system

Abstract

We provide the system. [Solution] A means of receiving the acquisition target and test date from the user, Means for generating a learning plan based on received information and a database, A means of inputting the user's learning progress, A means of evaluating the input learning progress and providing feedback, A means for comparing the received learning record with the generated learning plan, The means for providing the aforementioned feedback, 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, 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] While many individuals have the need to efficiently acquire qualifications and new skills, the conventional learning methods are uniform and it is difficult to create a plan optimized for an individual. Therefore, it is required to provide efficient learning support suitable for individual users by grasping the progress of learning and making necessary corrections.

Means for Solving the Problems

[0005] This invention is a system that receives acquisition goals and exam dates from users and generates a learning plan based on them. This system evaluates the learning progress entered by the user and provides feedback as needed, thereby supporting efficient learning by adjusting the plan. In addition, it provides tests to assess understanding and handles procedures such as purchasing learning materials and applying for exams on behalf of the user, thereby reducing the learning burden and enabling personalized support. In this way, the aim is to solve conventional problems and increase the success rate of obtaining qualifications.

[0006] A "user" refers to an individual who uses the system for the purpose of obtaining qualifications or acquiring skills.

[0007] "Acquisition goals" refer to specific qualifications or skill targets that the user aims to achieve.

[0008] "Exam date" refers to the date on which the user's target qualification exam or evaluation is scheduled to take place.

[0009] A "learning plan" refers to a plan that takes into account the user's learning goals and exam date, and defines the optimal learning content and schedule.

[0010] "Learning progress" refers to information that indicates the quantity and quality of learning that a user has performed based on their learning plan.

[0011] "Feedback" refers to information that provides necessary adjustments and advice based on the user's learning progress and test results.

[0012] "Comprehension level" refers to an indicator that shows how well a user has mastered the material they have learned.

[0013] "Purchasing learning materials" refers to the process of acquiring books and digital content necessary for learning.

[0014] "Exam application" refers to the registration process for taking a qualification exam.

[0015] "Test" refers to a test format in which questions or problems are set for the purpose of evaluating the user's understanding level.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Best Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, a 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.

[0020] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, a 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, and the like.

[0022] 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).

[0023] 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."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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".

[0037] This invention is a support system for users to efficiently acquire qualifications and skills, and is realized through the exchange of information between a terminal, a server, and the user. The operation of this system is described below in natural language.

[0038] First, the user uses their device to input their desired certification goal and exam date into the system. The device sends this information to the server, which then uses the received data to retrieve the necessary learning items and schedule from its database.

[0039] The server uses an AI algorithm to generate an efficient learning plan based on the entered goals and dates. This plan is optimized for the user, taking into account their past learning history and common learning methods. The generated learning plan is sent to the device and presented to the user.

[0040] Users input their daily learning progress into their devices, which the server receives. The server compares this learning progress to the existing plan and evaluates whether the progress is on schedule. If progress is behind, the server provides feedback to the user, including adjustments to the plan and additional learning items.

[0041] Furthermore, the server regularly conducts online tests to assess understanding and has a mechanism to further adjust the learning plan based on the results. This allows users to continue learning flexibly according to their own level of understanding.

[0042] Furthermore, the system can handle the purchase of necessary learning materials and the application process for exams. The server retrieves the necessary information and automates these tasks based on user instructions. However, an interface is also provided for user verification as needed.

[0043] For example, if a user aims to obtain a TOEIC qualification, they can input "TOEIC" and "Scheduled to take the exam on November 30, 2023" into their device. The server will then create a corresponding study plan, manage daily progress, and provide appropriate feedback. In this format, users can receive personalized learning support, which helps them efficiently obtain the qualification.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The user enters their desired certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0047] Step 2:

[0048] The server searches its database for relevant information based on the received credentials and exam date. This includes the learning items required to obtain the certification and an estimate of the appropriate study time.

[0049] Step 3:

[0050] The server uses an AI algorithm to generate an efficient study plan, working backward from the user's certification goal and exam date. This study plan is customized to take into account the user's past learning history and common success stories.

[0051] Step 4:

[0052] The server sends the generated learning plan to the terminal and presents it to the user. The user then proceeds with their learning based on this plan.

[0053] Step 5:

[0054] Users input their daily learning progress into their device. The device then sends this data to the server.

[0055] Step 6:

[0056] The server compares the received learning progress data with the existing learning plan and evaluates the progress. If there is a discrepancy between the plan and the actual progress, it adjusts the learning plan.

[0057] Step 7:

[0058] The server generates feedback based on the evaluation results. This feedback includes analyzing the causes of delays in the user's progress and modifying the learning method.

[0059] Step 8:

[0060] The server periodically provides users with online tests to measure their understanding. The test results are analyzed by the server, and the learning plan is modified as needed.

[0061] Step 9:

[0062] The server handles the purchase of necessary learning materials and exam registration on behalf of the user. This creates an environment where users can focus on their studies.

[0063] Step 10:

[0064] The user receives feedback from the server and, based on the provided learning plan or correction plan, begins the next learning session.

[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] Conventional learning support systems struggled to efficiently provide personalized learning plans, and their flexible feedback and plan adjustments based on progress were insufficient. Furthermore, the automation of comprehension assessments and learning material acquisition procedures was limited, making it difficult for users to have a consistent learning experience.

[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 goal achievement information and a specified date from the user, means for generating a learning plan based on the received information and stored information, and means for optimizing the learning plan using a generative AI model to realize flexible learning. This enables the provision of a learning plan optimized for each user and rapid feedback according to progress.

[0070] "Goal achievement information" refers to information about the specific goals and qualifications that the user aims to achieve.

[0071] The "specified date" refers to the specific date by which the user wants to achieve their goal.

[0072] "Accumulated information" refers to all data that has been stored in advance, such as past learning history and general learning methods.

[0073] A "learning plan" refers to a specific learning schedule and arrangement of items that enable a user to efficiently achieve their goals.

[0074] "Progress status" refers to information indicating whether the user's learning is progressing as planned.

[0075] "Feedback" refers to information that includes evaluations and advice tailored to the user's learning progress.

[0076] "Generative AI models" refer to artificial intelligence technologies used to generate effective learning plans for users.

[0077] "Evaluation procedures" refer to the process of tests and examinations conducted to measure the user's level of understanding.

[0078] "Learning materials" refer to the documents and learning materials that users need to progress in their studies.

[0079] "Qualification examination procedures" refers to the application process and related procedures for examinations that users wish to take.

[0080] This invention is a system that enables users to efficiently acquire qualifications and skills. This system consists of users, terminals, and a server, and functions through the transmission and reception of information. Embodiments of the invention are shown below.

[0081] First, the user uses their device to enter their desired qualification goal and exam date. This device refers to a standard computer or smartphone, designed to allow users to easily input data using its interface. For example, if a user aims for the TOEIC qualification, they would enter information such as "TOEIC" and "Scheduled to take the exam on November 30, 2023." This allows the server to understand the user's goal.

[0082] The terminal sends the user's input data to the server. The server receives this information and retrieves the necessary learning items and schedule from the database. Databases such as MongoDB and MySQL (registered trademark) are used. The server uses a generative AI model to generate an efficient learning plan based on the user's goals and specified dates. This generative AI model is implemented in Python or R and programmed to take the user's past learning history into consideration.

[0083] The generated learning plan is sent to the device and presented to the user. The plan can be provided using formats such as Adobe PDF. The user inputs their daily learning progress into the device, which is then sent to the server. The server evaluates the progress data and provides feedback as needed. This feedback may include adjusting the plan or adding learning content if progress is not on schedule.

[0084] Furthermore, the server administers online tests to assess comprehension and further adjusts the learning plan based on the results. LMS such as Moodle can be used for these online tests.

[0085] Furthermore, based on user instructions, the server automates the process of purchasing necessary learning materials and applying for certification exams through APIs on e-commerce sites and the official websites of examination organizations. Users can confirm these procedures and easily perform the necessary actions through an interface on their device.

[0086] An example of a prompt message is, "Please generate a study plan to obtain a TOEIC qualification by November 30, 2023." In this way, users can receive optimal learning support tailored to their goals.

[0087] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0088] Step 1:

[0089] The user enters their qualification goal and exam date via a terminal. The terminal receives this information and sends the entered data to the server. This input data may include, for example, "TOEIC" and "November 30, 2023". The server prepares this information for the next processing step.

[0090] Step 2:

[0091] Based on the goal and exam date data received from the terminal, the server searches the database to retrieve the relevant learning items and schedule from the stored information. The database contains qualification details and recommended schedules. The server extracts the relevant information from the database and retains it for use in the next step.

[0092] Step 3:

[0093] The server uses a generative AI model to generate an optimal learning plan based on the acquired learning items and schedule, as well as the user's past learning history. The input is the data acquired in the previous step and the user's history, and the output is a learning plan customized for the user. In this process, the AI ​​algorithm analyzes the data and creates an efficient plan.

[0094] Step 4:

[0095] The server sends the generated learning plan to the device. The device prepares to display the received data to the user. This plan is output in a format such as PDF, making it easy for the user to review. The device updates the UI appropriately so that the plan can be presented to the user.

[0096] Step 5:

[0097] Users input their daily learning progress into their device. The device receives this data and sends it to the server. The input data includes the items studied and the number of hours spent studying, and this information is used by the server to evaluate the user's progress.

[0098] Step 6:

[0099] The server compares the received learning progress with the existing learning plan to determine if the progress is on track. The server performs a comparison and, if there are problems with the progress, generates revisions to the plan or additional learning tasks. The output is feedback information, which is used in the next step.

[0100] Step 7:

[0101] The server sends the generated feedback to the terminal and presents it to the user. The terminal displays the feedback to the user, allowing them to understand their next learning actions. This feedback includes suggestions for adjusting the learning plan and recommended learning content.

[0102] Step 8:

[0103] The server periodically administers online tests to assess understanding. These tests are delivered to the user via a terminal, and the results are sent back to the server. The input is the test result, and the output is the plan adjustment instruction used in the next step.

[0104] Step 9:

[0105] The server further adjusts the learning plan based on the test results. The server then utilizes the AI ​​model again to generate a newly optimized learning plan. This plan is sent to the device as the basis for the next learning session.

[0106] (Application Example 1)

[0107] 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."

[0108] There is a lack of efficient planning for obtaining qualifications and acquiring skills, as well as a lack of smooth means for purchasing learning materials and applying for exams. Furthermore, insufficient feedback and adjustments to plans based on individual users' learning progress are also problematic.

[0109] 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.

[0110] In this invention, the server includes means for performing electronic payments for purchasing learning materials with an external system, means for automating the procedure for applying for exams, and means for evaluating the user's learning progress and providing feedback. This allows users to consistently purchase necessary learning materials and apply for exams based on an individually optimized learning plan without any hassle.

[0111] A "user" is an individual or group that uses the system with the aim of obtaining qualifications or acquiring skills.

[0112] "Acquisition goals" refer to the specific qualifications and skills that the user wants to achieve.

[0113] "Exam date" refers to the day the user is scheduled to take the exam.

[0114] A "data set" is accumulated data that includes information about a user's learning history and learning methods.

[0115] A "learning plan" is a specific learning schedule created based on the user's learning goals and schedule.

[0116] "Learning progress" refers to the progress a user has made in achieving their learning plan.

[0117] "Electronic payment" refers to a digital payment method used for purchasing educational materials and services online.

[0118] "Automation" means minimizing manual work and having a system automatically execute the process.

[0119] To implement a system based on this invention, a smartphone or tablet is first required as hardware. The user uses this device to input their qualification goals and exam dates. The application on the device then transmits this information to a server.

[0120] The server operates software built using Python (Django framework) and JavaScript (React Native). The server generates efficient learning plans using generative AI models and optimizes these plans for the user based on information retrieved from a database. Furthermore, to manage learning progress, it receives daily learning results from the device and provides progress evaluations and feedback.

[0121] Furthermore, the server integrates with an online payment system to automate the purchase of necessary learning materials. Exam registration is also handled automatically by the server communicating with an external exam management system.

[0122] As a concrete example, if a user aims to obtain the Fundamental Information Technology Engineer certification and enters "Fundamental Information Technology Engineer" and "Scheduled to take the exam on April 15, 2024," the server will create a study plan tailored to the qualification and smoothly handle the purchase of study materials and exam registration. An example of a prompt message to the generating AI model is: "Once the user enters the qualification name and desired exam date, create an optimal study plan, list the necessary study materials, and instruct the user to proceed with the purchase."

[0123] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0124] Step 1:

[0125] The user launches the application on their smartphone or tablet and enters their desired certification goal and exam date. The entered information is sent from the device to the server. The input here consists of the "certification name" and "exam date," and the output is the transmission of data to the server.

[0126] Step 2:

[0127] The server matches the received qualification name and exam date against the database and retrieves relevant learning items and past learning data. In this step, the input is qualification information, and the output is related learning data. The server inputs this data into a generating AI model to generate an efficient learning plan.

[0128] Step 3:

[0129] The generated learning plan is sent from the server to the terminal and presented to the user. The user then begins their daily learning based on this plan. Here, the input is the generated learning plan, and the output is what is presented to the user.

[0130] Step 4:

[0131] The user inputs their learning progress into the application daily. The device sends this progress data to the server. In this step, the input is learning progress data, and the output is transmission to the server.

