System

Abstract

To provide a system capable of supporting efficient skill acquisition and self-growth of a user.SOLUTION: The system includes means for generating a list of psychological test questions, means for presenting the psychological test questions to the user and collecting the user's responses, means for recording the user's behavioral data, means for analyzing the collected psychological test responses and behavioral data to characterize the user, means for generating a personalized curriculum based on the user's goals and desires, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, and means for periodically generating and providing feedback and counseling sessions to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] As our society ages, the decline in the working population and the rise in social security costs have become serious problems. Furthermore, many individuals have a declining interest in self-improvement, and are troubled by the question of what skills they should learn and how they should learn them. This makes it difficult for individuals to achieve self-development efficiently and effectively. This invention proposes a system for providing individualized curricula using generative AI to solve these problems. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means: By providing a system including a means for generating a list of psychological test questions, a means for presenting psychological test questions to a user and collecting the user's answers, a means for recording the user's behavioral data, a means for analyzing the collected psychological test answers and behavioral data to clarify the user's characteristics, a means for generating an individual curriculum based on the user's goals and aspirations, a means for presenting lessons to the user based on the generated curriculum, a means for recording the user's progress and analyzing the progress data to adjust the curriculum, and a means for periodically generating and providing feedback and counseling sessions to the user, it is possible to support the user's efficient skill acquisition and self-development.

[0006] A "psychological test" is a list of questions to assess a user's personality and characteristics.

[0007] A "question list" is a set of questions that are presented to a user in a psychological test.

[0008] A "user" is an individual who uses the system to learn and improve themselves.

[0009] "Behavioral data" is information that records the user's activity status and behavior history.

[0010] "Analysis" is a process for clarifying the characteristics of users based on collected data.

[0011] "Characteristics" refers to the user's personality, behavioral patterns, lifestyle, and other characteristics.

[0012] A "goal" is a specific outcome or skill that a user wants to achieve.

[0013] "Hope" refers to the user's requirements regarding the occupation, skills, and direction of growth they desire.

[0014] A "curriculum" is a learning plan designed based on a user's characteristics, desires, and goals.

[0015] A "lesson" is a unit of specific learning content or exercises included in the curriculum.

[0016] "Progress" is the learning progress a user makes through a lesson.

[0017] "Feedback" refers to learning evaluations and advice provided to users.

[0018] A "counseling session" is an interactive session designed to provide support to a user to effectively advance their personal growth.

[0019] "Generative AI" is a type of artificial intelligence that analyzes data based on user information and provides optimal curriculum, feedback, and counseling. [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] This invention relates to a system that uses generative AI to provide an individualized curriculum. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[0042] System configuration and operation

[0043] Psychological test submission

[0044] 1. The server generates a list of psychological test questions, which involves selecting appropriate questions from a number of templates and creating the list.

[0045] 2. The server sends the generated psychological test question list to the terminal.

[0046] 3. The device presents the received list of questions to the user. The device displays the questions through a user interface and prompts the user to answer them.

[0047] 4. The user enters an answer to each question.

[0048] 5. The device collects the user's answers and sends them to the server.

[0049] Obtaining user behavior data

[0050] 1. The device records user behavioral data, including app usage history and daily activity logs.

[0051] 2. The device sends the behavioral data collected over a certain period of time (for example, one week) to the server.

[0052] 3. The server analyzes the received behavioral data to identify the user's characteristics. This analysis is carried out to identify the user's daily rhythm and behavioral patterns.

[0053] Data analysis and curriculum generation

[0054] 1. The server integrates the psychological test answers and the analysis results of the behavioral data to analyze the user's detailed characteristics.

[0055] 2. The server takes into account the user's goals and aspirations and generates an optimal individual curriculum. This curriculum is designed to enable the user to gradually acquire the skills and knowledge they need.

[0056] 3. The server sends the generated curriculum to the terminal.

[0057] Lesson delivery and progress management

[0058] 1. The device presents lessons to the user based on the received curriculum, which includes textbooks and video content.

[0059] 2. Users take lessons and record their progress on their device, including by completing quizzes after each lesson.

[0060] 3. The device records the user's progress data and periodically sends it to the server.

[0061] 4. The server analyzes the progress data and adjusts the curriculum as needed.

[0062] Feedback and Counseling

[0063] 1. The server periodically generates feedback based on the user's progress and schedules counseling sessions.

[0064] 2. The server sends feedback and counseling information to the terminal.

[0065] 3. The device presents feedback and counseling information to the user, allowing them to self-assess and participate in a counseling session if necessary.

[0066] Specific examples

[0067] For example, consider the case where User A uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User A answers each question, and the device collects and sends the answers to the server. Next, the device records User A's behavioral data for one week and sends it to the server. The server analyzes this data, generates a curriculum tailored to User A's characteristics, and sends it to the device. User A takes lessons based on the received curriculum and records his or her progress on the device. Progress data is periodically sent to the server, and feedback and counseling information is generated.

[0068] This allows each user to be provided with the optimal learning plan, enabling efficient self-development.

[0069] The processing flow will be explained below.

[0070] Step 1:

[0071] The server generates a list of questions for the psychological test by selecting an appropriate set of questions from multiple question template databases and creating the list.

[0072] Step 2:

[0073] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and the questions are delivered to the device via an API.

[0074] Step 3:

[0075] The terminal presents the user with a list of questions for the psychological test, displays the questions on a user interface, and provides a form for the user to enter their answers.

[0076] Step 4:

[0077] The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[0078] Step 5:

[0079] The device collects the user's answers and sends them to the server, which formats the data before sending it to the server.

[0080] Step 6:

[0081] The device will record user behavior data over a one-week period, including app usage history, location information, and activity logs.

[0082] Step 7:

[0083] The device sends the collected behavioral data to a server, which uploads the data to the server periodically or in batches after a week.

[0084] Step 8:

[0085] The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle rhythms.

[0086] Step 9:

[0087] The server combines the results of the psychological test and the behavioral data analysis to perform a detailed analysis of the user's characteristics, and reflects the resulting characteristic data in the user profile.

[0088] Step 10:

[0089] The server generates a personalized curriculum based on the user's goals and preferences, and uses algorithms to create an optimal learning plan.

[0090] Step 11:

[0091] The server sends the generated curriculum to the device, converts the curriculum data into JSON or XML format, and distributes it to the device via API.

[0092] Step 12:

[0093] The device presents lessons to the user based on the received curriculum, displaying teaching materials, exercises, video content, and other information and notifying the user.

[0094] Step 13:

[0095] Users take lessons and enter their progress into the device. After completing the lesson, they enter progress data and record their evaluation and impressions.

[0096] Step 14:

[0097] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[0098] Step 15:

[0099] The server analyzes the progress data and adjusts the curriculum as needed, rebuilding lesson plans based on the user's progress.

[0100] Step 16:

[0101] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and analysis results.

[0102] Step 17:

[0103] The server transmits feedback and counseling information to the terminal, and distributes the created feedback and counseling schedule to the terminal.

[0104] Step 18:

[0105] The terminal presents the feedback and counseling information to the user, and displays and notifies the details of the feedback and counseling on the user interface.

[0106] Step 19:

[0107] Users receive feedback and participate in counseling sessions if necessary. Users complete self-assessments and schedule or attend counseling sessions.

[0108] Example 1

[0109] 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."

[0110] Conventional learning systems have difficulty providing an optimal learning curriculum based on the individual characteristics and behavioral patterns of each user, resulting in the inability to provide efficient learning support. Furthermore, conventional methods have difficulty dynamically adjusting the curriculum according to the user's progress, making it impossible to maximize learning effectiveness. Furthermore, the lack of regular feedback and counseling means that users are unable to effectively realize their own personal growth.

[0111] 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.

[0112] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individualized curriculum based on the user's goals and aspirations, means for generating an individualized curriculum using a generative AI model, means for inputting prompts into the generative AI model to obtain optimal instructions, means for transmitting the generated curriculum to a terminal, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, and means for periodically generating and providing feedback and counseling sessions to the user. This allows for the provision of an optimal learning curriculum based on the user's individual characteristics, dynamic adjustment of the curriculum according to the user's progress, and effective learning support through regular feedback and counseling.

[0113]

[0114] A "psychological test question list" is a list containing a series of questions used to assess a user's psychological state, learning style, etc.

[0115] The "means for presenting questions for a psychological test" is a means for displaying questions for a psychological test to a user via a terminal.

[0116] The "means for collecting user responses" refers to a means for recording the responses entered by the user to the questions in the psychological test.

[0117] "User behavior data" refers to data including a user's app usage history and daily activity log.

[0118] The "means for recording user behavior data" refers to a means for collecting and storing user behavior data.

[0119] The "means for analyzing psychological test answers and behavioral data" refers to a means for analyzing the collected psychological test answers and behavioral data to identify the characteristics of the user.

[0120] "User characteristics" are the individual characteristics and features of the user that are revealed based on the responses to the psychological test and behavioral data.

[0121] The "means for generating an individual curriculum" is a means for creating an optimal learning plan for an individual user based on the characteristics and goals of the user.

[0122] A "generative AI model" is a model that uses artificial intelligence to generate a response from an input prompt sentence.

[0123] A "prompt" is an instruction or question given to a generative AI model.

[0124] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet.

[0125] A "lesson" is content or learning material for users to study.

[0126] "Progress data" is data that indicates the learning progress of a user that is recorded as a result of the user taking a lesson.

[0127] "Feedback" is a response that includes evaluation and advice regarding the user's learning progress.

[0128] A "counseling session" is a session in which a user receives advice from an expert regarding their learning progress or concerns.

[0129]

[0130] This invention relates to a system that provides an individualized curriculum using generative AI. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[0131] System configuration and operation

[0132] Psychological test submission

[0133] The server randomly selects appropriate questions from a number of templates and generates a list of psychological test questions. This process is performed using a Python script. The generated list of questions is sent to the device via an HTTP POST request. The device then presents the psychological test questions to the user through a user interface built using React Native. The user then answers each question, and the answers are collected by the device and sent to the server again via an HTTP POST request.

[0134] For example, the server generates a question such as "What time of day do you study? (Morning, afternoon, or evening)" and the device presents this to the user. The user answers "Morning," and the device sends the answer data to the server.

[0135] Obtaining user behavior data

[0136] The device records the user's behavioral data, such as app usage history and daily activity logs. This data is recorded using the Google Analytics SDK. The behavioral data is collected over a certain period (for example, one week) and sent to the server in batch processing. The server analyzes the sent behavioral data using the Pandas library to identify the user's characteristics.

[0137] As a specific example, data is collected on how many times and at what times a user uses an app over the course of a week, and this data is used to identify daily rhythms and behavioral patterns.

[0138] Data analysis and curriculum generation

[0139] The server integrates the psychological test responses and behavioral data analysis results to reveal the user's detailed characteristics. Next, OpenAI's generative AI model is used to generate an optimal individualized curriculum that takes into account the user's goals and aspirations. The prompt sentence is then input into the generative AI model to obtain the expected curriculum proposal.

[0140] An example of a prompt sentence is, "User characteristics: morning person, prefers visual learning materials. Please generate the optimal curriculum." A curriculum is generated based on the response of the generative AI model and sent to the device via an HTTP POST request.

[0141] Lesson delivery and progress management

[0142] Based on the received curriculum, the device presents lessons to the user using textbooks, video content, etc. For example, this includes using YouTube's API to retrieve and display appropriate educational videos. The user takes the lessons and records their progress on the device. Progress data is stored locally using SQLite and periodically sent to the server via batch processing.

[0143] Feedback and Counseling

[0144] The server analyzes the progress data and generates regular feedback using a generative AI model. Additionally, it schedules necessary counseling sessions. The generated feedback and counseling information are sent to the device via an HTTP POST request. The device then presents the feedback to the user, who can then self-evaluate and participate in counseling sessions as needed.

[0145] In this way, a system using generative AI can provide an optimal learning curriculum based on each user's individual characteristics, dynamically adjust the curriculum according to the user's progress, and provide effective learning support through regular feedback and counseling.

[0146] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0147]

[0148] Step 1:

[0149] The server randomly selects appropriate questions from a large number of templates and generates a list of psychological test questions using a Python script. The generated list of questions is sent to the terminal via an HTTP POST request in JSON format.

[0150] Input: Template data

[0151] Output: Question list

[0152] Data processing: Randomly select questions from the template data and create a list

[0153] Specific behavior: questions = random.sample(all_templates, number_of_questions)

[0154] Step 2:

[0155] The device presents the received psychological test question list to the user. The questions are displayed through a user interface built using React Native, and the user inputs their answers.

[0156] Input: Question List

[0157] Output: User response data

[0158] Data calculation: None (display and input collection)

[0159] Specific operation: renderQuestions(questions)

[0160] Step 3:

[0161] The user inputs answers to each question, and the input answers are recorded on the terminal.

[0162] Input: Question display and answer input form

[0163] Output: Response data

[0164] Data processing: None (user input)

[0165] Specific behavior: The user answers "Morning" to the question "What time of day do you study? (Morning, afternoon, evening)"

[0166] Step 4:

[0167] The device collects the user's response data and sends it to the server in JSON format via an HTTP POST request.

[0168] Input: Answer data

[0169] Output: Response data to the server

[0170] Data calculation: None (data transmission)

[0171] Specific operation: response = requests.post(endpoint_url, json=user_answers)

[0172] Step 5:

[0173] The device uses the Google Analytics SDK to record user behavior data, such as app usage history and daily activity logs, which are collected periodically.

[0174] Input: User action

[0175] Output: Behavioral data

[0176] Data processing: recording behavioral data

[0177] Specific behavior: analytics.logEvent('page_view', {'page': currentPage})

[0178] Step 6:

[0179] The device sends behavioral data collected over a period of time (e.g., one week) to the server, where the data is compiled in batch processing and sent in JSON format.

