system

Abstract

Provide a system. [Solution] a means for inputting the user's learning needs and characteristics; means for transmitting the input data to a server; means for analyzing the transmitted data and selecting an appropriate learning method; means for generating a learning plan based on the selected learning method; means for providing the generated study plan to a user; a means for users to enter feedback or questions; A means for analyzing the input feedback and questions and correcting the learning plan; means for providing the user with a revised lesson plan; A system including:

Description

[Technical Field]

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

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

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

[0004] Currently, providing optimal learning methods for individual learners is a difficult challenge in many educational systems. Each learner has different needs and characteristics, making it difficult to achieve sufficient learning results with a uniform method. In addition, there is a lack of interactive systems that allow learners to provide feedback on their own learning process. There is a need to develop a system that solves this problem and provides learners with optimal learning methods. [Means for solving the problem]

[0005] In order to solve the above problems, the present invention provides the following means. First, the system includes a means for a user to input their own learning needs and characteristics. Next, the system includes a means for transmitting this data to a server and analyzing it. Next, the system includes a means for selecting a learning method suitable for the user based on the analyzed data and generating a learning plan. The generated learning plan is provided to the user, who can proceed with their studies according to this plan. Furthermore, the system includes a means for inputting feedback and questions from the user, and a means for transmitting this to the server, reanalyzing it, and appropriately modifying the learning plan. The modified learning plan is then provided to the user. In this way, an effective learning environment that meets the individual needs of the user can be provided.

[0006] "User" means an individual who uses this system to study.

[0007] "Learning needs" are the learning content and goals that users desire.

[0008] "Characteristics" are individual factors such as a user's learning style or time constraints.

[0009] "Input means" refers to the interface through which users input their learning needs and characteristics into the system.

[0010] The "server" is a computer system that analyzes the received data and selects and generates appropriate learning methods.

[0011] "Means of transmission" refers to the communications protocol and infrastructure used to transmit user input data to the server.

[0012] "Means of analysis" refers to the technology that identifies and analyzes the user's learning needs and characteristics based on the data received by the server.

[0013] "Learning methods" refer to the specific learning materials and methods provided to users. Examples include video learning materials and tests.

[0014] A "learning plan" is a personalized learning schedule and set of learning materials designed based on a user's learning needs and characteristics.

[0015] The "means for providing" is an interface for presenting the generated lesson plan to the user.

[0016] "Feedback" refers to comments, questions, and other reactions that users provide while learning.

[0017] A "question" is a question or question that a user sends to the system while studying.

[0018] "Modification" refers to the technology that allows the server to refine and update the lesson plan based on user feedback and questions.

[0019] "Natural language processing" is a computer technology for analyzing text data and understanding and extracting meaning. [Brief explanation of the drawings]

[0020] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0021] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0022] First, the terms used in the following description will be explained.

[0023] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0024] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0025] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0026] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0028] [First embodiment]

[0029] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0030] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0033] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0036] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0040] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0041] The present invention is a system for providing a user with an optimal learning method and making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[0042] Users use the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. Examples include items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[0043] The entered data is sent from the device to the server. This transmission is carried out using a secure communication protocol (e.g., HTTPS), ensuring data protection. The server analyzes the user's characteristics and needs based on the received data.

[0044] The server uses natural language processing technology to analyze the received data. This allows it to analyze the user's input in detail and identify the learning content and learning style that the user prefers. For example, it analyzes information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study."

[0045] Based on the analysis results, the server searches the database for learning methods and generates an optimal learning plan for the user. This plan includes the order of learning topics and the learning materials (videos, textbooks, tests, etc.) to be used. For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[0046] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[0047] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more information." The device then sends this feedback information to the server.

[0048] The server analyzes the received feedback and adjusts the learning plan as needed. The analysis uses natural language processing technology to understand the meaning of the feedback. For example, if the feedback is "difficult to understand," additional video materials or supplemental texts will be suggested.

[0049] The revised study plan is then sent back to the terminal from the server and provided to the user, who can then continue their studies based on the revised plan, ensuring a study process that is optimized to the user's needs.

[0050] As a concrete example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan may be generated that includes "watching five videos per week" and "taking check tests on weekends." If the user studies according to this plan and provides feedback along the way that "I'm having difficulty understanding events from a particular period," the server will revise the study plan by adding supplementary videos and text. The server then provides this revised plan to the user, who can continue their studies.

[0051] In this way, the present invention provides an effective learning environment that meets the individual needs of the user, and realizes continuous optimization of the learning process.

[0052] The processing flow will be explained below.

[0053] Step 1:

[0054] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0055] Step 2:

[0056] The device sends the entered learning needs and characteristics data to the server, using HTTP or HTTPS protocols to ensure data security.

[0057] Step 3:

[0058] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[0059] Step 4:

[0060] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[0061] Step 5:

[0062] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[0063] Step 6:

[0064] The server sends the generated learning plan to the terminal.

[0065] Step 7:

[0066] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[0067] Step 8:

[0068] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[0069] Step 9:

[0070] The user can input any questions or feedback they have during the learning process and send it to the server from their device. For example, they can give feedback such as, "I find it difficult to understand events from a particular period."

[0071] Step 10:

[0072] The server analyzes the feedback and questions received from users, using natural language processing techniques to understand the content of the feedback and identify areas for improvement.

[0073] Step 11:

[0074] The server then adjusts the lesson plan based on the feedback, for example adding additional videos or text for topics that are identified as difficult to understand.

[0075] Step 12:

[0076] The server transmits the revised lesson plan to the terminal.

[0077] Step 13:

[0078] The device displays the revised study plan again to the user, who can then continue studying according to the new plan.

[0079] Through this processing flow, the present system can provide an effective learning environment that meets the individual needs of the user.

[0080] Example 1

[0081] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0082] Conventional learning systems have difficulty responding flexibly to the learning needs and characteristics of individual users. As a result, users are unable to study efficiently, which often leads to a decline in motivation during the learning process and prevents effective knowledge acquisition. In addition, the process of modifying learning plans based on user feedback is often done manually, which is time-consuming and labor-intensive. To solve these issues, it is important to automatically generate and quickly modify learning plans based on user characteristics.

[0083] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0084] In this invention, the server includes means for analyzing the user's learning needs and characteristics using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, and means for analyzing input feedback and questions using natural language processing technology and revising the learning plan. This not only enables the automatic generation of an optimal learning plan tailored to the individual needs of the user, but also makes it possible to quickly revise the learning plan based on the user's feedback.

[0085] "User" refers to a person who uses the system to input learning needs and characteristics and provide feedback.

[0086] "Learning needs" refers to the specific topics or types of materials a user wants to learn, as well as their learning time and style.

[0087] "Characteristics" refers to a user's learning style, habits, and individual learning preferences and characteristics.

[0088] "Input means" refers to the interface through which a user provides learning needs and characteristics to the system. Examples include text entry fields and selection buttons.

[0089] "Information processing device" refers to a computer or server that receives and analyzes data sent by users and generates and modifies learning plans.

[0090] "Natural language processing technology" refers to technology used to analyze data and feedback entered by users. Specifically, it is technology used to understand the meaning of text data.

[0091] "Means for selecting an appropriate learning method" refers to the process of determining the optimal learning method based on automatically analyzed user learning needs and characteristics.

[0092] "Means for generating a learning plan" refers to a process for automatically creating specific learning schedules and learning materials based on the selected learning method.

[0093] "Means for inputting feedback and questions" refers to an interface that allows users to communicate their impressions and questions they have while studying to the system.

[0094] "Revising" refers to the process of reevaluating your learning plan based on feedback and questions and making changes as needed.

[0095] "Audio-visual materials" refers to learning materials that use video or audio, etc. These are materials that allow users to understand the learning content through their eyes and ears.

[0096] "Assessment test" refers to a test or quiz that assesses a user's understanding and progress. It is used to measure the effectiveness of learning.

[0097] The present invention is a system for providing a user with an optimal learning method and for making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[0098] System Configuration

[0099] The system primarily operates between three parties: the user, the device, and the server. The user inputs their learning needs and characteristics through the device interface, and the device sends the information to the server. The server analyzes the received data, generates an optimal learning plan, and provides it to the user via the device. During learning, feedback from the user is also sent to the server, and the learning plan is revised as needed.

[0100] Hardware and software used

[0101] Hardware: Devices (e.g., PCs, tablets, smartphones), servers (high-performance computers, cloud servers, etc.)

[0102] Software: Natural language processing technology (e.g., Python libraries spaCy and NLTK), secure communication protocols (HTTPS), databases (SQL)

[0103] Program processing

[0104] The user uses the device to input their learning needs and characteristics (e.g., "topics they want to learn," "preferred learning style," "daily study time," etc.) This input is done using interfaces such as text input fields and selection buttons.

[0105] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server analyzes the received data using natural language processing techniques to gain a detailed understanding of the user's learning needs and characteristics. For example, Python's spaCy or NLTK library can be used to analyze the user's input text.

[0106] Based on the analysis results, the server selects learning methods and generates a learning plan, which includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). This generation process is performed by retrieving relevant information from an SQL database and combining learning methods that are appropriate for the analysis results.

[0107] The generated study plan is sent from the server to the device and displayed on a dedicated dashboard on the device. The user can proceed with their studies according to this study plan. For example, it may include specific content such as "watch five 5-10 minute videos per week" or "take a check test on the weekend."

[0108] Specific examples

[0109] If a user inputs information such as "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials," the server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan might be generated that includes "watching five videos a week" and "taking check tests on the weekend."

[0110] While studying, users can submit feedback and questions through the interactive interface. For example, they can submit feedback such as, "I find it difficult to understand the events of a particular period." This feedback is sent to the server and analyzed using natural language processing technology. Based on the analysis results, the server generates a revised lesson plan by including additional video and text materials in the lesson plan. The revised lesson plan is then sent back to the device and provided to the user.

[0111] Prompt Sentence Examples

[0112] User: "I want to learn history efficiently. I only have 30 minutes a day to study, so I want to be as efficient as possible. I prefer to learn using video materials."

[0113] Server: "Generating lesson plan..."

[0114] In this way, the system provides a learning plan that meets the user's individual needs and allows for continuous optimization of the learning process.

[0115] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0116] Step 1:

[0117] The user inputs their learning needs and characteristics. Using the device interface, the user inputs information such as "topics they want to learn," "preferred learning style," and "daily study time." This information is input using text input fields and selection buttons.

[0118] Input: User's learning needs and characteristics (e.g., "I want to learn history efficiently," "I only have 30 minutes a day to study," "I prefer video learning materials")

[0119] Output: User input data

[0120] Step 2:

[0121] The device sends the input to the server using a secure communication protocol (HTTPS), and the data is encrypted during transmission.

[0122] Input: User-entered data

[0123] Output: Data sent to the server

[0124] Step 3:

[0125] The server analyzes the received data using natural language processing techniques, such as the Python spaCy and NLTK libraries, to analyze the user's learning needs and characteristics.

[0126] Input: User input data that reaches the server

[0127] Output: Analyzed user learning needs and characteristics

[0128] Step 4:

[0129] Based on the analysis results, the server selects an appropriate learning method from the database. In this step, SQL is used to search for related learning materials from the database and select a learning method.

[0130] Input: Analyzed user learning needs and characteristics

[0131] Output: The selected learning method

[0132] Step 5:

[0133] The server generates a study plan based on the selected learning method, for example, a study plan that includes watching five 5-10 minute videos per week and a weekend check test.

[0134] Input: Selected learning method

[0135] Output: The generated learning plan

[0136] Step 6:

[0137] The server then sends the generated learning plan to the device, which also uses HTTPS to ensure data protection.

[0138] Input: Generated lesson plan

[0139] Output: The lesson plan sent to the device

[0140] Step 7:

[0141] The device displays the received learning plan to the user, visually arranging the learning plan on a dedicated dashboard for easy user access.

[0142] Input: The lesson plan that arrived on the device

[0143] Output: A displayed lesson plan

[0144] Step 8:

[0145] As users progress through their studies, they can enter feedback and questions into the device's interactive interface, for example by entering text such as, "I'm having difficulty understanding events from a particular period."

[0146] Input: User feedback and questions

[0147] Output: Feedback or questions typed into the terminal

[0148] Step 9:

[0149] The device sends feedback and questions to the server, and this communication also uses a secure protocol (HTTPS).

[0150] Input: Feedback or questions typed into the device

[0151] Output: Feedback or questions sent to the server

[0152] Step 10:

[0153] The server analyzes the received feedback and questions using natural language processing technology. For example, it analyzes feedback such as "It's difficult to understand" and extracts specific problems.

[0154] Input: Feedback and questions that reach the server

[0155] Output: Parsed feedback and questions

[0156] Step 11:

[0157] The server then modifies the learning plan based on the analysis results, selecting additional learning materials as needed and creating a new learning plan.

[0158] Input: Parsed feedback and questions

[0159] Output: Revised lesson plan

[0160] Step 12:

[0161] The server then sends the revised lesson plan to the device, and this communication is also carried out via HTTPS.

[0162] Input: revised lesson plan

[0163] Output: The revised lesson plan sent to the device.

[0164] Step 13:

[0165] The device displays the revised study plan to the user, who then continues studying based on the new plan.

[0166] Input: revised lesson plan arriving at terminal

[0167] Output: The displayed revised lesson plan

[0168] (Application example 1)

[0169] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0170] In modern factories, efficient operation and maintenance of robots is important from the perspective of improving productivity and reducing costs. However, current systems do not individually optimize robot maintenance schedules or new work process learning plans, which can lead to inconsistencies and inefficiencies. Furthermore, the ability to revise learning and maintenance plans in real time based on robot status and feedback is limited. This can lead to problems such as reduced robot utilization rates, excessive or insufficient maintenance, and even stagnant learning progress.

[0171] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0172] In this invention, the server includes: means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for optimizing the robot's maintenance and learning process, means for collecting and transmitting operation data and sensor data of the robot to the server, means for the server to generate a learning plan and maintenance schedule for the robot, and means for providing the revised robot learning plan and maintenance schedule to the robot. This automates a series of plans for the robot's maintenance and learning process and enables plan revisions in real time, thereby improving productivity and reducing operating costs.

[0173] "User" means an individual or organization that uses this system.

[0174] "Learning needs" is information that indicates what the user wants to learn and their learning goals.

[0175] "Characteristics" is information that indicates personal characteristics such as the user's learning style and how they manage their time.

[0176] "Means" refers to elements that provide a method or function for achieving a specific purpose.

[0177] The "server" is a central computer system that analyzes input data and generates learning methods and plans.

[0178] "Data" refers to information entered by the user and operational and sensor data collected by the robot.

[0179] "Analysis" is the process of examining the input data in detail and selecting appropriate learning methods and maintenance plans based on that information.

[0180] "Learning methods" refer to the means and techniques that allow users to learn most effectively.

[0181] A "learning plan" is a plan that includes specific procedures and schedules for a user or a robot to proceed with learning.

[0182] "Feedback" refers to requests, questions, and opinions provided by users and robots as they progress through learning and maintenance.

[0183] "Revision" is the process of changing an existing plan based on feedback provided.

[0184] "Providing" is the act of passing the generated or modified learning plan or maintenance schedule to a user or robot.

[0185] "Maintenance" refers to activities including inspection and repair necessary to maintain the normal operation of the robot.

[0186] "Operational data" refers to information about the operating status of the robot, such as operating hours and error logs.

[0187] "Sensor data" is information collected from various sensors of the robot.

[0188] "Real-time" refers to near-instantaneous reactions and processing.

[0189] The present invention provides a system that provides an optimal learning method to a user and optimizes the maintenance and learning process of a robot. Specific embodiments for carrying out the invention are described below.

[0190] Users use a dedicated device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to make it easy for users to provide information. This includes items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[0191] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server uses natural language processing (NLP) technology to analyze the user's characteristics and needs based on the received data, and selects the optimal learning method based on the analysis results.

[0192] A learning plan is generated based on the selected learning method. This plan includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard. The user proceeds with their studies according to this plan.

[0193] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more materials." The device then sends this feedback information to the server. The server analyzes the received feedback, modifies the study plan as necessary, and provides it back to the device.

[0194] A similar system can also be applied to factory robots. The robot's operational and sensor data is collected autonomously and sent to a server. The server uses this data to generate and provide learning plans and maintenance schedules for the robot. For example, if a robot's operating time exceeds 500 hours, it may be determined that maintenance is necessary. It also generates video learning materials and on-the-job training plans to help the robot efficiently learn new tasks.

[0195] Feedback allows these plans to be revised in real time and the optimized plans provided back to the robot, preventing over- and under-maintenance and stagnation of learning.

[0196] As a concrete example, the following is an example of a prompt sentence for an AI model that generates a maintenance plan when a factory robot operates for 8 hours a day and the operation time reaches 500 hours.

[0197] Example prompt sentence:

[0198] "A robot with over 500 operating hours requires a comprehensive inspection of its motors and sensors within a week. Errors are occurring frequently during operation. Please suggest appropriate maintenance methods and an efficient learning menu for this robot, which prefers a 'video' and 'hands-on' learning style."

[0199] In this way, the present invention provides an effective learning environment that meets the individual needs of the user or robot, as well as appropriate maintenance of the robot, thereby achieving continuous optimization.

[0200] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0201] Step 1:

[0202] The user uses the terminal to input their learning needs and characteristics.

[0203] The input interface includes text entry fields and selection buttons, and is designed to allow users to provide information such as "topics they want to learn," "learning style preferences," and "daily study time."

[0204] Input: User learning needs and characteristics

[0205] Output: Input data

[0206] Step 2:

[0207] The terminal transmits the input data to the server using a secure communication protocol (HTTPS).

[0208] Specifically, the terminal encrypts the input data and communicates securely with the server.

[0209] Input: Input data

[0210] Output: Data sent to the server

[0211] Step 3:

[0212] The server uses natural language processing (NLP) techniques to analyze the user's characteristics and needs based on the received data.