[0132] Step 5:

[0133] The server compares the received progress data with the existing learning plan and evaluates whether the progress is on schedule. If the evaluation shows that the progress is behind schedule, it uses the generative AI model again to provide feedback and adjust the plan, and then sends it to the terminal. In this step, the progress data is the input, and the evaluation results and feedback are the outputs.

[0134] Step 6:

[0135] To support the user's learning progress, the server automatically purchases learning materials using an external electronic payment system. During this process, a generative AI model selects materials suitable for the user. The input is the required learning material information, and the output is a report confirming the purchase is complete.

[0136] Step 7:

[0137] As the exam date approaches, the server communicates with the exam management system to automate the exam registration process. The server inputs the exam information and notifies the user that the registration is complete. The input is the exam information, and the output is a registration confirmation.

[0138] 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.

[0139] This invention provides a system that enables users to acquire qualifications and skills in an efficient and personalized manner, and also provides emotional support by utilizing an emotion engine. Specifically, it links a terminal, a server, and an emotion engine to provide learning support tailored to the user's state.

[0140] First, the user enters their certification goals and exam dates into the system using a terminal. The terminal sends this information to the server, which then retrieves relevant information from its database based on the received certification information and exam dates.

[0141] The server uses an AI algorithm to generate an optimal learning plan, taking into account the user's goals and received data. A new emotion engine is now utilized, considering the user's emotional state as a component of the learning plan. For example, if the user is showing anxiety, adjustments are made, such as adding easier learning items to build confidence at the beginning.

[0142] Users input their daily learning progress into their devices and send it from their devices to the server. The server compares the user's learning progress data with their learning plan and evaluates their progress. This evaluation process also utilizes an emotion engine to provide optimal feedback tailored to the user's emotional state. Specifically, if a user is in a situation where they are likely to become discouraged, the system will make adjustments such as sending encouraging messages.

[0143] The server periodically provides tests to measure the user's understanding. The test results are analyzed by the server, and further learning plans are adjusted based on the user's level of understanding. The system also tracks the user's emotional state in real time and provides interactions that enhance motivation based on that state.

[0144] For example, if a user wants to obtain a math qualification and sets the goal of "taking the exam on December 1, 2023," the system will generate a study plan tailored to that goal. When the user is feeling stressed, the emotion engine recognizes this and adjusts the plan by including relaxing content or lighter tasks.

[0145] Furthermore, the system also supports the purchase of learning materials and the application process for exams, providing users with an environment where they can concentrate on their studies without being burdened by administrative procedures unrelated to learning. In this way, the entire system is designed to flexibly respond to users' needs to achieve the best possible results.

[0146] The following describes the processing flow.

[0147] Step 1:

[0148] The user enters their certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0149] Step 2:

[0150] The server uses the received information to retrieve relevant data from the database. This allows it to obtain the learning items and basic schedule proposals required for certification.

[0151] Step 3:

[0152] The server uses an emotion engine to evaluate the user's current emotional state. This takes into account the user's past emotional data and current input.

[0153] Step 4:

[0154] The server generates a personalized learning plan based on acquired data and emotional state through an AI algorithm. This plan is designed to maximize learning efficiency and minimize user stress.

[0155] Step 5:

[0156] The user enters their daily learning progress into the device. The device then sends the entered data to the server.

[0157] Step 6:

[0158] The server compares and evaluates the user's learning progress against the learning plan in real time, and also takes into account the user's emotional state at that time through an emotion engine.

[0159] Step 7:

[0160] Based on the evaluation results, the server generates feedback, taking into account the user's emotions, and sends it to the device. This feedback may include encouraging messages or modifications to the learning plan.

[0161] Step 8:

[0162] The server periodically provides users with tests to assess their understanding. The test results are analyzed by the server, and learning plans and feedback are adjusted according to the user's level of understanding.

[0163] Step 9:

[0164] When a user experiences stress, the server uses an emotion engine to recognize the situation and provides suggestions for enjoyable activities or recommendations for breaks via the user's device to improve their motivation.

[0165] Step 10:

[0166] The user receives feedback and an updated learning plan from the server and proceeds with the next learning session accordingly.

[0167] (Example 2)

[0168] 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".

[0169] In modern society, acquiring qualifications and skills requires flexible learning tailored to individual lifestyles and emotional states. However, conventional systems have difficulty providing learning plans that take into account the emotional state of individual users, resulting in situations where the effectiveness of learning is not maximized. Furthermore, the burden of administrative procedures in learning management is also a problem.

[0170] 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.

[0171] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and knowledge base, and means for analyzing the emotional state and reflecting it in the learning plan. This makes it possible to provide an optimal learning plan adapted to the user's individual emotional state, thereby improving the effectiveness of learning while reducing the burden of administrative procedures.

[0172] A "user" is an individual who aims to acquire qualifications or skills, and who uses the system to achieve their learning goals.

[0173] "Acquisition goals" refer to specific qualifications or skills that a user aims to achieve, and serve as indicators when creating a learning plan.

[0174] The "exam date" is the date on which the user takes an exam or assessment to achieve their set goals, and it serves as the basis for determining the schedule of their learning plan.

[0175] A "learning plan" is a specific schedule and curriculum generated to support the user in achieving their learning goals, and it is structured taking into account the information received and the user's emotional state.

[0176] "Emotional state" refers to the psychological state of a user during their learning process, and includes data such as anxiety and fluctuations in motivation.

[0177] "Feedback" refers to information and advice provided based on the user's learning progress and emotional state, and is a support measure to improve learning effectiveness.

[0178] "Means" refer to the specific methods or processes used to achieve a particular objective, and to the technical functions performed within a system.

[0179] This invention is a system for users to receive support in an efficient and personalized manner for obtaining qualifications and acquiring skills. The system mainly consists of a terminal, a server, an emotion engine, and related software modules.

[0180] First, the user uses their device to input their qualification goals and exam dates. The device then sends this information to a server via the internet. Based on the received information, the server retrieves relevant information from a designated database and uses an AI algorithm to generate an optimal learning plan for the user. This AI algorithm may include, for example, machine learning models or natural language processing models.

[0181] In addition, the server utilizes an emotion engine to dynamically adjust the learning plan according to the user's emotional state. This emotion engine infers the user's emotional state from their input data and learning history, and can change the learning content as needed. For example, if the user shows anxiety, the system will adjust the learning to prioritize easier and more confidence-building learning items.

[0182] Users record their learning progress on their devices, and this data is sent back to the server. The server analyzes this progress data and uses an emotion engine to provide personalized feedback to the user. Specific feedback may include encouraging messages or additional tasks to reinforce knowledge retention.

[0183] Furthermore, the server provides regular tests to measure the user's understanding and further adjusts the learning plan based on the test results. The system also supports administrative procedures such as purchasing learning materials and registering for exams, providing an environment where users can concentrate on their studies.

[0184] For example, if a user wants to obtain a mathematics qualification and sets the goal of "taking the exam on December 1, 2023," the server will generate a study plan tailored to that goal. At the same time, the generating AI model will suggest the optimal study approach using prompts such as, "The user is planning to study for a qualification exam; what methods can be suggested to reduce stress?"

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] The user uses a terminal to enter their certification goals and exam dates. This input serves as foundational data to identify the user's learning needs. The terminal sends this data to the server. As output, the server records the user's goals and exam dates.

[0188] Step 2:

[0189] Based on the received target and exam date, the server retrieves relevant information from the database. This includes data on the requirements for the relevant qualification and recommended study content. The server inputs this information into an AI algorithm to generate an optimal study plan. The output is a personalized study plan.

[0190] Step 3:

[0191] The server uses an emotion engine to understand the user's emotional state. It analyzes the user's past learning history and current input data to estimate their psychological state. Based on this emotional data, the server adjusts the learning plan. The input is the user's history and current data, and the output is the adjusted learning plan that takes the emotional state into account.

[0192] Step 4:

[0193] Users record their learning progress on their devices on a daily basis. This data is sent to a server, which receives it. The progress data is compared to the learning plan, and the current learning status is evaluated. The server analyzes the input data, re-evaluates the plan as needed, and generates feedback. As output, the user is provided with this feedback.

[0194] Step 5:

[0195] The server provides users with periodic tests to measure their understanding. Users take the tests from their devices and send the results to the server. The server analyzes the test results and further adjusts the learning plan. The input to this process is the test results, and the output is the updated learning plan.

[0196] Step 6:

[0197] The server handles tasks such as purchasing learning materials and registering for exams, allowing users to focus on their studies. It also provides incentives to maintain motivation. This is designed to allow users to concentrate on learning without being distracted by administrative tasks, eliminating the need for manual input. The output is smooth support regarding the content and procedures the user needs to learn.

[0198] (Application Example 2)

[0199] 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".

[0200] For users aiming to acquire qualifications or skills, there is a need to improve learning efficiency while simultaneously providing motivation and stress reduction tailored to their individual emotional state. Traditional learning systems cannot provide appropriate feedback or automatically adjust learning plans in response to the user's progress and emotional state, resulting in challenges such as efforts not leading to results or a lack of sustained motivation.

[0201] 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.

[0202] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and a database, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan accordingly. This enables the provision of an optimal learning plan that takes into account the user's individual learning progress and emotional state, as well as effective feedback based on emotions.

[0203] "Means for receiving acquisition goals and examination dates from users" refers to methods and devices for users to input their desired qualifications or skills and the dates for those examinations into a system via a digital terminal or similar device, and then receive that information.

[0204] "Means for generating a learning plan" refers to algorithms and devices that formulate the most effective learning schedule and methods for the user based on the received target data, exam dates, and database contents.

[0205] An "emotion engine" is a function or technology that analyzes a user's current emotional state and provides appropriate feedback or plan adjustments accordingly.

[0206] "Means for analyzing a user's emotional state and adjusting the learning plan based on that" refers to methods and devices for monitoring changes and states in a user's emotions and optimizing the content and progress of learning accordingly.

[0207] "Means of providing user-optimized interactions" refers to functions or processes for presenting dialogues and instructions that are adapted to the user's learning progress and emotional state.

[0208] "Means for acting as an agent for purchasing study materials and applying for exams" refers to methods and devices that perform these procedures on behalf of users, in order to reduce the time and effort required for users to manually purchase study materials and apply for exams.

[0209] "Means of suggesting breaks and motivational boosts" refers to functions and methods for encouraging users to take appropriate breaks or providing advice to boost their motivation when they are stuck or tired during their learning.

[0210] The system implementing this invention uses an advanced educational support system that integrates an emotion engine to personalize and efficiently facilitate user qualification acquisition and skill development.

[0211] First, the user enters their learning goals and exam dates into the system using their device. This information is transmitted to the server via the network. Based on the received information and its database, the server uses an AI algorithm to generate an optimal learning plan. At this time, the emotion engine analyzes the user's emotional data in real time and incorporates it into the generated learning plan.

[0212] The system uses programming languages ​​and frameworks such as Python and TENSORFLOW® to record and evaluate the user's learning progress. Progress information entered from the terminal is compared and evaluated on the server, and feedback is provided according to the user's emotional state. For example, if the user is losing motivation, the server may suggest that the user take a break or send an encouraging message.

[0213] The system supports users in purchasing learning materials and registering for exams, providing an environment where users can focus on their studies. This automated process reduces user effort and promotes efficient learning.

[0214] For example, if a user is feeling anxious about an upcoming certification exam, the system can suggest a relaxing plan, and the reduced stress will improve learning efficiency. Another prompt for the generative AI model is: "What kind of strategies would be effective in analyzing the user's current emotional state and providing learning approaches and feedback appropriate to that state? Please give specific examples."

[0215] In this way, by responding flexibly based on the user's emotional state, we provide a format that enables the best possible learning outcomes.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The user enters their acquisition goal and test date using a terminal. The entered data is transmitted to the server via the network. This input information serves as foundational data for subsequent processing.

[0219] Step 2:

[0220] Based on the received goals and exam dates, the server extracts the relevant data from the database. This provides the foundation for developing the optimal learning plan for the user.

[0221] Step 3:

[0222] The server uses an AI algorithm to generate a detailed learning plan based on the extracted data. During this process, the plan is customized to the user's skill level. The output is the generation of daily learning tasks based on the plan.

[0223] Step 4:

[0224] The server utilizes an emotion engine to analyze the user's emotional state. This analysis uses sensor information received from the terminal and user input. In this step, data calculations are performed according to the emotional state, and adjustments are made to the learning plan.

[0225] Step 5:

[0226] Users input their daily learning progress into their device. This input is sent to the server. The input here reflects the user's actual progress.

[0227] Step 6:

[0228] The server compares the user's learning progress with the generated learning plan. It evaluates how the progress stands against the plan and determines appropriate feedback based on that evaluation. As output, text messages and suggestions are generated for the user.

[0229] Step 7:

[0230] The server sends feedback to the terminal, tailored to the progress assessment. This feedback may include encouraging messages or suggestions for the next task.

[0231] Step 8:

[0232] The server periodically generates and sends tests to the user's terminal to assess their understanding. In this step, test materials are extracted from a database and adjusted to a difficulty level appropriate for the user.

[0233] Step 9:

[0234] The user enters their test results into a terminal, and that data is sent to a server. The server then re-evaluates the user's understanding and adjusts the learning plan further as needed.

[0235] Through these steps, we create a system that allows users to learn in a flexible and personalized way, tailored to their emotional state and progress.

