generating a personalized training plan. The user inputs their skill level and learning goals, and this information is sent to the server.

[0433] The server generates a customized training plan based on this user information. Furthermore, the server acquires data from the device's camera and microphone to understand the user's emotional state in real time. This utilizes facial expression and voice analysis libraries such as OpenCV. The server analyzes the user's facial expressions and voice tone to estimate their psychological state.

[0434] Based on the generated training plan and emotional data, the server can adjust the difficulty and content of the tasks. If the user's emotional state changes, the server immediately optimizes the learning tasks. This adjustment reduces the load and helps the user learn comfortably. Feedback includes advice and encouragement tailored to the user's emotions and learning content.

[0435] As a concrete example, if a user becomes frustrated while working on a math problem, the server will immediately lower the difficulty level of the problem and re-present it to the user. Another example of a prompt generated by the AI ​​model is, "How should the difficulty level of the problem be adjusted if the user is feeling frustrated?" This will further enhance the user's learning experience.

[0436] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0437] Step 1:

[0438] Users access the system and log in using a terminal. The terminal displays an interface for entering the user's skill level and learning goals. After the user enters this information, it is sent to the server. This information is the input and the user information sent to the server as output.

[0439] Step 2:

[0440] The server generates a customized training plan based on information received from the user. This process involves referencing past learning history and statistics from a database on the server to select appropriate tasks and content. User information is used as input, and a training plan is generated as output.

[0441] Step 3:

[0442] The server uses an emotion recognition algorithm to analyze camera and microphone data sent from the terminal. Specifically, it utilizes libraries such as OpenCV to analyze facial expressions and estimate the user's emotional state. The input is audio and video data, and the output is the analyzed emotional state.

[0443] Step 4:

[0444] The server combines the generated training plan with emotional state data to present the user with the most suitable task. At this stage, the difficulty level of the task is adjusted according to the emotional state. The training plan and emotional state are used as input, and the optimized task is output.

[0445] Step 5:

[0446] The user works on the presented task and sends their answer to the server via their device. The device acts as an input, forwarding the user's answer to the server. The output is the answer data.

[0447] Step 6:

[0448] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. Logical reasoning and originality are used as evaluation criteria. Evaluation data is generated and output using the user's responses as input.

[0449] Step 7:

[0450] The server generates appropriate feedback based on the evaluation results and the user's emotional state. This feedback includes specific areas for improvement and emotionally responsive advice. The input is evaluation data and emotional state, and the output is feedback information.

[0451] Step 8:

[0452] The device displays the generated feedback to the user and also tracks the user's progress. An example of this prompt is, "How should the difficulty of the task be adjusted if the user is feeling frustrated?" The input is feedback information, and the output is presented to the user.

[0453] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0454] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0455] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0456] [Third Embodiment]

[0457] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0458] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0459] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0460] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0461] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0462] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0463] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0464] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0465] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0466] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0467] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0468] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0469] This invention provides a system for effectively improving users' thinking and explanatory abilities. The system is primarily implemented through interactions between a server, a terminal, and the user.

[0470] Users first access the system via their device and log in. After logging in, users can enter their skill level and training goals. This information is sent to the server via the device.

[0471] The server receives information provided by the user and uses an AI algorithm to generate a customized training plan based on that information. This training plan is designed to improve the user's skills and includes specific tasks and simulated scenarios.

[0472] The generated training plan is presented to the user via a device. The user enters their answers to the presented tasks into the device and sends them to the server.

[0473] The server receives this response and uses AI to evaluate the user's thinking and explanatory abilities. The evaluation process uses indicators such as the logic, structure, and originality of the response. The server then generates feedback based on this evaluation result and provides it to the user through the terminal.

[0474] Users can improve their answers by referring to the feedback they receive. The server also tracks the progress of each training session and generates reports that evaluate the user's growth over time.

[0475] As a concrete example, a user might be given the task of "summarizing a new product's explanation to consumers in three points." By entering their answer and receiving feedback from the server, users can develop the ability to create more refined explanations. Through this process, users can gradually improve their skills.

[0476] The following describes the processing flow.

[0477] Step 1:

[0478] The user uses a device to access the system and perform a login operation. The device sends the user's authentication information to the server and attempts to log in.

[0479] Step 2:

[0480] The server verifies the received authentication information and authenticates the user. If authentication is successful, the server generates a user profile screen and sends it to the device.

[0481] Step 3:

[0482] The user enters their skill level and training goals on their device. The device then sends this information to the server.

[0483] Step 4:

[0484] The server uses an AI algorithm to generate a personalized training plan based on the user's skill level and goals. The generated plan is then sent to the device.

[0485] Step 5:

[0486] The terminal displays the training plan sent from the server to the user and presents the first assignment.

[0487] Step 6:

[0488] The user enters their answer to the task into their device and sends it to the server.

[0489] Step 7:

[0490] The server receives user responses and analyzes them using AI. It evaluates the content, logic, structure, and originality of the responses.

[0491] Step 8:

[0492] The server generates feedback based on the analysis results. This feedback includes areas for improvement and strengths in the response.

[0493] Step 9:

[0494] The server sends the generated feedback to the terminal, and the terminal displays the feedback to the user.

[0495] Step 10:

[0496] Users can review feedback, revise their responses as needed, and repeat the process.

[0497] Step 11:

[0498] The server stores the results of each user's training session and tracks their progress. It periodically generates reports evaluating the user's performance and provides them to the user via their terminal.

[0499] (Example 1)

[0500] Next, we will describe Example 1. 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."

[0501] To effectively improve users' thinking and communication skills, customized training tailored to individual skill levels and goals is necessary. However, conventional systems have limitations in adequately supporting user growth because they do not adequately present or evaluate appropriate tasks, and the quality of feedback is inconsistent.

[0502] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0503] In this invention, the server includes means for receiving user information and creating individually optimized training plans using a generation algorithm, means for presenting tasks based on the training plan, and means for receiving user responses and evaluating thinking and explanatory abilities using a generation model. This enables the provision of appropriate training content according to the user's skill level and detailed, personalized feedback.

[0504] "User information" refers to individual data such as the user's skill level and training goals, and a customized training plan is generated based on this information.

[0505] A "generative algorithm" refers to a set of methods and processes for creating an optimized training plan based on user information.

[0506] A "training plan" refers to a plan that includes specific tasks and simulated scenarios, designed to improve the user's skills.

[0507] "Presentation means" refers to a method or apparatus for the server to display training tasks on the user's connected device.

[0508] "Analysis means" refers to methods or processes used to analyze a user's responses and evaluate their thinking and explanatory abilities.

[0509] A "generative model" refers to an artificial intelligence algorithm or computational model used to evaluate user responses.

[0510] "Feedback" refers to evaluation results and reports of areas for improvement generated based on user responses, providing information to promote user growth.

[0511] "Logical structure" refers to the clarity of the logical framework and thought process in the user's response, and serves as a criterion for evaluation.