[0180] Input: Collected behavioral data

[0181] Output: Behavioral data to the server

[0182] Data Computation: Data Batching

[0183] Specific operation: response = requests.post(endpoint_url, json=collected_data)

[0184] Step 7:

[0185] The server analyzes the received behavioral data using Python's Pandas library to identify user characteristics, and the analysis results are stored in a database.

[0186] Input: Behavioral data

[0187] Output: User characteristics data

[0188] Data operations: transformation into data frames and statistical analysis

[0189] Specific behavior: df = pd.DataFrame(json_data), user_traits = df.describe()

[0190] Step 8:

[0191] The server integrates the psychological test response data with the analyzed behavioral data to reveal detailed user characteristics. It retrieves the data from the database using SQL queries and performs integrated analysis.

[0192] Input: Psychological test response data and behavioral data

[0193] Output: Integrated property data

[0194] Data Calculation: Joining Data with SQL Queries

[0195] Specific operation: SELECT FROM user_data WHERE userid = user_id

[0196] Step 9:

[0197] The server uses OpenAI's generative AI model to generate an individual curriculum based on the characteristic data. It inputs prompt statements into the generative AI model to obtain appropriate instructions.

[0198] Input: User characteristics data, prompt text

[0199] Output: Individual curriculum

[0200] Data Computation: Curriculum Generation with Generative AI Models

[0201] Specific behavior: response = openai.Completion.create(engine="davinci", prompt=prompt)

[0202] Step 10:

[0203] The server sends the generated curriculum to the device, again using an HTTP POST request.

[0204] Input: Individualized curriculum

[0205] Output: Curriculum to terminal

[0206] Data calculation: None (data transmission)

[0207] Specific operation: response = requests.post(endpoint_url, json=generated_curriculum)

[0208] Step 11:

[0209] Based on the received curriculum, the device presents the user with lessons using textbooks, video content, etc. It uses the YouTube API to retrieve and display appropriate educational videos.

[0210] Input: Individualized curriculum

[0211] Output:Lesson display

[0212] Data Computing: Acquiring and displaying video content

[0213] Specific operation: renderLessonContent(lesson_content)

[0214] Step 12:

[0215] Users take lessons and record their progress on their device, updating it by answering quizzes, for example.

[0216] Input: Lesson content

[0217] Output: Progress data

[0218] Data calculation: None (user input and recording)

[0219] Specific action: User answers a quiz

[0220] Step 13:

[0221] The device records progress data and periodically sends it to the server. The data is stored locally using SQLite and sent in batches.

[0222] Input: Progress data

[0223] Output: Progress data to the server

[0224] Data calculation: None (data transmission)

[0225] Specific behavior: db.insert('progress', progress_data), response = requests.post(endpoint_url, json=progress_data)

[0226] Step 14:

[0227] The server analyzes the progress data and generates feedback using a generative AI model, often using Python's Scikit-learn, to adjust the curriculum as needed.

[0228] Input: Progress data

[0229] Output: Feedback and adjusted curriculum

[0230] Data Computing: Progress in Data Analysis and Prediction

[0231] Specific behavior: model.predict(new_progress_data)

[0232] Step 15:

[0233] The server sends the generated feedback and counseling information to the device using an HTTP POST request.

[0234] Input: Feedback and Counseling Information

[0235] Output: Feedback and counseling information to the terminal

[0236] Data calculation: None (data transmission)

[0237] Specific operation: response = requests.post(endpoint_url, json=feedback_data)

[0238] Step 16:

[0239] The device provides feedback and counseling information to the user, displayed using a React Native UI.

[0240] Input: Feedback and Counseling Information

[0241] Output: Displaying information to the user

[0242] Data calculation: None (display)

[0243] Specific operation: displayFeedback(feedback_data)

[0244] (Application example 1)

[0245] 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."

[0246] Conventional learning support systems have difficulty providing an optimal curriculum based on the characteristics and behavioral data of each user, and have had limitations in providing effective training, particularly to factory workers. This has led to issues such as insufficient achievement of individual skill improvement and efficient work support.

[0247] 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.

[0248] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording user behavioral data, means for analyzing the collected psychological test answers and behavioral data to clarify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, and means for generating a training program to be executed by a robot terminal in the factory, thereby making it possible to provide factory workers with an optimal training curriculum tailored to their individual characteristics.

[0249] A "psychological test" is an evaluation method consisting of questions and tasks to measure a user's psychological characteristics and personality.

[0250] A "questionnaire" is a set of questions prepared for a psychological test.

[0251] "User" refers to an individual or employee who uses the System.

[0252] "Answer" refers to information entered by a user in response to a question in a psychological test.

[0253] "Behavioral data" is recorded data including the user's daily behavior, application usage history, work history, and the like.

[0254] A "curriculum" is a set of learning or training plans designed based on a user's characteristics and goals.

[0255] "Lesson" means a curriculum-based learning module or training session.

[0256] "Progress data" is data that shows the results of a user's learning or training in accordance with a curriculum.

[0257] "Feedback" refers to evaluation and advice regarding the user's learning or training progress.

[0258] A "counseling session" is an interactive support session to support a user's learning or training.

[0259] A "robot terminal" is a machine used in a factory or work environment, which has an interface with the user and provides training programs.

[0260] A "training program" is a series of learning activities or assignments designed to improve a user's skills.

[0261] This invention relates to a system that provides personalized training programs using a generative AI model. This system designs an optimal training plan based on the user's psychological test answers and behavioral data, provides lessons based on the plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[0262] System configuration and operation

[0263] Psychological test submission

[0264] The server generates a list of questions for the psychological test. This generation process involves selecting appropriate questions from a number of templates and creating a list. The generated list of questions for the psychological test is sent to the robot terminal. The terminal displays the questions through a user interface and prompts the user to answer them. The user enters answers to each question, and the terminal collects and sends them to the server.

[0265] Obtaining user behavior data

[0266] The device records the user's behavioral data, including factory activities, work history, and machine operation logs. The device then sends the behavioral data collected over a period of time (e.g., one week) to a server. The server analyzes the received behavioral data to identify the user's work characteristics. This analysis is performed to identify the user's work rhythm and behavioral patterns.

[0267] Data analysis and training program generation

[0268] The server combines the answers to the psychological test and the results of the behavioral data analysis to analyze the user's detailed characteristics. The server then generates an optimal individual training program, taking into account the user's work goals and wishes. This training program is designed to allow the user to gradually learn the skills and knowledge they need. The generated training program is then sent to the robot terminal.

[0269] Lesson delivery and progress management

[0270] The device presents training lessons to the user based on the received curriculum, which may include textbook materials and video content. The user takes the lessons and records their progress on the device. The device records the user's progress data and periodically sends it to the server. The server analyzes the progress data and adjusts the training program as needed.

[0271] Feedback and Counseling

[0272] The server periodically generates feedback based on the user's progress and schedules counseling sessions. The feedback and counseling information are provided to the user via their terminal, allowing the user to self-evaluate and participate in counseling sessions as needed.

[0273] Hardware and software used

[0274] Hardware: Factory robots, servers, IoT devices, etc.

[0275] Software: Python, database (e.g., PostgreSQL), AI library (e.g., TensorFlow)

[0276] Specific examples

[0277] For example, when factory employee A uses this system, the server first generates a list of psychological test questions and sends them to the robot terminal. The questions are presented to factory employee A through the user interface, and the employee enters their answers. These answers are sent to the server. The terminal then records factory employee A's behavioral data for one week and sends it to the server. The server analyzes the data, generates an optimal training program for factory employee A, and sends it to the terminal. Factory employee A takes lessons based on the training plan, and progress reports are periodically sent to the server. The server analyzes progress and schedules and provides feedback and counseling sessions.

[0278] Prompt Sentence Examples

[0279] Questions to ask the generative AI model:

[0280] "Generate an appropriate training curriculum based on the psychological test results and behavioral data of factory employees. Example questions include, 'How motivated are you to work?' and 'Are you interested in new technologies?' Examples of behavioral data include compliance with safety measures and machine maintenance history. The curriculum may include basic technology modules, applied technology modules, etc."

[0281] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0282] Step 1:

[0283] The server generates a list of questions for the psychological test. It selects appropriate questions from many templates and creates a list. This generated list of questions is sent to the terminal. The input is an existing template, and the output is the generated list of questions. Data processing involves selection and combination from templates.

[0284] Step 2:

[0285] The terminal presents the received psychological test question list to the user. The user answers each question, and the terminal collects the answers. The input is the question list sent from the server and the user's answers, and the output is the collected answers. Data calculation involves recording the user's input.

[0286] Step 3:

[0287] The terminal records user behavior data (e.g., work history, machine operation log) for a certain period (e.g., one week) and sends it to the server. The input is daily behavior data, and the output is a summary of the collected behavior data. Data processing involves storing and sending the recorded data.

[0288] Step 4:

[0289] The server analyzes the received psychological test answers and behavioral data to reveal the user's detailed characteristics. The input is the psychological test answers and behavioral data, and the output is the analysis results (user characteristics). Data calculation involves extracting characteristics using statistical analysis and machine learning models.

[0290] Step 5:

[0291] The server generates an optimal individual training program based on the user's goals and aspirations. The input is the user's characteristics, goals, and aspirations, and the output is the training program. Data calculation involves generating the program using a generative AI model.

[0292] Step 6:

[0293] The generated training program is sent to the terminal. The input is the generated training program, and the output is transmission to the terminal. Data processing involves format conversion and communication of the program data.

[0294] Step 7:

[0295] The terminal presents training lessons to the user based on the received training program. The input is the training program, and the output is the presentation to the user. Data processing includes displaying the lessons and providing an interactive user interface.

[0296] Step 8:

[0297] Users take lessons and record their progress on their devices. The input is the user's lesson data, and the output is their progress. Data processing involves recording and periodically sending the progress data.

[0298] Step 9:

[0299] The server analyzes the progress data and adjusts the training program as needed. The input is the progress data and the output is the adjusted training program. Data processing involves monitoring the progress and redesigning the program.

[0300] Step 10:

[0301] The server generates feedback and counseling sessions and provides them to users through terminals. The inputs are progress data and analysis results, and the output is feedback and counseling information. Data processing includes generating reports and scheduling counseling sessions.

[0302] 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.

[0303] This invention relates to a system that uses generative AI to provide an individualized curriculum. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback. This system designs an optimal learning plan based on the user's psychological test answers, behavioral data, and emotional data, provides lessons based on that plan, and manages progress. Below, we will specifically explain the operation of the entire system, focusing on the server, terminal, and user.

[0304] System configuration and operation

[0305] Psychological test submission

[0306] 1. The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list.

[0307] 2. The server sends the generated psychological test question list to the device. The data format is JSON or XML, and it is distributed to the device via API.

[0308] 3. The device presents the user with a list of questions for the psychological test, displaying the questions on the user interface and providing a form for the user to enter their answers.

[0309] 4. The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[0310] 5. The device collects the user's answers and sends them to the server, where they are reformatted and converted into the appropriate data format before being sent.

[0311] Obtaining user behavior data

[0312] 1. The device records user behavior data, including app usage history, location information, and activity logs.

[0313] 2. The device sends the collected behavioral data to the server, which uploads the data to the server periodically or in batches after a certain period of time.

[0314] 3. The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle.

[0315] Acquiring user emotion data

[0316] 1. The device collects user emotional data in real time, including facial recognition and voice analysis using sensors such as cameras and microphones.

[0317] 2. The device collects emotional data and sends it to a server, where it is converted into digital data using image analysis and voice processing technology.

[0318] 3. The server receives and analyzes the emotion data, identifies the user's emotional state, and reflects this in the user profile.

[0319] Data analysis and curriculum generation

[0320] 1. The server integrates the analysis results of the psychological test answers, behavioral data, and emotional data to analyze the user's detailed characteristics.

[0321] 2. The server generates an individual curriculum based on the user's goals, aspirations, and emotional state. This curriculum is designed to allow the user to gradually learn the skills and knowledge they need.

[0322] 3. The server sends the generated curriculum to the terminal.

[0323] Lesson delivery and progress management

[0324] 1. The device presents lessons to the user based on the curriculum received, which includes textbooks and video content.

[0325] 2. The user takes lessons and records their progress on the device. Progress is entered after each lesson, allowing the user to understand their learning status.

[0326] 3. The device records the user's progress data and sends it to the server, where it is uploaded to the server in real time or periodically.

[0327] 4. The server analyzes the progress data and adjusts the curriculum as needed. In addition to the progress data, it also analyzes emotional data and takes into account the state of the user's learning.

[0328] Feedback and Counseling

[0329] 1. The server periodically generates feedback based on the user's progress and emotional data and schedules counseling sessions.

[0330] 2. The server sends feedback and counseling information to the device, including learning evaluation and future advice.

[0331] 3. The device provides feedback and counseling information to the user, allowing the user to self-evaluate and manage their schedule.

[0332] Specific examples

[0333] For example, consider the case where User B uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User B answers each question, and the device collects and sends them to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends it to the server. The server analyzes this data, generates a curriculum based on User B's characteristics and emotional state, and sends it to the device. User B takes lessons based on the received curriculum and records his or her progress on the device. The progress data and emotional data are periodically sent to the server, and feedback and counseling information are generated.

[0334] This allows each user to be provided with the optimal learning plan, leading to efficient self-improvement. The introduction of the emotion engine enables a more precise learning experience.

[0335] The processing flow will be explained below.

[0336] Step 1:

[0337] The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list. The questions include personality assessment, behavioral tendencies, stress levels, etc.