[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 is a support system for users to efficiently acquire qualifications and skills, and is realized through the exchange of information between a terminal, a server, and the user. The operation of this system is described below in natural language.

[0253] First, the user uses their device to input their desired certification goal and exam date into the system. The device sends this information to the server, which then uses the received data to retrieve the necessary learning items and schedule from its database.

[0254] The server uses an AI algorithm to generate an efficient learning plan based on the entered goals and dates. This plan is optimized for the user, taking into account their past learning history and common learning methods. The generated learning plan is sent to the device and presented to the user.

[0255] Users input their daily learning progress into their devices, which the server receives. The server compares this learning progress to the existing plan and evaluates whether the progress is on schedule. If progress is behind, the server provides feedback to the user, including adjustments to the plan and additional learning items.

[0256] Furthermore, the server regularly conducts online tests to assess understanding and has a mechanism to further adjust the learning plan based on the results. This allows users to continue learning flexibly according to their own level of understanding.

[0257] Furthermore, the system can handle the purchase of necessary learning materials and the application process for exams. The server retrieves the necessary information and automates these tasks based on user instructions. However, an interface is also provided for user verification as needed.

[0258] For example, if a user aims to obtain a TOEIC qualification, they can input "TOEIC" and "Scheduled to take the exam on November 30, 2023" into their device. The server will then create a corresponding study plan, manage daily progress, and provide appropriate feedback. In this format, users can receive personalized learning support, which helps them efficiently obtain the qualification.

[0259] The following describes the processing flow.

[0260] Step 1:

[0261] The user enters their desired certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0262] Step 2:

[0263] The server searches its database for relevant information based on the received credentials and exam date. This includes the learning items required to obtain the certification and an estimate of the appropriate study time.

[0264] Step 3:

[0265] The server uses an AI algorithm to generate an efficient study plan, working backward from the user's certification goal and exam date. This study plan is customized to take into account the user's past learning history and common success stories.

[0266] Step 4:

[0267] The server sends the generated learning plan to the terminal and presents it to the user. The user then proceeds with their learning based on this plan.

[0268] Step 5:

[0269] Users input their daily learning progress into their device. The device then sends this data to the server.

[0270] Step 6:

[0271] The server compares the received learning progress data with the existing learning plan and evaluates the progress. If there is a discrepancy between the plan and the actual progress, it adjusts the learning plan.

[0272] Step 7:

[0273] The server generates feedback based on the evaluation results. This feedback includes analyzing the causes of delays in the user's progress and modifying the learning method.

[0274] Step 8:

[0275] The server regularly provides an online test for measuring the user's understanding level. The test results are analyzed by the server, and the learning plan is modified as necessary.

[0276] Step 9:

[0277] The server undertakes the purchase procedures for the teaching materials required by the user and the proxy application for the test. This creates an environment where the user can concentrate on learning.

[0278] Step 10:

[0279] The user receives feedback from the server and starts the next learning based on the provided learning plan or the modified plan.

[0280] (Example 1)

[0281] 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".

[0282] Conventional learning support systems have difficulty efficiently providing a learning plan optimized for each user, and flexible feedback and plan adjustment according to the progress situation are also insufficient. In addition, the evaluation of the understanding level and the automation of the acquisition procedures for learning materials are also limited, making it difficult for users to obtain a consistent learning experience.

[0283] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0284] In this invention, the server includes means for receiving target achievement information and a specified date from the user, means for generating a learning plan based on the received information and the accumulated information, and means for optimizing the learning plan using a generated AI model to realize flexible learning. This enables the provision of a learning plan optimized for each user and rapid feedback according to the progress.

[0285] "Goal achievement information" refers to information regarding specific goals or qualifications that a user aims to achieve.

[0286] "Specified date" indicates the specific date when the user wants to achieve the goal.

[0287] "Accumulated information" refers to all pre-stored data such as past learning histories and general learning methods.

[0288] "Learning plan" refers to the specific learning schedule and arrangement of items for the user to efficiently achieve the goal.

[0289] "Progress status" is information indicating whether the user's learning is progressing as planned.

[0290] "Feedback" refers to information including evaluations and advice according to the user's learning situation.

[0291] "Generative AI model" refers to artificial intelligence technology used to generate an effective learning plan for the user.

[0292] "Evaluation procedure" refers to the process of tests and examinations conducted to measure the user's understanding.

[0293] "Learning materials" refer to materials and teaching materials necessary for the user to proceed with learning. <000‎0928> "Qualification examination procedure" refers to the application and related procedures for the examination that the user takes.

[0295] The present invention is a system for the user to efficiently proceed with qualification acquisition and skill acquisition. This system is composed of a user, a terminal, and a server, and functions through the transmission and reception of information. Embodiments of the invention are shown below.

[0296] First, the user uses their device to enter their desired qualification goal and exam date. This device refers to a standard computer or smartphone, designed to allow users to easily input data using its interface. For example, if a user aims for the TOEIC qualification, they would enter information such as "TOEIC" and "Scheduled to take the exam on November 30, 2023." This allows the server to understand the user's goal.

[0297] The terminal sends the user's input data to the server. The server receives this information and retrieves the necessary learning items and schedule from the database. Databases such as MongoDB and MySQL are used. The server uses a generative AI model to generate an efficient learning plan based on the user's goals and specified dates. This generative AI model is implemented in Python or R and programmed to take the user's past learning history into account.

[0298] The generated learning plan is sent to the device and presented to the user. The plan can be provided using formats such as Adobe PDF. The user inputs their daily learning progress into the device, which is then sent to the server. The server evaluates the progress data and provides feedback as needed. This feedback may include adjusting the plan or adding learning content if progress is not on schedule.

[0299] Furthermore, the server administers online tests to assess comprehension and further adjusts the learning plan based on the results. LMS such as Moodle can be used for these online tests.

[0300] Furthermore, based on user instructions, the server automates the process of purchasing necessary learning materials and applying for certification exams through APIs on e-commerce sites and the official websites of examination organizations. Users can confirm these procedures and easily perform the necessary actions through an interface on their device.

[0301] Examples of prompt sentences include "Please generate a study plan to obtain a TOEIC qualification by November 30, 2023." In this way, users can receive optimal learning support according to their goals.

[0302] The flow of the specific process in Example 1 will be described using FIG. 11.

[0303] Step 1:

[0304] The user inputs the qualification acquisition goal and the test date via the terminal. The terminal receives this and sends the input data to the server. This input data includes, for example, "TOEIC" and "November 30, 2023". The server prepares this information for the next process.

[0305] Step 2:

[0306] Based on the goal and test date data received from the terminal, the server searches the database to obtain the corresponding learning items and schedule from the accumulated information. Here, the database stores qualification details and recommended schedules. The server extracts the corresponding information from the database and holds it for use in the next step.

[0307] Step 3:

[0308] The server uses the generation AI model to generate an optimal learning plan based on the acquired learning items and schedule, and the user's past learning history. The input is the data obtained in the previous step and the user's history, and the output is a learning plan customized for the user. In this process, the AI algorithm analyzes the data and makes an efficient plan.

[0309] Step 4:

[0310] The server sends the generated learning plan to the device. The device prepares to display the received data to the user. This plan is output in a format such as PDF, making it easy for the user to review. The device updates the UI appropriately so that the plan can be presented to the user.

[0311] Step 5:

[0312] Users input their daily learning progress into their device. The device receives this data and sends it to the server. The input data includes the items studied and the number of hours spent studying, and this information is used by the server to evaluate the user's progress.

[0313] Step 6:

[0314] The server compares the received learning progress with the existing learning plan to determine if the progress is on track. The server performs a comparison and, if there are problems with the progress, generates revisions to the plan or additional learning tasks. The output is feedback information, which is used in the next step.

[0315] Step 7:

[0316] The server sends the generated feedback to the terminal and presents it to the user. The terminal displays the feedback to the user, allowing them to understand their next learning actions. This feedback includes suggestions for adjusting the learning plan and recommended learning content.

[0317] Step 8:

[0318] The server periodically administers online tests to assess understanding. These tests are delivered to the user via a terminal, and the results are sent back to the server. The input is the test result, and the output is the plan adjustment instruction used in the next step.

[0319] Step 9:

[0320] The server further adjusts the learning plan based on the test results. The server then utilizes the AI ​​model again to generate a newly optimized learning plan. This plan is sent to the device as the basis for the next learning session.

[0321] (Application Example 1)

[0322] 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."

[0323] There is a lack of efficient planning for obtaining qualifications and acquiring skills, as well as a lack of smooth means for purchasing learning materials and applying for exams. Furthermore, insufficient feedback and adjustments to plans based on individual users' learning progress are also problematic.

[0324] 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.

[0325] In this invention, the server includes means for performing electronic payments for purchasing learning materials with an external system, means for automating the procedure for applying for exams, and means for evaluating the user's learning progress and providing feedback. This allows users to consistently purchase necessary learning materials and apply for exams based on an individually optimized learning plan without any hassle.

[0326] A "user" is an individual or group that uses the system with the aim of obtaining qualifications or acquiring skills.

[0327] "Acquisition goals" refer to the specific qualifications and skills that the user wants to achieve.

[0328] "Exam date" refers to the day the user is scheduled to take the exam.

[0329] A "data set" is accumulated data that includes information about a user's learning history and learning methods.

[0330] A "learning plan" is a specific learning schedule created based on the user's learning goals and schedule.

[0331] "Learning progress" refers to the progress a user has made in achieving their learning plan.

[0332] "Electronic payment" refers to a digital payment method used for purchasing educational materials and services online.

[0333] "Automation" means minimizing manual work and having a system automatically execute the process.

[0334] To implement a system based on this invention, a smartphone or tablet is first required as hardware. The user uses this device to input their qualification goals and exam dates. The application on the device then transmits this information to a server.

[0335] The server operates software built using Python (Django framework) and JavaScript (React Native). The server generates efficient learning plans using generative AI models and optimizes these plans for the user based on information retrieved from a database. Furthermore, to manage learning progress, it receives daily learning results from the device and provides progress evaluations and feedback.

[0336] Furthermore, the server integrates with an online payment system to automate the purchase of necessary learning materials. Exam registration is also handled automatically by the server communicating with an external exam management system.

[0337] As a concrete example, if a user aims to obtain the Fundamental Information Technology Engineer certification and enters "Fundamental Information Technology Engineer" and "Scheduled to take the exam on April 15, 2024," the server will create a study plan tailored to the qualification and smoothly handle the purchase of study materials and exam registration. An example of a prompt message to the generating AI model is: "Once the user enters the qualification name and desired exam date, create an optimal study plan, list the necessary study materials, and instruct the user to proceed with the purchase."

[0338] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0339] Step 1:

[0340] The user launches the application on their smartphone or tablet and enters their desired certification goal and exam date. The entered information is sent from the device to the server. The input here consists of the "certification name" and "exam date," and the output is the transmission of data to the server.

[0341] Step 2:

[0342] The server matches the received qualification name and exam date against the database and retrieves relevant learning items and past learning data. In this step, the input is qualification information, and the output is related learning data. The server inputs this data into a generating AI model to generate an efficient learning plan.

[0343] Step 3:

[0344] The generated learning plan is sent from the server to the terminal and presented to the user. The user then begins their daily learning based on this plan. Here, the input is the generated learning plan, and the output is what is presented to the user.

[0345] Step 4:

[0346] The user inputs their learning progress into the application daily. The device sends this progress data to the server. In this step, the input is learning progress data, and the output is transmission to the server.

[0347] Step 5:

[0348] The server compares the received progress data with the existing learning plan and evaluates whether the progress is on schedule. If the evaluation shows that the progress is behind schedule, it uses the generative AI model again to provide feedback and adjust the plan, and then sends it to the terminal. In this step, the progress data is the input, and the evaluation results and feedback are the outputs.

[0349] Step 6:

[0350] To support the user's learning progress, the server automatically purchases learning materials using an external electronic payment system. During this process, a generative AI model selects materials suitable for the user. The input is the required learning material information, and the output is a report confirming the purchase is complete.

[0351] Step 7:

[0352] As the exam date approaches, the server communicates with the exam management system to automate the exam registration process. The server inputs the exam information and notifies the user that the registration is complete. The input is the exam information, and the output is a registration confirmation.

[0353] 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.

[0354] This invention provides a system that enables users to acquire qualifications and skills in an efficient and personalized manner, and also provides emotional support by utilizing an emotion engine. Specifically, it links a terminal, a server, and an emotion engine to provide learning support tailored to the user's state.

[0355] First, the user enters their certification goals and exam dates into the system using a terminal. The terminal sends this information to the server, which then retrieves relevant information from its database based on the received certification information and exam dates.

[0356] The server uses an AI algorithm to generate an optimal learning plan, taking into account the user's goals and received data. A new emotion engine is now utilized, considering the user's emotional state as a component of the learning plan. For example, if the user is showing anxiety, adjustments are made, such as adding easier learning items to build confidence at the beginning.

[0357] Users input their daily learning progress into their devices and send it from their devices to the server. The server compares the user's learning progress data with their learning plan and evaluates their progress. This evaluation process also utilizes an emotion engine to provide optimal feedback tailored to the user's emotional state. Specifically, if a user is in a situation where they are likely to become discouraged, the system will make adjustments such as sending encouraging messages.

[0358] The server periodically provides tests to measure the user's understanding. The test results are analyzed by the server, and further learning plans are adjusted based on the user's level of understanding. The system also tracks the user's emotional state in real time and provides interactions that enhance motivation based on that state.