[0512] "Information structure" refers to the method of organizing and arranging the information contained in the user's response, and is an element that is paid attention to during evaluation.

[0513] "Original expression" refers to the creativity and unique perspective expressed by the user in their own words, and is considered as part of the evaluation.

[0514] The implementation of this system begins with the user accessing the server via a connecting device (e.g., personal computer, smartphone). The user logs into the system using the connecting device and enters user information such as skill level and training goals. The connecting device uses either a web browser or a dedicated application as its interface.

[0515] The server uses a generative AI model to analyze user information and generate a customized training plan. This generation process utilizes prompts to construct specific tasks and simulated scenarios. The generated training plan is then presented to the user via a connected device.

[0516] Upon receiving a user's input, the server analyzes the response using a generation AI model. The analysis evaluates the response's logical structure, information organization, and unique expression. Based on the analysis results, the server generates feedback for the user and provides improvement suggestions. This feedback is delivered to the user through a connected device.

[0517] As a concrete example, if a user submits an answer to the task "Summarize the explanation of a new product to consumers in three points," the server evaluates the answer and provides feedback from the perspectives of logic and originality. An example of a prompt to be input to the generative AI model is "Evaluate the input explanation and generate feedback including strengths and areas for improvement." Through this system, users can continuously improve their explanatory skills.

[0518] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0519] Step 1:

[0520] The user accesses the system and logs in using a terminal. They enter their username and password on the login screen, and this information is sent to the server. The server performs authentication and initiates a user session. The input is user information, and the output is a notification of successful authentication.

[0521] Step 2:

[0522] Users input their skill level and training goals via a terminal. This information is sent from the terminal to the server. The input is skill level and goal information, and the output is the result of registration in the database.

[0523] Step 3:

[0524] The server creates a training plan using a generated AI model based on the received user information. It inputs user data as prompts into the AI ​​model to generate an optimized training plan. The input consists of user information and prompts, and the output is a custom training plan.

[0525] Step 4:

[0526] The server sends the training plan to the terminal and presents it to the user. The terminal displays the received training plan in a format that is easy for the user to understand. The input is the training plan data, and the output is the screen display content.

[0527] Step 5:

[0528] The user inputs their answer to the presented task into a terminal and sends it to the server. The input is the user's answer, and the output is the data sent to the server.

[0529] Step 6:

[0530] The server evaluates the user's response using a generation AI model. It analyzes the logic, information structure, and originality of the response and generates results. The input is the user's response, and the output is the evaluation result.

[0531] Step 7:

[0532] The server generates feedback based on the evaluation results and provides it to the user via the terminal. The feedback points out specific areas for improvement. The input is the evaluation result, and the output is the feedback data.

[0533] Step 8:

[0534] The server records data from each training session and tracks user progress. This allows it to generate reports that evaluate user growth. The input is session data, and the output is the progress report.

[0535] (Application Example 1)

[0536] Next, we will explain Application Example 1. In the following explanation, 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."

[0537] Traditional education systems suffer from a lack of customized programs to effectively improve users' critical thinking and communication skills, as well as insufficient provision of feedback utilizing the results of these assessments. Furthermore, the limited means users have for honing these skills on a daily basis leads to inefficiencies in skill development.

[0538] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0539] In this invention, the server includes means for receiving information and generating a customized educational plan; means for presenting problems based on the educational plan; means for receiving user responses and analyzing them to evaluate thinking and explanatory abilities; means for generating feedback; means for providing feedback to the user and tracking the user's progress; and means for providing the educational plan through home devices or mobile terminals. This enables users to receive training on a daily basis to improve their thinking and explanatory abilities.

[0540] "Means for receiving information and generating customized educational plans" refers to devices or programs that have the function of creating individual educational plans based on data provided by users.

[0541] "Means for presenting problems based on an educational plan" refers to devices or programs that have the function of presenting appropriate tasks or problems to the user in accordance with a generated educational plan.

[0542] "An analytical means for receiving user responses and evaluating thinking and explanatory abilities" refers to a device or program that has the function of measuring a person's thinking and explanatory abilities by acquiring the answers submitted by the user, classifying them, and evaluating them.

[0543] "Means for generating feedback" refers to devices or programs that have the function of generating information and advice to encourage user skill improvement based on analysis results.

[0544] "Means of providing feedback to users and tracking user progress" refers to devices or programs that have the function of sending evaluation results to users and recording and analyzing their subsequent progress.

[0545] "Means of providing educational plans through home devices and mobile terminals" refers to devices or programs that have the function of distributing generated educational plans to users via home electronic devices or portable information terminals.

[0546] The system implementing this invention mainly consists of a server, terminals, and users, and has functions for realizing educational plans on various platforms.

[0547] The server receives information provided by the user and generates a customized educational plan using a generative AI model based on that information. The generated educational plan includes specific tasks designed to improve the user's thinking and explanation abilities. The server can deliver this educational plan to the user via home electronic devices or mobile devices.

[0548] The terminal is responsible for presenting problems to the user based on the received educational plan. The user inputs a response to the presented task. The user's response, received through the terminal, is sent to the server for analysis. Based on this response, the server evaluates the user's thinking and explanatory abilities and generates feedback.

[0549] The server uses AI to analyze the logic, structure, and uniqueness of responses, generating precise feedback. This feedback is provided to the user via their device, allowing them to attempt further improvements based on this information. The user's progress is continuously tracked, and this data is stored in a database on the server.

[0550] For example, when a user addresses a problem such as "I want to improve my skills in explaining new technologies," continuous training is possible using home devices or mobile devices. The server provides appropriate tasks using prompts. In this example, a prompt might be, "The user wants to improve their skills in explaining new technologies. Please suggest three pieces of advice to help them improve their explanatory skills." This allows users to improve their thinking and explanatory abilities in their daily lives without being limited by physical constraints.

[0551] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0552] Step 1:

[0553] The server receives login information from a user and authenticates the user's account. The input is the user's ID and password, and the output is the authentication success or failure status. Upon successful authentication, the user's skill level and goals are retrieved from the database.

[0554] Step 2:

[0555] The server uses a generative AI model to generate a customized educational plan based on the acquired user information. The input is the user's skill level and goals, and the output is a list of tasks tailored to the user's needs. This list of tasks is individually optimized for each user.

[0556] Step 3:

[0557] The terminal presents the user with tasks based on the educational plan received from the server. The input is a list of tasks included in the educational plan, and the output is the specific problem content displayed on the screen. The user then considers the answer.

[0558] Step 4:

[0559] The user inputs their response to a task via a terminal. The input is the user's thought-out response, and the output is the transmission of that response to the server. After the response is sent, the server is ready for analysis.

[0560] Step 5:

[0561] The server receives user responses and analyzes them using a generative AI model. The input is the user's response, and the output is an evaluation of the response's logic, structure, and originality. In this process, the AI ​​model precisely analyzes the response.

[0562] Step 6:

[0563] The server generates feedback based on the analysis results. The input is the analysis results, and the output is a feedback message to be returned to the user. The feedback includes areas for improvement and points of success.