[0213] Specifically, it analyzes the text data entered by the user, understands keywords and context, and extracts appropriate learning methods.

[0214] Input: Data sent to the server

[0215] Output: Analysis results

[0216] Step 4:

[0217] The server generates a lesson plan based on the analysis results.

[0218] This plan includes the order of topics to be studied and the materials to be used (videos, textbooks, tests, etc.).

[0219] Specifically, NLP technology is used to determine the optimal learning materials and learning order, and a learning plan is created in text format.

[0220] Input: Analysis results

[0221] Output: Learning plan

[0222] Step 5:

[0223] The server sends the generated learning plan to the terminal and displays it on a dedicated dashboard.

[0224] The dashboard has an intuitive interface for easy user access.

[0225] Input:Study plan

[0226] Output: The lesson plan displayed on the user's device

[0227] Step 6:

[0228] As users progress through their learning, they can submit feedback and questions from their device through an interactive interface.

[0229] Specific operations include a process in which users input feedback such as "It's difficult to understand" or "I'd like more information" on the dashboard.

[0230] Input: Feedback or Questions

[0231] Output: Feedback data

[0232] Step 7:

[0233] The terminal transmits the feedback data to the server.

[0234] Specifically, the terminal encrypts the feedback data and transmits it securely to the server.

[0235] Input: Feedback data

[0236] Output: Feedback sent to the server

[0237] Step 8:

[0238] The server analyzes the feedback it receives and modifies the learning plan as needed.

[0239] NLP technology is again used to understand the feedback, and for example, if the feedback is "difficult to understand," additional video materials or supplemental text are generated.

[0240] Input: Feedback data

[0241] Output: Revised lesson plan

[0242] Step 9:

[0243] The revised study plan is again transmitted from the server to the terminal and provided to the user.

[0244] This allows the user to view the updated study plan on the dashboard again and continue studying.

[0245] Input: revised lesson plan

[0246] Output: The revised lesson plan displayed on the user's device.

[0247] Step 10:

[0248] The robot autonomously collects operational data and sensor data and transmits it to a server.

[0249] Specifically, this includes a process in which the robot records operational data and sensor data and periodically transmits it to a server.

[0250] Input: Robot operation data and sensor data

[0251] Output: Robot data sent to the server

[0252] Step 11:

[0253] The server analyzes the robot's operational data and sensor data to generate optimal maintenance schedules and learning plans.

[0254] Specifically, machine learning algorithms are used to analyze data and identify necessary maintenance tasks and learning processes.

[0255] Input: Robot data

[0256] Output: Maintenance schedule and learning plan

[0257] Step 12:

[0258] Providing the robot with a modified robot learning plan and maintenance schedule.

[0259] The robot autonomously carries out maintenance and learning according to the provided plan.

[0260] Input: Maintenance schedule and study plan

[0261] Output: Maintenance and learning progress performed on the robot

[0262] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0263] The present invention is a system for providing users with an optimal learning method and promoting efficient learning, and in particular, is configured to incorporate an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention are described below.

[0264] The system begins with the user entering their learning needs and characteristics using a device. The input interface, which includes text input fields and selection buttons, is designed to allow users to provide information easily. For example, input items include "topics to learn," "learning style preferences," and "daily study hours."

[0265] The entered data is sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data.

[0266] The server uses natural language processing technology to analyze the user's input in detail and identify the learning content and learning style that the user desires. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be analyzed.

[0267] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[0268] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[0269] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[0270] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[0271] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[0272] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[0273] As a specific example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan including "watching five videos per week" and "taking check tests on weekends" may be generated. If the user provides feedback that "events from a particular era are difficult to understand" as they progress through their studies, and the emotion engine detects confusion or a low level of understanding, the server will create a revised study plan including supplementary materials and provide it to the user. In this way, the present invention provides comprehensive study support that even takes the user's emotions into consideration.

[0274] The processing flow will be explained below.

[0275] Step 1:

[0276] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0277] Step 2:

[0278] The device sends the input learning needs and characteristics data to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data security.

[0279] Step 3:

[0280] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[0281] Step 4:

[0282] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[0283] Step 5:

[0284] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[0285] Step 6:

[0286] The server sends the generated learning plan to the terminal.

[0287] Step 7:

[0288] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[0289] Step 8:

[0290] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[0291] Step 9:

[0292] The device's emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, the user's facial expressions and tone of voice are collected through a camera or microphone.

[0293] Step 10:

[0294] The server receives emotion data from the terminal and analyzes it. For example, if a user is confused while watching a video tutorial, the emotion engine will send this information to the server.

[0295] Step 11:

[0296] The server then modifies the lesson plan based on the received emotional data, for example by adding additional videos or texts to improve the lesson plan for areas where the user is struggling.

[0297] Step 12:

[0298] The server transmits the revised lesson plan to the terminal.

[0299] Step 13:

[0300] The device displays the revised lesson plan again to the user.

[0301] Step 14:

[0302] The user continues studying based on the revised study plan, for example, checking the added supplementary materials and studying again to improve their understanding.

[0303] Through this process flow, the system can provide an effective learning environment that responds to the individual needs and emotions of the user.

[0304] Example 2

[0305] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0306] Conventional learning systems have been unable to adequately address the individual learning needs and characteristics of users, making it difficult to maximize learning efficiency. Additionally, it is difficult to provide feedback or modify learning plans that take users' emotions into account, making it impossible to provide an optimal learning process for users.

[0307] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's learning needs and characteristics, means for transmitting the input data to the server, means for transmitting data using a secure communication protocol, means for analyzing the transmitted data using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for acquiring and analyzing the user's emotional data using an emotion engine, means for analyzing the input feedback, questions, and emotional data and revising the learning plan, and means for providing the user with the revised learning plan. This makes it possible to provide an optimized learning process based on the user's needs, characteristics, and emotional state.

[0308] "User" refers to an individual who uses the system to learn.

[0309] "Learning needs" refers to information that indicates a specific topic or purpose that a user wants to learn.

[0310] "Characteristics" refers to individual learning characteristics such as a user's learning style and study time.

[0311] "Input means" refers to the interface through which the user provides learning needs and characteristics to the system.

[0312] "Means for sending" refers to the technology for securely sending the user's input data to the server.

[0313] "Secure communication protocol" refers to a protocol (e.g., HTTPS) for encrypting data and communicating securely.

[0314] "Natural language processing technology" refers to artificial intelligence technology that analyzes user input data and understands the intent and content.

[0315] "Means of analysis" refers to the technology used to analyze input data and understand the needs and characteristics of users.

[0316] "Means of selection" refers to the technology used to select the optimal learning method based on the analysis results.

[0317] A "study plan" refers to a plan that includes specific steps and learning materials for a user to efficiently progress through their studies.

[0318] The "means for providing" refers to an interface for providing the generated lesson plan to the user in an easy-to-view format.

[0319] "Feedback" refers to information that users can use to report their thoughts and questions to the system while studying.

[0320] An "emotion engine" refers to technology for acquiring and analyzing emotions from a user's facial expressions, tone of voice, etc.

[0321] "Emotion data" refers to information about a user's emotional state obtained by an emotion engine.

[0322] "Remediation tools" refers to techniques used to analyze feedback and emotional data and refine lesson plans as needed.

[0323] The present invention is a system for providing a user with an optimal learning method and promoting efficient learning, and in particular has a configuration in which an emotion engine that recognizes the user's emotions is combined. Specific embodiments for carrying out the present invention are described below.

[0324] First, the user uses the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. For example, input items include "topics I want to learn," "preferred learning style," and "daily study time." Specific examples include a user entering "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0325] The entered data is then sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data. Natural language processing technology is used as the analysis method, and the user's input is analyzed in detail. This identifies the learning content the user desires and their appropriate learning style. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" may be analyzed.

[0326] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[0327] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[0328] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[0329] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[0330] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[0331] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[0332] Examples of prompts include:

[0333] "I want to learn history efficiently. I only have 30 minutes a day to study. I prefer video learning materials. Please suggest a specific study plan."

[0334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0335] Step 1: Enter your learning needs and characteristics

[0336] Users use the device's input interface to input their learning needs and characteristics. Input items include "topics they want to learn," "preferred learning style," and "daily study time." Specific examples include "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0337] Input: Learning needs and characteristics

[0338] Output: Input data (information on learning needs and characteristics)

[0339] Step 2: Sending data

[0340] The terminal sends the data entered by the user to the server using a secure communication protocol (e.g. HTTPS), and the data is encrypted during the process to ensure proper security.

[0341] Input: Input data (encoded learning needs and characteristics information)

[0342] Output: Data sent to the server

[0343] Step 3: Analyze the data

[0344] The server analyzes the received user data using natural language processing technology. Through this analysis, the user's learning needs and characteristics are extracted. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be identified.

[0345] Input: Data sent to the server

[0346] Output: Analysis results (detailed information on learning needs and characteristics)

[0347] Step 4: Find and choose the best learning method

[0348] The server searches the database for learning methods based on the analysis results and selects the most suitable learning method for the user. For example, for a user who prefers video learning materials, it selects a learning plan that focuses on short video learning materials.

[0349] Input: Analysis results

[0350] Output: Optimal learning method

[0351] Step 5: Generate a lesson plan

[0352] The server generates a specific study plan based on the selected learning method. This plan includes study topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[0353] Input: The best way to learn

[0354] Output: The generated learning plan

[0355] Step 6: Provide a learning plan

[0356] The server transmits the generated study plan to the terminal.

[0357] The device displays the received learning plan on a dedicated dashboard for easy access by the user.

[0358] Input: Generated learning plan

[0359] Output: The lesson plan displayed on the device

[0360] Step 7: Recognizing emotions while learning

[0361] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion engine, which analyzes them to identify the user's emotions, such as liking, confusion, or lack of understanding.

[0362] Input: facial expression data and voice data

[0363] Output: Emotion data

[0364] Step 8: Submit your feedback

[0365] Users can submit feedback and questions through an interactive interface while studying, such as "I'm having difficulty understanding events from a particular period."

[0366] Input: Feedback or Questions

[0367] Output: Feedback or questions sent to the server

[0368] Step 9: Analyze feedback and sentiment data

[0369] The server receives the feedback and emotional data and analyzes it using natural language processing technology and an emotional engine, for example, to determine whether a particularly confusing section requires additional learning materials.

[0370] Input: Feedback and emotion data

[0371] Output: Analysis results

[0372] Step 10: Modify your study plan

[0373] Based on the analysis, the server will modify the lesson plan as needed, for example adding supplementary video or text to difficult-to-understand sections.

[0374] Input: Analysis results

[0375] Output: revised lesson plan

[0376] Step 11: Provide a revised lesson plan

[0377] The server transmits the revised lesson plan to the terminal.

[0378] The terminal displays the resubmitted revised lesson plan on a dashboard for easy access by the user.

[0379] Input: revised lesson plan

[0380] Output: The revised lesson plan displayed on the terminal.

[0381] (Application example 2)

[0382] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0383] While conventional learning support systems can provide learning plans based on a user's learning needs and characteristics, they lack the ability to recognize the user's emotional state in real time and flexibly modify the learning content based on that. This can lead to problems such as users becoming confused or continuing their learning without fully understanding the material. This type of flexible response is particularly important when factory workers are learning new machines or procedures.

[0384] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0385] In this invention, the server includes means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for recognizing the user's emotions during the learning process and revising the learning plan based on the emotions, and means for providing supplementary learning materials based on the revised learning plan. This makes it possible to provide a flexible learning plan based on the user's emotional state, significantly improving the efficiency and effectiveness of learning.

[0386] A "user" is a person or agent who has something they want to learn using a learning system.

[0387] "Learning needs" are the topics or themes that users want to learn, as well as their more specific learning requirements and goals.

[0388] "Traits" are individual characteristics such as a user's unique learning style or the amount of time they spend studying.

[0389] "Input means" refers to the interface or device that allows the user to provide the system with the content and learning style they want to learn.

[0390] "Means of sending" refers to the function or mechanism that securely transports the entered data to the server.

[0391] "Means of analysis" refers to the techniques used to interpret the user's needs and characteristics using the received data and determine the most appropriate learning method.

[0392] The "means of selection" is a mechanism that selects the most suitable learning method and learning materials for the user based on the analyzed results.

[0393] A "learning plan" is a specific schedule of learning activities or materials provided to a user.

[0394] "Means of delivery" refers to the interface or mechanism by which the generated lesson plan is presented to the user and made accessible.

[0395] "Feedback" refers to opinions and comments that users make to communicate to the system any questions or level of understanding they may have while studying.

[0396] A "question" is an inquiry that a user makes to the system during learning about something they are unsure of or want to confirm.

[0397] "Avenues for modification" are the ability to make timely changes to your learning plan based on feedback and questions received.

[0398] The "emotion engine" is a technology that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[0399] "Supplemental Materials" are additional educational resources provided to users in response to content that they find difficult to understand or are confused about.

[0400] The present invention is a learning support system that takes into account the learning needs and characteristics of a user, provides an appropriate learning plan according to the learning process, and further recognizes the user's emotions in real time to optimize the learning process. Specifically, the present invention includes the following components.

[0401] 1. Input of user learning needs and characteristics

[0402] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, the amount of time they spend studying each day, etc. The input interface includes text entry fields and selection buttons, and is designed to make it easy for users to provide information.

[0403] 2. Data submission and analysis

[0404] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server then analyzes the received data using natural language processing technology (NLPAnalysis) to identify the optimal learning method based on the user's learning needs and characteristics.

[0405] 3. Creating and providing a learning plan

[0406] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan. This learning plan includes specific learning topics and learning materials (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard.

[0407] 4. Emotion Recognition by Emotion Engine

[0408] During learning, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state. For example, it can detect when the user is confused or interested.

[0409] 5. Feedback and revision of study plans

[0410] Users can submit feedback and questions through the interactive interface. The server analyzes the received feedback and sentiment data and modifies the lesson plan as needed. Modifications may include supplemental materials or additional videos.

[0411] 6. Hardware and Software Used

[0412] This system uses "EmotionEngine" as its emotion engine and "NLPAnalysis" for natural language processing. It uses HTTPS for communication and a relational database such as MongoDB or MySQL (registered trademark) for the database. It is intended for use on smartphones, tablets, or head-mounted displays (HMDs).

[0413] Prompt Sentence Examples

[0414] For example, if a worker wants to learn a new welding procedure, the following is entered:

[0415] "What topic would you like to learn about?"

[0416] "Welding Procedure"

[0417] "What is your learning style preference (video, text, etc.)?"

[0418] "video"

[0419] "How many minutes do you study per day?"

[0420] "half an hour"

[0421] Based on this, the server generates a study plan that includes watching six five-minute videos every day. If the server detects that the user is experiencing difficulty or has low comprehension during the study, it can incorporate supplementary videos or additional text into the plan.

[0422] In this way, the system of the present invention provides an efficient learning environment for the user, maximizing the effectiveness of learning.

[0423] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0424] Step 1:

[0425] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, and the amount of time they have to study each day.

[0426] Input: Topic you want to learn, your learning style, and the number of hours you want to study per day

[0427] Output: Data on learning needs and characteristics entered

[0428] How it works: The terminal provides text entry fields and choice buttons into which the user enters information.

[0429] Step 2:

[0430] The input data is sent to the server using a secure communication protocol (HTTPS).

[0431] Input: Data on learning needs and characteristics entered

[0432] Output: Data sent to the server

[0433] How it works: The device uses HTTPS to send the data entered by the user to the server.

[0434] Step 3:

[0435] The server analyzes the received data using natural language processing technology (NLPAnalysis) to analyze the user's learning needs and characteristics.

[0436] Input: Data sent to the server

[0437] Output: Analysis results (user learning needs and characteristics)

[0438] How it works: The server uses NLPAnalysis to analyze text data and identify user needs and characteristics.

[0439] Step 4:

[0440] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan.

[0441] Input: Analysis results

[0442] Output: Learning plan

[0443] Operation: The server checks the database based on the analysis results, selects relevant learning methods and materials, and generates a learning plan.

[0444] Step 5:

[0445] The generated learning plan is sent from the server to the terminal and displayed on a dedicated dashboard.

[0446] Input:Study plan

[0447] Output: The lesson plan displayed on the device.

[0448] Operation: The server sends the generated learning plan to the device, which displays it on the dashboard.

[0449] Step 6:

[0450] The device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state.

[0451] Input: User's facial expression data, voice data

[0452] Output: Emotion analysis results

[0453] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by the Emotion Engine.

[0454] Step 7:

[0455] Users can submit feedback and questions through an interactive interface while learning.

[0456] Input: Feedback, Questions

[0457] Output: Feedback or questions sent to the server

[0458] How it works: The user uses the device interface to input questions or comments that arise during the learning process and sends them to the server.

[0459] Step 8:

[0460] The server analyzes the received feedback and emotional data and adjusts the learning plan as needed.

[0461] Input: Feedback, emotion data

[0462] Output: Revised lesson plan

[0463] How it works: The server uses feedback and emotional data to revise the learning plan, for example by adding new learning materials.

[0464] Step 9:

[0465] The revised study plan is again sent from the server to the terminal and provided to the user.

[0466] Input: revised lesson plan

[0467] Output: The revised lesson plan displayed on the terminal.

[0468] Operation: The server sends the revised lesson plan to the device, which displays it again on the dashboard.

[0469] The above processing steps enable the learning assistance system to provide an efficient and effective learning experience while also responding to the user's emotional state.

[0470] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0471] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0472] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0473] [Second embodiment]

[0474] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0475] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0476] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0477] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0478] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0479] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0480] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0481] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0482] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0483] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0484] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0485] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0486] The present invention is a system for providing a user with an optimal learning method and making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[0487] Users use the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. Examples include items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[0488] The entered data is sent from the device to the server. This transmission is carried out using a secure communication protocol (e.g., HTTPS), ensuring data protection. The server analyzes the user's characteristics and needs based on the received data.