[0359] For example, if a user wants to obtain a math qualification and sets the goal of "taking the exam on December 1, 2023," the system will generate a study plan tailored to that goal. When the user is feeling stressed, the emotion engine recognizes this and adjusts the plan by including relaxing content or lighter tasks.

[0360] Furthermore, the system also supports the purchase of learning materials and the application process for exams, providing users with an environment where they can concentrate on their studies without being burdened by administrative procedures unrelated to learning. In this way, the entire system is designed to flexibly respond to users' needs to achieve the best possible results.

[0361] The following describes the processing flow.

[0362] Step 1:

[0363] The user enters their certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0364] Step 2:

[0365] The server uses the received information to retrieve relevant data from the database. This allows it to obtain the learning items and basic schedule proposals required for certification.

[0366] Step 3:

[0367] The server uses an emotion engine to evaluate the user's current emotional state. This takes into account the user's past emotional data and current input.

[0368] Step 4:

[0369] The server generates a personalized learning plan based on acquired data and emotional state through an AI algorithm. This plan is designed to maximize learning efficiency and minimize user stress.

[0370] Step 5:

[0371] The user enters their daily learning progress into the device. The device then sends the entered data to the server.

[0372] Step 6:

[0373] The server compares and evaluates the user's learning progress against the learning plan in real time, and also takes into account the user's emotional state at that time through an emotion engine.

[0374] Step 7:

[0375] Based on the evaluation results, the server generates feedback, taking into account the user's emotions, and sends it to the device. This feedback may include encouraging messages or modifications to the learning plan.

[0376] Step 8:

[0377] The server periodically provides users with tests to assess their understanding. The test results are analyzed by the server, and learning plans and feedback are adjusted according to the user's level of understanding.

[0378] Step 9:

[0379] When a user experiences stress, the server uses an emotion engine to recognize the situation and provides suggestions for enjoyable activities or recommendations for breaks via the user's device to improve their motivation.

[0380] Step 10:

[0381] The user receives feedback and an updated learning plan from the server and proceeds with the next learning session accordingly.

[0382] (Example 2)

[0383] 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".

[0384] In modern society, acquiring qualifications and skills requires flexible learning tailored to individual lifestyles and emotional states. However, conventional systems have difficulty providing learning plans that take into account the emotional state of individual users, resulting in situations where the effectiveness of learning is not maximized. Furthermore, the burden of administrative procedures in learning management is also a problem.

[0385] 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.

[0386] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and knowledge base, and means for analyzing the emotional state and reflecting it in the learning plan. This makes it possible to provide an optimal learning plan adapted to the user's individual emotional state, thereby improving the effectiveness of learning while reducing the burden of administrative procedures.

[0387] A "user" is an individual who aims to acquire qualifications or skills, and who uses the system to achieve their learning goals.

[0388] "Acquisition goals" refer to specific qualifications or skills that a user aims to achieve, and serve as indicators when creating a learning plan.

[0389] The "exam date" is the date on which the user takes an exam or assessment to achieve their set goals, and it serves as the basis for determining the schedule of their learning plan.

[0390] A "learning plan" is a specific schedule and curriculum generated to support the user in achieving their learning goals, and it is structured taking into account the information received and the user's emotional state.

[0391] "Emotional state" refers to the psychological state of a user during their learning process, and includes data such as anxiety and fluctuations in motivation.

[0392] "Feedback" refers to information and advice provided based on the user's learning progress and emotional state, and is a support measure to improve learning effectiveness.

[0393] "Means" refer to the specific methods or processes used to achieve a particular objective, and to the technical functions performed within a system.

[0394] This invention is a system for users to receive support in an efficient and personalized manner for obtaining qualifications and acquiring skills. The system mainly consists of a terminal, a server, an emotion engine, and related software modules.

[0395] First, the user uses their device to input their qualification goals and exam dates. The device then sends this information to a server via the internet. Based on the received information, the server retrieves relevant information from a designated database and uses an AI algorithm to generate an optimal learning plan for the user. This AI algorithm may include, for example, machine learning models or natural language processing models.

[0396] In addition, the server utilizes an emotion engine to dynamically adjust the learning plan according to the user's emotional state. This emotion engine infers the user's emotional state from their input data and learning history, and can change the learning content as needed. For example, if the user shows anxiety, the system will adjust the learning to prioritize easier and more confidence-building learning items.

[0397] Users record their learning progress on their devices, and this data is sent back to the server. The server analyzes this progress data and uses an emotion engine to provide personalized feedback to the user. Specific feedback may include encouraging messages or additional tasks to reinforce knowledge retention.

[0398] Furthermore, the server provides regular tests to measure the user's understanding and further adjusts the learning plan based on the test results. The system also supports administrative procedures such as purchasing learning materials and registering for exams, providing an environment where users can concentrate on their studies.

[0399] For example, if a user wants to obtain a mathematics qualification and sets the goal of "taking the exam on December 1, 2023," the server will generate a study plan tailored to that goal. At the same time, the generating AI model will suggest the optimal study approach using prompts such as, "The user is planning to study for a qualification exam; what methods can be suggested to reduce stress?"

[0400] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0401] Step 1:

[0402] The user uses a terminal to enter their certification goals and exam dates. This input serves as foundational data to identify the user's learning needs. The terminal sends this data to the server. As output, the server records the user's goals and exam dates.

[0403] Step 2:

[0404] Based on the received target and exam date, the server retrieves relevant information from the database. This includes data on the requirements for the relevant qualification and recommended study content. The server inputs this information into an AI algorithm to generate an optimal study plan. The output is a personalized study plan.

[0405] Step 3:

[0406] The server uses an emotion engine to understand the user's emotional state. It analyzes the user's past learning history and current input data to estimate their psychological state. Based on this emotional data, the server adjusts the learning plan. The input is the user's history and current data, and the output is the adjusted learning plan that takes the emotional state into account.

[0407] Step 4:

[0408] Users record their learning progress on their devices on a daily basis. This data is sent to a server, which receives it. The progress data is compared to the learning plan, and the current learning status is evaluated. The server analyzes the input data, re-evaluates the plan as needed, and generates feedback. As output, the user is provided with this feedback.

[0409] Step 5:

[0410] The server provides users with periodic tests to measure their understanding. Users take the tests from their devices and send the results to the server. The server analyzes the test results and further adjusts the learning plan. The input to this process is the test results, and the output is the updated learning plan.

[0411] Step 6:

[0412] The server handles tasks such as purchasing learning materials and registering for exams, allowing users to focus on their studies. It also provides incentives to maintain motivation. This is designed to allow users to concentrate on learning without being distracted by administrative tasks, eliminating the need for manual input. The output is smooth support regarding the content and procedures the user needs to learn.

[0413] (Application Example 2)

[0414] 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 will be referred to as the "terminal."

[0415] For users aiming to acquire qualifications or skills, there is a need to improve learning efficiency while simultaneously providing motivation and stress reduction tailored to their individual emotional state. Traditional learning systems cannot provide appropriate feedback or automatically adjust learning plans in response to the user's progress and emotional state, resulting in challenges such as efforts not leading to results or a lack of sustained motivation.

[0416] 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.

[0417] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and a database, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan accordingly. This enables the provision of an optimal learning plan that takes into account the user's individual learning progress and emotional state, as well as effective feedback based on emotions.

[0418] "Means for receiving acquisition goals and examination dates from users" refers to methods and devices for users to input their desired qualifications or skills and the dates for those examinations into a system via a digital terminal or similar device, and then receive that information.

[0419] "Means for generating a learning plan" refers to algorithms and devices that formulate the most effective learning schedule and methods for the user based on the received target data, exam dates, and database contents.

[0420] An "emotion engine" is a function or technology that analyzes a user's current emotional state and provides appropriate feedback or plan adjustments accordingly.

[0421] "Means for analyzing a user's emotional state and adjusting the learning plan based on that" refers to methods and devices for monitoring changes and states in a user's emotions and optimizing the content and progress of learning accordingly.

[0422] "Means of providing user-optimized interactions" refers to functions or processes for presenting dialogues and instructions that are adapted to the user's learning progress and emotional state.

[0423] "Means for acting as an agent for purchasing study materials and applying for exams" refers to methods and devices that perform these procedures on behalf of users, in order to reduce the time and effort required for users to manually purchase study materials and apply for exams.

[0424] "Means of suggesting breaks and motivational boosts" refers to functions and methods for encouraging users to take appropriate breaks or providing advice to boost their motivation when they are stuck or tired during their learning.

[0425] The system implementing this invention uses an advanced educational support system that integrates an emotion engine to personalize and efficiently facilitate user qualification acquisition and skill development.

[0426] First, the user enters their learning goals and exam dates into the system using their device. This information is transmitted to the server via the network. Based on the received information and its database, the server uses an AI algorithm to generate an optimal learning plan. At this time, the emotion engine analyzes the user's emotional data in real time and incorporates it into the generated learning plan.

[0427] The system uses programming languages ​​and frameworks such as Python and TensorFlow to record and evaluate the user's learning progress. Progress information entered from the terminal is compared and evaluated on the server, and feedback is provided according to the user's emotional state. For example, if the user is losing motivation, the server may suggest that the user take a break or send an encouraging message.

[0428] The system supports users in purchasing learning materials and registering for exams, providing an environment where users can focus on their studies. This automated process reduces user effort and promotes efficient learning.

[0429] For example, if a user is feeling anxious about an upcoming certification exam, the system can suggest a relaxing plan, and the reduced stress will improve learning efficiency. Another prompt for the generative AI model is: "What kind of strategies would be effective in analyzing the user's current emotional state and providing learning approaches and feedback appropriate to that state? Please give specific examples."

[0430] In this way, by responding flexibly based on the user's emotional state, we provide a format that enables the best possible learning outcomes.

[0431] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0432] Step 1:

[0433] The user enters their acquisition goal and test date using a terminal. The entered data is transmitted to the server via the network. This input information serves as foundational data for subsequent processing.

[0434] Step 2:

[0435] Based on the received goals and exam dates, the server extracts the relevant data from the database. This provides the foundation for developing the optimal learning plan for the user.

[0436] Step 3:

[0437] The server uses an AI algorithm to generate a detailed learning plan based on the extracted data. During this process, the plan is customized to the user's skill level. The output is the generation of daily learning tasks based on the plan.

[0438] Step 4:

[0439] The server utilizes an emotion engine to analyze the user's emotional state. This analysis uses sensor information received from the terminal and user input. In this step, data calculations are performed according to the emotional state, and adjustments are made to the learning plan.

[0440] Step 5:

[0441] Users input their daily learning progress into their device. This input is sent to the server. The input here reflects the user's actual progress.

[0442] Step 6:

[0443] The server compares the user's learning progress with the generated learning plan. It evaluates how the progress stands against the plan and determines appropriate feedback based on that evaluation. As output, text messages and suggestions are generated for the user.

[0444] Step 7:

[0445] The server sends feedback to the terminal, tailored to the progress assessment. This feedback may include encouraging messages or suggestions for the next task.

[0446] Step 8:

[0447] The server periodically generates and sends tests to the user's terminal to assess their understanding. In this step, test materials are extracted from a database and adjusted to a difficulty level appropriate for the user.

[0448] Step 9:

[0449] The user enters their test results into a terminal, and that data is sent to a server. The server then re-evaluates the user's understanding and adjusts the learning plan further as needed.

[0450] Through these steps, we create a system that allows users to learn in a flexible and personalized way, tailored to their emotional state and progress.

[0451] 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.

[0452] 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.

[0453] 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.

[0454] [Third Embodiment]

[0455] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0456] 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.

[0457] 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).

[0458] 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.

[0459] 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.

[0460] 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).

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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.

[0466] 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".

[0467] This invention is a support system for users to efficiently acquire qualifications and skills, and is realized through the exchange of information between a terminal, a server, and the user. The operation of this system is described below in natural language.

[0468] First, the user uses their device to input their desired certification goal and exam date into the system. The device sends this information to the server, which then uses the received data to retrieve the necessary learning items and schedule from its database.

[0469] The server uses an AI algorithm to generate an efficient learning plan based on the entered goals and dates. This plan is optimized for the user, taking into account their past learning history and common learning methods. The generated learning plan is sent to the device and presented to the user.

[0470] Users input their daily learning progress into their devices, which the server receives. The server compares this learning progress to the existing plan and evaluates whether the progress is on schedule. If progress is behind, the server provides feedback to the user, including adjustments to the plan and additional learning items.

[0471] Furthermore, the server regularly conducts online tests to assess understanding and has a mechanism to further adjust the learning plan based on the results. This allows users to continue learning flexibly according to their own level of understanding.

[0472] Furthermore, the system can handle the purchase of necessary learning materials and the application process for exams. The server retrieves the necessary information and automates these tasks based on user instructions. However, an interface is also provided for user verification as needed.

[0473] For example, if a user aims to obtain a TOEIC qualification, they can input "TOEIC" and "Scheduled to take the exam on November 30, 2023" into their device. The server will then create a corresponding study plan, manage daily progress, and provide appropriate feedback. In this format, users can receive personalized learning support, which helps them efficiently obtain the qualification.

[0474] The following describes the processing flow.

[0475] Step 1:

[0476] The user enters their desired certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0477] Step 2:

[0478] The server searches its database for relevant information based on the received credentials and exam date. This includes the learning items required to obtain the certification and an estimate of the appropriate study time.