[0564] Step 7:

[0565] The terminal receives feedback from the server and presents it to the user. The input is the feedback message, and the output is the screen display to the user. The user then reviews this and uses it to make improvements.

[0566] Step 8:

[0567] The server stores the user's learning history in a database and tracks their progress. The input is the details of the learning session, and the output is the updated user learning profile. The progress data is used to optimize future educational plans.

[0568] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0569] This invention combines an emotion engine with a system that enhances the user's thinking and explanatory abilities. This system recognizes the user's emotional state and optimizes the training plan based on that state.

[0570] The user accesses the system through a terminal and performs a login operation. After successful login, the terminal provides an interface for the user to input their current skill level and training goals. This information is sent to the server.

[0571] The server uses an emotion engine to analyze data from the user's camera and microphone to identify the user's emotional state in real time. The emotion engine estimates the user's emotions by combining facial expression analysis, voice tone analysis, and other methods.

[0572] The server takes this emotional data, along with the user's skill level and goals, into consideration to generate a customized training plan. This training plan includes tasks and scenarios optimized for the user's emotions.

[0573] The terminal presents the user with a training plan sent from the server. The user works on the tasks and enters their answers, which are then sent to the server via the terminal. The server analyzes the received answers and evaluates the user's thinking and explanatory abilities. Furthermore, an emotion engine monitors the user's emotional changes and adjusts the training content and difficulty level accordingly.

[0574] For example, if a user is feeling frustrated with an assignment, the server can adjust the difficulty level of the assignment, making it more manageable for the user. In this way, the user load can be appropriately controlled, and an optimal learning environment can be provided.

[0575] The feedback includes not only the results analyzed by the server, but also advice and encouragement tailored to the user's emotional state. This allows users to improve their skills while receiving psychological support.

[0576] As described above, the present invention provides a system that supports effective skill improvement by understanding the user's emotional state in real time and providing individually optimized training.

[0577] The following describes the processing flow.

[0578] Step 1:

[0579] The user accesses the system using a device and enters their login information. The device sends this information to the server to attempt authentication.

[0580] Step 2:

[0581] The server verifies the user's authentication information, and if authentication is successful, it generates a user profile screen and sends it to the device.

[0582] Step 3:

[0583] The user enters information into a form on their device indicating their skill level and training goals, and the device then sends this data to the server.

[0584] Step 4:

[0585] The server activates the emotion engine and acquires emotion data in real time from the user's face and voice. The emotion engine uses image analysis and voice analysis to identify the user's emotional state.

[0586] Step 5:

[0587] The server comprehensively evaluates the user's skill information, emotional data, and goals, and uses an AI algorithm to generate a customized training plan.

[0588] Step 6:

[0589] The device displays a training plan retrieved from the server to the user. The plan includes tasks and learning content tailored to the user's emotional state.

[0590] Step 7:

[0591] Users work on the assigned tasks, enter their answers into their device, and send them to the server. Answers include both text input and multiple-choice questions.

[0592] Step 8:

[0593] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. It also monitors changes in the user's emotions in real time using an emotion engine.

[0594] Step 9:

[0595] The server generates feedback based on collected sentiment data and responses. This feedback includes specific areas for improvement and advice tailored to the user's emotions.

[0596] Step 10:

[0597] The server sends the generated feedback to the terminal, and the terminal displays that feedback to the user.

[0598] Step 11:

[0599] Users can refer to feedback, revise their answers if necessary, and resubmit them. This allows users to iteratively improve their skills.

[0600] Step 12:

[0601] The server integrates data from all training sessions to track user progress. The server periodically generates and provides progress reports to the user.

[0602] (Example 2)

[0603] Next, we will describe Example 2. 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."

[0604] Traditional educational systems have a problem in that they cannot be customized to take into account the emotional state of users when aiming to improve their skills, and therefore cannot reduce the psychological burden on users. In addition, the provision of fixed assignments made it difficult to respond flexibly to the individual learning pace and emotional changes of users.

[0605] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0606] In this invention, the server includes means for receiving user information and generating a customized training plan, means for acquiring the user's emotional state and analyzing it in real time using an emotion engine, and means for dynamically adjusting the content and difficulty level of the tasks in accordance with the user's emotional changes. This enables efficient skill development while providing a training environment optimized for each individual user and offering psychological support to the user.

[0607] "User information" refers to the individual skill level, training goals, and other relevant data of a user that the system receives.

[0608] A "training plan" refers to a set of tasks and scenarios customized based on user information, designed to improve the user's skills.

[0609] "Response" refers to the data of answers or responses that users submit to a task.

[0610] "Cognitive ability" refers to a user's capacity to understand, judge, and interpret information.

[0611] "Explanatory ability" refers to a user's ability to clearly communicate their thoughts and logic to others.

[0612] "Analysis tools" refer to the in-system methods used to evaluate user responses and measure thinking and explanatory abilities.

[0613] "Emotional state" refers to a state based on facial expressions, voice tone, and other factors that reflect the user's psychological and emotional position.

[0614] An "emotion engine" refers to software or hardware that detects and evaluates a user's emotions based on data obtained from cameras and microphones.

[0615] "Feedback" refers to information that includes evaluation results from analytical methods, as well as advice and suggestions for improvement based on the user's emotional state.

[0616] "Psychological support" refers to the act of providing encouragement and advice tailored to the user's emotional state, thereby assisting in the learning process.

[0617] "Dynamic adjustment" refers to changing the content and difficulty level of the task in real time in response to changes in the user's emotions.

[0618] This invention is a system for providing training tailored to the individual learning needs of users. Users log in using an interface provided via a terminal and input their skill level and training goals. The terminal sends this information to a server, and processing begins once the server receives it.

[0619] The server, equipped with an emotion engine, acquires emotional data in real time from the user's camera and microphone. This data is processed through facial expression analysis and voice tone analysis to identify the user's emotional state. This emotional data is combined with user input to create a customized training plan using a generative AI model. The training plan includes tasks and scenarios optimized according to the user's emotional state.

[0620] The terminal presents the user with a training plan from the server. The user uses the terminal to complete tasks, inputs their results, and sends them to the server. The server analyzes the responses and evaluates cognitive and explanatory abilities. Furthermore, the emotion engine continuously monitors the user's emotional changes and can dynamically adjust the content and difficulty of the tasks as needed.

[0621] For example, if a user wants training to improve their presentation skills, the server will provide relaxing tasks when the user is nervous, and conversely, actively incorporate challenging tasks when the user is relaxed, thereby maximizing the learning effect.

[0622] A concrete example of a prompt message would be, "Please suggest an appropriate training plan so that the user can improve their skills while taking their emotions into consideration." In this way, the objective of the present invention is to provide a learning environment in which the user can efficiently improve their skills while being emotionally supported.

[0623] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0624] Step 1:

[0625] Users log in to the system via a terminal and input their skill level and training goals. The terminal sends the entered information to the server. This information is used as data necessary for creating subsequent training plans.