[0489] The server uses natural language processing technology to analyze the received data. This allows it to analyze the user's input in detail and identify the learning content and learning style that the user prefers. For example, it analyzes information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study."

[0490] Based on the analysis results, the server searches the database for learning methods and generates an optimal learning plan for the user. This plan includes the order of learning topics and the learning materials (videos, textbooks, tests, etc.) to be used. For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[0491] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[0492] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more information." The device then sends this feedback information to the server.

[0493] The server analyzes the received feedback and adjusts the learning plan as needed. The analysis uses natural language processing technology to understand the meaning of the feedback. For example, if the feedback is "difficult to understand," additional video materials or supplemental texts will be suggested.

[0494] The revised study plan is then sent back to the terminal from the server and provided to the user, who can then continue their studies based on the revised plan, ensuring a study process that is optimized to the user's needs.

[0495] As a concrete example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan may be generated that includes "watching five videos per week" and "taking check tests on weekends." If the user studies according to this plan and provides feedback along the way that "I'm having difficulty understanding events from a particular period," the server will revise the study plan by adding supplementary videos and text. The server then provides this revised plan to the user, who can continue their studies.

[0496] In this way, the present invention provides an effective learning environment that meets the individual needs of the user, and realizes continuous optimization of the learning process.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0500] Step 2:

[0501] The device sends the entered learning needs and characteristics data to the server, using HTTP or HTTPS protocols to ensure data security.

[0502] Step 3:

[0503] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[0504] Step 4:

[0505] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[0506] Step 5:

[0507] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[0508] Step 6:

[0509] The server sends the generated learning plan to the terminal.

[0510] Step 7:

[0511] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[0512] Step 8:

[0513] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[0514] Step 9:

[0515] The user can input any questions or feedback they have during the learning process and send it to the server from their device. For example, they can give feedback such as, "I find it difficult to understand events from a particular period."

[0516] Step 10:

[0517] The server analyzes the feedback and questions received from users, using natural language processing techniques to understand the content of the feedback and identify areas for improvement.

[0518] Step 11:

[0519] The server then adjusts the lesson plan based on the feedback, for example adding additional videos or text for topics that are identified as difficult to understand.

[0520] Step 12:

[0521] The server transmits the revised lesson plan to the terminal.

[0522] Step 13:

[0523] The device displays the revised study plan again to the user, who can then continue studying according to the new plan.

[0524] Through this processing flow, the present system can provide an effective learning environment that meets the individual needs of the user.

[0525] Example 1

[0526] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0527] Conventional learning systems have difficulty responding flexibly to the learning needs and characteristics of individual users. As a result, users are unable to study efficiently, which often leads to a decline in motivation during the learning process and prevents effective knowledge acquisition. In addition, the process of modifying learning plans based on user feedback is often done manually, which is time-consuming and labor-intensive. To solve these issues, it is important to automatically generate and quickly modify learning plans based on user characteristics.

[0528] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0529] In this invention, the server includes means for analyzing the user's learning needs and characteristics using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, and means for analyzing input feedback and questions using natural language processing technology and revising the learning plan. This not only enables the automatic generation of an optimal learning plan tailored to the individual needs of the user, but also makes it possible to quickly revise the learning plan based on the user's feedback.

[0530] "User" refers to a person who uses the system to input learning needs and characteristics and provide feedback.

[0531] "Learning needs" refers to the specific topics or types of materials a user wants to learn, as well as their learning time and style.

[0532] "Characteristics" refers to a user's learning style, habits, and individual learning preferences and characteristics.

[0533] "Input means" refers to the interface through which a user provides learning needs and characteristics to the system. Examples include text entry fields and selection buttons.

[0534] "Information processing device" refers to a computer or server that receives and analyzes data sent by users and generates and modifies learning plans.

[0535] "Natural language processing technology" refers to technology used to analyze data and feedback entered by users. Specifically, it is technology used to understand the meaning of text data.

[0536] "Means for selecting an appropriate learning method" refers to the process of determining the optimal learning method based on automatically analyzed user learning needs and characteristics.

[0537] "Means for generating a learning plan" refers to a process for automatically creating specific learning schedules and learning materials based on the selected learning method.

[0538] "Means for inputting feedback and questions" refers to an interface that allows users to communicate their impressions and questions they have while studying to the system.

[0539] "Revising" refers to the process of reevaluating your learning plan based on feedback and questions and making changes as needed.

[0540] "Audio-visual materials" refers to learning materials that use video or audio, etc. These are materials that allow users to understand the learning content through their eyes and ears.

[0541] "Assessment test" refers to a test or quiz that assesses a user's understanding and progress. It is used to measure the effectiveness of learning.

[0542] The present invention is a system for providing a user with an optimal learning method and for making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[0543] System Configuration

[0544] The system primarily operates between three parties: the user, the device, and the server. The user inputs their learning needs and characteristics through the device interface, and the device sends the information to the server. The server analyzes the received data, generates an optimal learning plan, and provides it to the user via the device. During learning, feedback from the user is also sent to the server, and the learning plan is revised as needed.

[0545] Hardware and software used

[0546] Hardware: Devices (e.g., PCs, tablets, smartphones), servers (high-performance computers, cloud servers, etc.)

[0547] Software: Natural language processing technology (e.g., Python libraries spaCy and NLTK), secure communication protocols (HTTPS), databases (SQL)

[0548] Program processing

[0549] The user uses the device to input their learning needs and characteristics (e.g., "topics they want to learn," "preferred learning style," "daily study time," etc.) This input is done using interfaces such as text input fields and selection buttons.

[0550] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server analyzes the received data using natural language processing techniques to gain a detailed understanding of the user's learning needs and characteristics. For example, Python's spaCy or NLTK library can be used to analyze the user's input text.

[0551] Based on the analysis results, the server selects learning methods and generates a learning plan, which includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). This generation process is performed by retrieving relevant information from an SQL database and combining learning methods that are appropriate for the analysis results.

[0552] The generated study plan is sent from the server to the device and displayed on a dedicated dashboard on the device. The user can proceed with their studies according to this study plan. For example, it may include specific content such as "watch five 5-10 minute videos per week" or "take a check test on the weekend."

[0553] Specific examples

[0554] If a user inputs information such as "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials," the server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan might be generated that includes "watching five videos a week" and "taking check tests on the weekend."

[0555] While studying, users can submit feedback and questions through the interactive interface. For example, they can submit feedback such as, "I find it difficult to understand the events of a particular period." This feedback is sent to the server and analyzed using natural language processing technology. Based on the analysis results, the server generates a revised lesson plan by including additional video and text materials in the lesson plan. The revised lesson plan is then sent back to the device and provided to the user.

[0556] Prompt Sentence Examples

[0557] User: "I want to learn history efficiently. I only have 30 minutes a day to study, so I want to be as efficient as possible. I prefer to learn using video materials."

[0558] Server: "Generating lesson plan..."

[0559] In this way, the system provides a learning plan that meets the user's individual needs and allows for continuous optimization of the learning process.

[0560] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0561] Step 1:

[0562] The user inputs their learning needs and characteristics. Using the device interface, the user inputs information such as "topics they want to learn," "preferred learning style," and "daily study time." This information is input using text input fields and selection buttons.

[0563] Input: User's learning needs and characteristics (e.g., "I want to learn history efficiently," "I only have 30 minutes a day to study," "I prefer video learning materials")

[0564] Output: User input data

[0565] Step 2:

[0566] The device sends the input to the server using a secure communication protocol (HTTPS), and the data is encrypted during transmission.

[0567] Input: User-entered data

[0568] Output: Data sent to the server

[0569] Step 3:

[0570] The server analyzes the received data using natural language processing techniques, such as the Python spaCy and NLTK libraries, to analyze the user's learning needs and characteristics.

[0571] Input: User input data that reaches the server

[0572] Output: Analyzed user learning needs and characteristics

[0573] Step 4:

[0574] Based on the analysis results, the server selects an appropriate learning method from the database. In this step, SQL is used to search for related learning materials from the database and select a learning method.

[0575] Input: Analyzed user learning needs and characteristics

[0576] Output: The selected learning method

[0577] Step 5:

[0578] The server generates a study plan based on the selected learning method, for example, a study plan that includes watching five 5-10 minute videos per week and a weekend check test.

[0579] Input: Selected learning method

[0580] Output: The generated learning plan

[0581] Step 6:

[0582] The server then sends the generated learning plan to the device, which also uses HTTPS to ensure data protection.

[0583] Input: Generated lesson plan

[0584] Output: The lesson plan sent to the device

[0585] Step 7:

[0586] The device displays the received learning plan to the user, visually arranging the learning plan on a dedicated dashboard for easy user access.

[0587] Input: The lesson plan that arrived on the device

[0588] Output: A displayed lesson plan

[0589] Step 8:

[0590] As users progress through their studies, they can enter feedback and questions into the device's interactive interface, for example by entering text such as, "I'm having difficulty understanding events from a particular period."

[0591] Input: User feedback and questions

[0592] Output: Feedback or questions typed into the terminal

[0593] Step 9:

[0594] The device sends feedback and questions to the server, and this communication also uses a secure protocol (HTTPS).

[0595] Input: Feedback or questions typed into the device

[0596] Output: Feedback or questions sent to the server

[0597] Step 10:

[0598] The server analyzes the received feedback and questions using natural language processing technology. For example, it analyzes feedback such as "It's difficult to understand" and extracts specific problems.

[0599] Input: Feedback and questions that reach the server

[0600] Output: Parsed feedback and questions

[0601] Step 11:

[0602] The server then modifies the learning plan based on the analysis results, selecting additional learning materials as needed and creating a new learning plan.

[0603] Input: Parsed feedback and questions

[0604] Output: Revised lesson plan

[0605] Step 12:

[0606] The server then sends the revised lesson plan to the device, and this communication is also carried out via HTTPS.

[0607] Input: revised lesson plan

[0608] Output: The revised lesson plan sent to the device.

[0609] Step 13:

[0610] The device displays the revised study plan to the user, who then continues studying based on the new plan.

[0611] Input: revised lesson plan arriving at terminal

[0612] Output: The displayed revised lesson plan

[0613] (Application example 1)

[0614] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0615] In modern factories, efficient operation and maintenance of robots is important from the perspective of improving productivity and reducing costs. However, current systems do not individually optimize robot maintenance schedules or new work process learning plans, which can lead to inconsistencies and inefficiencies. Furthermore, the ability to revise learning and maintenance plans in real time based on robot status and feedback is limited. This can lead to problems such as reduced robot utilization rates, excessive or insufficient maintenance, and even stagnant learning progress.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0617] In this invention, the server includes: means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for optimizing the robot's maintenance and learning process, means for collecting and transmitting operation data and sensor data of the robot to the server, means for the server to generate a learning plan and maintenance schedule for the robot, and means for providing the revised robot learning plan and maintenance schedule to the robot. This automates a series of plans for the robot's maintenance and learning process and enables plan revisions in real time, thereby improving productivity and reducing operating costs.

[0618] "User" means an individual or organization that uses this system.

[0619] "Learning needs" is information that indicates what the user wants to learn and their learning goals.

[0620] "Characteristics" is information that indicates personal characteristics such as the user's learning style and how they manage their time.

[0621] "Means" refers to elements that provide a method or function for achieving a specific purpose.

[0622] The "server" is a central computer system that analyzes input data and generates learning methods and plans.

[0623] "Data" refers to information entered by the user and operational and sensor data collected by the robot.

[0624] "Analysis" is the process of examining the input data in detail and selecting appropriate learning methods and maintenance plans based on that information.

[0625] "Learning methods" refer to the means and techniques that allow users to learn most effectively.

[0626] A "learning plan" is a plan that includes specific procedures and schedules for a user or a robot to proceed with learning.

[0627] "Feedback" refers to requests, questions, and opinions provided by users and robots as they progress through learning and maintenance.

[0628] "Revision" is the process of changing an existing plan based on feedback provided.

[0629] "Providing" is the act of passing the generated or modified learning plan or maintenance schedule to a user or robot.

[0630] "Maintenance" refers to activities including inspection and repair necessary to maintain the normal operation of the robot.

[0631] "Operational data" refers to information about the operating status of the robot, such as operating hours and error logs.

[0632] "Sensor data" is information collected from various sensors of the robot.

[0633] "Real-time" refers to near-instantaneous reactions and processing.

[0634] The present invention provides a system that provides an optimal learning method to a user and optimizes the maintenance and learning process of a robot. Specific embodiments for carrying out the invention are described below.

[0635] Users use a dedicated device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to make it easy for users to provide information. This includes items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[0636] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server uses natural language processing (NLP) technology to analyze the user's characteristics and needs based on the received data, and selects the optimal learning method based on the analysis results.

[0637] A learning plan is generated based on the selected learning method. This plan includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard. The user proceeds with their studies according to this plan.

[0638] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more materials." The device then sends this feedback information to the server. The server analyzes the received feedback, modifies the study plan as necessary, and provides it back to the device.

[0639] A similar system can also be applied to factory robots. The robot's operational and sensor data is collected autonomously and sent to a server. The server uses this data to generate and provide learning plans and maintenance schedules for the robot. For example, if a robot's operating time exceeds 500 hours, it may be determined that maintenance is necessary. It also generates video learning materials and on-the-job training plans to help the robot efficiently learn new tasks.

[0640] Feedback allows these plans to be revised in real time and the optimized plans provided back to the robot, preventing over- and under-maintenance and stagnation of learning.

[0641] As a concrete example, the following is an example of a prompt sentence for an AI model that generates a maintenance plan when a factory robot operates for 8 hours a day and the operation time reaches 500 hours.

[0642] Example prompt sentence:

[0643] "A robot with over 500 operating hours requires a comprehensive inspection of its motors and sensors within a week. Errors are occurring frequently during operation. Please suggest appropriate maintenance methods and an efficient learning menu for this robot, which prefers a 'video' and 'hands-on' learning style."

[0644] In this way, the present invention provides an effective learning environment that meets the individual needs of the user or robot, as well as appropriate maintenance of the robot, thereby achieving continuous optimization.

[0645] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0646] Step 1:

[0647] The user uses the terminal to input their learning needs and characteristics.

[0648] The input interface includes text entry fields and selection buttons, and is designed to allow users to provide information such as "topics they want to learn," "learning style preferences," and "daily study time."

[0649] Input: User learning needs and characteristics

[0650] Output: Input data

[0651] Step 2:

[0652] The terminal transmits the input data to the server using a secure communication protocol (HTTPS).

[0653] Specifically, the terminal encrypts the input data and communicates securely with the server.

[0654] Input: Input data

[0655] Output: Data sent to the server

[0656] Step 3:

[0657] The server uses natural language processing (NLP) techniques to analyze the user's characteristics and needs based on the received data.

[0658] Specifically, it analyzes the text data entered by the user, understands keywords and context, and extracts appropriate learning methods.

[0659] Input: Data sent to the server

[0660] Output: Analysis results

[0661] Step 4:

[0662] The server generates a lesson plan based on the analysis results.

[0663] This plan includes the order of topics to be studied and the materials to be used (videos, textbooks, tests, etc.).

[0664] Specifically, NLP technology is used to determine the optimal learning materials and learning order, and a learning plan is created in text format.

[0665] Input: Analysis results

[0666] Output: Learning plan

[0667] Step 5:

[0668] The server sends the generated learning plan to the terminal and displays it on a dedicated dashboard.

[0669] The dashboard has an intuitive interface for easy user access.

[0670] Input:Study plan

[0671] Output: The lesson plan displayed on the user's device

[0672] Step 6:

[0673] As users progress through their learning, they can submit feedback and questions from their device through an interactive interface.

[0674] Specific operations include a process in which users input feedback such as "It's difficult to understand" or "I'd like more information" on the dashboard.

[0675] Input: Feedback or Questions

[0676] Output: Feedback data

[0677] Step 7:

[0678] The terminal transmits the feedback data to the server.

[0679] Specifically, the terminal encrypts the feedback data and transmits it securely to the server.

[0680] Input: Feedback data

[0681] Output: Feedback sent to the server

[0682] Step 8:

[0683] The server analyzes the feedback it receives and modifies the learning plan as needed.

[0684] NLP technology is again used to understand the feedback, and for example, if the feedback is "difficult to understand," additional video materials or supplemental text are generated.

[0685] Input: Feedback data

[0686] Output: Revised lesson plan

[0687] Step 9:

[0688] The revised study plan is again transmitted from the server to the terminal and provided to the user.

[0689] This allows the user to view the updated study plan on the dashboard again and continue studying.

[0690] Input: revised lesson plan

[0691] Output: The revised lesson plan displayed on the user's device.

[0692] Step 10:

[0693] The robot autonomously collects operational data and sensor data and transmits it to a server.

[0694] Specifically, this includes a process in which the robot records operational data and sensor data and periodically transmits it to a server.

[0695] Input: Robot operation data and sensor data

[0696] Output: Robot data sent to the server

[0697] Step 11:

[0698] The server analyzes the robot's operational data and sensor data to generate optimal maintenance schedules and learning plans.

[0699] Specifically, machine learning algorithms are used to analyze data and identify necessary maintenance tasks and learning processes.

[0700] Input: Robot data

[0701] Output: Maintenance schedule and learning plan

[0702] Step 12:

[0703] Providing the robot with a modified robot learning plan and maintenance schedule.

[0704] The robot autonomously carries out maintenance and learning according to the provided plan.

[0705] Input: Maintenance schedule and study plan

[0706] Output: Maintenance and learning progress performed on the robot

[0707] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0708] The present invention is a system for providing users with an optimal learning method and promoting efficient learning, and in particular, is configured to incorporate an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention are described below.

[0709] The system begins with the user entering their learning needs and characteristics using a device. The input interface, which includes text input fields and selection buttons, is designed to allow users to provide information easily. For example, input items include "topics to learn," "learning style preferences," and "daily study hours."

[0710] The entered data is sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data.