[0338] Step 2:

[0339] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and communication is made to the device via an API.

[0340] Step 3:

[0341] The device presents the user with a list of psychological test questions, displays the questions on a user interface, and provides a form for the user to answer.

[0342] Step 4:

[0343] The user enters answers to each question in the psychological test. The user enters a choice or free response in the form.

[0344] Step 5:

[0345] The device collects the user's answers and sends them to the server, where they are formatted and converted into JSON format before being sent.

[0346] Step 6:

[0347] The device records user behavior data, including app usage history, location information, and activity logs.

[0348] Step 7:

[0349] The device sends the collected behavioral data to a server, either in real time or in batches over a fixed time span.

[0350] Step 8:

[0351] The server receives and analyzes the behavioral data, using machine learning algorithms to reveal the user's activity patterns and daily rhythms.

[0352] Step 9:

[0353] The device collects the user's emotional data in real time using a camera and microphone to recognize facial expressions and analyze voice.

[0354] Step 10:

[0355] The emotional data collected by the device is sent to a server, where it is converted into data using image analysis and voice processing technology.

[0356] Step 11:

[0357] The server receives and analyzes the emotional data, identifying each emotional state (e.g., joy, sadness, anger, etc.) and reflecting it in the user profile.

[0358] Step 12:

[0359] The server integrates the analysis results of the psychological test responses, behavioral data, and emotional data, thereby revealing detailed characteristics of the user.

[0360] Step 13:

[0361] The server generates an individual curriculum based on the user's goals, aspirations, and characteristics. The learning plan is automatically designed using an AI algorithm.

[0362] Step 14:

[0363] The server sends the generated curriculum to the device. The generated curriculum data is sent in JSON or XML format.

[0364] Step 15:

[0365] The device presents lessons to the user based on the received curriculum, which includes textbooks, video content, and practice questions.

[0366] Step 16:

[0367] Users take lessons and record their progress on their devices. Users enter progress data after each lesson.

[0368] Step 17:

[0369] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[0370] Step 18:

[0371] The server analyzes the progress data and adjusts the curriculum as needed. Based on the progress data and emotion data, the lesson plan is updated appropriately.

[0372] Step 19:

[0373] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and emotional state.

[0374] Step 20:

[0375] The server transmits feedback and counseling information to the terminal, and distributes the generated feedback and counseling schedule to the terminal.

[0376] Step 21:

[0377] The terminal presents the feedback and counseling information to the user, and displays the feedback and counseling details on the user interface.

[0378] Step 22:

[0379] Users receive feedback and participate in counseling sessions if necessary. Users complete a self-assessment and book or attend counseling sessions.

[0380] Example 2

[0381] 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."

[0382] Conventional learning systems have had difficulty providing a curriculum that fully takes into account the characteristics and emotional state of each user. Furthermore, they lack the ability to flexibly adjust the curriculum and provide feedback in response to the user's learning progress and emotional changes, which can hinder effective learning.

[0383] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for generating a list of questions for a psychological test, a means for recording user behavioral data and transmitting it to the server, a means for acquiring and analyzing user emotional data, a means for analyzing the collected psychological test answers, behavioral data, and emotional data to clarify the user's characteristics, and a means for generating an individual curriculum based on the user's goals, hopes, and emotional state. This makes it possible to provide an appropriate curriculum and feedback according to the user's characteristics and emotional state.

[0384] A "psychological test question list" is a collection of questions for assessing a user's psychological state and characteristics.

[0385] "User behavior data" refers to information including a user's application usage history, location information, and other activity logs.

[0386] "User emotion data" is information obtained by analyzing the user's emotional state obtained from facial expressions, voice, etc.

[0387] A "server" is a computer system for processing and analyzing data.

[0388] A "terminal" is a device such as a computer device or a smartphone that is directly operated by a user.

[0389] A "curriculum" is a collection of learning plans and teaching materials designed to meet a user's learning objectives.

[0390] "Feedback" refers to evaluations and advice provided based on a user's learning progress and achievements.

[0391] A "counseling session" is an interactive session to provide learning and psychological support to a user.

[0392] "Analysis" is the process of extracting and understanding useful information from collected data.

[0393] A "lesson" is a set of materials or activities designed to teach a specific skill or knowledge.

[0394] "Progress data" is data that indicates the progress of a user's learning activities.

[0395] This invention relates to a learning support system that provides an individualized curriculum using a generative AI model. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback.

[0396] System configuration

[0397] server

[0398] The server is a centralized computer system responsible for generating psychological test questions, analyzing behavioral and emotional data, generating personalized curriculum, and analyzing progress and generating feedback. The server includes software components such as the main database, AI models, analysis algorithms, and APIs (Application Programming Interfaces).

[0399] Terminal

[0400] The terminal is a device that the user directly operates, and is mainly composed of a smartphone or computer. The terminal has the functions of presenting a list of psychological test questions, recording the user's answers and behavioral data, acquiring emotional data, and presenting lessons based on an individual curriculum. The terminal also transmits the collected data to a server.

[0401] User

[0402] Users are individuals who use this system, answering psychological tests and providing daily behavioral and emotional data. Users also take lessons based on the provided curriculum and record their progress on their devices.

[0403] Example of operation

[0404] For example, when User B begins using this system, the server generates a list of psychological test questions and sends them to the device. User B answers the questions displayed on the device, and the answers are collected and sent to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends this data to the server. The server analyzes this data and generates an individual curriculum tailored to User B's characteristics and emotional state, which is then sent to the device.

[0405] User B takes lessons based on the received curriculum and records their progress on their device. Progress and emotion data are then periodically sent to the server, which analyzes the data and generates feedback and counseling information. The device then presents this information to User B, helping with self-evaluation and schedule management.

[0406] Specific hardware and software names used

[0407] The server configuration uses MySQL as the database, OpenAI GPT-3 for the generative AI model, Microsoft Azure Face API and Google Cloud Speech-to-Text for emotion analysis, etc. The terminal application is developed for iOS and Android smartphones, and frameworks such as React Native are used for real-time data collection and display.

[0408] Prompt Sentence Examples

[0409] Here is an example of a prompt for curriculum generation using a generative AI model:

[0410] "User B began using the system. He took a psychological test generated by the server, and an individual curriculum was generated based on the results. After that, the device continued to record behavioral and emotional data, and feedback was provided based on the analysis results."

[0411] In this way, an effective learning experience is achieved by providing optimal learning plans and feedback to each individual user.

[0412] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0413] Program processing flow

[0414] Step 1:

[0415] The server generates a list of questions for the psychological test. It selects template questions from the database and customizes the question set according to the user's characteristics. For example, it executes an SQL query such as "select from question_templates where category='Psychology'" and formats the retrieved questions in JSON format. The input is the template question database, and the output is the generated JSON-formatted question list.

[0416] Step 2:

[0417] The psychological test question list generated by the server is sent to the terminal. It is sent via API using an HTTP request. To ensure communication stability, error handling and resend functions are provided during transmission. The input is a JSON-formatted question list, and the output is the transmission status (success / failure).

[0418] Step 3:

[0419] The device presents the user with a list of questions for a psychological test. The questions are displayed on a user interface and options are provided. For example, React Native is used to render a question form for the user to answer. The input is a list of questions in JSON format, and the output is displayed on the screen.

[0420] Step 4:

[0421] The user answers questions in a psychological test. The user enters their answer in the displayed form and presses the submit button. For example, in response to the question "What are your hobbies?", the user enters "reading and watching movies" in the text box. The input is the user's free description, and the output is the user's answer data.

[0422] Step 5:

[0423] The device sends the user's answers to the psychological test to the server. The answer data is formatted in the appropriate JSON format and sent using an HTTP POST request. The input is the user's answer data, and the output is the submission status (success / failure).

[0424] Step 6:

[0425] The device records user behavior data, collecting application usage history and GPS location information. For example, it accumulates log information such as "App A used for 10 minutes, location information 35.6895, 139.6917." The input is the device's sensor and usage data, and the output is the recorded behavior data.

[0426] Step 7:

[0427] The behavioral data collected by the device is sent to the server. The data is formatted in batches at regular intervals and uploaded to the server using an HTTP POST request. The input is the recorded behavioral data, and the output is the transmission status (success / failure).

[0428] Step 8:

[0429] The server analyzes the behavioral data. It uses the Python pandas library to integrate it into a data frame and extract specific behavioral patterns. For example, it identifies behavioral characteristics such as "the user plays sports at least three times a week." The input is the behavioral data, and the output is the analysis results and behavioral patterns.

[0430] Step 9:

[0431] The device acquires the user's emotional data. It uses a camera and microphone to capture facial expressions and voice in real time and uses an emotion analysis API. For example, "detect happiness from facial expressions and determine low stress levels from voice." The input is real-time video and audio data, and the output is emotional data.

[0432] Step 10:

[0433] The emotion data acquired by the device is sent to the server. The data acquired from the emotion analysis API is formatted in JSON and sent via an HTTP POST request. The input is the emotion data, and the output is the transmission status (success / failure).

[0434] Step 11:

[0435] The server analyzes the emotional data. Using image and audio analysis technology, the server reflects the emotional state in a profile. For example, it might determine that "stress levels are high on Mondays, and emotions of joy are strong on Fridays." The input is the emotional data, and the output is the analysis results and the emotional profile.

[0436] Step 12:

[0437] The server integrates and analyzes psychological test responses, behavioral data, and emotional data. It then links the data stored in the database to extract insights for creating individualized curricula. For example, "The user is sociable and has a high level of concentration." The input is the integrated data, and the output is a detailed user profile.

[0438] Step 13:

[0439] The server generates an individual curriculum that is optimal for the user. Using a generative AI model, an appropriate curriculum is designed based on the analysis results. For example, "Week 1: Basic concepts, Week 2: Applied tasks." The input is a detailed user profile, and the output is the generated curriculum.

[0440] Step 14:

[0441] The server sends the generated curriculum to the terminal. The curriculum is formatted in JSON format and sent via an HTTP POST request. The input is the generated curriculum, and the output is the sending status (success / failure).

[0442] Step 15:

[0443] The terminal presents lessons to the user based on the curriculum. The lesson content is displayed on the user interface, providing an environment in which the user can learn. For example, "Today's lesson: Basic Python grammar." The input is the received curriculum, and the output is the display on the screen.

[0444] Step 16:

[0445] The user takes lessons and records their progress on the device. After each lesson, the progress is entered. For example, "Lesson completed" is recorded on the device. The input is the user's progress, and the output is the progress data.

[0446] Step 17:

[0447] The device sends the recorded progress data to the server. The progress data is formatted in JSON and sent via an HTTP POST request. The input is the progress data, and the output is the sending status (success / failure).

[0448] Step 18:

[0449] The server analyzes the progress data and adjusts the curriculum as needed. It analyzes the progress data and emotion data together and updates the curriculum to make it more adaptable for the user. For example, "Add supplementary lessons before moving on to applied tasks." The input is progress and emotion data, and the output is the adjusted curriculum.

[0450] Step 19:

[0451] The server generates feedback based on the progress and emotion data and suggests a counseling session. The feedback suggests, "You're doing well, let's move on to the applied task next." The input is the analyzed data, and the output is feedback and counseling information.

[0452] Step 20:

[0453] The server sends the generated feedback and counseling information to the terminal. The feedback information is formatted in JSON format and sent via an HTTP POST request. The input is the generated feedback, and the output is the sending status (success / failure).

[0454] Step 21:

[0455] The device presents feedback and counseling information to the user. The feedback and counseling suggestions are displayed on the screen. For example, "You're making good progress. Let's take the next step." The input is the feedback received, and the output is the display on the screen.

[0456] (Application example 2)

[0457] 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."

[0458] Conventional skill training systems struggle to fully understand and individually respond to the characteristics, goals, and aspirations of individual employees. Furthermore, they often provide a uniform curriculum without considering the emotional state of employees, resulting in ineffective training. Furthermore, they are unable to integrate and analyze work data and emotional data from industrial equipment, leaving challenges in improving employee productivity and work efficiency. This invention aims to solve these challenges by comprehensively analyzing the characteristics and emotional state of individual employees and providing an optimal training curriculum.

[0459] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to the user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, means for acquiring and analyzing the user's emotional data, means for generating an individual curriculum that reflects the user's emotional state, and means for recording employee work data on industrial equipment. This makes it possible to provide an optimal curriculum that takes into account the characteristics and emotional state of employees.

[0460] A "psychological test questionnaire" is a set of questions designed to measure a user's psychological state or characteristics.

[0461] "User" refers to an individual employee or other target person who uses this system for skill training or learning.

[0462] "Behavioral data" refers to data such as a user's daily behavioral patterns, activity logs, working hours, and movement patterns.

[0463] "Collected psychological test answers" refers to the answers given by the user to the psychological test.

[0464] "Emotion data" refers to data on the emotional state obtained by analyzing the user's facial expressions and voice.

[0465] "Individualized curriculum" refers to a learning or skills training plan that is customized based on a user's characteristics and goals.

[0466] "Lesson" refers to a specific learning content or training session provided to a User.

[0467] "Progress data" refers to data that indicates the progress of a user's learning or training.

[0468] "Feedback" refers to evaluations and advice provided based on a user's learning status and training results.

[0469] A "counseling session" refers to a time spent interviewing or consulting with a user, taking into account their characteristics and emotional state.

[0470] "Industrial equipment" refers to production facilities and machinery used in factories, etc.

[0471] "Work data" refers to recorded data regarding employees' operations and work when using industrial equipment.

[0472] This invention is a system that supports skill training for factory employees, generating individualized curricula using psychological test answers, behavioral data, and emotional data, and providing progress management and feedback. The system consists of a server, terminals (smartphones and industrial tablets), and industrial equipment.