[0626] Step 2:

[0627] The server uses an emotion engine to acquire video and audio data from the user's camera and microphone. Based on this input data, it performs facial expression analysis and voice tone analysis to identify the user's emotional state in real time. Emotional data is generated and used, along with the user's skill level information, to create a training plan.

[0628] Step 3:

[0629] The server generates training plans using a generative AI model. It incorporates optimized tasks and scenarios, taking into account the input skill level, goals, and emotional data. The generated training plans are individually customized to suit the user's needs.

[0630] Step 4:

[0631] The terminal receives the training plan sent from the server and presents it to the user. The user works on the presented tasks and inputs their responses into the terminal. These responses are sent to the server as output data.

[0632] Step 5:

[0633] The server analyzes the user's response data. Using analytical tools, it evaluates the user's cognitive and explanatory abilities. Based on this evaluation, it generates feedback and provides information to encourage further user growth.

[0634] Step 6:

[0635] The emotion engine continuously monitors the user's emotional changes and dynamically adjusts the content and difficulty of the training plan as needed. This process enables real-time responses to the user, allowing for efficient learning while reducing stress.

[0636] (Application Example 2)

[0637] Next, we will explain application example 2. In the following explanation, 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."

[0638] Conventional individualized learning systems present fixed tasks without considering the user's emotional state, leading to problems such as decreased learning efficiency and increased user stress. This invention aims to solve these problems by recognizing the user's emotional state in real time and providing an optimal learning environment tailored to individual needs.

[0639] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0640] In this invention, the server includes means for receiving user information and generating a customized training plan, means for detecting the user's emotional state in real time and optimizing and adjusting the training plan based on the detected emotions, and means for generating feedback that takes into account the evaluation results from the analysis means and the emotional state. This makes it possible to provide a flexible learning plan that meets the individual needs of the user.

[0641] "User information" refers to data that includes a user's individual attributes, skill level, and learning goals.

[0642] A "customized training plan" is a learning plan optimized based on the user's individual needs and skills.

[0643] A "task" is a problem, exercise, or activity that a user must work on, and it forms part of the learning process.

[0644] "Analytical methods for evaluating thinking and explanatory abilities" refer to methods that measure a user's level of logical understanding and explanatory ability based on their responses and reactions.

[0645] "Emotional state" refers to a user's psychological emotional state and reactions, and is a factor that affects learning efficiency.

[0646] "Means of optimization and adjustment" refers to methods that change the learning content and difficulty level in real time according to the user's emotional state and skill level.

[0647] "Feedback" refers to information that provides evaluation results and instructions for improvement regarding the user's learning progress.

[0648] "Means for tracking and adjusting progress" refers to methods for continuously monitoring the user's learning progress and dynamically updating the learning plan based on that.

[0649] The system that implements this application primarily consists of a server and a terminal. First, the user accesses the system through the terminal and logs in. After logging in, the terminal provides the user with an interface for generating a personalized training plan. The user inputs their skill level and learning goals, and this information is sent to the server.

[0650] The server generates a customized training plan based on this user information. Furthermore, the server acquires data from the device's camera and microphone to understand the user's emotional state in real time. This utilizes facial expression and voice analysis libraries such as OpenCV. The server analyzes the user's facial expressions and voice tone to estimate their psychological state.

[0651] Based on the generated training plan and emotional data, the server can adjust the difficulty and content of the tasks. If the user's emotional state changes, the server immediately optimizes the learning tasks. This adjustment reduces the load and helps the user learn comfortably. Feedback includes advice and encouragement tailored to the user's emotions and learning content.

[0652] As a concrete example, if a user becomes frustrated while working on a math problem, the server will immediately lower the difficulty level of the problem and re-present it to the user. Another example of a prompt generated by the AI ​​model is, "How should the difficulty level of the problem be adjusted if the user is feeling frustrated?" This will further enhance the user's learning experience.

[0653] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0654] Step 1:

[0655] Users access the system and log in using a terminal. The terminal displays an interface for entering the user's skill level and learning goals. After the user enters this information, it is sent to the server. This information is the input and the user information sent to the server as output.

[0656] Step 2:

[0657] The server generates a customized training plan based on information received from the user. This process involves referencing past learning history and statistics from a database on the server to select appropriate tasks and content. User information is used as input, and a training plan is generated as output.

[0658] Step 3:

[0659] The server uses an emotion recognition algorithm to analyze camera and microphone data sent from the terminal. Specifically, it utilizes libraries such as OpenCV to analyze facial expressions and estimate the user's emotional state. The input is audio and video data, and the output is the analyzed emotional state.

[0660] Step 4:

[0661] The server combines the generated training plan with emotional state data to present the user with the most suitable task. At this stage, the difficulty level of the task is adjusted according to the emotional state. The training plan and emotional state are used as input, and the optimized task is output.

[0662] Step 5:

[0663] The user works on the presented task and sends their answer to the server via their device. The device acts as an input, forwarding the user's answer to the server. The output is the answer data.

[0664] Step 6:

[0665] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. Logical reasoning and originality are used as evaluation criteria. Evaluation data is generated and output using the user's responses as input.

[0666] Step 7:

[0667] The server generates appropriate feedback based on the evaluation results and the user's emotional state. This feedback includes specific areas for improvement and emotionally responsive advice. The input is evaluation data and emotional state, and the output is feedback information.

[0668] Step 8:

[0669] The device displays the generated feedback to the user and also tracks the user's progress. An example of this prompt is, "How should the difficulty of the task be adjusted if the user is feeling frustrated?" The input is feedback information, and the output is presented to the user.

[0670] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0671] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0672] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0673] [Fourth Embodiment]

[0674] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0675] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0676] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0677] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0678] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0679] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0680] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0681] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0682] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0683] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0684] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0685] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0686] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0687] This invention provides a system for effectively improving users' thinking and explanatory abilities. The system is primarily implemented through interactions between a server, a terminal, and the user.

[0688] Users first access the system via their device and log in. After logging in, users can enter their skill level and training goals. This information is sent to the server via the device.

[0689] The server receives information provided by the user and uses an AI algorithm to generate a customized training plan based on that information. This training plan is designed to improve the user's skills and includes specific tasks and simulated scenarios.

[0690] The generated training plan is presented to the user via a device. The user enters their answers to the presented tasks into the device and sends them to the server.

[0691] The server receives this response and uses AI to evaluate the user's thinking and explanatory abilities. The evaluation process uses indicators such as the logic, structure, and originality of the response. The server then generates feedback based on this evaluation result and provides it to the user through the terminal.

[0692] Users can improve their answers by referring to the feedback they receive. The server also tracks the progress of each training session and generates reports that evaluate the user's growth over time.

[0693] As a concrete example, a user might be given the task of "summarizing a new product's explanation to consumers in three points." By entering their answer and receiving feedback from the server, users can develop the ability to create more refined explanations. Through this process, users can gradually improve their skills.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The user uses a device to access the system and perform a login operation. The device sends the user's authentication information to the server and attempts to log in.