[0479] Step 3:

[0480] The server uses an AI algorithm to generate an efficient study plan, working backward from the user's certification goal and exam date. This study plan is customized to take into account the user's past learning history and common success stories.

[0481] Step 4:

[0482] The server sends the generated learning plan to the terminal and presents it to the user. The user then proceeds with their learning based on this plan.

[0483] Step 5:

[0484] Users input their daily learning progress into their device. The device then sends this data to the server.

[0485] Step 6:

[0486] The server compares the received learning progress data with the existing learning plan and evaluates the progress. If there is a discrepancy between the plan and the actual progress, it adjusts the learning plan.

[0487] Step 7:

[0488] The server generates feedback based on the evaluation results. This feedback includes analyzing the causes of delays in the user's progress and modifying the learning method.

[0489] Step 8:

[0490] The server periodically provides users with online tests to measure their understanding. The test results are analyzed by the server, and the learning plan is modified as needed.

[0491] Step 9:

[0492] The server handles the purchase of necessary learning materials and exam registration on behalf of the user. This creates an environment where users can focus on their studies.

[0493] Step 10:

[0494] The user receives feedback from the server and, based on the provided learning plan or correction plan, begins the next learning session.

[0495] (Example 1)

[0496] 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."

[0497] Conventional learning support systems struggled to efficiently provide personalized learning plans, and their flexible feedback and plan adjustments based on progress were insufficient. Furthermore, the automation of comprehension assessments and learning material acquisition procedures was limited, making it difficult for users to have a consistent learning experience.

[0498] 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.

[0499] In this invention, the server includes means for receiving goal achievement information and a specified date from the user, means for generating a learning plan based on the received information and stored information, and means for optimizing the learning plan using a generative AI model to realize flexible learning. This enables the provision of a learning plan optimized for each user and rapid feedback according to progress.

[0500] "Goal achievement information" refers to information about the specific goals and qualifications that the user aims to achieve.

[0501] The "specified date" refers to the specific date by which the user wants to achieve their goal.

[0502] "Accumulated information" refers to all data that has been stored in advance, such as past learning history and general learning methods.

[0503] A "learning plan" refers to a specific learning schedule and arrangement of items that enable a user to efficiently achieve their goals.

[0504] "Progress status" refers to information indicating whether the user's learning is progressing as planned.

[0505] "Feedback" refers to information that includes evaluations and advice tailored to the user's learning progress.

[0506] "Generative AI models" refer to artificial intelligence technologies used to generate effective learning plans for users.

[0507] "Evaluation procedures" refer to the process of tests and examinations conducted to measure the user's level of understanding.

[0508] "Learning materials" refer to the documents and learning materials that users need to progress in their studies.

[0509] "Qualification examination procedures" refers to the application process and related procedures for examinations that users wish to take.

[0510] This invention is a system that enables users to efficiently acquire qualifications and skills. This system consists of users, terminals, and a server, and functions through the transmission and reception of information. Embodiments of the invention are shown below.

[0511] First, the user uses their device to enter their desired qualification goal and exam date. This device refers to a standard computer or smartphone, designed to allow users to easily input data using its interface. For example, if a user aims for the TOEIC qualification, they would enter information such as "TOEIC" and "Scheduled to take the exam on November 30, 2023." This allows the server to understand the user's goal.

[0512] The terminal sends the user's input data to the server. The server receives this information and retrieves the necessary learning items and schedule from the database. Databases such as MongoDB and MySQL are used. The server uses a generative AI model to generate an efficient learning plan based on the user's goals and specified dates. This generative AI model is implemented in Python or R and programmed to take the user's past learning history into account.

[0513] The generated learning plan is sent to the device and presented to the user. The plan can be provided using formats such as Adobe PDF. The user inputs their daily learning progress into the device, which is then sent to the server. The server evaluates the progress data and provides feedback as needed. This feedback may include adjusting the plan or adding learning content if progress is not on schedule.

[0514] Furthermore, the server administers online tests to assess comprehension and further adjusts the learning plan based on the results. LMS such as Moodle can be used for these online tests.

[0515] Furthermore, based on user instructions, the server automates the process of purchasing necessary learning materials and applying for certification exams through APIs on e-commerce sites and the official websites of examination organizations. Users can confirm these procedures and easily perform the necessary actions through an interface on their device.

[0516] An example of a prompt message is, "Please generate a study plan to obtain a TOEIC qualification by November 30, 2023." In this way, users can receive optimal learning support tailored to their goals.

[0517] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0518] Step 1:

[0519] The user enters their qualification goal and exam date via a terminal. The terminal receives this information and sends the entered data to the server. This input data may include, for example, "TOEIC" and "November 30, 2023". The server prepares this information for the next processing step.

[0520] Step 2:

[0521] Based on the goal and exam date data received from the terminal, the server searches the database to retrieve the relevant learning items and schedule from the stored information. The database contains qualification details and recommended schedules. The server extracts the relevant information from the database and retains it for use in the next step.

[0522] Step 3:

[0523] The server uses a generative AI model to generate an optimal learning plan based on the acquired learning items and schedule, as well as the user's past learning history. The input is the data acquired in the previous step and the user's history, and the output is a learning plan customized for the user. In this process, the AI ​​algorithm analyzes the data and creates an efficient plan.

[0524] Step 4:

[0525] The server sends the generated learning plan to the device. The device prepares to display the received data to the user. This plan is output in a format such as PDF, making it easy for the user to review. The device updates the UI appropriately so that the plan can be presented to the user.

[0526] Step 5:

[0527] Users input their daily learning progress into their device. The device receives this data and sends it to the server. The input data includes the items studied and the number of hours spent studying, and this information is used by the server to evaluate the user's progress.

[0528] Step 6:

[0529] The server compares the received learning progress with the existing learning plan to determine if the progress is on track. The server performs a comparison and, if there are problems with the progress, generates revisions to the plan or additional learning tasks. The output is feedback information, which is used in the next step.

[0530] Step 7:

[0531] The server sends the generated feedback to the terminal and presents it to the user. The terminal displays the feedback to the user, allowing them to understand their next learning actions. This feedback includes suggestions for adjusting the learning plan and recommended learning content.

[0532] Step 8:

[0533] The server periodically administers online tests to assess understanding. These tests are delivered to the user via a terminal, and the results are sent back to the server. The input is the test result, and the output is the plan adjustment instruction used in the next step.

[0534] Step 9:

[0535] The server further adjusts the learning plan based on the test results. The server then utilizes the AI ​​model again to generate a newly optimized learning plan. This plan is sent to the device as the basis for the next learning session.

[0536] (Application Example 1)

[0537] 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."

[0538] There is a lack of efficient planning for obtaining qualifications and acquiring skills, as well as a lack of smooth means for purchasing learning materials and applying for exams. Furthermore, insufficient feedback and adjustments to plans based on individual users' learning progress are also problematic.

[0539] 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.

[0540] In this invention, the server includes means for performing electronic payments for purchasing learning materials with an external system, means for automating the procedure for applying for exams, and means for evaluating the user's learning progress and providing feedback. This allows users to consistently purchase necessary learning materials and apply for exams based on an individually optimized learning plan without any hassle.

[0541] A "user" is an individual or group that uses the system with the aim of obtaining qualifications or acquiring skills.

[0542] "Acquisition goals" refer to the specific qualifications and skills that the user wants to achieve.

[0543] "Exam date" refers to the day the user is scheduled to take the exam.

[0544] A "data set" is accumulated data that includes information about a user's learning history and learning methods.

[0545] A "learning plan" is a specific learning schedule created based on the user's learning goals and schedule.

[0546] "Learning progress" refers to the progress a user has made in achieving their learning plan.

[0547] "Electronic payment" refers to a digital payment method used for purchasing educational materials and services online.

[0548] "Automation" means minimizing manual work and having a system automatically execute the process.

[0549] To implement a system based on this invention, a smartphone or tablet is first required as hardware. The user uses this device to input their qualification goals and exam dates. The application on the device then transmits this information to a server.

[0550] The server operates software built using Python (Django framework) and JavaScript (React Native). The server generates efficient learning plans using generative AI models and optimizes these plans for the user based on information retrieved from a database. Furthermore, to manage learning progress, it receives daily learning results from the device and provides progress evaluations and feedback.

[0551] Furthermore, the server integrates with an online payment system to automate the purchase of necessary learning materials. Exam registration is also handled automatically by the server communicating with an external exam management system.

[0552] As a concrete example, if a user aims to obtain the Fundamental Information Technology Engineer certification and enters "Fundamental Information Technology Engineer" and "Scheduled to take the exam on April 15, 2024," the server will create a study plan tailored to the qualification and smoothly handle the purchase of study materials and exam registration. An example of a prompt message to the generating AI model is: "Once the user enters the qualification name and desired exam date, create an optimal study plan, list the necessary study materials, and instruct the user to proceed with the purchase."

[0553] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0554] Step 1:

[0555] The user launches the application on their smartphone or tablet and enters their desired certification goal and exam date. The entered information is sent from the device to the server. The input here consists of the "certification name" and "exam date," and the output is the transmission of data to the server.

[0556] Step 2:

[0557] The server matches the received qualification name and exam date against the database and retrieves relevant learning items and past learning data. In this step, the input is qualification information, and the output is related learning data. The server inputs this data into a generating AI model to generate an efficient learning plan.

[0558] Step 3:

[0559] The generated learning plan is sent from the server to the terminal and presented to the user. The user then begins their daily learning based on this plan. Here, the input is the generated learning plan, and the output is what is presented to the user.

[0560] Step 4:

[0561] The user inputs their learning progress into the application daily. The device sends this progress data to the server. In this step, the input is learning progress data, and the output is transmission to the server.

[0562] Step 5:

[0563] The server compares the received progress data with the existing learning plan and evaluates whether the progress is on schedule. If the evaluation shows that the progress is behind schedule, it uses the generative AI model again to provide feedback and adjust the plan, and then sends it to the terminal. In this step, the progress data is the input, and the evaluation results and feedback are the outputs.

[0564] Step 6:

[0565] To support the user's learning progress, the server automatically purchases learning materials using an external electronic payment system. During this process, a generative AI model selects materials suitable for the user. The input is the required learning material information, and the output is a report confirming the purchase is complete.

[0566] Step 7:

[0567] As the exam date approaches, the server communicates with the exam management system to automate the exam registration process. The server inputs the exam information and notifies the user that the registration is complete. The input is the exam information, and the output is a registration confirmation.

[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 provides a system that enables users to acquire qualifications and skills in an efficient and personalized manner, and also provides emotional support by utilizing an emotion engine. Specifically, it links a terminal, a server, and an emotion engine to provide learning support tailored to the user's state.

[0570] First, the user enters their certification goals and exam dates into the system using a terminal. The terminal sends this information to the server, which then retrieves relevant information from its database based on the received certification information and exam dates.

[0571] The server uses an AI algorithm to generate an optimal learning plan, taking into account the user's goals and received data. A new emotion engine is now utilized, considering the user's emotional state as a component of the learning plan. For example, if the user is showing anxiety, adjustments are made, such as adding easier learning items to build confidence at the beginning.

[0572] Users input their daily learning progress into their devices and send it from their devices to the server. The server compares the user's learning progress data with their learning plan and evaluates their progress. This evaluation process also utilizes an emotion engine to provide optimal feedback tailored to the user's emotional state. Specifically, if a user is in a situation where they are likely to become discouraged, the system will make adjustments such as sending encouraging messages.

[0573] The server periodically provides tests to measure the user's understanding. The test results are analyzed by the server, and further learning plans are adjusted based on the user's level of understanding. The system also tracks the user's emotional state in real time and provides interactions that enhance motivation based on that state.

[0574] For example, if a user wants to obtain a math qualification and sets the goal of "taking the exam on December 1, 2023," the system will generate a study plan tailored to that goal. When the user is feeling stressed, the emotion engine recognizes this and adjusts the plan by including relaxing content or lighter tasks.

[0575] Furthermore, the system also supports the purchase of learning materials and the application process for exams, providing users with an environment where they can concentrate on their studies without being burdened by administrative procedures unrelated to learning. In this way, the entire system is designed to flexibly respond to users' needs to achieve the best possible results.

[0576] The following describes the processing flow.

[0577] Step 1:

[0578] The user enters their certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0579] Step 2:

[0580] The server uses the received information to retrieve relevant data from the database. This allows it to obtain the learning items and basic schedule proposals required for certification.

[0581] Step 3:

[0582] The server uses an emotion engine to evaluate the user's current emotional state. This takes into account the user's past emotional data and current input.

[0583] Step 4:

[0584] The server generates a personalized learning plan based on acquired data and emotional state through an AI algorithm. This plan is designed to maximize learning efficiency and minimize user stress.

[0585] Step 5:

[0586] The user enters their daily learning progress into the device. The device then sends the entered data to the server.

[0587] Step 6:

[0588] The server compares and evaluates the user's learning progress against the learning plan in real time, and also takes into account the user's emotional state at that time through an emotion engine.

[0589] Step 7:

[0590] Based on the evaluation results, the server generates feedback, taking into account the user's emotions, and sends it to the device. This feedback may include encouraging messages or modifications to the learning plan.

[0591] Step 8:

[0592] The server periodically provides users with tests to assess their understanding. The test results are analyzed by the server, and learning plans and feedback are adjusted according to the user's level of understanding.