[0473] First, the server generates a list of psychological test questions. To do this, it selects an appropriate set of questions from a question template database and creates a list in JSON or XML format. Next, the server sends the generated psychological test question list to the terminal. The terminal presents the psychological test questions to the user, and the answers are collected as the user answers them. These answers are sent to the server for data analysis.

[0474] In addition, the device records the user's behavioral data, including work status, movement patterns, and work time, and uploads the data to a server periodically or in batches. At the same time, the device also acquires the user's emotional data using sensors such as a camera and microphone. The server analyzes the emotional data using facial recognition and voice analysis technologies (e.g., OpenCV and Google Cloud Speech-to-Text) to identify the user's emotional state.

[0475] This data is analyzed comprehensively to generate an individual curriculum based on the user's characteristics and goals. The curriculum is designed as a learning plan specific to the user and sent to the device. The device then presents lessons to the user based on this curriculum and records the user's progress as they take the lessons. Progress data is also periodically sent to the server for analysis. This allows the server to adjust the curriculum as needed, providing the user with the optimal learning experience.

[0476] In addition, the server periodically generates feedback and counseling sessions and sends them to the terminal, allowing the user to self-evaluate and manage their schedule based on this information, resulting in efficient skill training.

[0477] As a concrete example, consider the case where Employee A is learning a new welding technique in a factory. The server generates a psychological test such as "work stress level" and sends it to a terminal. Employee A answers the questions on their smartphone and sends the answers to the server. Factory robots and smartphones monitor Employee A's working hours and facial expressions, collecting behavioral and emotional data. The server analyzes this data, generates a "welding training curriculum" optimal for Employee A, and sends it to the terminal. Employee A progresses through the training according to the curriculum and records his or her progress, and the server provides feedback based on the progress data and emotional data.

[0478] An example of a prompt is:

[0479] AI prompts to generate optimal curriculum based on user behavioral and emotional data:

[0480] The user's emotional state is "{emotion}" and their current behavior pattern is "{behavior}". Based on this, please generate the optimal curriculum for the user.

[0481] Examples include:

[0482] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0483] Step 1:

[0484] The server generates a list of psychological test questions, which involves selecting an appropriate set of questions from a database, creating a list in JSON or XML format, and sending the list to the device.

[0485] Input: Question template database

[0486] Data processing / calculation: Select appropriate questions and generate lists

[0487] Output: Psychological test question list (JSON or XML format)

[0488] Step 2:

[0489] The terminal presents the user with a list of psychological test questions received from the server, and the user answers the questions, which are then collected and stored on the terminal.

[0490] Input: Psychological test question list

[0491] Data processing / calculation: Present psychological test questions to users

[0492] Output: User's answer

[0493] Step 3:

[0494] The device formats the user's answers, converts them into JSON or XML format, and sends them to the server.

[0495] Input: User's answer

[0496] Data processing / calculation: Formatting answers and converting them to data formats

[0497] Output: Formatted response data (JSON or XML format)

[0498] Step 4:

[0499] The device records user behavior data, including work status, movement patterns, work time, etc. The data is uploaded to a server periodically or in batches.

[0500] Input: User behavior data

[0501] Data processing / calculation: Data recording, periodic uploading

[0502] Output: Uploaded behavioral data

[0503] Step 5:

[0504] The device uses sensors such as a camera and microphone to capture the user's emotional data in real time, including facial expression recognition and voice analysis, and transmits the emotional data to a server.

[0505] Input: Real-time video and audio data of the user

[0506] Data processing / calculation: facial expression recognition, voice analysis

[0507] Output: Emotion data

[0508] Step 6:

[0509] The server analyzes the collected psychological test answers, behavioral data, and emotional data, and generates an individual curriculum based on the user's characteristics and goals.

[0510] Input: Psychological test answers, behavioral data, emotional data

[0511] Data processing / calculation: Data integration, analysis, and creation of individual curriculum

[0512] Output: Individual curriculum

[0513] Step 7:

[0514] The terminal presents lessons to the user based on the curriculum received from the server, records the user's progress as they take the lessons, and periodically transmits the progress data to the server.

[0515] Input: Individualized curriculum

[0516] Data processing / calculation: Presenting lessons and recording progress

[0517] Output: Progress data

[0518] Step 8:

[0519] The server generates feedback and counseling sessions based on the progress data and emotion data and transmits them to the terminal.

[0520] Input: Progress data, emotion data

[0521] Data processing / calculation: Data analysis, feedback and generation of counseling sessions

[0522] Output: Feedback, counseling sessions

[0523] Step 9:

[0524] The terminal provides the user with feedback from the server and counseling sessions, allowing the user to self-evaluate and manage their schedule.

[0525] Input: Feedback, counseling sessions

[0526] Data processing / calculation: Presentation to the user

[0527] Output: Self-evaluation, schedule management

[0528] 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.

[0529] 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.

[0530] 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.

[0531] [Second embodiment]

[0532] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0533] 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.

[0534] 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).

[0535] 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.

[0536] 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.

[0537] 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).

[0538] 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.

[0539] 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.

[0540] 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.

[0541] 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.

[0542] 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.

[0543] 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."

[0544] This invention relates to a system that uses generative AI to provide an individualized curriculum. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[0545] System configuration and operation

[0546] Psychological test submission

[0547] 1. The server generates a list of psychological test questions, which involves selecting appropriate questions from a number of templates and creating the list.

[0548] 2. The server sends the generated psychological test question list to the terminal.

[0549] 3. The device presents the received list of questions to the user. The device displays the questions through a user interface and prompts the user to answer them.

[0550] 4. The user enters an answer to each question.

[0551] 5. The device collects the user's answers and sends them to the server.

[0552] Obtaining user behavior data

[0553] 1. The device records user behavioral data, including app usage history and daily activity logs.

[0554] 2. The device sends the behavioral data collected over a certain period of time (for example, one week) to the server.

[0555] 3. The server analyzes the received behavioral data to identify the user's characteristics. This analysis is carried out to identify the user's daily rhythm and behavioral patterns.

[0556] Data analysis and curriculum generation

[0557] 1. The server integrates the psychological test answers and the analysis results of the behavioral data to analyze the user's detailed characteristics.

[0558] 2. The server takes into account the user's goals and aspirations and generates an optimal individual curriculum. This curriculum is designed to enable the user to gradually acquire the skills and knowledge they need.

[0559] 3. The server sends the generated curriculum to the terminal.

[0560] Lesson delivery and progress management

[0561] 1. The device presents lessons to the user based on the received curriculum, which includes textbooks and video content.

[0562] 2. Users take lessons and record their progress on their device, including by completing quizzes after each lesson.

[0563] 3. The device records the user's progress data and periodically sends it to the server.

[0564] 4. The server analyzes the progress data and adjusts the curriculum as needed.

[0565] Feedback and Counseling

[0566] 1. The server periodically generates feedback based on the user's progress and schedules counseling sessions.

[0567] 2. The server sends feedback and counseling information to the terminal.

[0568] 3. The device presents feedback and counseling information to the user, allowing them to self-assess and participate in a counseling session if necessary.

[0569] Specific examples

[0570] For example, consider the case where User A uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User A answers each question, and the device collects and sends the answers to the server. Next, the device records User A's behavioral data for one week and sends it to the server. The server analyzes this data, generates a curriculum tailored to User A's characteristics, and sends it to the device. User A takes lessons based on the received curriculum and records his or her progress on the device. Progress data is periodically sent to the server, and feedback and counseling information is generated.

[0571] This allows each user to be provided with the optimal learning plan, enabling efficient self-development.

[0572] The processing flow will be explained below.

[0573] Step 1:

[0574] The server generates a list of questions for the psychological test by selecting an appropriate set of questions from multiple question template databases and creating the list.

[0575] Step 2:

[0576] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and the questions are delivered to the device via an API.

[0577] Step 3:

[0578] The terminal presents the user with a list of questions for the psychological test, displays the questions on a user interface, and provides a form for the user to enter their answers.

[0579] Step 4:

[0580] The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[0581] Step 5:

[0582] The device collects the user's answers and sends them to the server, which formats the data before sending it to the server.

[0583] Step 6:

[0584] The device will record user behavior data over a one-week period, including app usage history, location information, and activity logs.

[0585] Step 7:

[0586] The device sends the collected behavioral data to a server, which uploads the data to the server periodically or in batches after a week.

[0587] Step 8:

[0588] The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle rhythms.

[0589] Step 9:

[0590] The server combines the results of the psychological test and the behavioral data analysis to perform a detailed analysis of the user's characteristics, and reflects the resulting characteristic data in the user profile.

[0591] Step 10:

[0592] The server generates a personalized curriculum based on the user's goals and preferences, and uses algorithms to create an optimal learning plan.

[0593] Step 11:

[0594] The server sends the generated curriculum to the device, converts the curriculum data into JSON or XML format, and distributes it to the device via API.

[0595] Step 12:

[0596] The device presents lessons to the user based on the received curriculum, displaying teaching materials, exercises, video content, and other information and notifying the user.

[0597] Step 13:

[0598] Users take lessons and enter their progress into the device. After completing the lesson, they enter progress data and record their evaluation and impressions.

[0599] Step 14:

[0600] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[0601] Step 15:

[0602] The server analyzes the progress data and adjusts the curriculum as needed, rebuilding lesson plans based on the user's progress.

[0603] Step 16:

[0604] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and analysis results.

[0605] Step 17:

[0606] The server transmits feedback and counseling information to the terminal, and distributes the created feedback and counseling schedule to the terminal.

[0607] Step 18:

[0608] The terminal presents the feedback and counseling information to the user, and displays and notifies the details of the feedback and counseling on the user interface.

[0609] Step 19:

[0610] Users receive feedback and participate in counseling sessions if necessary. Users complete self-assessments and schedule or attend counseling sessions.

[0611] Example 1

[0612] 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."

[0613] Conventional learning systems have difficulty providing an optimal learning curriculum based on the individual characteristics and behavioral patterns of each user, resulting in the inability to provide efficient learning support. Furthermore, conventional methods have difficulty dynamically adjusting the curriculum according to the user's progress, making it impossible to maximize learning effectiveness. Furthermore, the lack of regular feedback and counseling means that users are unable to effectively realize their own personal growth.

[0614] 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.

[0615] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individualized curriculum based on the user's goals and aspirations, means for generating an individualized curriculum using a generative AI model, means for inputting prompts into the generative AI model to obtain optimal instructions, means for transmitting the generated curriculum to a terminal, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, and means for periodically generating and providing feedback and counseling sessions to the user. This allows for the provision of an optimal learning curriculum based on the user's individual characteristics, dynamic adjustment of the curriculum according to the user's progress, and effective learning support through regular feedback and counseling.

[0616]

[0617] A "psychological test question list" is a list containing a series of questions used to assess a user's psychological state, learning style, etc.

[0618] The "means for presenting questions for a psychological test" is a means for displaying questions for a psychological test to a user via a terminal.

[0619] The "means for collecting user responses" refers to a means for recording the responses entered by the user to the questions in the psychological test.

[0620] "User behavior data" refers to data including a user's app usage history and daily activity log.

[0621] The "means for recording user behavior data" refers to a means for collecting and storing user behavior data.

[0622] The "means for analyzing psychological test answers and behavioral data" refers to a means for analyzing the collected psychological test answers and behavioral data to identify the characteristics of the user.

[0623] "User characteristics" are the individual characteristics and features of the user that are revealed based on the responses to the psychological test and behavioral data.

[0624] The "means for generating an individual curriculum" is a means for creating an optimal learning plan for an individual user based on the characteristics and goals of the user.

[0625] A "generative AI model" is a model that uses artificial intelligence to generate a response from an input prompt sentence.

[0626] A "prompt" is an instruction or question given to a generative AI model.

[0627] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet.

[0628] A "lesson" is content or learning material for users to study.

[0629] "Progress data" is data that indicates the learning progress of a user that is recorded as a result of the user taking a lesson.

[0630] "Feedback" is a response that includes evaluation and advice regarding the user's learning progress.

[0631] A "counseling session" is a session in which a user receives advice from an expert regarding their learning progress or concerns.

[0632]

[0633] This invention relates to a system that provides an individualized curriculum using generative AI. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[0634] System configuration and operation

[0635] Psychological test submission

[0636] The server randomly selects appropriate questions from a number of templates and generates a list of psychological test questions. This process is performed using a Python script. The generated list of questions is sent to the device via an HTTP POST request. The device then presents the psychological test questions to the user through a user interface built using React Native. The user then answers each question, and the answers are collected by the device and sent to the server again via an HTTP POST request.

[0637] For example, the server generates a question such as "What time of day do you study? (Morning, afternoon, or evening)" and the device presents this to the user. The user answers "Morning," and the device sends the answer data to the server.

[0638] Obtaining user behavior data

[0639] The device records the user's behavioral data, such as app usage history and daily activity logs. This data is recorded using the Google Analytics SDK. The behavioral data is collected over a certain period (for example, one week) and sent to the server in batch processing. The server analyzes the sent behavioral data using the Pandas library to identify the user's characteristics.

[0640] As a specific example, data is collected on how many times and at what times a user uses an app over the course of a week, and this data is used to identify daily rhythms and behavioral patterns.

[0641] Data analysis and curriculum generation

[0642] The server integrates the psychological test responses and behavioral data analysis results to reveal the user's detailed characteristics. Next, OpenAI's generative AI model is used to generate an optimal individualized curriculum that takes into account the user's goals and aspirations. The prompt sentence is then input into the generative AI model to obtain the expected curriculum proposal.

[0643] An example of a prompt sentence is, "User characteristics: morning person, prefers visual learning materials. Please generate the optimal curriculum." A curriculum is generated based on the response of the generative AI model and sent to the device via an HTTP POST request.