[0711] The server uses natural language processing technology to analyze the user's input in detail and identify the learning content and learning style that the user desires. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be analyzed.

[0712] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[0713] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[0714] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[0715] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[0716] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[0717] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[0718] As a specific example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan including "watching five videos per week" and "taking check tests on weekends" may be generated. If the user provides feedback that "events from a particular era are difficult to understand" as they progress through their studies, and the emotion engine detects confusion or a low level of understanding, the server will create a revised study plan including supplementary materials and provide it to the user. In this way, the present invention provides comprehensive study support that even takes the user's emotions into consideration.

[0719] The processing flow will be explained below.

[0720] Step 1:

[0721] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0722] Step 2:

[0723] The device sends the input learning needs and characteristics data to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data security.

[0724] Step 3:

[0725] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[0726] Step 4:

[0727] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[0728] Step 5:

[0729] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[0730] Step 6:

[0731] The server sends the generated learning plan to the terminal.

[0732] Step 7:

[0733] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[0734] Step 8:

[0735] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[0736] Step 9:

[0737] The device's emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, the user's facial expressions and tone of voice are collected through a camera or microphone.

[0738] Step 10:

[0739] The server receives emotion data from the terminal and analyzes it. For example, if a user is confused while watching a video tutorial, the emotion engine will send this information to the server.

[0740] Step 11:

[0741] The server then modifies the lesson plan based on the received emotional data, for example by adding additional videos or texts to improve the lesson plan for areas where the user is struggling.

[0742] Step 12:

[0743] The server transmits the revised lesson plan to the terminal.

[0744] Step 13:

[0745] The device displays the revised lesson plan again to the user.

[0746] Step 14:

[0747] The user continues studying based on the revised study plan, for example, checking the added supplementary materials and studying again to improve their understanding.

[0748] Through this process flow, the system can provide an effective learning environment that responds to the individual needs and emotions of the user.

[0749] Example 2

[0750] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0751] Conventional learning systems have been unable to adequately address the individual learning needs and characteristics of users, making it difficult to maximize learning efficiency. Additionally, it is difficult to provide feedback or modify learning plans that take users' emotions into account, making it impossible to provide an optimal learning process for users.

[0752] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's learning needs and characteristics, means for transmitting the input data to the server, means for transmitting data using a secure communication protocol, means for analyzing the transmitted data using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for acquiring and analyzing the user's emotional data using an emotion engine, means for analyzing the input feedback, questions, and emotional data and revising the learning plan, and means for providing the user with the revised learning plan. This makes it possible to provide an optimized learning process based on the user's needs, characteristics, and emotional state.

[0753] "User" refers to an individual who uses the system to learn.

[0754] "Learning needs" refers to information that indicates a specific topic or purpose that a user wants to learn.

[0755] "Characteristics" refers to individual learning characteristics such as a user's learning style and study time.

[0756] "Input means" refers to the interface through which the user provides learning needs and characteristics to the system.

[0757] "Means for sending" refers to the technology for securely sending the user's input data to the server.

[0758] "Secure communication protocol" refers to a protocol (e.g., HTTPS) for encrypting data and communicating securely.

[0759] "Natural language processing technology" refers to artificial intelligence technology that analyzes user input data and understands the intent and content.

[0760] "Means of analysis" refers to the technology used to analyze input data and understand the needs and characteristics of users.

[0761] "Means of selection" refers to the technology used to select the optimal learning method based on the analysis results.

[0762] A "study plan" refers to a plan that includes specific steps and learning materials for a user to efficiently progress through their studies.

[0763] The "means for providing" refers to an interface for providing the generated lesson plan to the user in an easy-to-view format.

[0764] "Feedback" refers to information that users can use to report their thoughts and questions to the system while studying.

[0765] An "emotion engine" refers to technology for acquiring and analyzing emotions from a user's facial expressions, tone of voice, etc.

[0766] "Emotion data" refers to information about a user's emotional state obtained by an emotion engine.

[0767] "Remediation tools" refers to techniques used to analyze feedback and emotional data and refine lesson plans as needed.

[0768] The present invention is a system for providing a user with an optimal learning method and promoting efficient learning, and in particular has a configuration in which an emotion engine that recognizes the user's emotions is combined. Specific embodiments for carrying out the present invention are described below.

[0769] First, the user uses the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. For example, input items include "topics I want to learn," "preferred learning style," and "daily study time." Specific examples include a user entering "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0770] The entered data is then sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data. Natural language processing technology is used as the analysis method, and the user's input is analyzed in detail. This identifies the learning content the user desires and their appropriate learning style. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" may be analyzed.

[0771] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[0772] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[0773] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[0774] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[0775] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[0776] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[0777] Examples of prompts include:

[0778] "I want to learn history efficiently. I only have 30 minutes a day to study. I prefer video learning materials. Please suggest a specific study plan."

[0779] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0780] Step 1: Enter your learning needs and characteristics

[0781] Users use the device's input interface to input their learning needs and characteristics. Input items include "topics they want to learn," "preferred learning style," and "daily study time." Specific examples include "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0782] Input: Learning needs and characteristics

[0783] Output: Input data (information on learning needs and characteristics)

[0784] Step 2: Sending data

[0785] The terminal sends the data entered by the user to the server using a secure communication protocol (e.g. HTTPS), and the data is encrypted during the process to ensure proper security.

[0786] Input: Input data (encoded learning needs and characteristics information)

[0787] Output: Data sent to the server

[0788] Step 3: Analyze the data

[0789] The server analyzes the received user data using natural language processing technology. Through this analysis, the user's learning needs and characteristics are extracted. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be identified.

[0790] Input: Data sent to the server

[0791] Output: Analysis results (detailed information on learning needs and characteristics)

[0792] Step 4: Find and choose the best learning method

[0793] The server searches the database for learning methods based on the analysis results and selects the most suitable learning method for the user. For example, for a user who prefers video learning materials, it selects a learning plan that focuses on short video learning materials.

[0794] Input: Analysis results

[0795] Output: Optimal learning method

[0796] Step 5: Generate a lesson plan

[0797] The server generates a specific study plan based on the selected learning method. This plan includes study topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[0798] Input: The best way to learn

[0799] Output: The generated learning plan

[0800] Step 6: Provide a learning plan

[0801] The server transmits the generated study plan to the terminal.

[0802] The device displays the received learning plan on a dedicated dashboard for easy access by the user.

[0803] Input: Generated learning plan

[0804] Output: The lesson plan displayed on the device

[0805] Step 7: Recognizing emotions while learning

[0806] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion engine, which analyzes them to identify the user's emotions, such as liking, confusion, or lack of understanding.

[0807] Input: facial expression data and voice data

[0808] Output: Emotion data

[0809] Step 8: Submit your feedback

[0810] Users can submit feedback and questions through an interactive interface while studying, such as "I'm having difficulty understanding events from a particular period."

[0811] Input: Feedback or Questions

[0812] Output: Feedback or questions sent to the server

[0813] Step 9: Analyze feedback and sentiment data

[0814] The server receives the feedback and emotional data and analyzes it using natural language processing technology and an emotional engine, for example, to determine whether a particularly confusing section requires additional learning materials.

[0815] Input: Feedback and emotion data

[0816] Output: Analysis results

[0817] Step 10: Modify your study plan

[0818] Based on the analysis, the server will modify the lesson plan as needed, for example adding supplementary video or text to difficult-to-understand sections.

[0819] Input: Analysis results

[0820] Output: revised lesson plan

[0821] Step 11: Provide a revised lesson plan

[0822] The server transmits the revised lesson plan to the terminal.

[0823] The terminal displays the resubmitted revised lesson plan on a dashboard for easy access by the user.

[0824] Input: revised lesson plan

[0825] Output: The revised lesson plan displayed on the terminal.

[0826] (Application example 2)

[0827] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0828] While conventional learning support systems can provide learning plans based on a user's learning needs and characteristics, they lack the ability to recognize the user's emotional state in real time and flexibly modify the learning content based on that. This can lead to problems such as users becoming confused or continuing their learning without fully understanding the material. This type of flexible response is particularly important when factory workers are learning new machines or procedures.

[0829] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0830] In this invention, the server includes means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for recognizing the user's emotions during the learning process and revising the learning plan based on the emotions, and means for providing supplementary learning materials based on the revised learning plan. This makes it possible to provide a flexible learning plan based on the user's emotional state, significantly improving the efficiency and effectiveness of learning.

[0831] A "user" is a person or agent who has something they want to learn using a learning system.

[0832] "Learning needs" are the topics or themes that users want to learn, as well as their more specific learning requirements and goals.

[0833] "Traits" are individual characteristics such as a user's unique learning style or the amount of time they spend studying.

[0834] "Input means" refers to the interface or device that allows the user to provide the system with the content and learning style they want to learn.

[0835] "Means of sending" refers to the function or mechanism that securely transports the entered data to the server.

[0836] "Means of analysis" refers to the techniques used to interpret the user's needs and characteristics using the received data and determine the most appropriate learning method.

[0837] The "means of selection" is a mechanism that selects the most suitable learning method and learning materials for the user based on the analyzed results.

[0838] A "learning plan" is a specific schedule of learning activities or materials provided to a user.

[0839] "Means of delivery" refers to the interface or mechanism by which the generated lesson plan is presented to the user and made accessible.

[0840] "Feedback" refers to opinions and comments that users make to communicate to the system any questions or level of understanding they may have while studying.

[0841] A "question" is an inquiry that a user makes to the system during learning about something they are unsure of or want to confirm.

[0842] "Avenues for modification" are the ability to make timely changes to your learning plan based on feedback and questions received.

[0843] The "emotion engine" is a technology that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[0844] "Supplemental Materials" are additional educational resources provided to users in response to content that they find difficult to understand or are confused about.

[0845] The present invention is a learning support system that takes into account the learning needs and characteristics of a user, provides an appropriate learning plan according to the learning process, and further recognizes the user's emotions in real time to optimize the learning process. Specifically, the present invention includes the following components.

[0846] 1. Input of user learning needs and characteristics

[0847] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, the amount of time they spend studying each day, etc. The input interface includes text entry fields and selection buttons, and is designed to make it easy for users to provide information.

[0848] 2. Data submission and analysis

[0849] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server then analyzes the received data using natural language processing technology (NLPAnalysis) to identify the optimal learning method based on the user's learning needs and characteristics.

[0850] 3. Creating and providing a learning plan

[0851] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan. This learning plan includes specific learning topics and learning materials (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard.

[0852] 4. Emotion Recognition by Emotion Engine

[0853] During learning, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state. For example, it can detect when the user is confused or interested.

[0854] 5. Feedback and revision of study plans

[0855] Users can submit feedback and questions through the interactive interface. The server analyzes the received feedback and sentiment data and modifies the lesson plan as needed. Modifications may include supplemental materials or additional videos.

[0856] 6. Hardware and Software Used

[0857] This system uses "EmotionEngine" as an emotion engine and "NLPAnalysis" for natural language processing. It uses HTTPS for communication and a relational database such as MongoDB or MySQL for the database. It is intended for use on smartphones, tablets, or head-mounted displays (HMDs).

[0858] Prompt Sentence Examples

[0859] For example, if a worker wants to learn a new welding procedure, the following is entered:

[0860] "What topic would you like to learn about?"

[0861] "Welding Procedure"

[0862] "What is your learning style preference (video, text, etc.)?"

[0863] "video"

[0864] "How many minutes do you study per day?"

[0865] "half an hour"

[0866] Based on this, the server generates a study plan that includes watching six five-minute videos every day. If the server detects that the user is experiencing difficulty or has low comprehension during the study, it can incorporate supplementary videos or additional text into the plan.

[0867] In this way, the system of the present invention provides an efficient learning environment for the user, maximizing the effectiveness of learning.

[0868] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0869] Step 1:

[0870] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, and the amount of time they have to study each day.

[0871] Input: Topic you want to learn, your learning style, and the number of hours you want to study per day

[0872] Output: Data on learning needs and characteristics entered

[0873] How it works: The terminal provides text entry fields and choice buttons into which the user enters information.

[0874] Step 2:

[0875] The input data is sent to the server using a secure communication protocol (HTTPS).

[0876] Input: Data on learning needs and characteristics entered

[0877] Output: Data sent to the server

[0878] How it works: The device uses HTTPS to send the data entered by the user to the server.

[0879] Step 3:

[0880] The server analyzes the received data using natural language processing technology (NLPAnalysis) to analyze the user's learning needs and characteristics.

[0881] Input: Data sent to the server

[0882] Output: Analysis results (user learning needs and characteristics)

[0883] How it works: The server uses NLPAnalysis to analyze text data and identify user needs and characteristics.

[0884] Step 4:

[0885] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan.

[0886] Input: Analysis results

[0887] Output: Learning plan

[0888] Operation: The server checks the database based on the analysis results, selects relevant learning methods and materials, and generates a learning plan.

[0889] Step 5:

[0890] The generated learning plan is sent from the server to the terminal and displayed on a dedicated dashboard.

[0891] Input:Study plan

[0892] Output: The lesson plan displayed on the device.

[0893] Operation: The server sends the generated learning plan to the device, which displays it on the dashboard.

[0894] Step 6:

[0895] The device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state.

[0896] Input: User's facial expression data, voice data

[0897] Output: Emotion analysis results

[0898] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by the Emotion Engine.

[0899] Step 7:

[0900] Users can submit feedback and questions through an interactive interface while learning.

[0901] Input: Feedback, Questions

[0902] Output: Feedback or questions sent to the server

[0903] How it works: The user uses the device interface to input questions or comments that arise during the learning process and sends them to the server.

[0904] Step 8:

[0905] The server analyzes the received feedback and emotional data and adjusts the learning plan as needed.

[0906] Input: Feedback, emotion data

[0907] Output: Revised lesson plan

[0908] How it works: The server uses feedback and emotional data to revise the learning plan, for example by adding new learning materials.

[0909] Step 9:

[0910] The revised study plan is again sent from the server to the terminal and provided to the user.

[0911] Input: revised lesson plan

[0912] Output: The revised lesson plan displayed on the terminal.

[0913] Operation: The server sends the revised lesson plan to the device, which displays it again on the dashboard.

[0914] The above processing steps enable the learning assistance system to provide an efficient and effective learning experience while also responding to the user's emotional state.

[0915] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0916] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0917] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0918] [Third embodiment]

[0919] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0920] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0921] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0922] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0923] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0924] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0925] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0926] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0927] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0928] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0929] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0930] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0931] The present invention is a system for providing a user with an optimal learning method and making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[0932] Users use the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. Examples include items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[0933] The entered data is sent from the device to the server. This transmission is carried out using a secure communication protocol (e.g., HTTPS), ensuring data protection. The server analyzes the user's characteristics and needs based on the received data.

[0934] The server uses natural language processing technology to analyze the received data. This allows it to analyze the user's input in detail and identify the learning content and learning style that the user prefers. For example, it analyzes information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study."

[0935] Based on the analysis results, the server searches the database for learning methods and generates an optimal learning plan for the user. This plan includes the order of learning topics and the learning materials (videos, textbooks, tests, etc.) to be used. For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[0936] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[0937] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more information." The device then sends this feedback information to the server.

[0938] The server analyzes the received feedback and adjusts the learning plan as needed. The analysis uses natural language processing technology to understand the meaning of the feedback. For example, if the feedback is "difficult to understand," additional video materials or supplemental texts will be suggested.

[0939] The revised study plan is then sent back to the terminal from the server and provided to the user, who can then continue their studies based on the revised plan, ensuring a study process that is optimized to the user's needs.

[0940] As a concrete example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan may be generated that includes "watching five videos per week" and "taking check tests on weekends." If the user studies according to this plan and provides feedback along the way that "I'm having difficulty understanding events from a particular period," the server will revise the study plan by adding supplementary videos and text. The server then provides this revised plan to the user, who can continue their studies.

[0941] In this way, the present invention provides an effective learning environment that meets the individual needs of the user, and realizes continuous optimization of the learning process.

[0942] The processing flow will be explained below.

[0943] Step 1:

[0944] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[0945] Step 2:

[0946] The device sends the entered learning needs and characteristics data to the server, using HTTP or HTTPS protocols to ensure data security.

[0947] Step 3:

[0948] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[0949] Step 4:

[0950] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[0951] Step 5:

[0952] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[0953] Step 6:

[0954] The server sends the generated learning plan to the terminal.

[0955] Step 7:

[0956] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[0957] Step 8:

[0958] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[0959] Step 9:

[0960] The user can input any questions or feedback they have during the learning process and send it to the server from their device. For example, they can give feedback such as, "I find it difficult to understand events from a particular period."

[0961] Step 10:

[0962] The server analyzes the feedback and questions received from users, using natural language processing techniques to understand the content of the feedback and identify areas for improvement.

[0963] Step 11:

[0964] The server then adjusts the lesson plan based on the feedback, for example adding additional videos or text for topics that are identified as difficult to understand.

[0965] Step 12:

[0966] The server transmits the revised lesson plan to the terminal.

[0967] Step 13:

[0968] The device displays the revised study plan again to the user, who can then continue studying according to the new plan.

[0969] Through this processing flow, the present system can provide an effective learning environment that meets the individual needs of the user.

[0970] Example 1

[0971] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0972] Conventional learning systems have difficulty responding flexibly to the learning needs and characteristics of individual users. As a result, users are unable to study efficiently, which often leads to a decline in motivation during the learning process and prevents effective knowledge acquisition. In addition, the process of modifying learning plans based on user feedback is often done manually, which is time-consuming and labor-intensive. To solve these issues, it is important to automatically generate and quickly modify learning plans based on user characteristics.

[0973] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0974] In this invention, the server includes means for analyzing the user's learning needs and characteristics using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, and means for analyzing input feedback and questions using natural language processing technology and revising the learning plan. This not only enables the automatic generation of an optimal learning plan tailored to the individual needs of the user, but also makes it possible to quickly revise the learning plan based on the user's feedback.

[0975] "User" refers to a person who uses the system to input learning needs and characteristics and provide feedback.