[0593] Step 9:

[0594] When a user experiences stress, the server uses an emotion engine to recognize the situation and provides suggestions for enjoyable activities or recommendations for breaks via the user's device to improve their motivation.

[0595] Step 10:

[0596] The user receives feedback and an updated learning plan from the server and proceeds with the next learning session accordingly.

[0597] (Example 2)

[0598] 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."

[0599] In modern society, acquiring qualifications and skills requires flexible learning tailored to individual lifestyles and emotional states. However, conventional systems have difficulty providing learning plans that take into account the emotional state of individual users, resulting in situations where the effectiveness of learning is not maximized. Furthermore, the burden of administrative procedures in learning management is also a problem.

[0600] 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.

[0601] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and knowledge base, and means for analyzing the emotional state and reflecting it in the learning plan. This makes it possible to provide an optimal learning plan adapted to the user's individual emotional state, thereby improving the effectiveness of learning while reducing the burden of administrative procedures.

[0602] A "user" is an individual who aims to acquire qualifications or skills, and who uses the system to achieve their learning goals.

[0603] "Acquisition goals" refer to specific qualifications or skills that a user aims to achieve, and serve as indicators when creating a learning plan.

[0604] The "exam date" is the date on which the user takes an exam or assessment to achieve their set goals, and it serves as the basis for determining the schedule of their learning plan.

[0605] A "learning plan" is a specific schedule and curriculum generated to support the user in achieving their learning goals, and it is structured taking into account the information received and the user's emotional state.

[0606] "Emotional state" refers to the psychological state of a user during their learning process, and includes data such as anxiety and fluctuations in motivation.

[0607] "Feedback" refers to information and advice provided based on the user's learning progress and emotional state, and is a support measure to improve learning effectiveness.

[0608] "Means" refer to the specific methods or processes used to achieve a particular objective, and to the technical functions performed within a system.

[0609] This invention is a system for users to receive support in an efficient and personalized manner for obtaining qualifications and acquiring skills. The system mainly consists of a terminal, a server, an emotion engine, and related software modules.

[0610] First, the user uses their device to input their qualification goals and exam dates. The device then sends this information to a server via the internet. Based on the received information, the server retrieves relevant information from a designated database and uses an AI algorithm to generate an optimal learning plan for the user. This AI algorithm may include, for example, machine learning models or natural language processing models.

[0611] In addition, the server utilizes an emotion engine to dynamically adjust the learning plan according to the user's emotional state. This emotion engine infers the user's emotional state from their input data and learning history, and can change the learning content as needed. For example, if the user shows anxiety, the system will adjust the learning to prioritize easier and more confidence-building learning items.

[0612] Users record their learning progress on their devices, and this data is sent back to the server. The server analyzes this progress data and uses an emotion engine to provide personalized feedback to the user. Specific feedback may include encouraging messages or additional tasks to reinforce knowledge retention.

[0613] Furthermore, the server provides regular tests to measure the user's understanding and further adjusts the learning plan based on the test results. The system also supports administrative procedures such as purchasing learning materials and registering for exams, providing an environment where users can concentrate on their studies.

[0614] For example, if a user wants to obtain a mathematics qualification and sets the goal of "taking the exam on December 1, 2023," the server will generate a study plan tailored to that goal. At the same time, the generating AI model will suggest the optimal study approach using prompts such as, "The user is planning to study for a qualification exam; what methods can be suggested to reduce stress?"

[0615] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0616] Step 1:

[0617] The user uses a terminal to enter their certification goals and exam dates. This input serves as foundational data to identify the user's learning needs. The terminal sends this data to the server. As output, the server records the user's goals and exam dates.

[0618] Step 2:

[0619] Based on the received target and exam date, the server retrieves relevant information from the database. This includes data on the requirements for the relevant qualification and recommended study content. The server inputs this information into an AI algorithm to generate an optimal study plan. The output is a personalized study plan.

[0620] Step 3:

[0621] The server uses an emotion engine to understand the user's emotional state. It analyzes the user's past learning history and current input data to estimate their psychological state. Based on this emotional data, the server adjusts the learning plan. The input is the user's history and current data, and the output is the adjusted learning plan that takes the emotional state into account.

[0622] Step 4:

[0623] Users record their learning progress on their devices on a daily basis. This data is sent to a server, which receives it. The progress data is compared to the learning plan, and the current learning status is evaluated. The server analyzes the input data, re-evaluates the plan as needed, and generates feedback. As output, the user is provided with this feedback.

[0624] Step 5:

[0625] The server provides users with periodic tests to measure their understanding. Users take the tests from their devices and send the results to the server. The server analyzes the test results and further adjusts the learning plan. The input to this process is the test results, and the output is the updated learning plan.

[0626] Step 6:

[0627] The server handles tasks such as purchasing learning materials and registering for exams, allowing users to focus on their studies. It also provides incentives to maintain motivation. This is designed to allow users to concentrate on learning without being distracted by administrative tasks, eliminating the need for manual input. The output is smooth support regarding the content and procedures the user needs to learn.

[0628] (Application Example 2)

[0629] 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."

[0630] For users aiming to acquire qualifications or skills, there is a need to improve learning efficiency while simultaneously providing motivation and stress reduction tailored to their individual emotional state. Traditional learning systems cannot provide appropriate feedback or automatically adjust learning plans in response to the user's progress and emotional state, resulting in challenges such as efforts not leading to results or a lack of sustained motivation.

[0631] 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.

[0632] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and a database, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan accordingly. This enables the provision of an optimal learning plan that takes into account the user's individual learning progress and emotional state, as well as effective feedback based on emotions.

[0633] "Means for receiving acquisition goals and examination dates from users" refers to methods and devices for users to input their desired qualifications or skills and the dates for those examinations into a system via a digital terminal or similar device, and then receive that information.

[0634] "Means for generating a learning plan" refers to algorithms and devices that formulate the most effective learning schedule and methods for the user based on the received target data, exam dates, and database contents.

[0635] An "emotion engine" is a function or technology that analyzes a user's current emotional state and provides appropriate feedback or plan adjustments accordingly.

[0636] "Means for analyzing a user's emotional state and adjusting the learning plan based on that" refers to methods and devices for monitoring changes and states in a user's emotions and optimizing the content and progress of learning accordingly.

[0637] "Means of providing user-optimized interactions" refers to functions or processes for presenting dialogues and instructions that are adapted to the user's learning progress and emotional state.

[0638] "Means for acting as an agent for purchasing study materials and applying for exams" refers to methods and devices that perform these procedures on behalf of users, in order to reduce the time and effort required for users to manually purchase study materials and apply for exams.

[0639] "Means of suggesting breaks and motivational boosts" refers to functions and methods for encouraging users to take appropriate breaks or providing advice to boost their motivation when they are stuck or tired during their learning.

[0640] The system implementing this invention uses an advanced educational support system that integrates an emotion engine to personalize and efficiently facilitate user qualification acquisition and skill development.

[0641] First, the user enters their learning goals and exam dates into the system using their device. This information is transmitted to the server via the network. Based on the received information and its database, the server uses an AI algorithm to generate an optimal learning plan. At this time, the emotion engine analyzes the user's emotional data in real time and incorporates it into the generated learning plan.

[0642] The system uses programming languages ​​and frameworks such as Python and TensorFlow to record and evaluate the user's learning progress. Progress information entered from the terminal is compared and evaluated on the server, and feedback is provided according to the user's emotional state. For example, if the user is losing motivation, the server may suggest that the user take a break or send an encouraging message.

[0643] The system supports users in purchasing learning materials and registering for exams, providing an environment where users can focus on their studies. This automated process reduces user effort and promotes efficient learning.

[0644] For example, if a user is feeling anxious about an upcoming certification exam, the system can suggest a relaxing plan, and the reduced stress will improve learning efficiency. Another prompt for the generative AI model is: "What kind of strategies would be effective in analyzing the user's current emotional state and providing learning approaches and feedback appropriate to that state? Please give specific examples."

[0645] In this way, by responding flexibly based on the user's emotional state, we provide a format that enables the best possible learning outcomes.

[0646] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0647] Step 1:

[0648] The user enters their acquisition goal and test date using a terminal. The entered data is transmitted to the server via the network. This input information serves as foundational data for subsequent processing.

[0649] Step 2:

[0650] Based on the received goals and exam dates, the server extracts the relevant data from the database. This provides the foundation for developing the optimal learning plan for the user.

[0651] Step 3:

[0652] The server uses an AI algorithm to generate a detailed learning plan based on the extracted data. During this process, the plan is customized to the user's skill level. The output is the generation of daily learning tasks based on the plan.

[0653] Step 4:

[0654] The server utilizes an emotion engine to analyze the user's emotional state. This analysis uses sensor information received from the terminal and user input. In this step, data calculations are performed according to the emotional state, and adjustments are made to the learning plan.

[0655] Step 5:

[0656] Users input their daily learning progress into their device. This input is sent to the server. The input here reflects the user's actual progress.

[0657] Step 6:

[0658] The server compares the user's learning progress with the generated learning plan. It evaluates how the progress stands against the plan and determines appropriate feedback based on that evaluation. As output, text messages and suggestions are generated for the user.

[0659] Step 7:

[0660] The server sends feedback to the terminal, tailored to the progress assessment. This feedback may include encouraging messages or suggestions for the next task.

[0661] Step 8:

[0662] The server periodically generates and sends tests to the user's terminal to assess their understanding. In this step, test materials are extracted from a database and adjusted to a difficulty level appropriate for the user.

[0663] Step 9:

[0664] The user enters their test results into a terminal, and that data is sent to a server. The server then re-evaluates the user's understanding and adjusts the learning plan further as needed.

[0665] Through these steps, we create a system that allows users to learn in a flexible and personalized way, tailored to their emotional state and progress.

[0666] 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.

[0667] 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.

[0668] 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.

[0669] [Fourth Embodiment]

[0670] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0671] 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.

[0672] 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).

[0673] 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.

[0674] 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.

[0675] 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).

[0676] 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.

[0677] 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.

[0678] 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.

[0679] 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.

[0680] 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.

[0681] 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.

[0682] 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".

[0683] This invention is a support system for users to efficiently acquire qualifications and skills, and is realized through the exchange of information between a terminal, a server, and the user. The operation of this system is described below in natural language.

[0684] First, the user uses their device to input their desired certification goal and exam date into the system. The device sends this information to the server, which then uses the received data to retrieve the necessary learning items and schedule from its database.

[0685] The server uses an AI algorithm to generate an efficient learning plan based on the entered goals and dates. This plan is optimized for the user, taking into account their past learning history and common learning methods. The generated learning plan is sent to the device and presented to the user.

[0686] Users input their daily learning progress into their devices, which the server receives. The server compares this learning progress to the existing plan and evaluates whether the progress is on schedule. If progress is behind, the server provides feedback to the user, including adjustments to the plan and additional learning items.

[0687] Furthermore, the server regularly conducts online tests to assess understanding and has a mechanism to further adjust the learning plan based on the results. This allows users to continue learning flexibly according to their own level of understanding.

[0688] Furthermore, the system can handle the purchase of necessary learning materials and the application process for exams. The server retrieves the necessary information and automates these tasks based on user instructions. However, an interface is also provided for user verification as needed.

[0689] For example, if a user aims to obtain a TOEIC qualification, they can input "TOEIC" and "Scheduled to take the exam on November 30, 2023" into their device. The server will then create a corresponding study plan, manage daily progress, and provide appropriate feedback. In this format, users can receive personalized learning support, which helps them efficiently obtain the qualification.

[0690] The following describes the processing flow.

[0691] Step 1:

[0692] The user enters their desired certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0693] Step 2:

[0694] The server searches its database for relevant information based on the received credentials and exam date. This includes the learning items required to obtain the certification and an estimate of the appropriate study time.

[0695] Step 3:

[0696] The server uses an AI algorithm to generate an efficient study plan, working backward from the user's certification goal and exam date. This study plan is customized to take into account the user's past learning history and common success stories.

[0697] Step 4:

[0698] The server sends the generated learning plan to the terminal and presents it to the user. The user then proceeds with their learning based on this plan.

[0699] Step 5:

[0700] Users input their daily learning progress into their device. The device then sends this data to the server.

[0701] Step 6:

[0702] The server compares the received learning progress data with the existing learning plan and evaluates the progress. If there is a discrepancy between the plan and the actual progress, it adjusts the learning plan.

[0703] Step 7:

[0704] The server generates feedback based on the evaluation results. This feedback includes analyzing the causes of delays in the user's progress and modifying the learning method.

[0705] Step 8:

[0706] The server periodically provides users with online tests to measure their understanding. The test results are analyzed by the server, and the learning plan is modified as needed.

[0707] Step 9:

[0708] The server handles the purchase of necessary learning materials and exam registration on behalf of the user. This creates an environment where users can focus on their studies.

[0709] Step 10:

[0710] The user receives feedback from the server and, based on the provided learning plan or correction plan, begins the next learning session.

[0711] (Example 1)

[0712] 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".

[0713] Conventional learning support systems struggled to efficiently provide personalized learning plans, and their flexible feedback and plan adjustments based on progress were insufficient. Furthermore, the automation of comprehension assessments and learning material acquisition procedures was limited, making it difficult for users to have a consistent learning experience.

[0714] 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.