[0697] Step 2:

[0698] The server verifies the received authentication information and authenticates the user. If authentication is successful, the server generates a user profile screen and sends it to the device.

[0699] Step 3:

[0700] The user enters their skill level and training goals on their device. The device then sends this information to the server.

[0701] Step 4:

[0702] The server uses an AI algorithm to generate a personalized training plan based on the user's skill level and goals. The generated plan is then sent to the device.

[0703] Step 5:

[0704] The terminal displays the training plan sent from the server to the user and presents the first assignment.

[0705] Step 6:

[0706] The user enters their answer to the task into their device and sends it to the server.

[0707] Step 7:

[0708] The server receives user responses and analyzes them using AI. It evaluates the content, logic, structure, and originality of the responses.

[0709] Step 8:

[0710] The server generates feedback based on the analysis results. This feedback includes areas for improvement and strengths in the response.

[0711] Step 9:

[0712] The server sends the generated feedback to the terminal, and the terminal displays the feedback to the user.

[0713] Step 10:

[0714] Users can review feedback, revise their responses as needed, and repeat the process.

[0715] Step 11:

[0716] The server stores the results of each user's training session and tracks their progress. It periodically generates reports evaluating the user's performance and provides them to the user via their terminal.

[0717] (Example 1)

[0718] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0719] To effectively improve users' thinking and communication skills, customized training tailored to individual skill levels and goals is necessary. However, conventional systems have limitations in adequately supporting user growth because they do not adequately present or evaluate appropriate tasks, and the quality of feedback is inconsistent.

[0720] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0721] In this invention, the server includes means for receiving user information and creating individually optimized training plans using a generation algorithm, means for presenting tasks based on the training plan, and means for receiving user responses and evaluating thinking and explanatory abilities using a generation model. This enables the provision of appropriate training content according to the user's skill level and detailed, personalized feedback.

[0722] "User information" refers to individual data such as the user's skill level and training goals, and a customized training plan is generated based on this information.

[0723] A "generative algorithm" refers to a set of methods and processes for creating an optimized training plan based on user information.

[0724] A "training plan" refers to a plan that includes specific tasks and simulated scenarios, designed to improve the user's skills.

[0725] "Presentation means" refers to a method or apparatus for the server to display training tasks on the user's connected device.

[0726] "Analysis means" refers to methods or processes used to analyze a user's responses and evaluate their thinking and explanatory abilities.

[0727] A "generative model" refers to an artificial intelligence algorithm or computational model used to evaluate user responses.

[0728] "Feedback" refers to evaluation results and reports of areas for improvement generated based on user responses, providing information to promote user growth.

[0729] "Logical structure" refers to the clarity of the logical framework and thought process in the user's response, and serves as a criterion for evaluation.

[0730] "Information structure" refers to the method of organizing and arranging the information contained in the user's response, and is an element that is paid attention to during evaluation.

[0731] "Original expression" refers to the creativity and unique perspective expressed by the user in their own words, and is considered as part of the evaluation.

[0732] The implementation of this system begins with the user accessing the server via a connecting device (e.g., personal computer, smartphone). The user logs into the system using the connecting device and enters user information such as skill level and training goals. The connecting device uses either a web browser or a dedicated application as its interface.

[0733] The server uses a generative AI model to analyze user information and generate a customized training plan. This generation process utilizes prompts to construct specific tasks and simulated scenarios. The generated training plan is then presented to the user via a connected device.

[0734] Upon receiving a user's input, the server analyzes the response using a generation AI model. The analysis evaluates the response's logical structure, information organization, and unique expression. Based on the analysis results, the server generates feedback for the user and provides improvement suggestions. This feedback is delivered to the user through a connected device.

[0735] As a concrete example, if a user submits an answer to the task "Summarize the explanation of a new product to consumers in three points," the server evaluates the answer and provides feedback from the perspectives of logic and originality. An example of a prompt to be input to the generative AI model is "Evaluate the input explanation and generate feedback including strengths and areas for improvement." Through this system, users can continuously improve their explanatory skills.

[0736] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0737] Step 1:

[0738] The user accesses the system and logs in using a terminal. They enter their username and password on the login screen, and this information is sent to the server. The server performs authentication and initiates a user session. The input is user information, and the output is a notification of successful authentication.

[0739] Step 2:

[0740] Users input their skill level and training goals via a terminal. This information is sent from the terminal to the server. The input is skill level and goal information, and the output is the result of registration in the database.

[0741] Step 3:

[0742] The server creates a training plan using a generated AI model based on the received user information. It inputs user data as prompts into the AI ​​model to generate an optimized training plan. The input consists of user information and prompts, and the output is a custom training plan.

[0743] Step 4:

[0744] The server sends the training plan to the terminal and presents it to the user. The terminal displays the received training plan in a format that is easy for the user to understand. The input is the training plan data, and the output is the screen display content.

[0745] Step 5:

[0746] The user inputs their answer to the presented task into a terminal and sends it to the server. The input is the user's answer, and the output is the data sent to the server.

[0747] Step 6:

[0748] The server evaluates the user's response using a generation AI model. It analyzes the logic, information structure, and originality of the response and generates results. The input is the user's response, and the output is the evaluation result.

[0749] Step 7:

[0750] The server generates feedback based on the evaluation results and provides it to the user via the terminal. The feedback points out specific areas for improvement. The input is the evaluation result, and the output is the feedback data.

[0751] Step 8:

[0752] The server records data from each training session and tracks user progress. This allows it to generate reports that evaluate user growth. The input is session data, and the output is the progress report.

[0753] (Application Example 1)

[0754] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0755] Traditional education systems suffer from a lack of customized programs to effectively improve users' critical thinking and communication skills, as well as insufficient provision of feedback utilizing the results of these assessments. Furthermore, the limited means users have for honing these skills on a daily basis leads to inefficiencies in skill development.

[0756] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0757] In this invention, the server includes means for receiving information and generating a customized educational plan; means for presenting problems based on the educational plan; means for receiving user responses and analyzing them to evaluate thinking and explanatory abilities; means for generating feedback; means for providing feedback to the user and tracking the user's progress; and means for providing the educational plan through home devices or mobile terminals. This enables users to receive training on a daily basis to improve their thinking and explanatory abilities.

[0758] "Means for receiving information and generating customized educational plans" refers to devices or programs that have the function of creating individual educational plans based on data provided by users.

[0759] "Means for presenting problems based on an educational plan" refers to devices or programs that have the function of presenting appropriate tasks or problems to the user in accordance with a generated educational plan.

[0760] "An analytical means for receiving user responses and evaluating thinking and explanatory abilities" refers to a device or program that has the function of measuring a person's thinking and explanatory abilities by acquiring the answers submitted by the user, classifying them, and evaluating them.

[0761] "Means for generating feedback" refers to devices or programs that have the function of generating information and advice to encourage user skill improvement based on analysis results.