[0976] "Learning needs" refers to the specific topics or types of materials a user wants to learn, as well as their learning time and style.

[0977] "Characteristics" refers to a user's learning style, habits, and individual learning preferences and characteristics.

[0978] "Input means" refers to the interface through which a user provides learning needs and characteristics to the system. Examples include text entry fields and selection buttons.

[0979] "Information processing device" refers to a computer or server that receives and analyzes data sent by users and generates and modifies learning plans.

[0980] "Natural language processing technology" refers to technology used to analyze data and feedback entered by users. Specifically, it is technology used to understand the meaning of text data.

[0981] "Means for selecting an appropriate learning method" refers to the process of determining the optimal learning method based on automatically analyzed user learning needs and characteristics.

[0982] "Means for generating a learning plan" refers to a process for automatically creating specific learning schedules and learning materials based on the selected learning method.

[0983] "Means for inputting feedback and questions" refers to an interface that allows users to communicate their impressions and questions they have while studying to the system.

[0984] "Revising" refers to the process of reevaluating your learning plan based on feedback and questions and making changes as needed.

[0985] "Audio-visual materials" refers to learning materials that use video or audio, etc. These are materials that allow users to understand the learning content through their eyes and ears.

[0986] "Assessment test" refers to a test or quiz that assesses a user's understanding and progress. It is used to measure the effectiveness of learning.

[0987] The present invention is a system for providing a user with an optimal learning method and for making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[0988] System Configuration

[0989] The system primarily operates between three parties: the user, the device, and the server. The user inputs their learning needs and characteristics through the device interface, and the device sends the information to the server. The server analyzes the received data, generates an optimal learning plan, and provides it to the user via the device. During learning, feedback from the user is also sent to the server, and the learning plan is revised as needed.

[0990] Hardware and software used

[0991] Hardware: Devices (e.g., PCs, tablets, smartphones), servers (high-performance computers, cloud servers, etc.)

[0992] Software: Natural language processing technology (e.g., Python libraries spaCy and NLTK), secure communication protocols (HTTPS), databases (SQL)

[0993] Program processing

[0994] The user uses the device to input their learning needs and characteristics (e.g., "topics they want to learn," "preferred learning style," "daily study time," etc.) This input is done using interfaces such as text input fields and selection buttons.

[0995] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server analyzes the received data using natural language processing techniques to gain a detailed understanding of the user's learning needs and characteristics. For example, Python's spaCy or NLTK library can be used to analyze the user's input text.

[0996] Based on the analysis results, the server selects learning methods and generates a learning plan, which includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). This generation process is performed by retrieving relevant information from an SQL database and combining learning methods that are appropriate for the analysis results.

[0997] The generated study plan is sent from the server to the device and displayed on a dedicated dashboard on the device. The user can proceed with their studies according to this study plan. For example, it may include specific content such as "watch five 5-10 minute videos per week" or "take a check test on the weekend."

[0998] Specific examples

[0999] If a user inputs information such as "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials," the server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan might be generated that includes "watching five videos a week" and "taking check tests on the weekend."

[1000] While studying, users can submit feedback and questions through the interactive interface. For example, they can submit feedback such as, "I find it difficult to understand the events of a particular period." This feedback is sent to the server and analyzed using natural language processing technology. Based on the analysis results, the server generates a revised lesson plan by including additional video and text materials in the lesson plan. The revised lesson plan is then sent back to the device and provided to the user.

[1001] Prompt Sentence Examples

[1002] User: "I want to learn history efficiently. I only have 30 minutes a day to study, so I want to be as efficient as possible. I prefer to learn using video materials."

[1003] Server: "Generating lesson plan..."

[1004] In this way, the system provides a learning plan that meets the user's individual needs and allows for continuous optimization of the learning process.

[1005] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1006] Step 1:

[1007] The user inputs their learning needs and characteristics. Using the device interface, the user inputs information such as "topics they want to learn," "preferred learning style," and "daily study time." This information is input using text input fields and selection buttons.

[1008] Input: User's learning needs and characteristics (e.g., "I want to learn history efficiently," "I only have 30 minutes a day to study," "I prefer video learning materials")

[1009] Output: User input data

[1010] Step 2:

[1011] The device sends the input to the server using a secure communication protocol (HTTPS), and the data is encrypted during transmission.

[1012] Input: User-entered data

[1013] Output: Data sent to the server

[1014] Step 3:

[1015] The server analyzes the received data using natural language processing techniques, such as the Python spaCy and NLTK libraries, to analyze the user's learning needs and characteristics.

[1016] Input: User input data that reaches the server

[1017] Output: Analyzed user learning needs and characteristics

[1018] Step 4:

[1019] Based on the analysis results, the server selects an appropriate learning method from the database. In this step, SQL is used to search for related learning materials from the database and select a learning method.

[1020] Input: Analyzed user learning needs and characteristics

[1021] Output: The selected learning method

[1022] Step 5:

[1023] The server generates a study plan based on the selected learning method, for example, a study plan that includes watching five 5-10 minute videos per week and a weekend check test.

[1024] Input: Selected learning method

[1025] Output: The generated learning plan

[1026] Step 6:

[1027] The server then sends the generated learning plan to the device, which also uses HTTPS to ensure data protection.

[1028] Input: Generated lesson plan

[1029] Output: The lesson plan sent to the device

[1030] Step 7:

[1031] The device displays the received learning plan to the user, visually arranging the learning plan on a dedicated dashboard for easy user access.

[1032] Input: The lesson plan that arrived on the device

[1033] Output: A displayed lesson plan

[1034] Step 8:

[1035] As users progress through their studies, they can enter feedback and questions into the device's interactive interface, for example by entering text such as, "I'm having difficulty understanding events from a particular period."

[1036] Input: User feedback and questions

[1037] Output: Feedback or questions typed into the terminal

[1038] Step 9:

[1039] The device sends feedback and questions to the server, and this communication also uses a secure protocol (HTTPS).

[1040] Input: Feedback or questions typed into the device

[1041] Output: Feedback or questions sent to the server

[1042] Step 10:

[1043] The server analyzes the received feedback and questions using natural language processing technology. For example, it analyzes feedback such as "It's difficult to understand" and extracts specific problems.

[1044] Input: Feedback and questions that reach the server

[1045] Output: Parsed feedback and questions

[1046] Step 11:

[1047] The server then modifies the learning plan based on the analysis results, selecting additional learning materials as needed and creating a new learning plan.

[1048] Input: Parsed feedback and questions

[1049] Output: Revised lesson plan

[1050] Step 12:

[1051] The server then sends the revised lesson plan to the device, and this communication is also carried out via HTTPS.

[1052] Input: revised lesson plan

[1053] Output: The revised lesson plan sent to the device.

[1054] Step 13:

[1055] The device displays the revised study plan to the user, who then continues studying based on the new plan.

[1056] Input: revised lesson plan arriving at terminal

[1057] Output: The displayed revised lesson plan

[1058] (Application example 1)

[1059] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1060] In modern factories, efficient operation and maintenance of robots is important from the perspective of improving productivity and reducing costs. However, current systems do not individually optimize robot maintenance schedules or new work process learning plans, which can lead to inconsistencies and inefficiencies. Furthermore, the ability to revise learning and maintenance plans in real time based on robot status and feedback is limited. This can lead to problems such as reduced robot utilization rates, excessive or insufficient maintenance, and even stagnant learning progress.

[1061] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1062] In this invention, the server includes: means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for optimizing the robot's maintenance and learning process, means for collecting and transmitting operation data and sensor data of the robot to the server, means for the server to generate a learning plan and maintenance schedule for the robot, and means for providing the revised robot learning plan and maintenance schedule to the robot. This automates a series of plans for the robot's maintenance and learning process and enables plan revisions in real time, thereby improving productivity and reducing operating costs.

[1063] "User" means an individual or organization that uses this system.

[1064] "Learning needs" is information that indicates what the user wants to learn and their learning goals.

[1065] "Characteristics" is information that indicates personal characteristics such as the user's learning style and how they manage their time.

[1066] "Means" refers to elements that provide a method or function for achieving a specific purpose.

[1067] The "server" is a central computer system that analyzes input data and generates learning methods and plans.

[1068] "Data" refers to information entered by the user and operational and sensor data collected by the robot.

[1069] "Analysis" is the process of examining the input data in detail and selecting appropriate learning methods and maintenance plans based on that information.

[1070] "Learning methods" refer to the means and techniques that allow users to learn most effectively.

[1071] A "learning plan" is a plan that includes specific procedures and schedules for a user or a robot to proceed with learning.

[1072] "Feedback" refers to requests, questions, and opinions provided by users and robots as they progress through learning and maintenance.

[1073] "Revision" is the process of changing an existing plan based on feedback provided.

[1074] "Providing" is the act of passing the generated or modified learning plan or maintenance schedule to a user or robot.

[1075] "Maintenance" refers to activities including inspection and repair necessary to maintain the normal operation of the robot.

[1076] "Operational data" refers to information about the operating status of the robot, such as operating hours and error logs.

[1077] "Sensor data" is information collected from various sensors of the robot.

[1078] "Real-time" refers to near-instantaneous reactions and processing.

[1079] The present invention provides a system that provides an optimal learning method to a user and optimizes the maintenance and learning process of a robot. Specific embodiments for carrying out the invention are described below.

[1080] Users use a dedicated device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to make it easy for users to provide information. This includes items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[1081] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server uses natural language processing (NLP) technology to analyze the user's characteristics and needs based on the received data, and selects the optimal learning method based on the analysis results.

[1082] A learning plan is generated based on the selected learning method. This plan includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard. The user proceeds with their studies according to this plan.

[1083] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more materials." The device then sends this feedback information to the server. The server analyzes the received feedback, modifies the study plan as necessary, and provides it back to the device.

[1084] A similar system can also be applied to factory robots. The robot's operational and sensor data is collected autonomously and sent to a server. The server uses this data to generate and provide learning plans and maintenance schedules for the robot. For example, if a robot's operating time exceeds 500 hours, it may be determined that maintenance is necessary. It also generates video learning materials and on-the-job training plans to help the robot efficiently learn new tasks.

[1085] Feedback allows these plans to be revised in real time and the optimized plans provided back to the robot, preventing over- and under-maintenance and stagnation of learning.

[1086] As a concrete example, the following is an example of a prompt sentence for an AI model that generates a maintenance plan when a factory robot operates for 8 hours a day and the operation time reaches 500 hours.

[1087] Example prompt sentence:

[1088] "A robot with over 500 operating hours requires a comprehensive inspection of its motors and sensors within a week. Errors are occurring frequently during operation. Please suggest appropriate maintenance methods and an efficient learning menu for this robot, which prefers a 'video' and 'hands-on' learning style."

[1089] In this way, the present invention provides an effective learning environment that meets the individual needs of the user or robot, as well as appropriate maintenance of the robot, thereby achieving continuous optimization.

[1090] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1091] Step 1:

[1092] The user uses the terminal to input their learning needs and characteristics.

[1093] The input interface includes text entry fields and selection buttons, and is designed to allow users to provide information such as "topics they want to learn," "learning style preferences," and "daily study time."

[1094] Input: User learning needs and characteristics

[1095] Output: Input data

[1096] Step 2:

[1097] The terminal transmits the input data to the server using a secure communication protocol (HTTPS).

[1098] Specifically, the terminal encrypts the input data and communicates securely with the server.

[1099] Input: Input data

[1100] Output: Data sent to the server

[1101] Step 3:

[1102] The server uses natural language processing (NLP) techniques to analyze the user's characteristics and needs based on the received data.

[1103] Specifically, it analyzes the text data entered by the user, understands keywords and context, and extracts appropriate learning methods.

[1104] Input: Data sent to the server

[1105] Output: Analysis results

[1106] Step 4:

[1107] The server generates a lesson plan based on the analysis results.

[1108] This plan includes the order of topics to be studied and the materials to be used (videos, textbooks, tests, etc.).

[1109] Specifically, NLP technology is used to determine the optimal learning materials and learning order, and a learning plan is created in text format.

[1110] Input: Analysis results

[1111] Output: Learning plan

[1112] Step 5:

[1113] The server sends the generated learning plan to the terminal and displays it on a dedicated dashboard.

[1114] The dashboard has an intuitive interface for easy user access.

[1115] Input:Study plan

[1116] Output: The lesson plan displayed on the user's device

[1117] Step 6:

[1118] As users progress through their learning, they can submit feedback and questions from their device through an interactive interface.

[1119] Specific operations include a process in which users input feedback such as "It's difficult to understand" or "I'd like more information" on the dashboard.

[1120] Input: Feedback or Questions

[1121] Output: Feedback data

[1122] Step 7:

[1123] The terminal transmits the feedback data to the server.

[1124] Specifically, the terminal encrypts the feedback data and transmits it securely to the server.

[1125] Input: Feedback data

[1126] Output: Feedback sent to the server

[1127] Step 8:

[1128] The server analyzes the feedback it receives and modifies the learning plan as needed.

[1129] NLP technology is again used to understand the feedback, and for example, if the feedback is "difficult to understand," additional video materials or supplemental text are generated.

[1130] Input: Feedback data

[1131] Output: Revised lesson plan

[1132] Step 9:

[1133] The revised study plan is again transmitted from the server to the terminal and provided to the user.

[1134] This allows the user to view the updated study plan on the dashboard again and continue studying.

[1135] Input: revised lesson plan

[1136] Output: The revised lesson plan displayed on the user's device.

[1137] Step 10:

[1138] The robot autonomously collects operational data and sensor data and transmits it to a server.

[1139] Specifically, this includes a process in which the robot records operational data and sensor data and periodically transmits it to a server.

[1140] Input: Robot operation data and sensor data

[1141] Output: Robot data sent to the server

[1142] Step 11:

[1143] The server analyzes the robot's operational data and sensor data to generate optimal maintenance schedules and learning plans.

[1144] Specifically, machine learning algorithms are used to analyze data and identify necessary maintenance tasks and learning processes.

[1145] Input: Robot data

[1146] Output: Maintenance schedule and learning plan

[1147] Step 12:

[1148] Providing the robot with a modified robot learning plan and maintenance schedule.

[1149] The robot autonomously carries out maintenance and learning according to the provided plan.

[1150] Input: Maintenance schedule and study plan

[1151] Output: Maintenance and learning progress performed on the robot

[1152] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1153] The present invention is a system for providing users with an optimal learning method and promoting efficient learning, and in particular, is configured to incorporate an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention are described below.

[1154] The system begins with the user entering their learning needs and characteristics using a device. The input interface, which includes text input fields and selection buttons, is designed to allow users to provide information easily. For example, input items include "topics to learn," "learning style preferences," and "daily study hours."

[1155] The entered data is sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data.

[1156] The server uses natural language processing technology to analyze the user's input in detail and identify the learning content and learning style that the user desires. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be analyzed.

[1157] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[1158] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[1159] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[1160] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[1161] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[1162] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[1163] As a specific example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan including "watching five videos per week" and "taking check tests on weekends" may be generated. If the user provides feedback that "events from a particular era are difficult to understand" as they progress through their studies, and the emotion engine detects confusion or a low level of understanding, the server will create a revised study plan including supplementary materials and provide it to the user. In this way, the present invention provides comprehensive study support that even takes the user's emotions into consideration.

[1164] The processing flow will be explained below.

[1165] Step 1:

[1166] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[1167] Step 2:

[1168] The device sends the input learning needs and characteristics data to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data security.

[1169] Step 3:

[1170] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[1171] Step 4:

[1172] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[1173] Step 5:

[1174] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[1175] Step 6:

[1176] The server sends the generated learning plan to the terminal.

[1177] Step 7:

[1178] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[1179] Step 8:

[1180] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[1181] Step 9:

[1182] The device's emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, the user's facial expressions and tone of voice are collected through a camera or microphone.

[1183] Step 10:

[1184] The server receives emotion data from the terminal and analyzes it. For example, if a user is confused while watching a video tutorial, the emotion engine will send this information to the server.

[1185] Step 11:

[1186] The server then modifies the lesson plan based on the received emotional data, for example by adding additional videos or texts to improve the lesson plan for areas where the user is struggling.

[1187] Step 12:

[1188] The server transmits the revised lesson plan to the terminal.

[1189] Step 13:

[1190] The device displays the revised lesson plan again to the user.

[1191] Step 14:

[1192] The user continues studying based on the revised study plan, for example, checking the added supplementary materials and studying again to improve their understanding.

[1193] Through this process flow, the system can provide an effective learning environment that responds to the individual needs and emotions of the user.

[1194] Example 2

[1195] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1196] Conventional learning systems have been unable to adequately address the individual learning needs and characteristics of users, making it difficult to maximize learning efficiency. Additionally, it is difficult to provide feedback or modify learning plans that take users' emotions into account, making it impossible to provide an optimal learning process for users.

[1197] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's learning needs and characteristics, means for transmitting the input data to the server, means for transmitting data using a secure communication protocol, means for analyzing the transmitted data using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for acquiring and analyzing the user's emotional data using an emotion engine, means for analyzing the input feedback, questions, and emotional data and revising the learning plan, and means for providing the user with the revised learning plan. This makes it possible to provide an optimized learning process based on the user's needs, characteristics, and emotional state.

[1198] "User" refers to an individual who uses the system to learn.

[1199] "Learning needs" refers to information that indicates a specific topic or purpose that a user wants to learn.

[1200] "Characteristics" refers to individual learning characteristics such as a user's learning style and study time.

[1201] "Input means" refers to the interface through which the user provides learning needs and characteristics to the system.

[1202] "Means for sending" refers to the technology for securely sending the user's input data to the server.

[1203] "Secure communication protocol" refers to a protocol (e.g., HTTPS) for encrypting data and communicating securely.

[1204] "Natural language processing technology" refers to artificial intelligence technology that analyzes user input data and understands the intent and content.

[1205] "Means of analysis" refers to the technology used to analyze input data and understand the needs and characteristics of users.