[0715] In this invention, the server includes means for receiving goal achievement information and a specified date from the user, means for generating a learning plan based on the received information and stored information, and means for optimizing the learning plan using a generative AI model to realize flexible learning. This enables the provision of a learning plan optimized for each user and rapid feedback according to progress.

[0716] "Goal achievement information" refers to information about the specific goals and qualifications that the user aims to achieve.

[0717] The "specified date" refers to the specific date by which the user wants to achieve their goal.

[0718] "Accumulated information" refers to all data that has been stored in advance, such as past learning history and general learning methods.

[0719] A "learning plan" refers to a specific learning schedule and arrangement of items that enable a user to efficiently achieve their goals.

[0720] "Progress status" refers to information indicating whether the user's learning is progressing as planned.

[0721] "Feedback" refers to information that includes evaluations and advice tailored to the user's learning progress.

[0722] "Generative AI models" refer to artificial intelligence technologies used to generate effective learning plans for users.

[0723] "Evaluation procedures" refer to the process of tests and examinations conducted to measure the user's level of understanding.

[0724] "Learning materials" refer to the documents and learning materials that users need to progress in their studies.

[0725] "Qualification examination procedures" refers to the application process and related procedures for examinations that users wish to take.

[0726] This invention is a system that enables users to efficiently acquire qualifications and skills. This system consists of users, terminals, and a server, and functions through the transmission and reception of information. Embodiments of the invention are shown below.

[0727] First, the user uses their device to enter their desired qualification goal and exam date. This device refers to a standard computer or smartphone, designed to allow users to easily input data using its interface. For example, if a user aims for the TOEIC qualification, they would enter information such as "TOEIC" and "Scheduled to take the exam on November 30, 2023." This allows the server to understand the user's goal.

[0728] The terminal sends the user's input data to the server. The server receives this information and retrieves the necessary learning items and schedule from the database. Databases such as MongoDB and MySQL are used. The server uses a generative AI model to generate an efficient learning plan based on the user's goals and specified dates. This generative AI model is implemented in Python or R and programmed to take the user's past learning history into account.

[0729] The generated learning plan is sent to the device and presented to the user. The plan can be provided using formats such as Adobe PDF. The user inputs their daily learning progress into the device, which is then sent to the server. The server evaluates the progress data and provides feedback as needed. This feedback may include adjusting the plan or adding learning content if progress is not on schedule.

[0730] Furthermore, the server administers online tests to assess comprehension and further adjusts the learning plan based on the results. LMS such as Moodle can be used for these online tests.

[0731] Furthermore, based on user instructions, the server automates the process of purchasing necessary learning materials and applying for certification exams through APIs on e-commerce sites and the official websites of examination organizations. Users can confirm these procedures and easily perform the necessary actions through an interface on their device.

[0732] An example of a prompt message is, "Please generate a study plan to obtain a TOEIC qualification by November 30, 2023." In this way, users can receive optimal learning support tailored to their goals.

[0733] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0734] Step 1:

[0735] The user enters their qualification goal and exam date via a terminal. The terminal receives this information and sends the entered data to the server. This input data may include, for example, "TOEIC" and "November 30, 2023". The server prepares this information for the next processing step.

[0736] Step 2:

[0737] Based on the goal and exam date data received from the terminal, the server searches the database to retrieve the relevant learning items and schedule from the stored information. The database contains qualification details and recommended schedules. The server extracts the relevant information from the database and retains it for use in the next step.

[0738] Step 3:

[0739] The server uses a generative AI model to generate an optimal learning plan based on the acquired learning items and schedule, as well as the user's past learning history. The input is the data acquired in the previous step and the user's history, and the output is a learning plan customized for the user. In this process, the AI ​​algorithm analyzes the data and creates an efficient plan.

[0740] Step 4:

[0741] The server sends the generated learning plan to the device. The device prepares to display the received data to the user. This plan is output in a format such as PDF, making it easy for the user to review. The device updates the UI appropriately so that the plan can be presented to the user.

[0742] Step 5:

[0743] Users input their daily learning progress into their device. The device receives this data and sends it to the server. The input data includes the items studied and the number of hours spent studying, and this information is used by the server to evaluate the user's progress.

[0744] Step 6:

[0745] The server compares the received learning progress with the existing learning plan to determine if the progress is on track. The server performs a comparison and, if there are problems with the progress, generates revisions to the plan or additional learning tasks. The output is feedback information, which is used in the next step.

[0746] Step 7:

[0747] The server sends the generated feedback to the terminal and presents it to the user. The terminal displays the feedback to the user, allowing them to understand their next learning actions. This feedback includes suggestions for adjusting the learning plan and recommended learning content.

[0748] Step 8:

[0749] The server periodically administers online tests to assess understanding. These tests are delivered to the user via a terminal, and the results are sent back to the server. The input is the test result, and the output is the plan adjustment instruction used in the next step.

[0750] Step 9:

[0751] The server further adjusts the learning plan based on the test results. The server then utilizes the AI ​​model again to generate a newly optimized learning plan. This plan is sent to the device as the basis for the next learning session.

[0752] (Application Example 1)

[0753] 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".

[0754] There is a lack of efficient planning for obtaining qualifications and acquiring skills, as well as a lack of smooth means for purchasing learning materials and applying for exams. Furthermore, insufficient feedback and adjustments to plans based on individual users' learning progress are also problematic.

[0755] 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.

[0756] In this invention, the server includes means for performing electronic payments for purchasing learning materials with an external system, means for automating the procedure for applying for exams, and means for evaluating the user's learning progress and providing feedback. This allows users to consistently purchase necessary learning materials and apply for exams based on an individually optimized learning plan without any hassle.

[0757] A "user" is an individual or group that uses the system with the aim of obtaining qualifications or acquiring skills.

[0758] "Acquisition goals" refer to the specific qualifications and skills that the user wants to achieve.

[0759] "Exam date" refers to the day the user is scheduled to take the exam.

[0760] A "data set" is accumulated data that includes information about a user's learning history and learning methods.

[0761] A "learning plan" is a specific learning schedule created based on the user's learning goals and schedule.

[0762] "Learning progress" refers to the progress a user has made in achieving their learning plan.

[0763] "Electronic payment" refers to a digital payment method used for purchasing educational materials and services online.

[0764] "Automation" means minimizing manual work and having a system automatically execute the process.

[0765] To implement a system based on this invention, a smartphone or tablet is first required as hardware. The user uses this device to input their qualification goals and exam dates. The application on the device then transmits this information to a server.

[0766] The server operates software built using Python (Django framework) and JavaScript (React Native). The server generates efficient learning plans using generative AI models and optimizes these plans for the user based on information retrieved from a database. Furthermore, to manage learning progress, it receives daily learning results from the device and provides progress evaluations and feedback.

[0767] Furthermore, the server integrates with an online payment system to automate the purchase of necessary learning materials. Exam registration is also handled automatically by the server communicating with an external exam management system.

[0768] As a concrete example, if a user aims to obtain the Fundamental Information Technology Engineer certification and enters "Fundamental Information Technology Engineer" and "Scheduled to take the exam on April 15, 2024," the server will create a study plan tailored to the qualification and smoothly handle the purchase of study materials and exam registration. An example of a prompt message to the generating AI model is: "Once the user enters the qualification name and desired exam date, create an optimal study plan, list the necessary study materials, and instruct the user to proceed with the purchase."

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

[0770] Step 1:

[0771] The user launches the application on their smartphone or tablet and enters their desired certification goal and exam date. The entered information is sent from the device to the server. The input here consists of the "certification name" and "exam date," and the output is the transmission of data to the server.

[0772] Step 2:

[0773] The server matches the received qualification name and exam date against the database and retrieves relevant learning items and past learning data. In this step, the input is qualification information, and the output is related learning data. The server inputs this data into a generating AI model to generate an efficient learning plan.

[0774] Step 3:

[0775] The generated learning plan is sent from the server to the terminal and presented to the user. The user then begins their daily learning based on this plan. Here, the input is the generated learning plan, and the output is what is presented to the user.

[0776] Step 4:

[0777] The user inputs their learning progress into the application daily. The device sends this progress data to the server. In this step, the input is learning progress data, and the output is transmission to the server.

[0778] Step 5:

[0779] The server compares the received progress data with the existing learning plan and evaluates whether the progress is on schedule. If the evaluation shows that the progress is behind schedule, it uses the generative AI model again to provide feedback and adjust the plan, and then sends it to the terminal. In this step, the progress data is the input, and the evaluation results and feedback are the outputs.

[0780] Step 6:

[0781] To support the user's learning progress, the server automatically purchases learning materials using an external electronic payment system. During this process, a generative AI model selects materials suitable for the user. The input is the required learning material information, and the output is a report confirming the purchase is complete.

[0782] Step 7:

[0783] As the exam date approaches, the server communicates with the exam management system to automate the exam registration process. The server inputs the exam information and notifies the user that the registration is complete. The input is the exam information, and the output is a registration confirmation.

[0784] 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.

[0785] This invention provides a system that enables users to acquire qualifications and skills in an efficient and personalized manner, and also provides emotional support by utilizing an emotion engine. Specifically, it links a terminal, a server, and an emotion engine to provide learning support tailored to the user's state.

[0786] First, the user enters their certification goals and exam dates into the system using a terminal. The terminal sends this information to the server, which then retrieves relevant information from its database based on the received certification information and exam dates.

[0787] The server uses an AI algorithm to generate an optimal learning plan, taking into account the user's goals and received data. A new emotion engine is now utilized, considering the user's emotional state as a component of the learning plan. For example, if the user is showing anxiety, adjustments are made, such as adding easier learning items to build confidence at the beginning.

[0788] Users input their daily learning progress into their devices and send it from their devices to the server. The server compares the user's learning progress data with their learning plan and evaluates their progress. This evaluation process also utilizes an emotion engine to provide optimal feedback tailored to the user's emotional state. Specifically, if a user is in a situation where they are likely to become discouraged, the system will make adjustments such as sending encouraging messages.

[0789] The server periodically provides tests to measure the user's understanding. The test results are analyzed by the server, and further learning plans are adjusted based on the user's level of understanding. The system also tracks the user's emotional state in real time and provides interactions that enhance motivation based on that state.

[0790] For example, if a user wants to obtain a math qualification and sets the goal of "taking the exam on December 1, 2023," the system will generate a study plan tailored to that goal. When the user is feeling stressed, the emotion engine recognizes this and adjusts the plan by including relaxing content or lighter tasks.

[0791] Furthermore, the system also supports the purchase of learning materials and the application process for exams, providing users with an environment where they can concentrate on their studies without being burdened by administrative procedures unrelated to learning. In this way, the entire system is designed to flexibly respond to users' needs to achieve the best possible results.

[0792] The following describes the processing flow.

[0793] Step 1:

[0794] The user enters their certification goal and exam date into the terminal. The terminal then sends this information to the server.

[0795] Step 2:

[0796] The server uses the received information to retrieve relevant data from the database. This allows it to obtain the learning items and basic schedule proposals required for certification.

[0797] Step 3:

[0798] The server uses an emotion engine to evaluate the user's current emotional state. This takes into account the user's past emotional data and current input.

[0799] Step 4:

[0800] The server generates a personalized learning plan based on acquired data and emotional state through an AI algorithm. This plan is designed to maximize learning efficiency and minimize user stress.

[0801] Step 5:

[0802] The user enters their daily learning progress into the device. The device then sends the entered data to the server.

[0803] Step 6:

[0804] The server compares and evaluates the user's learning progress against the learning plan in real time, and also takes into account the user's emotional state at that time through an emotion engine.

[0805] Step 7:

[0806] Based on the evaluation results, the server generates feedback, taking into account the user's emotions, and sends it to the device. This feedback may include encouraging messages or modifications to the learning plan.

[0807] Step 8:

[0808] The server periodically provides users with tests to assess their understanding. The test results are analyzed by the server, and learning plans and feedback are adjusted according to the user's level of understanding.

[0809] Step 9:

[0810] When a user experiences stress, the server uses an emotion engine to recognize the situation and provides suggestions for enjoyable activities or recommendations for breaks via the user's device to improve their motivation.

[0811] Step 10:

[0812] The user receives feedback and an updated learning plan from the server and proceeds with the next learning session accordingly.

[0813] (Example 2)

[0814] 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".

[0815] In modern society, acquiring qualifications and skills requires flexible learning tailored to individual lifestyles and emotional states. However, conventional systems have difficulty providing learning plans that take into account the emotional state of individual users, resulting in situations where the effectiveness of learning is not maximized. Furthermore, the burden of administrative procedures in learning management is also a problem.

[0816] 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.

[0817] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and knowledge base, and means for analyzing the emotional state and reflecting it in the learning plan. This makes it possible to provide an optimal learning plan adapted to the user's individual emotional state, thereby improving the effectiveness of learning while reducing the burden of administrative procedures.

[0818] A "user" is an individual who aims to acquire qualifications or skills, and who uses the system to achieve their learning goals.

[0819] "Acquisition goals" refer to specific qualifications or skills that a user aims to achieve, and serve as indicators when creating a learning plan.

[0820] The "exam date" is the date on which the user takes an exam or assessment to achieve their set goals, and it serves as the basis for determining the schedule of their learning plan.

[0821] A "learning plan" is a specific schedule and curriculum generated to support the user in achieving their learning goals, and it is structured taking into account the information received and the user's emotional state.