[0644] Lesson delivery and progress management

[0645] Based on the received curriculum, the device presents lessons to the user using textbooks, video content, etc. For example, this includes using YouTube's API to retrieve and display appropriate educational videos. The user takes the lessons and records their progress on the device. Progress data is stored locally using SQLite and periodically sent to the server via batch processing.

[0646] Feedback and Counseling

[0647] The server analyzes the progress data and generates regular feedback using a generative AI model. Additionally, it schedules necessary counseling sessions. The generated feedback and counseling information are sent to the device via an HTTP POST request. The device then presents the feedback to the user, who can then self-evaluate and participate in counseling sessions as needed.

[0648] In this way, a system using generative AI can provide an optimal learning curriculum based on each user's individual characteristics, dynamically adjust the curriculum according to the user's progress, and provide effective learning support through regular feedback and counseling.

[0649] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0650]

[0651] Step 1:

[0652] The server randomly selects appropriate questions from a large number of templates and generates a list of psychological test questions using a Python script. The generated list of questions is sent to the terminal via an HTTP POST request in JSON format.

[0653] Input: Template data

[0654] Output: Question list

[0655] Data processing: Randomly select questions from the template data and create a list

[0656] Specific behavior: questions = random.sample(all_templates, number_of_questions)

[0657] Step 2:

[0658] The device presents the received psychological test question list to the user. The questions are displayed through a user interface built using React Native, and the user inputs their answers.

[0659] Input: Question List

[0660] Output: User response data

[0661] Data calculation: None (display and input collection)

[0662] Specific operation: renderQuestions(questions)

[0663] Step 3:

[0664] The user inputs answers to each question, and the input answers are recorded on the terminal.

[0665] Input: Question display and answer input form

[0666] Output: Response data

[0667] Data processing: None (user input)

[0668] Specific behavior: The user answers "Morning" to the question "What time of day do you study? (Morning, afternoon, evening)"

[0669] Step 4:

[0670] The device collects the user's response data and sends it to the server in JSON format via an HTTP POST request.

[0671] Input: Answer data

[0672] Output: Response data to the server

[0673] Data calculation: None (data transmission)

[0674] Specific operation: response = requests.post(endpoint_url, json=user_answers)

[0675] Step 5:

[0676] The device uses the Google Analytics SDK to record user behavior data, such as app usage history and daily activity logs, which are collected periodically.

[0677] Input: User action

[0678] Output: Behavioral data

[0679] Data processing: recording behavioral data

[0680] Specific behavior: analytics.logEvent('page_view', {'page': currentPage})

[0681] Step 6:

[0682] The device sends behavioral data collected over a period of time (e.g., one week) to the server, where the data is compiled in batch processing and sent in JSON format.

[0683] Input: Collected behavioral data

[0684] Output: Behavioral data to the server

[0685] Data Computation: Data Batching

[0686] Specific operation: response = requests.post(endpoint_url, json=collected_data)

[0687] Step 7:

[0688] The server analyzes the received behavioral data using Python's Pandas library to identify user characteristics, and the analysis results are stored in a database.

[0689] Input: Behavioral data

[0690] Output: User characteristics data

[0691] Data operations: transformation into data frames and statistical analysis

[0692] Specific behavior: df = pd.DataFrame(json_data), user_traits = df.describe()

[0693] Step 8:

[0694] The server integrates the psychological test response data with the analyzed behavioral data to reveal detailed user characteristics. It retrieves the data from the database using SQL queries and performs integrated analysis.

[0695] Input: Psychological test response data and behavioral data

[0696] Output: Integrated property data

[0697] Data Calculation: Joining Data with SQL Queries

[0698] Specific operation: SELECT FROM user_data WHERE userid = user_id

[0699] Step 9:

[0700] The server uses OpenAI's generative AI model to generate an individual curriculum based on the characteristic data. It inputs prompt statements into the generative AI model to obtain appropriate instructions.

[0701] Input: User characteristics data, prompt text

[0702] Output: Individual curriculum

[0703] Data Computation: Curriculum Generation with Generative AI Models

[0704] Specific behavior: response = openai.Completion.create(engine="davinci", prompt=prompt)

[0705] Step 10:

[0706] The server sends the generated curriculum to the device, again using an HTTP POST request.

[0707] Input: Individualized curriculum

[0708] Output: Curriculum to terminal

[0709] Data calculation: None (data transmission)

[0710] Specific operation: response = requests.post(endpoint_url, json=generated_curriculum)

[0711] Step 11:

[0712] Based on the received curriculum, the device presents the user with lessons using textbooks, video content, etc. It uses the YouTube API to retrieve and display appropriate educational videos.

[0713] Input: Individualized curriculum

[0714] Output:Lesson display

[0715] Data Computing: Acquiring and displaying video content

[0716] Specific operation: renderLessonContent(lesson_content)

[0717] Step 12:

[0718] Users take lessons and record their progress on their device, updating it by answering quizzes, for example.

[0719] Input: Lesson content

[0720] Output: Progress data

[0721] Data calculation: None (user input and recording)

[0722] Specific action: User answers a quiz

[0723] Step 13:

[0724] The device records progress data and periodically sends it to the server. The data is stored locally using SQLite and sent in batches.

[0725] Input: Progress data

[0726] Output: Progress data to the server

[0727] Data calculation: None (data transmission)

[0728] Specific behavior: db.insert('progress', progress_data), response = requests.post(endpoint_url, json=progress_data)

[0729] Step 14:

[0730] The server analyzes the progress data and generates feedback using a generative AI model, often using Python's Scikit-learn, to adjust the curriculum as needed.

[0731] Input: Progress data

[0732] Output: Feedback and adjusted curriculum

[0733] Data Computing: Progress in Data Analysis and Prediction

[0734] Specific behavior: model.predict(new_progress_data)

[0735] Step 15:

[0736] The server sends the generated feedback and counseling information to the device using an HTTP POST request.

[0737] Input: Feedback and Counseling Information

[0738] Output: Feedback and counseling information to the terminal

[0739] Data calculation: None (data transmission)

[0740] Specific operation: response = requests.post(endpoint_url, json=feedback_data)

[0741] Step 16:

[0742] The device provides feedback and counseling information to the user, displayed using a React Native UI.

[0743] Input: Feedback and Counseling Information

[0744] Output: Displaying information to the user

[0745] Data calculation: None (display)

[0746] Specific operation: displayFeedback(feedback_data)

[0747] (Application example 1)

[0748] 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."

[0749] Conventional learning support systems have difficulty providing an optimal curriculum based on the characteristics and behavioral data of each user, and have had limitations in providing effective training, particularly to factory workers. This has led to issues such as insufficient achievement of individual skill improvement and efficient work support.

[0750] 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.

[0751] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording user behavioral data, means for analyzing the collected psychological test answers and behavioral data to clarify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, and means for generating a training program to be executed by a robot terminal in the factory, thereby making it possible to provide factory workers with an optimal training curriculum tailored to their individual characteristics.

[0752] A "psychological test" is an evaluation method consisting of questions and tasks to measure a user's psychological characteristics and personality.

[0753] A "questionnaire" is a set of questions prepared for a psychological test.

[0754] "User" refers to an individual or employee who uses the System.

[0755] "Answer" refers to information entered by a user in response to a question in a psychological test.

[0756] "Behavioral data" is recorded data including the user's daily behavior, application usage history, work history, and the like.

[0757] A "curriculum" is a set of learning or training plans designed based on a user's characteristics and goals.

[0758] "Lesson" means a curriculum-based learning module or training session.

[0759] "Progress data" is data that shows the results of a user's learning or training in accordance with a curriculum.

[0760] "Feedback" refers to evaluation and advice regarding the user's learning or training progress.

[0761] A "counseling session" is an interactive support session to support a user's learning or training.

[0762] A "robot terminal" is a machine used in a factory or work environment, which has an interface with the user and provides training programs.

[0763] A "training program" is a series of learning activities or assignments designed to improve a user's skills.

[0764] This invention relates to a system that provides personalized training programs using a generative AI model. This system designs an optimal training plan based on the user's psychological test answers and behavioral data, provides lessons based on the plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[0765] System configuration and operation

[0766] Psychological test submission

[0767] The server generates a list of questions for the psychological test. This generation process involves selecting appropriate questions from a number of templates and creating a list. The generated list of questions for the psychological test is sent to the robot terminal. The terminal displays the questions through a user interface and prompts the user to answer them. The user enters answers to each question, and the terminal collects and sends them to the server.

[0768] Obtaining user behavior data

[0769] The device records the user's behavioral data, including factory activities, work history, and machine operation logs. The device then sends the behavioral data collected over a period of time (e.g., one week) to a server. The server analyzes the received behavioral data to identify the user's work characteristics. This analysis is performed to identify the user's work rhythm and behavioral patterns.

[0770] Data analysis and training program generation

[0771] The server combines the answers to the psychological test and the results of the behavioral data analysis to analyze the user's detailed characteristics. The server then generates an optimal individual training program, taking into account the user's work goals and wishes. This training program is designed to allow the user to gradually learn the skills and knowledge they need. The generated training program is then sent to the robot terminal.

[0772] Lesson delivery and progress management

[0773] The device presents training lessons to the user based on the received curriculum, which may include textbook materials and video content. The user takes the lessons and records their progress on the device. The device records the user's progress data and periodically sends it to the server. The server analyzes the progress data and adjusts the training program as needed.

[0774] Feedback and Counseling

[0775] The server periodically generates feedback based on the user's progress and schedules counseling sessions. The feedback and counseling information are provided to the user via their terminal, allowing the user to self-evaluate and participate in counseling sessions as needed.

[0776] Hardware and software used

[0777] Hardware: Factory robots, servers, IoT devices, etc.

[0778] Software: Python, database (e.g., PostgreSQL), AI library (e.g., TensorFlow)

[0779] Specific examples

[0780] For example, when factory employee A uses this system, the server first generates a list of psychological test questions and sends them to the robot terminal. The questions are presented to factory employee A through the user interface, and the employee enters their answers. These answers are sent to the server. The terminal then records factory employee A's behavioral data for one week and sends it to the server. The server analyzes the data, generates an optimal training program for factory employee A, and sends it to the terminal. Factory employee A takes lessons based on the training plan, and progress reports are periodically sent to the server. The server analyzes progress and schedules and provides feedback and counseling sessions.

[0781] Prompt Sentence Examples

[0782] Questions to ask the generative AI model:

[0783] "Generate an appropriate training curriculum based on the psychological test results and behavioral data of factory employees. Example questions include, 'How motivated are you to work?' and 'Are you interested in new technologies?' Examples of behavioral data include compliance with safety measures and machine maintenance history. The curriculum may include basic technology modules, applied technology modules, etc."

[0784] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0785] Step 1:

[0786] The server generates a list of questions for the psychological test. It selects appropriate questions from many templates and creates a list. This generated list of questions is sent to the terminal. The input is an existing template, and the output is the generated list of questions. Data processing involves selection and combination from templates.

[0787] Step 2:

[0788] The terminal presents the received psychological test question list to the user. The user answers each question, and the terminal collects the answers. The input is the question list sent from the server and the user's answers, and the output is the collected answers. Data calculation involves recording the user's input.

[0789] Step 3:

[0790] The terminal records user behavior data (e.g., work history, machine operation log) for a certain period (e.g., one week) and sends it to the server. The input is daily behavior data, and the output is a summary of the collected behavior data. Data processing involves storing and sending the recorded data.

[0791] Step 4:

[0792] The server analyzes the received psychological test answers and behavioral data to reveal the user's detailed characteristics. The input is the psychological test answers and behavioral data, and the output is the analysis results (user characteristics). Data calculation involves extracting characteristics using statistical analysis and machine learning models.

[0793] Step 5:

[0794] The server generates an optimal individual training program based on the user's goals and aspirations. The input is the user's characteristics, goals, and aspirations, and the output is the training program. Data calculation involves generating the program using a generative AI model.

[0795] Step 6:

[0796] The generated training program is sent to the terminal. The input is the generated training program, and the output is transmission to the terminal. Data processing involves format conversion and communication of the program data.

[0797] Step 7:

[0798] The terminal presents training lessons to the user based on the received training program. The input is the training program, and the output is the presentation to the user. Data processing includes displaying the lessons and providing an interactive user interface.

[0799] Step 8:

[0800] Users take lessons and record their progress on their devices. The input is the user's lesson data, and the output is their progress. Data processing involves recording and periodically sending the progress data.

[0801] Step 9:

[0802] The server analyzes the progress data and adjusts the training program as needed. The input is the progress data and the output is the adjusted training program. Data processing involves monitoring the progress and redesigning the program.

[0803] Step 10:

[0804] The server generates feedback and counseling sessions and provides them to users through terminals. The inputs are progress data and analysis results, and the output is feedback and counseling information. Data processing includes generating reports and scheduling counseling sessions.

[0805] 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.

[0806] This invention relates to a system that uses generative AI to provide an individualized curriculum. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback. This system designs an optimal learning plan based on the user's psychological test answers, behavioral data, and emotional data, provides lessons based on that plan, and manages progress. Below, we will specifically explain the operation of the entire system, focusing on the server, terminal, and user.

[0807] System configuration and operation

[0808] Psychological test submission

[0809] 1. The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list.

[0810] 2. The server sends the generated psychological test question list to the device. The data format is JSON or XML, and it is distributed to the device via API.

[0811] 3. The device presents the user with a list of questions for the psychological test, displaying the questions on the user interface and providing a form for the user to enter their answers.

[0812] 4. The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[0813] 5. The device collects the user's answers and sends them to the server, where they are reformatted and converted into the appropriate data format before being sent.