[1206] "Means of selection" refers to the technology used to select the optimal learning method based on the analysis results.

[1207] A "study plan" refers to a plan that includes specific steps and learning materials for a user to efficiently progress through their studies.

[1208] The "means for providing" refers to an interface for providing the generated lesson plan to the user in an easy-to-view format.

[1209] "Feedback" refers to information that users can use to report their thoughts and questions to the system while studying.

[1210] An "emotion engine" refers to technology for acquiring and analyzing emotions from a user's facial expressions, tone of voice, etc.

[1211] "Emotion data" refers to information about a user's emotional state obtained by an emotion engine.

[1212] "Remediation tools" refers to techniques used to analyze feedback and emotional data and refine lesson plans as needed.

[1213] The present invention is a system for providing a user with an optimal learning method and promoting efficient learning, and in particular has a configuration in which an emotion engine that recognizes the user's emotions is combined. Specific embodiments for carrying out the present invention are described below.

[1214] First, the user uses the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. For example, input items include "topics I want to learn," "preferred learning style," and "daily study time." Specific examples include a user entering "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[1215] The entered data is then sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data. Natural language processing technology is used as the analysis method, and the user's input is analyzed in detail. This identifies the learning content the user desires and their appropriate learning style. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" may be analyzed.

[1216] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[1217] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[1218] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[1219] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[1220] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[1221] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[1222] Examples of prompts include:

[1223] "I want to learn history efficiently. I only have 30 minutes a day to study. I prefer video learning materials. Please suggest a specific study plan."

[1224] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1225] Step 1: Enter your learning needs and characteristics

[1226] Users use the device's input interface to input their learning needs and characteristics. Input items include "topics they want to learn," "preferred learning style," and "daily study time." Specific examples include "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[1227] Input: Learning needs and characteristics

[1228] Output: Input data (information on learning needs and characteristics)

[1229] Step 2: Sending data

[1230] The terminal sends the data entered by the user to the server using a secure communication protocol (e.g. HTTPS), and the data is encrypted during the process to ensure proper security.

[1231] Input: Input data (encoded learning needs and characteristics information)

[1232] Output: Data sent to the server

[1233] Step 3: Analyze the data

[1234] The server analyzes the received user data using natural language processing technology. Through this analysis, the user's learning needs and characteristics are extracted. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be identified.

[1235] Input: Data sent to the server

[1236] Output: Analysis results (detailed information on learning needs and characteristics)

[1237] Step 4: Find and choose the best learning method

[1238] The server searches the database for learning methods based on the analysis results and selects the most suitable learning method for the user. For example, for a user who prefers video learning materials, it selects a learning plan that focuses on short video learning materials.

[1239] Input: Analysis results

[1240] Output: Optimal learning method

[1241] Step 5: Generate a lesson plan

[1242] The server generates a specific study plan based on the selected learning method. This plan includes study topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[1243] Input: The best way to learn

[1244] Output: The generated learning plan

[1245] Step 6: Provide a learning plan

[1246] The server transmits the generated study plan to the terminal.

[1247] The device displays the received learning plan on a dedicated dashboard for easy access by the user.

[1248] Input: Generated learning plan

[1249] Output: The lesson plan displayed on the device

[1250] Step 7: Recognizing emotions while learning

[1251] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion engine, which analyzes them to identify the user's emotions, such as liking, confusion, or lack of understanding.

[1252] Input: facial expression data and voice data

[1253] Output: Emotion data

[1254] Step 8: Submit your feedback

[1255] Users can submit feedback and questions through an interactive interface while studying, such as "I'm having difficulty understanding events from a particular period."

[1256] Input: Feedback or Questions

[1257] Output: Feedback or questions sent to the server

[1258] Step 9: Analyze feedback and sentiment data

[1259] The server receives the feedback and emotional data and analyzes it using natural language processing technology and an emotional engine, for example, to determine whether a particularly confusing section requires additional learning materials.

[1260] Input: Feedback and emotion data

[1261] Output: Analysis results

[1262] Step 10: Modify your study plan

[1263] Based on the analysis, the server will modify the lesson plan as needed, for example adding supplementary video or text to difficult-to-understand sections.

[1264] Input: Analysis results

[1265] Output: revised lesson plan

[1266] Step 11: Provide a revised lesson plan

[1267] The server transmits the revised lesson plan to the terminal.

[1268] The terminal displays the resubmitted revised lesson plan on a dashboard for easy access by the user.

[1269] Input: revised lesson plan

[1270] Output: The revised lesson plan displayed on the terminal.

[1271] (Application example 2)

[1272] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1273] While conventional learning support systems can provide learning plans based on a user's learning needs and characteristics, they lack the ability to recognize the user's emotional state in real time and flexibly modify the learning content based on that. This can lead to problems such as users becoming confused or continuing their learning without fully understanding the material. This type of flexible response is particularly important when factory workers are learning new machines or procedures.

[1274] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1275] In this invention, the server includes means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for recognizing the user's emotions during the learning process and revising the learning plan based on the emotions, and means for providing supplementary learning materials based on the revised learning plan. This makes it possible to provide a flexible learning plan based on the user's emotional state, significantly improving the efficiency and effectiveness of learning.

[1276] A "user" is a person or agent who has something they want to learn using a learning system.

[1277] "Learning needs" are the topics or themes that users want to learn, as well as their more specific learning requirements and goals.

[1278] "Traits" are individual characteristics such as a user's unique learning style or the amount of time they spend studying.

[1279] "Input means" refers to the interface or device that allows the user to provide the system with the content and learning style they want to learn.

[1280] "Means of sending" refers to the function or mechanism that securely transports the entered data to the server.

[1281] "Means of analysis" refers to the techniques used to interpret the user's needs and characteristics using the received data and determine the most appropriate learning method.

[1282] The "means of selection" is a mechanism that selects the most suitable learning method and learning materials for the user based on the analyzed results.

[1283] A "learning plan" is a specific schedule of learning activities or materials provided to a user.

[1284] "Means of delivery" refers to the interface or mechanism by which the generated lesson plan is presented to the user and made accessible.

[1285] "Feedback" refers to opinions and comments that users make to communicate to the system any questions or level of understanding they may have while studying.

[1286] A "question" is an inquiry that a user makes to the system during learning about something they are unsure of or want to confirm.

[1287] "Avenues for modification" are the ability to make timely changes to your learning plan based on feedback and questions received.

[1288] The "emotion engine" is a technology that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[1289] "Supplemental Materials" are additional educational resources provided to users in response to content that they find difficult to understand or are confused about.

[1290] The present invention is a learning support system that takes into account the learning needs and characteristics of a user, provides an appropriate learning plan according to the learning process, and further recognizes the user's emotions in real time to optimize the learning process. Specifically, the present invention includes the following components.

[1291] 1. Input of user learning needs and characteristics

[1292] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, the amount of time they spend studying each day, etc. The input interface includes text entry fields and selection buttons, and is designed to make it easy for users to provide information.

[1293] 2. Data submission and analysis

[1294] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server then analyzes the received data using natural language processing technology (NLPAnalysis) to identify the optimal learning method based on the user's learning needs and characteristics.

[1295] 3. Creating and providing a learning plan

[1296] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan. This learning plan includes specific learning topics and learning materials (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard.

[1297] 4. Emotion Recognition by Emotion Engine

[1298] During learning, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state. For example, it can detect when the user is confused or interested.

[1299] 5. Feedback and revision of study plans

[1300] Users can submit feedback and questions through the interactive interface. The server analyzes the received feedback and sentiment data and modifies the lesson plan as needed. Modifications may include supplemental materials or additional videos.

[1301] 6. Hardware and Software Used

[1302] This system uses "EmotionEngine" as an emotion engine and "NLPAnalysis" for natural language processing. It uses HTTPS for communication and a relational database such as MongoDB or MySQL for the database. It is intended for use on smartphones, tablets, or head-mounted displays (HMDs).

[1303] Prompt Sentence Examples

[1304] For example, if a worker wants to learn a new welding procedure, the following is entered:

[1305] "What topic would you like to learn about?"

[1306] "Welding Procedure"

[1307] "What is your learning style preference (video, text, etc.)?"

[1308] "video"

[1309] "How many minutes do you study per day?"

[1310] "half an hour"

[1311] Based on this, the server generates a study plan that includes watching six five-minute videos every day. If the server detects that the user is experiencing difficulty or has low comprehension during the study, it can incorporate supplementary videos or additional text into the plan.

[1312] In this way, the system of the present invention provides an efficient learning environment for the user, maximizing the effectiveness of learning.

[1313] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1314] Step 1:

[1315] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, and the amount of time they have to study each day.

[1316] Input: Topic you want to learn, your learning style, and the number of hours you want to study per day

[1317] Output: Data on learning needs and characteristics entered

[1318] How it works: The terminal provides text entry fields and choice buttons into which the user enters information.

[1319] Step 2:

[1320] The input data is sent to the server using a secure communication protocol (HTTPS).

[1321] Input: Data on learning needs and characteristics entered

[1322] Output: Data sent to the server

[1323] How it works: The device uses HTTPS to send the data entered by the user to the server.

[1324] Step 3:

[1325] The server analyzes the received data using natural language processing technology (NLPAnalysis) to analyze the user's learning needs and characteristics.

[1326] Input: Data sent to the server

[1327] Output: Analysis results (user learning needs and characteristics)

[1328] How it works: The server uses NLPAnalysis to analyze text data and identify user needs and characteristics.

[1329] Step 4:

[1330] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan.

[1331] Input: Analysis results

[1332] Output: Learning plan

[1333] Operation: The server checks the database based on the analysis results, selects relevant learning methods and materials, and generates a learning plan.

[1334] Step 5:

[1335] The generated learning plan is sent from the server to the terminal and displayed on a dedicated dashboard.

[1336] Input:Study plan

[1337] Output: The lesson plan displayed on the device.

[1338] Operation: The server sends the generated learning plan to the device, which displays it on the dashboard.

[1339] Step 6:

[1340] The device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state.

[1341] Input: User's facial expression data, voice data

[1342] Output: Emotion analysis results

[1343] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by the Emotion Engine.

[1344] Step 7:

[1345] Users can submit feedback and questions through an interactive interface while learning.

[1346] Input: Feedback, Questions

[1347] Output: Feedback or questions sent to the server

[1348] How it works: The user uses the device interface to input questions or comments that arise during the learning process and sends them to the server.

[1349] Step 8:

[1350] The server analyzes the received feedback and emotional data and adjusts the learning plan as needed.

[1351] Input: Feedback, emotion data

[1352] Output: Revised lesson plan

[1353] How it works: The server uses feedback and emotional data to revise the learning plan, for example by adding new learning materials.

[1354] Step 9:

[1355] The revised study plan is again sent from the server to the terminal and provided to the user.

[1356] Input: revised lesson plan

[1357] Output: The revised lesson plan displayed on the terminal.

[1358] Operation: The server sends the revised lesson plan to the device, which displays it again on the dashboard.

[1359] The above processing steps enable the learning assistance system to provide an efficient and effective learning experience while also responding to the user's emotional state.

[1360] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1361] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1362] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1363] [Fourth embodiment]

[1364] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1365] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1366] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1367] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1368] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1369] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1370] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1371] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1372] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1373] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1374] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1375] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1376] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1377] The present invention is a system for providing a user with an optimal learning method and making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[1378] Users use the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. Examples include items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[1379] The entered data is sent from the device to the server. This transmission is carried out using a secure communication protocol (e.g., HTTPS), ensuring data protection. The server analyzes the user's characteristics and needs based on the received data.

[1380] The server uses natural language processing technology to analyze the received data. This allows it to analyze the user's input in detail and identify the learning content and learning style that the user prefers. For example, it analyzes information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study."

[1381] Based on the analysis results, the server searches the database for learning methods and generates an optimal learning plan for the user. This plan includes the order of learning topics and the learning materials (videos, textbooks, tests, etc.) to be used. For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[1382] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[1383] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more information." The device then sends this feedback information to the server.

[1384] The server analyzes the received feedback and adjusts the learning plan as needed. The analysis uses natural language processing technology to understand the meaning of the feedback. For example, if the feedback is "difficult to understand," additional video materials or supplemental texts will be suggested.

[1385] The revised study plan is then sent back to the terminal from the server and provided to the user, who can then continue their studies based on the revised plan, ensuring a study process that is optimized to the user's needs.

[1386] As a concrete example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan may be generated that includes "watching five videos per week" and "taking check tests on weekends." If the user studies according to this plan and provides feedback along the way that "I'm having difficulty understanding events from a particular period," the server will revise the study plan by adding supplementary videos and text. The server then provides this revised plan to the user, who can continue their studies.

[1387] In this way, the present invention provides an effective learning environment that meets the individual needs of the user, and realizes continuous optimization of the learning process.

[1388] The processing flow will be explained below.

[1389] Step 1:

[1390] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[1391] Step 2:

[1392] The device sends the entered learning needs and characteristics data to the server, using HTTP or HTTPS protocols to ensure data security.

[1393] Step 3:

[1394] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[1395] Step 4:

[1396] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[1397] Step 5:

[1398] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[1399] Step 6:

[1400] The server sends the generated learning plan to the terminal.

[1401] Step 7:

[1402] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[1403] Step 8:

[1404] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[1405] Step 9:

[1406] The user can input any questions or feedback they have during the learning process and send it to the server from their device. For example, they can give feedback such as, "I find it difficult to understand events from a particular period."

[1407] Step 10:

[1408] The server analyzes the feedback and questions received from users, using natural language processing techniques to understand the content of the feedback and identify areas for improvement.

[1409] Step 11:

[1410] The server then adjusts the lesson plan based on the feedback, for example adding additional videos or text for topics that are identified as difficult to understand.

[1411] Step 12:

[1412] The server transmits the revised lesson plan to the terminal.

[1413] Step 13:

[1414] The device displays the revised study plan again to the user, who can then continue studying according to the new plan.

[1415] Through this processing flow, the present system can provide an effective learning environment that meets the individual needs of the user.

[1416] Example 1

[1417] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1418] Conventional learning systems have difficulty responding flexibly to the learning needs and characteristics of individual users. As a result, users are unable to study efficiently, which often leads to a decline in motivation during the learning process and prevents effective knowledge acquisition. In addition, the process of modifying learning plans based on user feedback is often done manually, which is time-consuming and labor-intensive. To solve these issues, it is important to automatically generate and quickly modify learning plans based on user characteristics.

[1419] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1420] In this invention, the server includes means for analyzing the user's learning needs and characteristics using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, and means for analyzing input feedback and questions using natural language processing technology and revising the learning plan. This not only enables the automatic generation of an optimal learning plan tailored to the individual needs of the user, but also makes it possible to quickly revise the learning plan based on the user's feedback.

[1421] "User" refers to a person who uses the system to input learning needs and characteristics and provide feedback.

[1422] "Learning needs" refers to the specific topics or types of materials a user wants to learn, as well as their learning time and style.

[1423] "Characteristics" refers to a user's learning style, habits, and individual learning preferences and characteristics.

[1424] "Input means" refers to the interface through which a user provides learning needs and characteristics to the system. Examples include text entry fields and selection buttons.

[1425] "Information processing device" refers to a computer or server that receives and analyzes data sent by users and generates and modifies learning plans.

[1426] "Natural language processing technology" refers to technology used to analyze data and feedback entered by users. Specifically, it is technology used to understand the meaning of text data.

[1427] "Means for selecting an appropriate learning method" refers to the process of determining the optimal learning method based on automatically analyzed user learning needs and characteristics.

[1428] "Means for generating a learning plan" refers to a process for automatically creating specific learning schedules and learning materials based on the selected learning method.

[1429] "Means for inputting feedback and questions" refers to an interface that allows users to communicate their impressions and questions they have while studying to the system.

[1430] "Revising" refers to the process of reevaluating your learning plan based on feedback and questions and making changes as needed.

[1431] "Audio-visual materials" refers to learning materials that use video or audio, etc. These are materials that allow users to understand the learning content through their eyes and ears.

[1432] "Assessment test" refers to a test or quiz that assesses a user's understanding and progress. It is used to measure the effectiveness of learning.

[1433] The present invention is a system for providing a user with an optimal learning method and for making the learning process more efficient. Specific embodiments for carrying out the present invention are described below.

[1434] System Configuration

[1435] The system primarily operates between three parties: the user, the device, and the server. The user inputs their learning needs and characteristics through the device interface, and the device sends the information to the server. The server analyzes the received data, generates an optimal learning plan, and provides it to the user via the device. During learning, feedback from the user is also sent to the server, and the learning plan is revised as needed.

[1436] Hardware and software used

[1437] Hardware: Devices (e.g., PCs, tablets, smartphones), servers (high-performance computers, cloud servers, etc.)

[1438] Software: Natural language processing technology (e.g., Python libraries spaCy and NLTK), secure communication protocols (HTTPS), databases (SQL)

[1439] Program processing

[1440] The user uses the device to input their learning needs and characteristics (e.g., "topics they want to learn," "preferred learning style," "daily study time," etc.) This input is done using interfaces such as text input fields and selection buttons.

[1441] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server analyzes the received data using natural language processing techniques to gain a detailed understanding of the user's learning needs and characteristics. For example, Python's spaCy or NLTK library can be used to analyze the user's input text.

[1442] Based on the analysis results, the server selects learning methods and generates a learning plan, which includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). This generation process is performed by retrieving relevant information from an SQL database and combining learning methods that are appropriate for the analysis results.

[1443] The generated study plan is sent from the server to the device and displayed on a dedicated dashboard on the device. The user can proceed with their studies according to this study plan. For example, it may include specific content such as "watch five 5-10 minute videos per week" or "take a check test on the weekend."

[1444] Specific examples

[1445] If a user inputs information such as "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials," the server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan might be generated that includes "watching five videos a week" and "taking check tests on the weekend."