[0822] "Emotional state" refers to the psychological state of a user during their learning process, and includes data such as anxiety and fluctuations in motivation.

[0823] "Feedback" refers to information and advice provided based on the user's learning progress and emotional state, and is a support measure to improve learning effectiveness.

[0824] "Means" refer to the specific methods or processes used to achieve a particular objective, and to the technical functions performed within a system.

[0825] This invention is a system for users to receive support in an efficient and personalized manner for obtaining qualifications and acquiring skills. The system mainly consists of a terminal, a server, an emotion engine, and related software modules.

[0826] First, the user uses their device to input their qualification goals and exam dates. The device then sends this information to a server via the internet. Based on the received information, the server retrieves relevant information from a designated database and uses an AI algorithm to generate an optimal learning plan for the user. This AI algorithm may include, for example, machine learning models or natural language processing models.

[0827] In addition, the server utilizes an emotion engine to dynamically adjust the learning plan according to the user's emotional state. This emotion engine infers the user's emotional state from their input data and learning history, and can change the learning content as needed. For example, if the user shows anxiety, the system will adjust the learning to prioritize easier and more confidence-building learning items.

[0828] Users record their learning progress on their devices, and this data is sent back to the server. The server analyzes this progress data and uses an emotion engine to provide personalized feedback to the user. Specific feedback may include encouraging messages or additional tasks to reinforce knowledge retention.

[0829] Furthermore, the server provides regular tests to measure the user's understanding and further adjusts the learning plan based on the test results. The system also supports administrative procedures such as purchasing learning materials and registering for exams, providing an environment where users can concentrate on their studies.

[0830] For example, if a user wants to obtain a mathematics qualification and sets the goal of "taking the exam on December 1, 2023," the server will generate a study plan tailored to that goal. At the same time, the generating AI model will suggest the optimal study approach using prompts such as, "The user is planning to study for a qualification exam; what methods can be suggested to reduce stress?"

[0831] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0832] Step 1:

[0833] The user uses a terminal to enter their certification goals and exam dates. This input serves as foundational data to identify the user's learning needs. The terminal sends this data to the server. As output, the server records the user's goals and exam dates.

[0834] Step 2:

[0835] Based on the received target and exam date, the server retrieves relevant information from the database. This includes data on the requirements for the relevant qualification and recommended study content. The server inputs this information into an AI algorithm to generate an optimal study plan. The output is a personalized study plan.

[0836] Step 3:

[0837] The server uses an emotion engine to understand the user's emotional state. It analyzes the user's past learning history and current input data to estimate their psychological state. Based on this emotional data, the server adjusts the learning plan. The input is the user's history and current data, and the output is the adjusted learning plan that takes the emotional state into account.

[0838] Step 4:

[0839] Users record their learning progress on their devices on a daily basis. This data is sent to a server, which receives it. The progress data is compared to the learning plan, and the current learning status is evaluated. The server analyzes the input data, re-evaluates the plan as needed, and generates feedback. As output, the user is provided with this feedback.

[0840] Step 5:

[0841] The server provides users with periodic tests to measure their understanding. Users take the tests from their devices and send the results to the server. The server analyzes the test results and further adjusts the learning plan. The input to this process is the test results, and the output is the updated learning plan.

[0842] Step 6:

[0843] The server handles tasks such as purchasing learning materials and registering for exams, allowing users to focus on their studies. It also provides incentives to maintain motivation. This is designed to allow users to concentrate on learning without being distracted by administrative tasks, eliminating the need for manual input. The output is smooth support regarding the content and procedures the user needs to learn.

[0844] (Application Example 2)

[0845] 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".

[0846] For users aiming to acquire qualifications or skills, there is a need to improve learning efficiency while simultaneously providing motivation and stress reduction tailored to their individual emotional state. Traditional learning systems cannot provide appropriate feedback or automatically adjust learning plans in response to the user's progress and emotional state, resulting in challenges such as efforts not leading to results or a lack of sustained motivation.

[0847] 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.

[0848] In this invention, the server includes means for receiving acquisition goals and test dates from the user, means for generating a learning plan based on the received information and a database, and means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan accordingly. This enables the provision of an optimal learning plan that takes into account the user's individual learning progress and emotional state, as well as effective feedback based on emotions.

[0849] "Means for receiving acquisition goals and examination dates from users" refers to methods and devices for users to input their desired qualifications or skills and the dates for those examinations into a system via a digital terminal or similar device, and then receive that information.

[0850] "Means for generating a learning plan" refers to algorithms and devices that formulate the most effective learning schedule and methods for the user based on the received target data, exam dates, and database contents.

[0851] An "emotion engine" is a function or technology that analyzes a user's current emotional state and provides appropriate feedback or plan adjustments accordingly.

[0852] "Means for analyzing a user's emotional state and adjusting the learning plan based on that" refers to methods and devices for monitoring changes and states in a user's emotions and optimizing the content and progress of learning accordingly.

[0853] "Means of providing user-optimized interactions" refers to functions or processes for presenting dialogues and instructions that are adapted to the user's learning progress and emotional state.

[0854] "Means for acting as an agent for purchasing study materials and applying for exams" refers to methods and devices that perform these procedures on behalf of users, in order to reduce the time and effort required for users to manually purchase study materials and apply for exams.

[0855] "Means of suggesting breaks and motivational boosts" refers to functions and methods for encouraging users to take appropriate breaks or providing advice to boost their motivation when they are stuck or tired during their learning.

[0856] The system implementing this invention uses an advanced educational support system that integrates an emotion engine to personalize and efficiently facilitate user qualification acquisition and skill development.

[0857] First, the user enters their learning goals and exam dates into the system using their device. This information is transmitted to the server via the network. Based on the received information and its database, the server uses an AI algorithm to generate an optimal learning plan. At this time, the emotion engine analyzes the user's emotional data in real time and incorporates it into the generated learning plan.

[0858] The system uses programming languages ​​and frameworks such as Python and TensorFlow to record and evaluate the user's learning progress. Progress information entered from the terminal is compared and evaluated on the server, and feedback is provided according to the user's emotional state. For example, if the user is losing motivation, the server may suggest that the user take a break or send an encouraging message.

[0859] The system supports users in purchasing learning materials and registering for exams, providing an environment where users can focus on their studies. This automated process reduces user effort and promotes efficient learning.

[0860] For example, if a user is feeling anxious about an upcoming certification exam, the system can suggest a relaxing plan, and the reduced stress will improve learning efficiency. Another prompt for the generative AI model is: "What kind of strategies would be effective in analyzing the user's current emotional state and providing learning approaches and feedback appropriate to that state? Please give specific examples."

[0861] In this way, by responding flexibly based on the user's emotional state, we provide a format that enables the best possible learning outcomes.

[0862] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0863] Step 1:

[0864] The user enters their acquisition goal and test date using a terminal. The entered data is transmitted to the server via the network. This input information serves as foundational data for subsequent processing.

[0865] Step 2:

[0866] Based on the received goals and exam dates, the server extracts the relevant data from the database. This provides the foundation for developing the optimal learning plan for the user.

[0867] Step 3:

[0868] The server uses an AI algorithm to generate a detailed learning plan based on the extracted data. During this process, the plan is customized to the user's skill level. The output is the generation of daily learning tasks based on the plan.

[0869] Step 4:

[0870] The server utilizes an emotion engine to analyze the user's emotional state. This analysis uses sensor information received from the terminal and user input. In this step, data calculations are performed according to the emotional state, and adjustments are made to the learning plan.

[0871] Step 5:

[0872] Users input their daily learning progress into their device. This input is sent to the server. The input here reflects the user's actual progress.

[0873] Step 6:

[0874] The server compares the user's learning progress with the generated learning plan. It evaluates how the progress stands against the plan and determines appropriate feedback based on that evaluation. As output, text messages and suggestions are generated for the user.

[0875] Step 7:

[0876] The server sends feedback to the terminal, tailored to the progress assessment. This feedback may include encouraging messages or suggestions for the next task.

[0877] Step 8:

[0878] The server periodically generates and sends tests to the user's terminal to assess their understanding. In this step, test materials are extracted from a database and adjusted to a difficulty level appropriate for the user.

[0879] Step 9:

[0880] The user enters their test results into a terminal, and that data is sent to a server. The server then re-evaluates the user's understanding and adjusts the learning plan further as needed.

[0881] Through these steps, we create a system that allows users to learn in a flexible and personalized way, tailored to their emotional state and progress.

[0882] 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.

[0883] 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.

[0884] 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.

[0885] 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.

[0886] 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. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, 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.

[0887] 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.

[0888] 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.

[0889] 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.

[0890] 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."

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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.

[0895] 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.

[0896] 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.

[0897] 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.

[0898] 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.

[0899] 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.

[0900] 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.

[0901] 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.

[0902] 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.

[0903] The following is further disclosed regarding the embodiments described above.

[0904] (Claim 1)

[0905] A means of receiving the acquisition target and test date from the user,

[0906] Means for generating a learning plan based on received information and a database,

[0907] A means of inputting the user's learning progress,

[0908] A means of evaluating the input learning progress and providing feedback,

[0909] A means for comparing the received learning record with the generated learning plan,

[0910] The means for providing the aforementioned feedback,

[0911] A system that includes this.

[0912] (Claim 2)

[0913] The system according to claim 1, further comprising means for providing a test to evaluate the user's level of understanding.

[0914] (Claim 3)

[0915] The system according to claim 1, further comprising means for acting as an agent for the procedures of purchasing educational materials and applying for examinations.

[0916] "Example 1"

[0917] (Claim 1)

[0918] A means of receiving goal achievement information and a specified date from the user,

[0919] A means for generating a learning plan based on received information and stored information,

[0920] A means of inputting the user's progress,

[0921] A means of evaluating the entered progress and providing feedback,

[0922] A means of comparing the received learning results with the generated plan,

[0923] A means of adjusting the plan when progress is not on schedule and generating feedback that includes additional learning content,

[0924] A means of optimizing the learning plan using a generative AI model to achieve flexible learning,

[0925] A system that includes this.

[0926] (Claim 2)

[0927] The system according to claim 1, further comprising means for providing an evaluation procedure for evaluating the user's level of understanding.

[0928] (Claim 3)

[0929] The system according to claim 1, further comprising means for obtaining learning materials and acting as an agent for qualification examination procedures.

[0930] "Application Example 1"

[0931] (Claim 1)

[0932] A means of receiving the acquisition target and test date from the user,

[0933] A means for generating a learning plan based on the received information and data set,

[0934] A means of inputting the user's learning progress,

[0935] A means of evaluating the input learning progress and providing feedback,

[0936] A means for comparing the received learning record with the generated learning plan,

[0937] A means of making electronic payments to purchase educational materials with an external system,

[0938] A means to automate the exam application process,

[0939] A system that includes this.

[0940] (Claim 2)

[0941] The system according to claim 1, further comprising means for providing a test to evaluate the user's level of understanding.

[0942] (Claim 3)

[0943] The system according to claim 1, further comprising means for acting as an agent for the procedures of purchasing educational materials and applying for examinations.

[0944] "Example 2 of combining an emotion engine"

[0945] (Claim 1)

[0946] A means of receiving the acquisition target and test date from the user,

[0947] A means for generating a learning plan based on received information and a knowledge base,

[0948] A means of analyzing emotional states and reflecting them in learning plans,

[0949] A means of inputting the user's learning progress,

[0950] A means of evaluating the input learning progress and providing feedback,

[0951] A means of adjusting feedback according to emotional state,

[0952] A means for comparing the received learning record with the generated learning plan,

[0953] The means for providing the aforementioned feedback,

[0954] A system that includes this.

[0955] (Claim 2)

[0956] The system according to claim 1, further comprising means for providing a test to assess the user's level of understanding and for adjusting the learning plan based on the level of understanding.

[0957] (Claim 3)

[0958] The system according to claim 1, further comprising means for acting as an agent for the procedures of purchasing educational materials and applying for examinations.

[0959] "Application example 2 when combining with an emotional engine"

[0960] (Claim 1)

[0961] A means of receiving the acquisition target and test date from the user,

[0962] Means for generating a learning plan based on received information and a database,

[0963] A means of inputting the user's learning progress,

[0964] A means of evaluating the input learning progress and providing feedback,

[0965] A means for comparing the received learning record with the generated learning plan,

[0966] A means for analyzing the user's emotional state using an emotion engine and adjusting the learning plan based on that analysis,

[0967] A means of providing user-optimized interactions based on a tailored learning plan,

[0968] A system that includes this.

[0969] (Claim 2)

[0970] The system according to claim 1, further comprising means for providing a test to evaluate the user's level of understanding, and means for providing appropriate feedback and learning approaches based on the user's emotional state.

[0971] (Claim 3)

[0972] The system according to claim 1, further comprising means for acting as an agent for the procedures of purchasing educational materials and applying for exams, and means for providing suggestions for breaks and motivation enhancements 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 the acquisition target and test date from the user, Means for generating a learning plan based on received information and a database, A means of inputting the user's learning progress, A means of evaluating the input learning progress and providing feedback, A means for comparing the received learning record with the generated learning plan, The means for providing the aforementioned feedback, A system that includes this.

2. The system according to claim 1, further comprising means for providing a test to evaluate the user's level of understanding.

3. The system according to claim 1, further comprising means for acting as an agent for the procedures of purchasing teaching materials and applying for examinations.

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