[0762] "Means of providing feedback to users and tracking user progress" refers to devices or programs that have the function of sending evaluation results to users and recording and analyzing their subsequent progress.

[0763] "Means of providing educational plans through home devices and mobile terminals" refers to devices or programs that have the function of distributing generated educational plans to users via home electronic devices or portable information terminals.

[0764] The system implementing this invention mainly consists of a server, terminals, and users, and has functions for realizing educational plans on various platforms.

[0765] The server receives information provided by the user and generates a customized educational plan using a generative AI model based on that information. The generated educational plan includes specific tasks designed to improve the user's thinking and explanation abilities. The server can deliver this educational plan to the user via home electronic devices or mobile devices.

[0766] The terminal is responsible for presenting problems to the user based on the received educational plan. The user inputs a response to the presented task. The user's response, received through the terminal, is sent to the server for analysis. Based on this response, the server evaluates the user's thinking and explanatory abilities and generates feedback.

[0767] The server uses AI to analyze the logic, structure, and uniqueness of responses, generating precise feedback. This feedback is provided to the user via their device, allowing them to attempt further improvements based on this information. The user's progress is continuously tracked, and this data is stored in a database on the server.

[0768] For example, when a user addresses a problem such as "I want to improve my skills in explaining new technologies," continuous training is possible using home devices or mobile devices. The server provides appropriate tasks using prompts. In this example, a prompt might be, "The user wants to improve their skills in explaining new technologies. Please suggest three pieces of advice to help them improve their explanatory skills." This allows users to improve their thinking and explanatory abilities in their daily lives without being limited by physical constraints.

[0769] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0770] Step 1:

[0771] The server receives login information from a user and authenticates the user's account. The input is the user's ID and password, and the output is the authentication success or failure status. Upon successful authentication, the user's skill level and goals are retrieved from the database.

[0772] Step 2:

[0773] The server uses a generative AI model to generate a customized educational plan based on the acquired user information. The input is the user's skill level and goals, and the output is a list of tasks tailored to the user's needs. This list of tasks is individually optimized for each user.

[0774] Step 3:

[0775] The terminal presents the user with tasks based on the educational plan received from the server. The input is a list of tasks included in the educational plan, and the output is the specific problem content displayed on the screen. The user then considers the answer.

[0776] Step 4:

[0777] The user inputs their response to a task via a terminal. The input is the user's thought-out response, and the output is the transmission of that response to the server. After the response is sent, the server is ready for analysis.

[0778] Step 5:

[0779] The server receives user responses and analyzes them using a generative AI model. The input is the user's response, and the output is an evaluation of the response's logic, structure, and originality. In this process, the AI ​​model precisely analyzes the response.

[0780] Step 6:

[0781] The server generates feedback based on the analysis results. The input is the analysis results, and the output is a feedback message to be returned to the user. The feedback includes areas for improvement and points of success.

[0782] Step 7:

[0783] The terminal receives feedback from the server and presents it to the user. The input is the feedback message, and the output is the screen display to the user. The user then reviews this and uses it to make improvements.

[0784] Step 8:

[0785] The server stores the user's learning history in a database and tracks their progress. The input is the details of the learning session, and the output is the updated user learning profile. The progress data is used to optimize future educational plans.

[0786] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0787] This invention combines an emotion engine with a system that enhances the user's thinking and explanatory abilities. This system recognizes the user's emotional state and optimizes the training plan based on that state.

[0788] The user accesses the system through a terminal and performs a login operation. After successful login, the terminal provides an interface for the user to input their current skill level and training goals. This information is sent to the server.

[0789] The server uses an emotion engine to analyze data from the user's camera and microphone to identify the user's emotional state in real time. The emotion engine estimates the user's emotions by combining facial expression analysis, voice tone analysis, and other methods.

[0790] The server takes this emotional data, along with the user's skill level and goals, into consideration to generate a customized training plan. This training plan includes tasks and scenarios optimized for the user's emotions.

[0791] The terminal presents the user with a training plan sent from the server. The user works on the tasks and enters their answers, which are then sent to the server via the terminal. The server analyzes the received answers and evaluates the user's thinking and explanatory abilities. Furthermore, an emotion engine monitors the user's emotional changes and adjusts the training content and difficulty level accordingly.

[0792] For example, if a user is feeling frustrated with an assignment, the server can adjust the difficulty level of the assignment, making it more manageable for the user. In this way, the user load can be appropriately controlled, and an optimal learning environment can be provided.

[0793] The feedback includes not only the results analyzed by the server, but also advice and encouragement tailored to the user's emotional state. This allows users to improve their skills while receiving psychological support.

[0794] As described above, the present invention provides a system that supports effective skill improvement by understanding the user's emotional state in real time and providing individually optimized training.

[0795] The following describes the processing flow.

[0796] Step 1:

[0797] The user accesses the system using a device and enters their login information. The device sends this information to the server to attempt authentication.

[0798] Step 2:

[0799] The server verifies the user's authentication information, and if authentication is successful, it generates a user profile screen and sends it to the device.

[0800] Step 3:

[0801] The user enters information into a form on their device indicating their skill level and training goals, and the device then sends this data to the server.

[0802] Step 4:

[0803] The server activates the emotion engine and acquires emotion data in real time from the user's face and voice. The emotion engine uses image analysis and voice analysis to identify the user's emotional state.

[0804] Step 5:

[0805] The server comprehensively evaluates the user's skill information, emotional data, and goals, and uses an AI algorithm to generate a customized training plan.

[0806] Step 6:

[0807] The device displays a training plan retrieved from the server to the user. The plan includes tasks and learning content tailored to the user's emotional state.

[0808] Step 7:

[0809] Users work on the assigned tasks, enter their answers into their device, and send them to the server. Answers include both text input and multiple-choice questions.

[0810] Step 8:

[0811] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. It also monitors changes in the user's emotions in real time using an emotion engine.

[0812] Step 9:

[0813] The server generates feedback based on collected sentiment data and responses. This feedback includes specific areas for improvement and advice tailored to the user's emotions.

[0814] Step 10:

[0815] The server sends the generated feedback to the terminal, and the terminal displays that feedback to the user.

[0816] Step 11:

[0817] Users can refer to feedback, revise their answers if necessary, and resubmit them. This allows users to iteratively improve their skills.

[0818] Step 12:

[0819] The server integrates data from all training sessions to track user progress. The server periodically generates and provides progress reports to the user.

[0820] (Example 2)

[0821] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0822] Traditional educational systems have a problem in that they cannot be customized to take into account the emotional state of users when aiming to improve their skills, and therefore cannot reduce the psychological burden on users. In addition, the provision of fixed assignments made it difficult to respond flexibly to the individual learning pace and emotional changes of users.

[0823] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0824] In this invention, the server includes means for receiving user information and generating a customized training plan, means for acquiring the user's emotional state and analyzing it in real time using an emotion engine, and means for dynamically adjusting the content and difficulty level of the tasks in accordance with the user's emotional changes. This enables efficient skill development while providing a training environment optimized for each individual user and offering psychological support to the user.