[1446] While studying, users can submit feedback and questions through the interactive interface. For example, they can submit feedback such as, "I find it difficult to understand the events of a particular period." This feedback is sent to the server and analyzed using natural language processing technology. Based on the analysis results, the server generates a revised lesson plan by including additional video and text materials in the lesson plan. The revised lesson plan is then sent back to the device and provided to the user.

[1447] Prompt Sentence Examples

[1448] User: "I want to learn history efficiently. I only have 30 minutes a day to study, so I want to be as efficient as possible. I prefer to learn using video materials."

[1449] Server: "Generating lesson plan..."

[1450] In this way, the system provides a learning plan that meets the user's individual needs and allows for continuous optimization of the learning process.

[1451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1452] Step 1:

[1453] The user inputs their learning needs and characteristics. Using the device interface, the user inputs information such as "topics they want to learn," "preferred learning style," and "daily study time." This information is input using text input fields and selection buttons.

[1454] Input: User's learning needs and characteristics (e.g., "I want to learn history efficiently," "I only have 30 minutes a day to study," "I prefer video learning materials")

[1455] Output: User input data

[1456] Step 2:

[1457] The device sends the input to the server using a secure communication protocol (HTTPS), and the data is encrypted during transmission.

[1458] Input: User-entered data

[1459] Output: Data sent to the server

[1460] Step 3:

[1461] The server analyzes the received data using natural language processing techniques, such as the Python spaCy and NLTK libraries, to analyze the user's learning needs and characteristics.

[1462] Input: User input data that reaches the server

[1463] Output: Analyzed user learning needs and characteristics

[1464] Step 4:

[1465] Based on the analysis results, the server selects an appropriate learning method from the database. In this step, SQL is used to search for related learning materials from the database and select a learning method.

[1466] Input: Analyzed user learning needs and characteristics

[1467] Output: The selected learning method

[1468] Step 5:

[1469] The server generates a study plan based on the selected learning method, for example, a study plan that includes watching five 5-10 minute videos per week and a weekend check test.

[1470] Input: Selected learning method

[1471] Output: The generated learning plan

[1472] Step 6:

[1473] The server then sends the generated learning plan to the device, which also uses HTTPS to ensure data protection.

[1474] Input: Generated lesson plan

[1475] Output: The lesson plan sent to the device

[1476] Step 7:

[1477] The device displays the received learning plan to the user, visually arranging the learning plan on a dedicated dashboard for easy user access.

[1478] Input: The lesson plan that arrived on the device

[1479] Output: A displayed lesson plan

[1480] Step 8:

[1481] As users progress through their studies, they can enter feedback and questions into the device's interactive interface, for example by entering text such as, "I'm having difficulty understanding events from a particular period."

[1482] Input: User feedback and questions

[1483] Output: Feedback or questions typed into the terminal

[1484] Step 9:

[1485] The device sends feedback and questions to the server, and this communication also uses a secure protocol (HTTPS).

[1486] Input: Feedback or questions typed into the device

[1487] Output: Feedback or questions sent to the server

[1488] Step 10:

[1489] The server analyzes the received feedback and questions using natural language processing technology. For example, it analyzes feedback such as "It's difficult to understand" and extracts specific problems.

[1490] Input: Feedback and questions that reach the server

[1491] Output: Parsed feedback and questions

[1492] Step 11:

[1493] The server then modifies the learning plan based on the analysis results, selecting additional learning materials as needed and creating a new learning plan.

[1494] Input: Parsed feedback and questions

[1495] Output: Revised lesson plan

[1496] Step 12:

[1497] The server then sends the revised lesson plan to the device, and this communication is also carried out via HTTPS.

[1498] Input: revised lesson plan

[1499] Output: The revised lesson plan sent to the device.

[1500] Step 13:

[1501] The device displays the revised study plan to the user, who then continues studying based on the new plan.

[1502] Input: revised lesson plan arriving at terminal

[1503] Output: The displayed revised lesson plan

[1504] (Application example 1)

[1505] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1506] In modern factories, efficient operation and maintenance of robots is important from the perspective of improving productivity and reducing costs. However, current systems do not individually optimize robot maintenance schedules or new work process learning plans, which can lead to inconsistencies and inefficiencies. Furthermore, the ability to revise learning and maintenance plans in real time based on robot status and feedback is limited. This can lead to problems such as reduced robot utilization rates, excessive or insufficient maintenance, and even stagnant learning progress.

[1507] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1508] In this invention, the server includes: means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for optimizing the robot's maintenance and learning process, means for collecting and transmitting operation data and sensor data of the robot to the server, means for the server to generate a learning plan and maintenance schedule for the robot, and means for providing the revised robot learning plan and maintenance schedule to the robot. This automates a series of plans for the robot's maintenance and learning process and enables plan revisions in real time, thereby improving productivity and reducing operating costs.

[1509] "User" means an individual or organization that uses this system.

[1510] "Learning needs" is information that indicates what the user wants to learn and their learning goals.

[1511] "Characteristics" is information that indicates personal characteristics such as the user's learning style and how they manage their time.

[1512] "Means" refers to elements that provide a method or function for achieving a specific purpose.

[1513] The "server" is a central computer system that analyzes input data and generates learning methods and plans.

[1514] "Data" refers to information entered by the user and operational and sensor data collected by the robot.

[1515] "Analysis" is the process of examining the input data in detail and selecting appropriate learning methods and maintenance plans based on that information.

[1516] "Learning methods" refer to the means and techniques that allow users to learn most effectively.

[1517] A "learning plan" is a plan that includes specific procedures and schedules for a user or a robot to proceed with learning.

[1518] "Feedback" refers to requests, questions, and opinions provided by users and robots as they progress through learning and maintenance.

[1519] "Revision" is the process of changing an existing plan based on feedback provided.

[1520] "Providing" is the act of passing the generated or modified learning plan or maintenance schedule to a user or robot.

[1521] "Maintenance" refers to activities including inspection and repair necessary to maintain the normal operation of the robot.

[1522] "Operational data" refers to information about the operating status of the robot, such as operating hours and error logs.

[1523] "Sensor data" is information collected from various sensors of the robot.

[1524] "Real-time" refers to near-instantaneous reactions and processing.

[1525] The present invention provides a system that provides an optimal learning method to a user and optimizes the maintenance and learning process of a robot. Specific embodiments for carrying out the invention are described below.

[1526] Users use a dedicated device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to make it easy for users to provide information. This includes items such as "topics you want to learn," "learning style preferences," and "daily study hours."

[1527] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server uses natural language processing (NLP) technology to analyze the user's characteristics and needs based on the received data, and selects the optimal learning method based on the analysis results.

[1528] A learning plan is generated based on the selected learning method. This plan includes the order of learning topics and the learning materials to be used (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard. The user proceeds with their studies according to this plan.

[1529] As users progress through their studies, they can send feedback and questions from their devices through the interactive interface. For example, they can provide specific feedback such as "I find this topic difficult to understand" or "I would like more materials." The device then sends this feedback information to the server. The server analyzes the received feedback, modifies the study plan as necessary, and provides it back to the device.

[1530] A similar system can also be applied to factory robots. The robot's operational and sensor data is collected autonomously and sent to a server. The server uses this data to generate and provide learning plans and maintenance schedules for the robot. For example, if a robot's operating time exceeds 500 hours, it may be determined that maintenance is necessary. It also generates video learning materials and on-the-job training plans to help the robot efficiently learn new tasks.

[1531] Feedback allows these plans to be revised in real time and the optimized plans provided back to the robot, preventing over- and under-maintenance and stagnation of learning.

[1532] As a concrete example, the following is an example of a prompt sentence for an AI model that generates a maintenance plan when a factory robot operates for 8 hours a day and the operation time reaches 500 hours.

[1533] Example prompt sentence:

[1534] "A robot with over 500 operating hours requires a comprehensive inspection of its motors and sensors within a week. Errors are occurring frequently during operation. Please suggest appropriate maintenance methods and an efficient learning menu for this robot, which prefers a 'video' and 'hands-on' learning style."

[1535] In this way, the present invention provides an effective learning environment that meets the individual needs of the user or robot, as well as appropriate maintenance of the robot, thereby achieving continuous optimization.

[1536] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1537] Step 1:

[1538] The user uses the terminal to input their learning needs and characteristics.

[1539] The input interface includes text entry fields and selection buttons, and is designed to allow users to provide information such as "topics they want to learn," "learning style preferences," and "daily study time."

[1540] Input: User learning needs and characteristics

[1541] Output: Input data

[1542] Step 2:

[1543] The terminal transmits the input data to the server using a secure communication protocol (HTTPS).

[1544] Specifically, the terminal encrypts the input data and communicates securely with the server.

[1545] Input: Input data

[1546] Output: Data sent to the server

[1547] Step 3:

[1548] The server uses natural language processing (NLP) techniques to analyze the user's characteristics and needs based on the received data.

[1549] Specifically, it analyzes the text data entered by the user, understands keywords and context, and extracts appropriate learning methods.

[1550] Input: Data sent to the server

[1551] Output: Analysis results

[1552] Step 4:

[1553] The server generates a lesson plan based on the analysis results.

[1554] This plan includes the order of topics to be studied and the materials to be used (videos, textbooks, tests, etc.).

[1555] Specifically, NLP technology is used to determine the optimal learning materials and learning order, and a learning plan is created in text format.

[1556] Input: Analysis results

[1557] Output: Learning plan

[1558] Step 5:

[1559] The server sends the generated learning plan to the terminal and displays it on a dedicated dashboard.

[1560] The dashboard has an intuitive interface for easy user access.

[1561] Input:Study plan

[1562] Output: The lesson plan displayed on the user's device

[1563] Step 6:

[1564] As users progress through their learning, they can submit feedback and questions from their device through an interactive interface.

[1565] Specific operations include a process in which users input feedback such as "It's difficult to understand" or "I'd like more information" on the dashboard.

[1566] Input: Feedback or Questions

[1567] Output: Feedback data

[1568] Step 7:

[1569] The terminal transmits the feedback data to the server.

[1570] Specifically, the terminal encrypts the feedback data and transmits it securely to the server.

[1571] Input: Feedback data

[1572] Output: Feedback sent to the server

[1573] Step 8:

[1574] The server analyzes the feedback it receives and modifies the learning plan as needed.

[1575] NLP technology is again used to understand the feedback, and for example, if the feedback is "difficult to understand," additional video materials or supplemental text are generated.

[1576] Input: Feedback data

[1577] Output: Revised lesson plan

[1578] Step 9:

[1579] The revised study plan is again transmitted from the server to the terminal and provided to the user.

[1580] This allows the user to view the updated study plan on the dashboard again and continue studying.

[1581] Input: revised lesson plan

[1582] Output: The revised lesson plan displayed on the user's device.

[1583] Step 10:

[1584] The robot autonomously collects operational data and sensor data and transmits it to a server.

[1585] Specifically, this includes a process in which the robot records operational data and sensor data and periodically transmits it to a server.

[1586] Input: Robot operation data and sensor data

[1587] Output: Robot data sent to the server

[1588] Step 11:

[1589] The server analyzes the robot's operational data and sensor data to generate optimal maintenance schedules and learning plans.

[1590] Specifically, machine learning algorithms are used to analyze data and identify necessary maintenance tasks and learning processes.

[1591] Input: Robot data

[1592] Output: Maintenance schedule and learning plan

[1593] Step 12:

[1594] Providing the robot with a modified robot learning plan and maintenance schedule.

[1595] The robot autonomously carries out maintenance and learning according to the provided plan.

[1596] Input: Maintenance schedule and study plan

[1597] Output: Maintenance and learning progress performed on the robot

[1598] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1599] The present invention is a system for providing users with an optimal learning method and promoting efficient learning, and in particular, is configured to incorporate an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention are described below.

[1600] The system begins with the user entering their learning needs and characteristics using a device. The input interface, which includes text input fields and selection buttons, is designed to allow users to provide information easily. For example, input items include "topics to learn," "learning style preferences," and "daily study hours."

[1601] The entered data is sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data.

[1602] The server uses natural language processing technology to analyze the user's input in detail and identify the learning content and learning style that the user desires. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be analyzed.

[1603] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[1604] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[1605] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[1606] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[1607] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[1608] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[1609] As a specific example, consider the case where a user inputs "I want to study history efficiently," "I only have 30 minutes a day to study," and "I prefer video learning materials." The server analyzes this information and generates a study plan centered around 5-10 minute videos. For example, a plan including "watching five videos per week" and "taking check tests on weekends" may be generated. If the user provides feedback that "events from a particular era are difficult to understand" as they progress through their studies, and the emotion engine detects confusion or a low level of understanding, the server will create a revised study plan including supplementary materials and provide it to the user. In this way, the present invention provides comprehensive study support that even takes the user's emotions into consideration.

[1610] The processing flow will be explained below.

[1611] Step 1:

[1612] Users operate the device to input their own learning needs and characteristics, such as "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[1613] Step 2:

[1614] The device sends the input learning needs and characteristics data to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data security.

[1615] Step 3:

[1616] The server analyzes the received user data. Using natural language processing technology, the server extracts the meaning of the input text and understands the user's desired learning method. For example, it extracts keywords such as "30 minutes a day" and "video learning materials."

[1617] Step 4:

[1618] The server searches through the database of learning methods and selects the most suitable method for the user. For example, a learning method centered on 5-10 minute video materials may be selected.

[1619] Step 5:

[1620] Based on the analysis results, the server generates a personalized learning plan for the user. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five videos per week" and "take a check test on the weekend."

[1621] Step 6:

[1622] The server sends the generated learning plan to the terminal.

[1623] Step 7:

[1624] The device then displays the received learning plan to the user, using an interface such as a dashboard to allow the user to easily check the plan.

[1625] Step 8:

[1626] The user follows a displayed study plan, for example, watching 30 minutes of videos every day and taking a check test on the weekend.

[1627] Step 9:

[1628] The device's emotion engine analyzes the user's facial expressions and voice in real time to obtain emotional data. For example, the user's facial expressions and tone of voice are collected through a camera or microphone.

[1629] Step 10:

[1630] The server receives emotion data from the terminal and analyzes it. For example, if a user is confused while watching a video tutorial, the emotion engine will send this information to the server.

[1631] Step 11:

[1632] The server then modifies the lesson plan based on the received emotional data, for example by adding additional videos or texts to improve the lesson plan for areas where the user is struggling.

[1633] Step 12:

[1634] The server transmits the revised lesson plan to the terminal.

[1635] Step 13:

[1636] The device displays the revised lesson plan again to the user.

[1637] Step 14:

[1638] The user continues studying based on the revised study plan, for example, checking the added supplementary materials and studying again to improve their understanding.

[1639] Through this process flow, the system can provide an effective learning environment that responds to the individual needs and emotions of the user.

[1640] Example 2

[1641] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1642] Conventional learning systems have been unable to adequately address the individual learning needs and characteristics of users, making it difficult to maximize learning efficiency. Additionally, it is difficult to provide feedback or modify learning plans that take users' emotions into account, making it impossible to provide an optimal learning process for users.

[1643] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes means for inputting the user's learning needs and characteristics, means for transmitting the input data to the server, means for transmitting data using a secure communication protocol, means for analyzing the transmitted data using natural language processing technology and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for acquiring and analyzing the user's emotional data using an emotion engine, means for analyzing the input feedback, questions, and emotional data and revising the learning plan, and means for providing the user with the revised learning plan. This makes it possible to provide an optimized learning process based on the user's needs, characteristics, and emotional state.

[1644] "User" refers to an individual who uses the system to learn.

[1645] "Learning needs" refers to information that indicates a specific topic or purpose that a user wants to learn.

[1646] "Characteristics" refers to individual learning characteristics such as a user's learning style and study time.

[1647] "Input means" refers to the interface through which the user provides learning needs and characteristics to the system.

[1648] "Means for sending" refers to the technology for securely sending the user's input data to the server.

[1649] "Secure communication protocol" refers to a protocol (e.g., HTTPS) for encrypting data and communicating securely.

[1650] "Natural language processing technology" refers to artificial intelligence technology that analyzes user input data and understands the intent and content.

[1651] "Means of analysis" refers to the technology used to analyze input data and understand the needs and characteristics of users.

[1652] "Means of selection" refers to the technology used to select the optimal learning method based on the analysis results.

[1653] A "study plan" refers to a plan that includes specific steps and learning materials for a user to efficiently progress through their studies.

[1654] The "means for providing" refers to an interface for providing the generated lesson plan to the user in an easy-to-view format.

[1655] "Feedback" refers to information that users can use to report their thoughts and questions to the system while studying.

[1656] An "emotion engine" refers to technology for acquiring and analyzing emotions from a user's facial expressions, tone of voice, etc.

[1657] "Emotion data" refers to information about a user's emotional state obtained by an emotion engine.

[1658] "Remediation tools" refers to techniques used to analyze feedback and emotional data and refine lesson plans as needed.

[1659] The present invention is a system for providing a user with an optimal learning method and promoting efficient learning, and in particular has a configuration in which an emotion engine that recognizes the user's emotions is combined. Specific embodiments for carrying out the present invention are described below.

[1660] First, the user uses the device to input their learning needs and characteristics. The input interface includes text input fields and selection buttons, and is designed to allow users to provide information easily. For example, input items include "topics I want to learn," "preferred learning style," and "daily study time." Specific examples include a user entering "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[1661] The entered data is then sent from the device to the server. During this transmission process, a secure communication protocol (e.g., HTTPS) is used to ensure data safety. The server analyzes the user's characteristics and needs based on the received data. Natural language processing technology is used as the analysis method, and the user's input is analyzed in detail. This identifies the learning content the user desires and their appropriate learning style. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" may be analyzed.

[1662] Based on the analysis results, the server searches for and selects the most suitable learning method for the user from the database. It then generates a learning plan based on this selected learning method. This plan includes specific learning topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watching five 5-10 minute videos per week" and "taking a check test on the weekend."