[0814] Obtaining user behavior data

[0815] 1. The device records user behavior data, including app usage history, location information, and activity logs.

[0816] 2. The device sends the collected behavioral data to the server, which uploads the data to the server periodically or in batches after a certain period of time.

[0817] 3. The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle.

[0818] Acquiring user emotion data

[0819] 1. The device collects user emotional data in real time, including facial recognition and voice analysis using sensors such as cameras and microphones.

[0820] 2. The device collects emotional data and sends it to a server, where it is converted into digital data using image analysis and voice processing technology.

[0821] 3. The server receives and analyzes the emotion data, identifies the user's emotional state, and reflects this in the user profile.

[0822] Data analysis and curriculum generation

[0823] 1. The server integrates the analysis results of the psychological test answers, behavioral data, and emotional data to analyze the user's detailed characteristics.

[0824] 2. The server generates an individual curriculum based on the user's goals, aspirations, and emotional state. This curriculum is designed to allow the user to gradually learn the skills and knowledge they need.

[0825] 3. The server sends the generated curriculum to the terminal.

[0826] Lesson delivery and progress management

[0827] 1. The device presents lessons to the user based on the curriculum received, which includes textbooks and video content.

[0828] 2. The user takes lessons and records their progress on the device. Progress is entered after each lesson, allowing the user to understand their learning status.

[0829] 3. The device records the user's progress data and sends it to the server, where it is uploaded to the server in real time or periodically.

[0830] 4. The server analyzes the progress data and adjusts the curriculum as needed. In addition to the progress data, it also analyzes emotional data and takes into account the state of the user's learning.

[0831] Feedback and Counseling

[0832] 1. The server periodically generates feedback based on the user's progress and emotional data and schedules counseling sessions.

[0833] 2. The server sends feedback and counseling information to the device, including learning evaluation and future advice.

[0834] 3. The device provides feedback and counseling information to the user, allowing the user to self-evaluate and manage their schedule.

[0835] Specific examples

[0836] For example, consider the case where User B uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User B answers each question, and the device collects and sends them to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends it to the server. The server analyzes this data, generates a curriculum based on User B's characteristics and emotional state, and sends it to the device. User B takes lessons based on the received curriculum and records his or her progress on the device. The progress data and emotional data are periodically sent to the server, and feedback and counseling information are generated.

[0837] This allows each user to be provided with the optimal learning plan, leading to efficient self-improvement. The introduction of the emotion engine enables a more precise learning experience.

[0838] The processing flow will be explained below.

[0839] Step 1:

[0840] The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list. The questions include personality assessment, behavioral tendencies, stress levels, etc.

[0841] Step 2:

[0842] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and communication is made to the device via an API.

[0843] Step 3:

[0844] The device presents the user with a list of psychological test questions, displays the questions on a user interface, and provides a form for the user to answer.

[0845] Step 4:

[0846] The user enters answers to each question in the psychological test. The user enters a choice or free response in the form.

[0847] Step 5:

[0848] The device collects the user's answers and sends them to the server, where they are formatted and converted into JSON format before being sent.

[0849] Step 6:

[0850] The device records user behavior data, including app usage history, location information, and activity logs.

[0851] Step 7:

[0852] The device sends the collected behavioral data to a server, either in real time or in batches over a fixed time span.

[0853] Step 8:

[0854] The server receives and analyzes the behavioral data, using machine learning algorithms to reveal the user's activity patterns and daily rhythms.

[0855] Step 9:

[0856] The device collects the user's emotional data in real time using a camera and microphone to recognize facial expressions and analyze voice.

[0857] Step 10:

[0858] The emotional data collected by the device is sent to a server, where it is converted into data using image analysis and voice processing technology.

[0859] Step 11:

[0860] The server receives and analyzes the emotional data, identifying each emotional state (e.g., joy, sadness, anger, etc.) and reflecting it in the user profile.

[0861] Step 12:

[0862] The server integrates the analysis results of the psychological test responses, behavioral data, and emotional data, thereby revealing detailed characteristics of the user.

[0863] Step 13:

[0864] The server generates an individual curriculum based on the user's goals, aspirations, and characteristics. The learning plan is automatically designed using an AI algorithm.

[0865] Step 14:

[0866] The server sends the generated curriculum to the device. The generated curriculum data is sent in JSON or XML format.

[0867] Step 15:

[0868] The device presents lessons to the user based on the received curriculum, which includes textbooks, video content, and practice questions.

[0869] Step 16:

[0870] Users take lessons and record their progress on their devices. Users enter progress data after each lesson.

[0871] Step 17:

[0872] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[0873] Step 18:

[0874] The server analyzes the progress data and adjusts the curriculum as needed. Based on the progress data and emotion data, the lesson plan is updated appropriately.

[0875] Step 19:

[0876] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and emotional state.

[0877] Step 20:

[0878] The server transmits feedback and counseling information to the terminal, and distributes the generated feedback and counseling schedule to the terminal.

[0879] Step 21:

[0880] The terminal presents the feedback and counseling information to the user, and displays the feedback and counseling details on the user interface.

[0881] Step 22:

[0882] Users receive feedback and participate in counseling sessions if necessary. Users complete a self-assessment and book or attend counseling sessions.

[0883] Example 2

[0884] 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."

[0885] Conventional learning systems have had difficulty providing a curriculum that fully takes into account the characteristics and emotional state of each user. Furthermore, they lack the ability to flexibly adjust the curriculum and provide feedback in response to the user's learning progress and emotional changes, which can hinder effective learning.

[0886] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for generating a list of questions for a psychological test, a means for recording user behavioral data and transmitting it to the server, a means for acquiring and analyzing user emotional data, a means for analyzing the collected psychological test answers, behavioral data, and emotional data to clarify the user's characteristics, and a means for generating an individual curriculum based on the user's goals, hopes, and emotional state. This makes it possible to provide an appropriate curriculum and feedback according to the user's characteristics and emotional state.

[0887] A "psychological test question list" is a collection of questions for assessing a user's psychological state and characteristics.

[0888] "User behavior data" refers to information including a user's application usage history, location information, and other activity logs.

[0889] "User emotion data" is information obtained by analyzing the user's emotional state obtained from facial expressions, voice, etc.

[0890] A "server" is a computer system for processing and analyzing data.

[0891] A "terminal" is a device such as a computer device or a smartphone that is directly operated by a user.

[0892] A "curriculum" is a collection of learning plans and teaching materials designed to meet a user's learning objectives.

[0893] "Feedback" refers to evaluations and advice provided based on a user's learning progress and achievements.

[0894] A "counseling session" is an interactive session to provide learning and psychological support to a user.

[0895] "Analysis" is the process of extracting and understanding useful information from collected data.

[0896] A "lesson" is a set of materials or activities designed to teach a specific skill or knowledge.

[0897] "Progress data" is data that indicates the progress of a user's learning activities.

[0898] This invention relates to a learning support system that provides an individualized curriculum using a generative AI model. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback.

[0899] System configuration

[0900] server

[0901] The server is a centralized computer system responsible for generating psychological test questions, analyzing behavioral and emotional data, generating personalized curriculum, and analyzing progress and generating feedback. The server includes software components such as the main database, AI models, analysis algorithms, and APIs (Application Programming Interfaces).

[0902] Terminal

[0903] The terminal is a device that the user directly operates, and is mainly composed of a smartphone or computer. The terminal has the functions of presenting a list of psychological test questions, recording the user's answers and behavioral data, acquiring emotional data, and presenting lessons based on an individual curriculum. The terminal also transmits the collected data to a server.

[0904] User

[0905] Users are individuals who use this system, answering psychological tests and providing daily behavioral and emotional data. Users also take lessons based on the provided curriculum and record their progress on their devices.

[0906] Example of operation

[0907] For example, when User B begins using this system, the server generates a list of psychological test questions and sends them to the device. User B answers the questions displayed on the device, and the answers are collected and sent to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends this data to the server. The server analyzes this data and generates an individual curriculum tailored to User B's characteristics and emotional state, which is then sent to the device.

[0908] User B takes lessons based on the received curriculum and records their progress on their device. Progress and emotion data are then periodically sent to the server, which analyzes the data and generates feedback and counseling information. The device then presents this information to User B, helping with self-evaluation and schedule management.

[0909] Specific hardware and software names used

[0910] The server configuration uses MySQL as the database, OpenAI GPT-3 for the generative AI model, Microsoft Azure Face API and Google Cloud Speech-to-Text for emotion analysis, etc. The terminal application is developed for iOS and Android smartphones, and frameworks such as React Native are used for real-time data collection and display.

[0911] Prompt Sentence Examples

[0912] Here is an example of a prompt for curriculum generation using a generative AI model:

[0913] "User B began using the system. He took a psychological test generated by the server, and an individual curriculum was generated based on the results. After that, the device continued to record behavioral and emotional data, and feedback was provided based on the analysis results."

[0914] In this way, an effective learning experience is achieved by providing optimal learning plans and feedback to each individual user.

[0915] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0916] Program processing flow

[0917] Step 1:

[0918] The server generates a list of questions for the psychological test. It selects template questions from the database and customizes the question set according to the user's characteristics. For example, it executes an SQL query such as "select from question_templates where category='Psychology'" and formats the retrieved questions in JSON format. The input is the template question database, and the output is the generated JSON-formatted question list.

[0919] Step 2:

[0920] The psychological test question list generated by the server is sent to the terminal. It is sent via API using an HTTP request. To ensure communication stability, error handling and resend functions are provided during transmission. The input is a JSON-formatted question list, and the output is the transmission status (success / failure).

[0921] Step 3:

[0922] The device presents the user with a list of questions for a psychological test. The questions are displayed on a user interface and options are provided. For example, React Native is used to render a question form for the user to answer. The input is a list of questions in JSON format, and the output is displayed on the screen.

[0923] Step 4:

[0924] The user answers questions in a psychological test. The user enters their answer in the displayed form and presses the submit button. For example, in response to the question "What are your hobbies?", the user enters "reading and watching movies" in the text box. The input is the user's free description, and the output is the user's answer data.

[0925] Step 5:

[0926] The device sends the user's answers to the psychological test to the server. The answer data is formatted in the appropriate JSON format and sent using an HTTP POST request. The input is the user's answer data, and the output is the submission status (success / failure).

[0927] Step 6:

[0928] The device records user behavior data, collecting application usage history and GPS location information. For example, it accumulates log information such as "App A used for 10 minutes, location information 35.6895, 139.6917." The input is the device's sensor and usage data, and the output is the recorded behavior data.

[0929] Step 7:

[0930] The behavioral data collected by the device is sent to the server. The data is formatted in batches at regular intervals and uploaded to the server using an HTTP POST request. The input is the recorded behavioral data, and the output is the transmission status (success / failure).

[0931] Step 8:

[0932] The server analyzes the behavioral data. It uses the Python pandas library to integrate it into a data frame and extract specific behavioral patterns. For example, it identifies behavioral characteristics such as "the user plays sports at least three times a week." The input is the behavioral data, and the output is the analysis results and behavioral patterns.

[0933] Step 9:

[0934] The device acquires the user's emotional data. It uses a camera and microphone to capture facial expressions and voice in real time and uses an emotion analysis API. For example, "detect happiness from facial expressions and determine low stress levels from voice." The input is real-time video and audio data, and the output is emotional data.

[0935] Step 10:

[0936] The emotion data acquired by the device is sent to the server. The data acquired from the emotion analysis API is formatted in JSON and sent via an HTTP POST request. The input is the emotion data, and the output is the transmission status (success / failure).

[0937] Step 11:

[0938] The server analyzes the emotional data. Using image and audio analysis technology, the server reflects the emotional state in a profile. For example, it might determine that "stress levels are high on Mondays, and emotions of joy are strong on Fridays." The input is the emotional data, and the output is the analysis results and the emotional profile.

[0939] Step 12:

[0940] The server integrates and analyzes psychological test responses, behavioral data, and emotional data. It then links the data stored in the database to extract insights for creating individualized curricula. For example, "The user is sociable and has a high level of concentration." The input is the integrated data, and the output is a detailed user profile.

[0941] Step 13:

[0942] The server generates an individual curriculum that is optimal for the user. Using a generative AI model, an appropriate curriculum is designed based on the analysis results. For example, "Week 1: Basic concepts, Week 2: Applied tasks." The input is a detailed user profile, and the output is the generated curriculum.

[0943] Step 14:

[0944] The server sends the generated curriculum to the terminal. The curriculum is formatted in JSON format and sent via an HTTP POST request. The input is the generated curriculum, and the output is the sending status (success / failure).

[0945] Step 15:

[0946] The terminal presents lessons to the user based on the curriculum. The lesson content is displayed on the user interface, providing an environment in which the user can learn. For example, "Today's lesson: Basic Python grammar." The input is the received curriculum, and the output is the display on the screen.

[0947] Step 16:

[0948] The user takes lessons and records their progress on the device. After each lesson, the progress is entered. For example, "Lesson completed" is recorded on the device. The input is the user's progress, and the output is the progress data.

[0949] Step 17:

[0950] The device sends the recorded progress data to the server. The progress data is formatted in JSON and sent via an HTTP POST request. The input is the progress data, and the output is the sending status (success / failure).

[0951] Step 18:

[0952] The server analyzes the progress data and adjusts the curriculum as needed. It analyzes the progress data and emotion data together and updates the curriculum to make it more adaptable for the user. For example, "Add supplementary lessons before moving on to applied tasks." The input is progress and emotion data, and the output is the adjusted curriculum.

[0953] Step 19:

[0954] The server generates feedback based on the progress and emotion data and suggests a counseling session. The feedback suggests, "You're doing well, let's move on to the applied task next." The input is the analyzed data, and the output is feedback and counseling information.