[0825] "User information" refers to the individual skill level, training goals, and other relevant data of a user that the system receives.

[0826] A "training plan" refers to a set of tasks and scenarios customized based on user information, designed to improve the user's skills.

[0827] "Response" refers to the data of answers or responses that users submit to a task.

[0828] "Cognitive ability" refers to a user's capacity to understand, judge, and interpret information.

[0829] "Explanatory ability" refers to a user's ability to clearly communicate their thoughts and logic to others.

[0830] "Analysis tools" refer to the in-system methods used to evaluate user responses and measure thinking and explanatory abilities.

[0831] "Emotional state" refers to a state based on facial expressions, voice tone, and other factors that reflect the user's psychological and emotional position.

[0832] An "emotion engine" refers to software or hardware that detects and evaluates a user's emotions based on data obtained from cameras and microphones.

[0833] "Feedback" refers to information that includes evaluation results from analytical methods, as well as advice and suggestions for improvement based on the user's emotional state.

[0834] "Psychological support" refers to the act of providing encouragement and advice tailored to the user's emotional state, thereby assisting in the learning process.

[0835] "Dynamic adjustment" refers to changing the content and difficulty level of the task in real time in response to changes in the user's emotions.

[0836] This invention is a system for providing training tailored to the individual learning needs of users. Users log in using an interface provided via a terminal and input their skill level and training goals. The terminal sends this information to a server, and processing begins once the server receives it.

[0837] The server, equipped with an emotion engine, acquires emotional data in real time from the user's camera and microphone. This data is processed through facial expression analysis and voice tone analysis to identify the user's emotional state. This emotional data is combined with user input to create a customized training plan using a generative AI model. The training plan includes tasks and scenarios optimized according to the user's emotional state.

[0838] The terminal presents the user with a training plan from the server. The user uses the terminal to complete tasks, inputs their results, and sends them to the server. The server analyzes the responses and evaluates cognitive and explanatory abilities. Furthermore, the emotion engine continuously monitors the user's emotional changes and can dynamically adjust the content and difficulty of the tasks as needed.

[0839] For example, if a user wants training to improve their presentation skills, the server will provide relaxing tasks when the user is nervous, and conversely, actively incorporate challenging tasks when the user is relaxed, thereby maximizing the learning effect.

[0840] A concrete example of a prompt message would be, "Please suggest an appropriate training plan so that the user can improve their skills while taking their emotions into consideration." In this way, the objective of the present invention is to provide a learning environment in which the user can efficiently improve their skills while being emotionally supported.

[0841] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0842] Step 1:

[0843] Users log in to the system via a terminal and input their skill level and training goals. The terminal sends the entered information to the server. This information is used as data necessary for creating subsequent training plans.

[0844] Step 2:

[0845] The server uses an emotion engine to acquire video and audio data from the user's camera and microphone. Based on this input data, it performs facial expression analysis and voice tone analysis to identify the user's emotional state in real time. Emotional data is generated and used, along with the user's skill level information, to create a training plan.

[0846] Step 3:

[0847] The server generates training plans using a generative AI model. It incorporates optimized tasks and scenarios, taking into account the input skill level, goals, and emotional data. The generated training plans are individually customized to suit the user's needs.

[0848] Step 4:

[0849] The terminal receives the training plan sent from the server and presents it to the user. The user works on the presented tasks and inputs their responses into the terminal. These responses are sent to the server as output data.

[0850] Step 5:

[0851] The server analyzes the user's response data. Using analytical tools, it evaluates the user's cognitive and explanatory abilities. Based on this evaluation, it generates feedback and provides information to encourage further user growth.

[0852] Step 6:

[0853] The emotion engine continuously monitors the user's emotional changes and dynamically adjusts the content and difficulty of the training plan as needed. This process enables real-time responses to the user, allowing for efficient learning while reducing stress.

[0854] (Application Example 2)

[0855] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0856] Conventional individualized learning systems present fixed tasks without considering the user's emotional state, leading to problems such as decreased learning efficiency and increased user stress. This invention aims to solve these problems by recognizing the user's emotional state in real time and providing an optimal learning environment tailored to individual needs.

[0857] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0858] In this invention, the server includes means for receiving user information and generating a customized training plan, means for detecting the user's emotional state in real time and optimizing and adjusting the training plan based on the detected emotions, and means for generating feedback that takes into account the evaluation results from the analysis means and the emotional state. This makes it possible to provide a flexible learning plan that meets the individual needs of the user.

[0859] "User information" refers to data that includes a user's individual attributes, skill level, and learning goals.

[0860] A "customized training plan" is a learning plan optimized based on the user's individual needs and skills.

[0861] A "task" is a problem, exercise, or activity that a user must work on, and it forms part of the learning process.

[0862] "Analytical methods for evaluating thinking and explanatory abilities" refer to methods that measure a user's level of logical understanding and explanatory ability based on their responses and reactions.

[0863] "Emotional state" refers to a user's psychological emotional state and reactions, and is a factor that affects learning efficiency.

[0864] "Means of optimization and adjustment" refers to methods that change the learning content and difficulty level in real time according to the user's emotional state and skill level.

[0865] "Feedback" refers to information that provides evaluation results and instructions for improvement regarding the user's learning progress.

[0866] "Means for tracking and adjusting progress" refers to methods for continuously monitoring the user's learning progress and dynamically updating the learning plan based on that.

[0867] The system that implements this application primarily consists of a server and a terminal. First, the user accesses the system through the terminal and logs in. After logging in, the terminal provides the user with an interface for generating a personalized training plan. The user inputs their skill level and learning goals, and this information is sent to the server.

[0868] The server generates a customized training plan based on this user information. Furthermore, the server acquires data from the device's camera and microphone to understand the user's emotional state in real time. This utilizes facial expression and voice analysis libraries such as OpenCV. The server analyzes the user's facial expressions and voice tone to estimate their psychological state.

[0869] Based on the generated training plan and emotional data, the server can adjust the difficulty and content of the tasks. If the user's emotional state changes, the server immediately optimizes the learning tasks. This adjustment reduces the load and helps the user learn comfortably. Feedback includes advice and encouragement tailored to the user's emotions and learning content.

[0870] As a concrete example, if a user becomes frustrated while working on a math problem, the server will immediately lower the difficulty level of the problem and re-present it to the user. Another example of a prompt generated by the AI ​​model is, "How should the difficulty level of the problem be adjusted if the user is feeling frustrated?" This will further enhance the user's learning experience.

[0871] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0872] Step 1:

[0873] Users access the system and log in using a terminal. The terminal displays an interface for entering the user's skill level and learning goals. After the user enters this information, it is sent to the server. This information is the input and the user information sent to the server as output.