[1663] The generated learning plan is sent from the server to the terminal and provided to the user. The terminal displays this learning plan on a dedicated dashboard, making it easily accessible to the user. The user can proceed with their studies according to this plan.

[1664] Furthermore, an emotion engine is built in to recognize the user's emotions while learning. The emotion engine acquires and analyzes emotional data from the user's facial expressions and voice. For example, it analyzes the user's facial expressions and voice tone through a camera or microphone to understand how the user's emotions, such as liking, confusion, or low level of understanding, fluctuate.

[1665] As users progress through their studies, they can submit feedback and questions through the interactive interface. For example, they may say, "I find it difficult to understand events from a particular period." This feedback is sent from the device to the server. In addition, an emotion engine analyzes the user's emotional data and reflects the results in the learning plan.

[1666] The server analyzes the received feedback and emotional data and modifies the learning plan as necessary. The analysis uses an emotional engine and natural language processing technology. For example, if the emotional data indicates that the user is highly confused about a section of the content that is "difficult to understand," the server modifies the learning plan by adding additional supplementary videos or text.

[1667] The revised study plan is then sent from the server to the terminal and provided to the user, who can then continue studying based on the revised plan, thereby providing a learning process optimized based on the user's needs and emotions.

[1668] Examples of prompts include:

[1669] "I want to learn history efficiently. I only have 30 minutes a day to study. I prefer video learning materials. Please suggest a specific study plan."

[1670] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1671] Step 1: Enter your learning needs and characteristics

[1672] Users use the device's input interface to input their learning needs and characteristics. Input items include "topics they want to learn," "preferred learning style," and "daily study time." Specific examples include "I want to learn history efficiently," "I can only spare 30 minutes a day to study," and "I prefer video learning materials."

[1673] Input: Learning needs and characteristics

[1674] Output: Input data (information on learning needs and characteristics)

[1675] Step 2: Sending data

[1676] The terminal sends the data entered by the user to the server using a secure communication protocol (e.g. HTTPS), and the data is encrypted during the process to ensure proper security.

[1677] Input: Input data (encoded learning needs and characteristics information)

[1678] Output: Data sent to the server

[1679] Step 3: Analyze the data

[1680] The server analyzes the received user data using natural language processing technology. Through this analysis, the user's learning needs and characteristics are extracted. For example, information such as "I prefer video learning materials" or "I can only find 30 minutes a day to study" can be identified.

[1681] Input: Data sent to the server

[1682] Output: Analysis results (detailed information on learning needs and characteristics)

[1683] Step 4: Find and choose the best learning method

[1684] The server searches the database for learning methods based on the analysis results and selects the most suitable learning method for the user. For example, for a user who prefers video learning materials, it selects a learning plan that focuses on short video learning materials.

[1685] Input: Analysis results

[1686] Output: Optimal learning method

[1687] Step 5: Generate a lesson plan

[1688] The server generates a specific study plan based on the selected learning method. This plan includes study topics and learning materials (videos, textbooks, tests, etc.). For example, it might include "watch five 5-10 minute videos per week" and "take a check test on the weekend."

[1689] Input: The best way to learn

[1690] Output: The generated learning plan

[1691] Step 6: Provide a learning plan

[1692] The server transmits the generated study plan to the terminal.

[1693] The device displays the received learning plan on a dedicated dashboard for easy access by the user.

[1694] Input: Generated learning plan

[1695] Output: The lesson plan displayed on the device

[1696] Step 7: Recognizing emotions while learning

[1697] The device uses a camera and microphone to capture the user's facial expressions and voice, and sends them to an emotion engine, which analyzes them to identify the user's emotions, such as liking, confusion, or lack of understanding.

[1698] Input: facial expression data and voice data

[1699] Output: Emotion data

[1700] Step 8: Submit your feedback

[1701] Users can submit feedback and questions through an interactive interface while studying, such as "I'm having difficulty understanding events from a particular period."

[1702] Input: Feedback or Questions

[1703] Output: Feedback or questions sent to the server

[1704] Step 9: Analyze feedback and sentiment data

[1705] The server receives the feedback and emotional data and analyzes it using natural language processing technology and an emotional engine, for example, to determine whether a particularly confusing section requires additional learning materials.

[1706] Input: Feedback and emotion data

[1707] Output: Analysis results

[1708] Step 10: Modify your study plan

[1709] Based on the analysis, the server will modify the lesson plan as needed, for example adding supplementary video or text to difficult-to-understand sections.

[1710] Input: Analysis results

[1711] Output: revised lesson plan

[1712] Step 11: Provide a revised lesson plan

[1713] The server transmits the revised lesson plan to the terminal.

[1714] The terminal displays the resubmitted revised lesson plan on a dashboard for easy access by the user.

[1715] Input: revised lesson plan

[1716] Output: The revised lesson plan displayed on the terminal.

[1717] (Application example 2)

[1718] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1719] While conventional learning support systems can provide learning plans based on a user's learning needs and characteristics, they lack the ability to recognize the user's emotional state in real time and flexibly modify the learning content based on that. This can lead to problems such as users becoming confused or continuing their learning without fully understanding the material. This type of flexible response is particularly important when factory workers are learning new machines or procedures.

[1720] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1721] In this invention, the server includes means for inputting a user's learning needs and characteristics, means for transmitting the input data to the server, means for analyzing the transmitted data and selecting an appropriate learning method, means for generating a learning plan based on the selected learning method, means for providing the generated learning plan to the user, means for inputting feedback and questions from the user, means for analyzing the input feedback and questions and revising the learning plan, means for providing the revised learning plan to the user, means for recognizing the user's emotions during the learning process and revising the learning plan based on the emotions, and means for providing supplementary learning materials based on the revised learning plan. This makes it possible to provide a flexible learning plan based on the user's emotional state, significantly improving the efficiency and effectiveness of learning.

[1722] A "user" is a person or agent who has something they want to learn using a learning system.

[1723] "Learning needs" are the topics or themes that users want to learn, as well as their more specific learning requirements and goals.

[1724] "Traits" are individual characteristics such as a user's unique learning style or the amount of time they spend studying.

[1725] "Input means" refers to the interface or device that allows the user to provide the system with the content and learning style they want to learn.

[1726] "Means of sending" refers to the function or mechanism that securely transports the entered data to the server.

[1727] "Means of analysis" refers to the techniques used to interpret the user's needs and characteristics using the received data and determine the most appropriate learning method.

[1728] The "means of selection" is a mechanism that selects the most suitable learning method and learning materials for the user based on the analyzed results.

[1729] A "learning plan" is a specific schedule of learning activities or materials provided to a user.

[1730] "Means of delivery" refers to the interface or mechanism by which the generated lesson plan is presented to the user and made accessible.

[1731] "Feedback" refers to opinions and comments that users make to communicate to the system any questions or level of understanding they may have while studying.

[1732] A "question" is an inquiry that a user makes to the system during learning about something they are unsure of or want to confirm.

[1733] "Avenues for modification" are the ability to make timely changes to your learning plan based on feedback and questions received.

[1734] The "emotion engine" is a technology that analyzes the user's emotions from their facial expressions and voice and recognizes their state.

[1735] "Supplemental Materials" are additional educational resources provided to users in response to content that they find difficult to understand or are confused about.

[1736] The present invention is a learning support system that takes into account the learning needs and characteristics of a user, provides an appropriate learning plan according to the learning process, and further recognizes the user's emotions in real time to optimize the learning process. Specifically, the present invention includes the following components.

[1737] 1. Input of user learning needs and characteristics

[1738] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, the amount of time they spend studying each day, etc. The input interface includes text entry fields and selection buttons, and is designed to make it easy for users to provide information.

[1739] 2. Data submission and analysis

[1740] The input data is sent from the device to the server using a secure communication protocol (e.g., HTTPS). The server then analyzes the received data using natural language processing technology (NLPAnalysis) to identify the optimal learning method based on the user's learning needs and characteristics.

[1741] 3. Creating and providing a learning plan

[1742] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan. This learning plan includes specific learning topics and learning materials (videos, texts, tests, etc.). The generated learning plan is sent from the server to the device and displayed on a dedicated dashboard.

[1743] 4. Emotion Recognition by Emotion Engine

[1744] During learning, the device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state. For example, it can detect when the user is confused or interested.

[1745] 5. Feedback and revision of study plans

[1746] Users can submit feedback and questions through the interactive interface. The server analyzes the received feedback and sentiment data and modifies the lesson plan as needed. Modifications may include supplemental materials or additional videos.

[1747] 6. Hardware and Software Used

[1748] This system uses "EmotionEngine" as an emotion engine and "NLPAnalysis" for natural language processing. It uses HTTPS for communication and a relational database such as MongoDB or MySQL for the database. It is intended for use on smartphones, tablets, or head-mounted displays (HMDs).

[1749] Prompt Sentence Examples

[1750] For example, if a worker wants to learn a new welding procedure, the following is entered:

[1751] "What topic would you like to learn about?"

[1752] "Welding Procedure"

[1753] "What is your learning style preference (video, text, etc.)?"

[1754] "video"

[1755] "How many minutes do you study per day?"

[1756] "half an hour"

[1757] Based on this, the server generates a study plan that includes watching six five-minute videos every day. If the server detects that the user is experiencing difficulty or has low comprehension during the study, it can incorporate supplementary videos or additional text into the plan.

[1758] In this way, the system of the present invention provides an efficient learning environment for the user, maximizing the effectiveness of learning.

[1759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1760] Step 1:

[1761] Users use the device to input their learning needs and characteristics, such as the topics they want to learn, their preferred learning style, and the amount of time they have to study each day.

[1762] Input: Topic you want to learn, your learning style, and the number of hours you want to study per day

[1763] Output: Data on learning needs and characteristics entered

[1764] How it works: The terminal provides text entry fields and choice buttons into which the user enters information.

[1765] Step 2:

[1766] The input data is sent to the server using a secure communication protocol (HTTPS).

[1767] Input: Data on learning needs and characteristics entered

[1768] Output: Data sent to the server

[1769] How it works: The device uses HTTPS to send the data entered by the user to the server.

[1770] Step 3:

[1771] The server analyzes the received data using natural language processing technology (NLPAnalysis) to analyze the user's learning needs and characteristics.

[1772] Input: Data sent to the server

[1773] Output: Analysis results (user learning needs and characteristics)

[1774] How it works: The server uses NLPAnalysis to analyze text data and identify user needs and characteristics.

[1775] Step 4:

[1776] The server searches the database for the optimal learning method based on the analysis results and generates a learning plan.

[1777] Input: Analysis results

[1778] Output: Learning plan

[1779] Operation: The server checks the database based on the analysis results, selects relevant learning methods and materials, and generates a learning plan.

[1780] Step 5:

[1781] The generated learning plan is sent from the server to the terminal and displayed on a dedicated dashboard.

[1782] Input:Study plan

[1783] Output: The lesson plan displayed on the device.

[1784] Operation: The server sends the generated learning plan to the device, which displays it on the dashboard.

[1785] Step 6:

[1786] The device's camera and microphone are used to analyze the user's facial expressions and voice, and the Emotion Engine recognizes the user's emotional state.

[1787] Input: User's facial expression data, voice data

[1788] Output: Emotion analysis results

[1789] How it works: The device uses a camera and microphone to collect the user's facial expressions and voice, which are then analyzed by the Emotion Engine.

[1790] Step 7:

[1791] Users can submit feedback and questions through an interactive interface while learning.

[1792] Input: Feedback, Questions

[1793] Output: Feedback or questions sent to the server

[1794] How it works: The user uses the device interface to input questions or comments that arise during the learning process and sends them to the server.

[1795] Step 8:

[1796] The server analyzes the received feedback and emotional data and adjusts the learning plan as needed.

[1797] Input: Feedback, emotion data

[1798] Output: Revised lesson plan

[1799] How it works: The server uses feedback and emotional data to revise the learning plan, for example by adding new learning materials.

[1800] Step 9:

[1801] The revised study plan is again sent from the server to the terminal and provided to the user.

[1802] Input: revised lesson plan

[1803] Output: The revised lesson plan displayed on the terminal.

[1804] Operation: The server sends the revised lesson plan to the device, which displays it again on the dashboard.

[1805] The above processing steps enable the learning assistance system to provide an efficient and effective learning experience while also responding to the user's emotional state.

[1806] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1807] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1808] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1809] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1810] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1811] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1812] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1813] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1814] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1815] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1816] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1817] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1818] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1819] 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.

[1820] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1821] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1822] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1823] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1824] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1825] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1826] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1827] The following is further disclosed regarding the above embodiment.

[1828] (Claim 1)

[1829] a means for inputting the user's learning needs and characteristics;

[1830] means for transmitting the input data to a server;

[1831] means for analyzing the transmitted data and selecting an appropriate learning method;

[1832] means for generating a learning plan based on the selected learning method;

[1833] means for providing the generated study plan to a user;

[1834] a means for users to enter feedback or questions;

[1835] A means for analyzing the input feedback and questions and correcting the learning plan;

[1836] means for providing the user with a revised lesson plan;

[1837] A system including:

[1838] (Claim 2)

[1839] 10. The system according to claim 1, further comprising means for analyzing the user's learning needs and characteristics using natural language processing.

[1840] (Claim 3)

[1841] 10. The system of claim 1, wherein the provision of the lesson plan includes a series of video instructional materials and tests for the user.

[1842] "Example 1"

[1843] (Claim 1)

[1844] a means for inputting the user's learning needs and characteristics;

[1845] means for transmitting the input data to an information processing device;

[1846] means for analyzing the transmitted data using natural language processing technology and selecting an appropriate learning method;

[1847] means for generating a learning plan based on the selected learning method;

[1848] means for providing the generated study plan to a user;

[1849] a means for users to enter feedback or questions;

[1850] a means for analyzing the input feedback and questions using natural language processing technology and correcting the learning plan;

[1851] means for providing the user with a revised lesson plan;

[1852] A system including:

[1853] (Claim 2)

[1854] 10. The system according to claim 1, further comprising means for analyzing the user's learning needs and characteristics using natural language processing.

[1855] (Claim 3)

[1856] 10. The system of claim 1, wherein providing the learning plan includes a series of audiovisual materials and an assessment test for the user.

[1857] "Application Example 1"

[1858] (Claim 1)

[1859] a means for inputting the user's learning needs and characteristics;

[1860] means for transmitting the input data to a server;

[1861] means for analyzing the transmitted data and selecting an appropriate learning method;

[1862] means for generating a learning plan based on the selected learning method;

[1863] means for providing the generated study plan to a user;

[1864] a means for users to enter feedback or questions;

[1865] A means for analyzing the input feedback and questions and correcting the learning plan;

[1866] means for providing the user with a revised lesson plan;

[1867] including means to optimize the robot's maintenance and learning processes;

[1868] means for collecting operational data and sensor data of the robot and transmitting the data to a server;

[1869] means for generating a learning plan and a maintenance schedule for the robot by the server;

[1870] means for providing the robot with a modified robot learning plan and maintenance schedule;

[1871] A system including:

[1872] (Claim 2)

[1873] 10. The system of claim 1, further comprising means for analyzing the user's learning needs and characteristics using natural language processing.

[1874] (Claim 3)

[1875] 10. The system of claim 1, wherein providing the user with a study plan includes a series of video instructional materials and a test.

[1876] "Example 2: Combining Emotion Engines"

[1877] (Claim 1)

[1878] a means for inputting the user's learning needs and characteristics;

[1879] means for transmitting the input data to a server;

[1880] means for transmitting data utilizing a secure communications protocol;

[1881] means for analyzing the transmitted data using natural language processing technology and selecting an appropriate learning method;

[1882] means for generating a learning plan based on the selected learning method;

[1883] means for providing the generated study plan to a user;

[1884] a means for users to enter feedback or questions;

[1885] A means for acquiring and analyzing user emotion data using an emotion engine;

[1886] A means for analyzing the input feedback, questions, and emotion data and modifying the learning plan;

[1887] means for providing the user with a revised lesson plan;

[1888] A system including:

[1889] (Claim 2)

[1890] 10. The system according to claim 1, further comprising means for analyzing the user's learning needs and characteristics using natural language processing techniques.

[1891] (Claim 3)

[1892] 10. The system of claim 1, wherein the provision of the lesson plan includes a series of video instructional materials and tests for the user.

[1893] "Application example 2 when combining emotion engines"

[1894] (Claim 1)

[1895] a means for inputting the user's learning needs and characteristics;

[1896] means for transmitting the input data to a server;

[1897] means for analyzing the transmitted data and selecting an appropriate learning method;

[1898] means for generating a learning plan based on the selected learning method;

[1899] means for providing the generated study plan to a user;

[1900] a means for users to enter feedback or questions;

[1901] A means for analyzing the input feedback and questions and correcting the learning plan;

[1902] means for providing the user with a revised lesson plan;

[1903] means for recognizing a user's emotions during the learning process and modifying the learning plan based on said emotions;

[1904] a means for providing supplemental learning materials based on the revised lesson plan;

[1905] A system including:

[1906] (Claim 2)

[1907] 2. The system according to claim 1, further comprising: means for analyzing the user's learning needs and characteristics using natural language processing; and means for recognizing the user's emotions using the emotion engine and reflecting the emotions in the learning plan.

[1908] (Claim 3)

[1909] 10. The system of claim 1, wherein providing the learning plan includes a series of video instructional materials and tests for the user, and supplemental video and text based on emotion recognition. [Explanation of symbols]

[1910] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a means for inputting the user's learning needs and characteristics; means for transmitting the input data to a server; means for analyzing the transmitted data and selecting an appropriate learning method; means for generating a learning plan based on the selected learning method; means for providing the generated study plan to a user; a means for users to enter feedback or questions; A means for analyzing the input feedback and questions and correcting the learning plan; means for providing the user with a revised lesson plan; A system including:

2. The system of claim 1 , further comprising means for analyzing the user's learning needs and characteristics using natural language processing.

3. 10. The system of claim 1, wherein the provision of the lesson plan includes a series of video instructional materials and tests for the user.

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