[0955] Step 20:

[0956] The server sends the generated feedback and counseling information to the terminal. The feedback information is formatted in JSON format and sent via an HTTP POST request. The input is the generated feedback, and the output is the sending status (success / failure).

[0957] Step 21:

[0958] The device presents feedback and counseling information to the user. The feedback and counseling suggestions are displayed on the screen. For example, "You're making good progress. Let's take the next step." The input is the feedback received, and the output is the display on the screen.

[0959] (Application example 2)

[0960] 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."

[0961] Conventional skill training systems struggle to fully understand and individually respond to the characteristics, goals, and aspirations of individual employees. Furthermore, they often provide a uniform curriculum without considering the emotional state of employees, resulting in ineffective training. Furthermore, they are unable to integrate and analyze work data and emotional data from industrial equipment, leaving challenges in improving employee productivity and work efficiency. This invention aims to solve these challenges by comprehensively analyzing the characteristics and emotional state of individual employees and providing an optimal training curriculum.

[0962] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to the user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, means for acquiring and analyzing the user's emotional data, means for generating an individual curriculum that reflects the user's emotional state, and means for recording employee work data on industrial equipment. This makes it possible to provide an optimal curriculum that takes into account the characteristics and emotional state of employees.

[0963] A "psychological test questionnaire" is a set of questions designed to measure a user's psychological state or characteristics.

[0964] "User" refers to an individual employee or other target person who uses this system for skill training or learning.

[0965] "Behavioral data" refers to data such as a user's daily behavioral patterns, activity logs, working hours, and movement patterns.

[0966] "Collected psychological test answers" refers to the answers given by the user to the psychological test.

[0967] "Emotion data" refers to data on the emotional state obtained by analyzing the user's facial expressions and voice.

[0968] "Individualized curriculum" refers to a learning or skills training plan that is customized based on a user's characteristics and goals.

[0969] "Lesson" refers to a specific learning content or training session provided to a User.

[0970] "Progress data" refers to data that indicates the progress of a user's learning or training.

[0971] "Feedback" refers to evaluations and advice provided based on a user's learning status and training results.

[0972] A "counseling session" refers to a time spent interviewing or consulting with a user, taking into account their characteristics and emotional state.

[0973] "Industrial equipment" refers to production facilities and machinery used in factories, etc.

[0974] "Work data" refers to recorded data regarding employees' operations and work when using industrial equipment.

[0975] This invention is a system that supports skill training for factory employees, generating individualized curricula using psychological test answers, behavioral data, and emotional data, and providing progress management and feedback. The system consists of a server, terminals (smartphones and industrial tablets), and industrial equipment.

[0976] First, the server generates a list of psychological test questions. To do this, it selects an appropriate set of questions from a question template database and creates a list in JSON or XML format. Next, the server sends the generated psychological test question list to the terminal. The terminal presents the psychological test questions to the user, and the answers are collected as the user answers them. These answers are sent to the server for data analysis.

[0977] In addition, the device records the user's behavioral data, including work status, movement patterns, and work time, and uploads the data to a server periodically or in batches. At the same time, the device also acquires the user's emotional data using sensors such as a camera and microphone. The server analyzes the emotional data using facial recognition and voice analysis technologies (e.g., OpenCV and Google Cloud Speech-to-Text) to identify the user's emotional state.

[0978] This data is analyzed comprehensively to generate an individual curriculum based on the user's characteristics and goals. The curriculum is designed as a learning plan specific to the user and sent to the device. The device then presents lessons to the user based on this curriculum and records the user's progress as they take the lessons. Progress data is also periodically sent to the server for analysis. This allows the server to adjust the curriculum as needed, providing the user with the optimal learning experience.

[0979] In addition, the server periodically generates feedback and counseling sessions and sends them to the terminal, allowing the user to self-evaluate and manage their schedule based on this information, resulting in efficient skill training.

[0980] As a concrete example, consider the case where Employee A is learning a new welding technique in a factory. The server generates a psychological test such as "work stress level" and sends it to a terminal. Employee A answers the questions on their smartphone and sends the answers to the server. Factory robots and smartphones monitor Employee A's working hours and facial expressions, collecting behavioral and emotional data. The server analyzes this data, generates a "welding training curriculum" optimal for Employee A, and sends it to the terminal. Employee A progresses through the training according to the curriculum and records his or her progress, and the server provides feedback based on the progress data and emotional data.

[0981] An example of a prompt is:

[0982] AI prompts to generate optimal curriculum based on user behavioral and emotional data:

[0983] The user's emotional state is "{emotion}" and their current behavior pattern is "{behavior}". Based on this, please generate the optimal curriculum for the user.

[0984] Examples include:

[0985] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0986] Step 1:

[0987] The server generates a list of psychological test questions, which involves selecting an appropriate set of questions from a database, creating a list in JSON or XML format, and sending the list to the device.

[0988] Input: Question template database

[0989] Data processing / calculation: Select appropriate questions and generate lists

[0990] Output: Psychological test question list (JSON or XML format)

[0991] Step 2:

[0992] The terminal presents the user with a list of psychological test questions received from the server, and the user answers the questions, which are then collected and stored on the terminal.

[0993] Input: Psychological test question list

[0994] Data processing / calculation: Present psychological test questions to users

[0995] Output: User's answer

[0996] Step 3:

[0997] The device formats the user's answers, converts them into JSON or XML format, and sends them to the server.

[0998] Input: User's answer

[0999] Data processing / calculation: Formatting answers and converting them to data formats

[1000] Output: Formatted response data (JSON or XML format)

[1001] Step 4:

[1002] The device records user behavior data, including work status, movement patterns, work time, etc. The data is uploaded to a server periodically or in batches.

[1003] Input: User behavior data

[1004] Data processing / calculation: Data recording, periodic uploading

[1005] Output: Uploaded behavioral data

[1006] Step 5:

[1007] The device uses sensors such as a camera and microphone to capture the user's emotional data in real time, including facial expression recognition and voice analysis, and transmits the emotional data to a server.

[1008] Input: Real-time video and audio data of the user

[1009] Data processing / calculation: facial expression recognition, voice analysis

[1010] Output: Emotion data

[1011] Step 6:

[1012] The server analyzes the collected psychological test answers, behavioral data, and emotional data, and generates an individual curriculum based on the user's characteristics and goals.

[1013] Input: Psychological test answers, behavioral data, emotional data

[1014] Data processing / calculation: Data integration, analysis, and creation of individual curriculum

[1015] Output: Individual curriculum

[1016] Step 7:

[1017] The terminal presents lessons to the user based on the curriculum received from the server, records the user's progress as they take the lessons, and periodically transmits the progress data to the server.

[1018] Input: Individualized curriculum

[1019] Data processing / calculation: Presenting lessons and recording progress

[1020] Output: Progress data

[1021] Step 8:

[1022] The server generates feedback and counseling sessions based on the progress data and emotion data and transmits them to the terminal.

[1023] Input: Progress data, emotion data

[1024] Data processing / calculation: Data analysis, feedback and generation of counseling sessions

[1025] Output: Feedback, counseling sessions

[1026] Step 9:

[1027] The terminal provides the user with feedback from the server and counseling sessions, allowing the user to self-evaluate and manage their schedule.

[1028] Input: Feedback, counseling sessions

[1029] Data processing / calculation: Presentation to the user

[1030] Output: Self-evaluation, schedule management

[1031] 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.

[1032] 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.

[1033] 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.

[1034] [Third embodiment]

[1035] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1036] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1037] 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).

[1038] 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.

[1039] 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.

[1040] 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).

[1041] 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.

[1042] 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.

[1043] 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.

[1044] 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.

[1045] 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.

[1046] 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."

[1047] This invention relates to a system that uses generative AI to provide an individualized curriculum. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[1048] System configuration and operation

[1049] Psychological test submission

[1050] 1. The server generates a list of psychological test questions, which involves selecting appropriate questions from a number of templates and creating the list.

[1051] 2. The server sends the generated psychological test question list to the terminal.

[1052] 3. The device presents the received list of questions to the user. The device displays the questions through a user interface and prompts the user to answer them.

[1053] 4. The user enters an answer to each question.

[1054] 5. The device collects the user's answers and sends them to the server.

[1055] Obtaining user behavior data

[1056] 1. The device records user behavioral data, including app usage history and daily activity logs.

[1057] 2. The device sends the behavioral data collected over a certain period of time (for example, one week) to the server.

[1058] 3. The server analyzes the received behavioral data to identify the user's characteristics. This analysis is carried out to identify the user's daily rhythm and behavioral patterns.

[1059] Data analysis and curriculum generation

[1060] 1. The server integrates the psychological test answers and the analysis results of the behavioral data to analyze the user's detailed characteristics.

[1061] 2. The server takes into account the user's goals and aspirations and generates an optimal individual curriculum. This curriculum is designed to enable the user to gradually acquire the skills and knowledge they need.

[1062] 3. The server sends the generated curriculum to the terminal.

[1063] Lesson delivery and progress management

[1064] 1. The device presents lessons to the user based on the received curriculum, which includes textbooks and video content.

[1065] 2. Users take lessons and record their progress on their device, including by completing quizzes after each lesson.

[1066] 3. The device records the user's progress data and periodically sends it to the server.

[1067] 4. The server analyzes the progress data and adjusts the curriculum as needed.

[1068] Feedback and Counseling

[1069] 1. The server periodically generates feedback based on the user's progress and schedules counseling sessions.

[1070] 2. The server sends feedback and counseling information to the terminal.

[1071] 3. The device presents feedback and counseling information to the user, allowing them to self-assess and participate in a counseling session if necessary.

[1072] Specific examples

[1073] For example, consider the case where User A uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User A answers each question, and the device collects and sends the answers to the server. Next, the device records User A's behavioral data for one week and sends it to the server. The server analyzes this data, generates a curriculum tailored to User A's characteristics, and sends it to the device. User A takes lessons based on the received curriculum and records his or her progress on the device. Progress data is periodically sent to the server, and feedback and counseling information is generated.

[1074] This allows each user to be provided with the optimal learning plan, enabling efficient self-development.

[1075] The processing flow will be explained below.

[1076] Step 1:

[1077] The server generates a list of questions for the psychological test by selecting an appropriate set of questions from multiple question template databases and creating the list.

[1078] Step 2:

[1079] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and the questions are delivered to the device via an API.

[1080] Step 3:

[1081] The terminal presents the user with a list of questions for the psychological test, displays the questions on a user interface, and provides a form for the user to enter their answers.

[1082] Step 4:

[1083] The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[1084] Step 5:

[1085] The device collects the user's answers and sends them to the server, which formats the data before sending it to the server.

[1086] Step 6:

[1087] The device will record user behavior data over a one-week period, including app usage history, location information, and activity logs.

[1088] Step 7:

[1089] The device sends the collected behavioral data to a server, which uploads the data to the server periodically or in batches after a week.

[1090] Step 8:

[1091] The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle rhythms.

[1092] Step 9:

[1093] The server combines the results of the psychological test and the behavioral data analysis to perform a detailed analysis of the user's characteristics, and reflects the resulting characteristic data in the user profile.

[1094] Step 10:

[1095] The server generates a personalized curriculum based on the user's goals and preferences, and uses algorithms to create an optimal learning plan.

[1096] Step 11:

[1097] The server sends the generated curriculum to the device, converts the curriculum data into JSON or XML format, and distributes it to the device via API.

[1098] Step 12:

[1099] The device presents lessons to the user based on the received curriculum, displaying teaching materials, exercises, video content, and other information and notifying the user.

[1100] Step 13:

[1101] Users take lessons and enter their progress into the device. After completing the lesson, they enter progress data and record their evaluation and impressions.

[1102] Step 14:

[1103] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[1104] Step 15:

[1105] The server analyzes the progress data and adjusts the curriculum as needed, rebuilding lesson plans based on the user's progress.

[1106] Step 16:

[1107] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and analysis results.

[1108] Step 17:

[1109] The server transmits feedback and counseling information to the terminal, and distributes the created feedback and counseling schedule to the terminal.

[1110] Step 18:

[1111] The terminal presents the feedback and counseling information to the user, and displays and notifies the details of the feedback and counseling on the user interface.

[1112] Step 19:

[1113] Users receive feedback and participate in counseling sessions if necessary. Users complete self-assessments and schedule or attend counseling sessions.

[1114] Example 1

[1115] 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."

[1116] Conventional learning systems have difficulty providing an optimal learning curriculum based on the individual characteristics and behavioral patterns of each user, resulting in the inability to provide efficient learning support. Furthermore, conventional methods have difficulty dynamically adjusting the curriculum according to the user's progress, making it impossible to maximize learning effectiveness. Furthermore, the lack of regular feedback and counseling means that users are unable to effectively realize their own personal growth.

[1117] 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.

[1118] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individualized curriculum based on the user's goals and aspirations, means for generating an individualized curriculum using a generative AI model, means for inputting prompts into the generative AI model to obtain optimal instructions, means for transmitting the generated curriculum to a terminal, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, and means for periodically generating and providing feedback and counseling sessions to the user. This allows for the provision of an optimal learning curriculum based on the user's individual characteristics, dynamic adjustment of the curriculum according to the user's progress, and effective learning support through regular feedback and counseling.

[1119]

[1120] A "psychological test question list" is a list containing a series of questions used to assess a user's psychological state, learning style, etc.

[1121] The "means for presenting questions for a psychological test" is a means for displaying questions for a psychological test to a user via a terminal.

[1122] The "means for collecting user responses" refers to a means for recording the responses entered by the user to the questions in the psychological test.

[1123] "User behavior data" refers to data including a user's app usage history and daily activity log.

[1124] The "means for recording user behavior data" refers to a means for collecting and storing user behavior data.