[0874] Step 2:

[0875] The server generates a customized training plan based on information received from the user. This process involves referencing past learning history and statistics from a database on the server to select appropriate tasks and content. User information is used as input, and a training plan is generated as output.

[0876] Step 3:

[0877] The server uses an emotion recognition algorithm to analyze camera and microphone data sent from the terminal. Specifically, it utilizes libraries such as OpenCV to analyze facial expressions and estimate the user's emotional state. The input is audio and video data, and the output is the analyzed emotional state.

[0878] Step 4:

[0879] The server combines the generated training plan with emotional state data to present the user with the most suitable task. At this stage, the difficulty level of the task is adjusted according to the emotional state. The training plan and emotional state are used as input, and the optimized task is output.

[0880] Step 5:

[0881] The user works on the presented task and sends their answer to the server via their device. The device acts as an input, forwarding the user's answer to the server. The output is the answer data.

[0882] Step 6:

[0883] The server analyzes the user's responses and evaluates their thinking and explanatory abilities. Logical reasoning and originality are used as evaluation criteria. Evaluation data is generated and output using the user's responses as input.

[0884] Step 7:

[0885] The server generates appropriate feedback based on the evaluation results and the user's emotional state. This feedback includes specific areas for improvement and emotionally responsive advice. The input is evaluation data and emotional state, and the output is feedback information.

[0886] Step 8:

[0887] The device displays the generated feedback to the user and also tracks the user's progress. An example of this prompt is, "How should the difficulty of the task be adjusted if the user is feeling frustrated?" The input is feedback information, and the output is presented to the user.

[0888] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0889] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0890] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0891] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0892] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. In the upper and lower directions of the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. Also, the upper side of the concentric circles is where "pleasant" emotions are located, and the lower side is where "unpleasant" emotions are located. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0893] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0894] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0895] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0896] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0897] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0898] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0899] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0900] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0901] 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.

[0902] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0903] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0904] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0905] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0906] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0907] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0908] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0909] The following is further disclosed regarding the embodiments described above.

[0910] (Claim 1)

[0911] A means for receiving user information and generating a customized training plan,

[0912] A means of presenting tasks based on the aforementioned training plan,

[0913] An analytical means for receiving user responses and evaluating thinking and explanatory skills,

[0914] A means for generating feedback based on the evaluation results from the aforementioned analysis means,

[0915] A means of providing the aforementioned feedback to the user and tracking the user's progress,

[0916] A system that includes this.

[0917] (Claim 2)

[0918] The system according to claim 1, further comprising means for adjusting the training plan based on the user's skill level and goals.

[0919] (Claim 3)

[0920] The system according to claim 1, wherein the feedback evaluates the logic, structure, and originality of the user's response and suggests areas for improvement.

[0921] "Example 1"

[0922] (Claim 1)

[0923] A means for receiving user information and creating individually optimized training plans using a generation algorithm,

[0924] A presentation means for presenting tasks based on the aforementioned training plan,

[0925] An analytical means that receives user responses and evaluates thinking ability and explanatory ability using a generative model,

[0926] A means for generating and providing feedback based on the evaluation results obtained by the aforementioned analysis means,

[0927] A means of providing feedback via the user's connected device and tracking the user's progress,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, further comprising means for dynamically adjusting a training plan generated based on the user's skill level and goals.

[0931] (Claim 3)

[0932] The system according to claim 1, wherein the feedback evaluates the logical structure, information organization, and unique expression of the user's response and specifically suggests areas for improvement.

[0933] "Application Example 1"

[0934] (Claim 1)

[0935] A means of receiving information and generating a customized educational plan,

[0936] A means of presenting problems based on the aforementioned educational plan,

[0937] An analytical means for receiving user responses and evaluating thinking ability and explanatory ability,

[0938] A means for generating feedback based on the evaluation results from the aforementioned analysis means,

[0939] A means of providing the aforementioned feedback to the user and tracking the user's progress,

[0940] Means of providing educational plans through home devices and mobile devices,

[0941] A system that includes this.

[0942] (Claim 2)

[0943] The system according to claim 1, further comprising means for adjusting the educational plan based on the user's ability level and goals.

[0944] (Claim 3)

[0945] The system according to claim 1, wherein the feedback evaluates the logic, structure, and originality of the user's response and suggests areas for improvement.

[0946] "Example 2 of combining an emotion engine"

[0947] (Claim 1)

[0948] A means for receiving user information and generating a customized training plan,

[0949] A means of presenting tasks based on the aforementioned training plan,

[0950] An analytical means for receiving user responses and evaluating cognitive and explanatory abilities,

[0951] A method for acquiring the user's emotional state and analyzing it in real time using an emotion engine,

[0952] A means for generating feedback based on the evaluation results and emotional state obtained by the aforementioned analytical means,

[0953] A means of providing psychological support to the user by offering advice tailored to the evaluation results, including the aforementioned feedback, and their emotional state,

[0954] A means for dynamically adjusting the content and difficulty level of the task in response to the user's emotional changes,

[0955] A system that includes this.

[0956] (Claim 2)

[0957] The system according to claim 1, which adjusts the training plan based on the user's skill level and goals and uses the results of analysis by the emotion engine.

[0958] (Claim 3)

[0959] The system according to claim 1, wherein the feedback evaluates the logic, structure, and originality of the user's response, suggests areas for further improvement, and includes encouragement tailored to the user's emotional state.

[0960] "Application example 2 when combining with an emotional engine"

[0961] (Claim 1)

[0962] A means for receiving user information and generating a customized training plan,

[0963] A means of presenting tasks based on the aforementioned training plan,

[0964] An analytical means for receiving user responses and evaluating thinking and explanatory skills,

[0965] A means for detecting the user's emotional state in real time and optimizing and adjusting the training plan based on the detected emotions,

[0966] Means for generating feedback that takes into account the evaluation results and emotional state obtained by the aforementioned analysis means,

[0967] Means for providing the aforementioned feedback to the user and for tracking and adjusting the user's progress,

[0968] A system that includes this.

[0969] (Claim 2)

[0970] The system according to claim 1, further comprising means for adjusting the training plan based on the user's skill level and goals, and for flexibly changing the content or difficulty level of the learning tasks in accordance with the detected emotional state.

[0971] (Claim 3)

[0972] The system according to claim 1, wherein the feedback evaluates the logic, structure, and originality of the user's response and provides individual areas for improvement and motivation according to the user's emotional state. [Explanation of Symbols]

[0973] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving information and generating a customized educational plan, A means of presenting problems based on the aforementioned educational plan, An analytical means for receiving user responses and evaluating thinking ability and explanatory ability, A means for generating feedback based on the evaluation results from the aforementioned analysis means, A means of providing the aforementioned feedback to the user and tracking the user's progress, Means of providing educational plans through home devices and mobile devices, A system that includes this.

2. The system according to claim 1, further comprising means for adjusting the educational plan based on the user's ability level and goals.

3. The system according to claim 1, wherein the feedback evaluates the logic, structure, and originality of the user's response and suggests areas for improvement.

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