[1125] The "means for analyzing psychological test answers and behavioral data" refers to a means for analyzing the collected psychological test answers and behavioral data to identify the characteristics of the user.

[1126] "User characteristics" are the individual characteristics and features of the user that are revealed based on the responses to the psychological test and behavioral data.

[1127] The "means for generating an individual curriculum" is a means for creating an optimal learning plan for an individual user based on the characteristics and goals of the user.

[1128] A "generative AI model" is a model that uses artificial intelligence to generate a response from an input prompt sentence.

[1129] A "prompt" is an instruction or question given to a generative AI model.

[1130] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet.

[1131] A "lesson" is content or learning material for users to study.

[1132] "Progress data" is data that indicates the learning progress of a user that is recorded as a result of the user taking a lesson.

[1133] "Feedback" is a response that includes evaluation and advice regarding the user's learning progress.

[1134] A "counseling session" is a session in which a user receives advice from an expert regarding their learning progress or concerns.

[1135]

[1136] This invention relates to a system that provides an individualized curriculum using generative AI. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[1137] System configuration and operation

[1138] Psychological test submission

[1139] The server randomly selects appropriate questions from a number of templates and generates a list of psychological test questions. This process is performed using a Python script. The generated list of questions is sent to the device via an HTTP POST request. The device then presents the psychological test questions to the user through a user interface built using React Native. The user then answers each question, and the answers are collected by the device and sent to the server again via an HTTP POST request.

[1140] For example, the server generates a question such as "What time of day do you study? (Morning, afternoon, or evening)" and the device presents this to the user. The user answers "Morning," and the device sends the answer data to the server.

[1141] Obtaining user behavior data

[1142] The device records the user's behavioral data, such as app usage history and daily activity logs. This data is recorded using the Google Analytics SDK. The behavioral data is collected over a certain period (for example, one week) and sent to the server in batch processing. The server analyzes the sent behavioral data using the Pandas library to identify the user's characteristics.

[1143] As a specific example, data is collected on how many times and at what times a user uses an app over the course of a week, and this data is used to identify daily rhythms and behavioral patterns.

[1144] Data analysis and curriculum generation

[1145] The server integrates the psychological test responses and behavioral data analysis results to reveal the user's detailed characteristics. Next, OpenAI's generative AI model is used to generate an optimal individualized curriculum that takes into account the user's goals and aspirations. The prompt sentence is then input into the generative AI model to obtain the expected curriculum proposal.

[1146] An example of a prompt sentence is, "User characteristics: morning person, prefers visual learning materials. Please generate the optimal curriculum." A curriculum is generated based on the response of the generative AI model and sent to the device via an HTTP POST request.

[1147] Lesson delivery and progress management

[1148] Based on the received curriculum, the device presents lessons to the user using textbooks, video content, etc. For example, this includes using YouTube's API to retrieve and display appropriate educational videos. The user takes the lessons and records their progress on the device. Progress data is stored locally using SQLite and periodically sent to the server via batch processing.

[1149] Feedback and Counseling

[1150] The server analyzes the progress data and generates regular feedback using a generative AI model. Additionally, it schedules necessary counseling sessions. The generated feedback and counseling information are sent to the device via an HTTP POST request. The device then presents the feedback to the user, who can then self-evaluate and participate in counseling sessions as needed.

[1151] In this way, a system using generative AI can provide an optimal learning curriculum based on each user's individual characteristics, dynamically adjust the curriculum according to the user's progress, and provide effective learning support through regular feedback and counseling.

[1152] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1153]

[1154] Step 1:

[1155] The server randomly selects appropriate questions from a large number of templates and generates a list of psychological test questions using a Python script. The generated list of questions is sent to the terminal via an HTTP POST request in JSON format.

[1156] Input: Template data

[1157] Output: Question list

[1158] Data processing: Randomly select questions from the template data and create a list

[1159] Specific behavior: questions = random.sample(all_templates, number_of_questions)

[1160] Step 2:

[1161] The device presents the received psychological test question list to the user. The questions are displayed through a user interface built using React Native, and the user inputs their answers.

[1162] Input: Question List

[1163] Output: User response data

[1164] Data calculation: None (display and input collection)

[1165] Specific operation: renderQuestions(questions)

[1166] Step 3:

[1167] The user inputs answers to each question, and the input answers are recorded on the terminal.

[1168] Input: Question display and answer input form

[1169] Output: Response data

[1170] Data processing: None (user input)

[1171] Specific behavior: The user answers "Morning" to the question "What time of day do you study? (Morning, afternoon, evening)"

[1172] Step 4:

[1173] The device collects the user's response data and sends it to the server in JSON format via an HTTP POST request.

[1174] Input: Answer data

[1175] Output: Response data to the server

[1176] Data calculation: None (data transmission)

[1177] Specific operation: response = requests.post(endpoint_url, json=user_answers)

[1178] Step 5:

[1179] The device uses the Google Analytics SDK to record user behavior data, such as app usage history and daily activity logs, which are collected periodically.

[1180] Input: User action

[1181] Output: Behavioral data

[1182] Data processing: recording behavioral data

[1183] Specific behavior: analytics.logEvent('page_view', {'page': currentPage})

[1184] Step 6:

[1185] The device sends behavioral data collected over a period of time (e.g., one week) to the server, where the data is compiled in batch processing and sent in JSON format.

[1186] Input: Collected behavioral data

[1187] Output: Behavioral data to the server

[1188] Data Computation: Data Batching

[1189] Specific operation: response = requests.post(endpoint_url, json=collected_data)

[1190] Step 7:

[1191] The server analyzes the received behavioral data using Python's Pandas library to identify user characteristics, and the analysis results are stored in a database.

[1192] Input: Behavioral data

[1193] Output: User characteristics data

[1194] Data operations: transformation into data frames and statistical analysis

[1195] Specific behavior: df = pd.DataFrame(json_data), user_traits = df.describe()

[1196] Step 8:

[1197] The server integrates the psychological test response data with the analyzed behavioral data to reveal detailed user characteristics. It retrieves the data from the database using SQL queries and performs integrated analysis.

[1198] Input: Psychological test response data and behavioral data

[1199] Output: Integrated property data

[1200] Data Calculation: Joining Data with SQL Queries

[1201] Specific operation: SELECT FROM user_data WHERE userid = user_id

[1202] Step 9:

[1203] The server uses OpenAI's generative AI model to generate an individual curriculum based on the characteristic data. It inputs prompt statements into the generative AI model to obtain appropriate instructions.

[1204] Input: User characteristics data, prompt text

[1205] Output: Individual curriculum

[1206] Data Computation: Curriculum Generation with Generative AI Models

[1207] Specific behavior: response = openai.Completion.create(engine="davinci", prompt=prompt)

[1208] Step 10:

[1209] The server sends the generated curriculum to the device, again using an HTTP POST request.

[1210] Input: Individualized curriculum

[1211] Output: Curriculum to terminal

[1212] Data calculation: None (data transmission)

[1213] Specific operation: response = requests.post(endpoint_url, json=generated_curriculum)

[1214] Step 11:

[1215] Based on the received curriculum, the device presents the user with lessons using textbooks, video content, etc. It uses the YouTube API to retrieve and display appropriate educational videos.

[1216] Input: Individualized curriculum

[1217] Output:Lesson display

[1218] Data Computing: Acquiring and displaying video content

[1219] Specific operation: renderLessonContent(lesson_content)

[1220] Step 12:

[1221] Users take lessons and record their progress on their device, updating it by answering quizzes, for example.

[1222] Input: Lesson content

[1223] Output: Progress data

[1224] Data calculation: None (user input and recording)

[1225] Specific action: User answers a quiz

[1226] Step 13:

[1227] The device records progress data and periodically sends it to the server. The data is stored locally using SQLite and sent in batches.

[1228] Input: Progress data

[1229] Output: Progress data to the server

[1230] Data calculation: None (data transmission)

[1231] Specific behavior: db.insert('progress', progress_data), response = requests.post(endpoint_url, json=progress_data)

[1232] Step 14:

[1233] The server analyzes the progress data and generates feedback using a generative AI model, often using Python's Scikit-learn, to adjust the curriculum as needed.

[1234] Input: Progress data

[1235] Output: Feedback and adjusted curriculum

[1236] Data Computing: Progress in Data Analysis and Prediction

[1237] Specific behavior: model.predict(new_progress_data)

[1238] Step 15:

[1239] The server sends the generated feedback and counseling information to the device using an HTTP POST request.

[1240] Input: Feedback and Counseling Information

[1241] Output: Feedback and counseling information to the terminal

[1242] Data calculation: None (data transmission)

[1243] Specific operation: response = requests.post(endpoint_url, json=feedback_data)

[1244] Step 16:

[1245] The device provides feedback and counseling information to the user, displayed using a React Native UI.

[1246] Input: Feedback and Counseling Information

[1247] Output: Displaying information to the user

[1248] Data calculation: None (display)

[1249] Specific operation: displayFeedback(feedback_data)

[1250] (Application example 1)

[1251] 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."

[1252] Conventional learning support systems have difficulty providing an optimal curriculum based on the characteristics and behavioral data of each user, and have had limitations in providing effective training, particularly to factory workers. This has led to issues such as insufficient achievement of individual skill improvement and efficient work support.

[1253] 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.

[1254] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording user behavioral data, means for analyzing the collected psychological test answers and behavioral data to clarify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, and means for generating a training program to be executed by a robot terminal in the factory, thereby making it possible to provide factory workers with an optimal training curriculum tailored to their individual characteristics.

[1255] A "psychological test" is an evaluation method consisting of questions and tasks to measure a user's psychological characteristics and personality.

[1256] A "questionnaire" is a set of questions prepared for a psychological test.

[1257] "User" refers to an individual or employee who uses the System.

[1258] "Answer" refers to information entered by a user in response to a question in a psychological test.

[1259] "Behavioral data" is recorded data including the user's daily behavior, application usage history, work history, and the like.

[1260] A "curriculum" is a set of learning or training plans designed based on a user's characteristics and goals.

[1261] "Lesson" means a curriculum-based learning module or training session.

[1262] "Progress data" is data that shows the results of a user's learning or training in accordance with a curriculum.

[1263] "Feedback" refers to evaluation and advice regarding the user's learning or training progress.

[1264] A "counseling session" is an interactive support session to support a user's learning or training.

[1265] A "robot terminal" is a machine used in a factory or work environment, which has an interface with the user and provides training programs.

[1266] A "training program" is a series of learning activities or assignments designed to improve a user's skills.

[1267] This invention relates to a system that provides personalized training programs using a generative AI model. This system designs an optimal training plan based on the user's psychological test answers and behavioral data, provides lessons based on the plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[1268] System configuration and operation

[1269] Psychological test submission

[1270] The server generates a list of questions for the psychological test. This generation process involves selecting appropriate questions from a number of templates and creating a list. The generated list of questions for the psychological test is sent to the robot terminal. The terminal displays the questions through a user interface and prompts the user to answer them. The user enters answers to each question, and the terminal collects and sends them to the server.

[1271] Obtaining user behavior data

[1272] The device records the user's behavioral data, including factory activities, work history, and machine operation logs. The device then sends the behavioral data collected over a period of time (e.g., one week) to a server. The server analyzes the received behavioral data to identify the user's work characteristics. This analysis is performed to identify the user's work rhythm and behavioral patterns.

[1273] Data analysis and training program generation

[1274] The server combines the answers to the psychological test and the results of the behavioral data analysis to analyze the user's detailed characteristics. The server then generates an optimal individual training program, taking into account the user's work goals and wishes. This training program is designed to allow the user to gradually learn the skills and knowledge they need. The generated training program is then sent to the robot terminal.

[1275] Lesson delivery and progress management

[1276] The device presents training lessons to the user based on the received curriculum, which may include textbook materials and video content. The user takes the lessons and records their progress on the device. The device records the user's progress data and periodically sends it to the server. The server analyzes the progress data and adjusts the training program as needed.

[1277] Feedback and Counseling

[1278] The server periodically generates feedback based on the user's progress and schedules counseling sessions. The feedback and counseling information are provided to the user via their terminal, allowing the user to self-evaluate and participate in counseling sessions as needed.

[1279] Hardware and software used

[1280] Hardware: Factory robots, servers, IoT devices, etc.

[1281] Software: Python, database (e.g., PostgreSQL), AI library (e.g., TensorFlow)

[1282] Specific examples

[1283] For example, when factory employee A uses this system, the server first generates a list of psychological test questions and sends them to the robot terminal. The questions are presented to factory employee A through the user interface, and the employee enters their answers. These answers are sent to the server. The terminal then records factory employee A's behavioral data for one week and sends it to the server. The server analyzes the data, generates an optimal training program for factory employee A, and sends it to the terminal. Factory employee A takes lessons based on the training plan, and progress reports are periodically sent to the server. The server analyzes progress and schedules and provides feedback and counseling sessions.

[1284] Prompt Sentence Examples

[1285] Questions to ask the generative AI model:

[1286] "Generate an appropriate training curriculum based on the psychological test results and behavioral data of factory employees. Example questions include, 'How motivated are you to work?' and 'Are you interested in new technologies?' Examples of behavioral data include compliance with safety measures and machine maintenance history. The curriculum may include basic technology modules, applied technology modules, etc."

[1287] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1288] Step 1:

[1289] The server generates a list of questions for the psychological test. It selects appropriate questions from many templates and creates a list. This generated list of questions is sent to the terminal. The input is an existing template, and the output is the generated list of questions. Data processing involves selection and combination from templates.

[1290] Step 2:

[1291] The terminal presents the received psychological test question list to the user. The user answers each question, and the terminal collects the answers. The input is the question list sent from the server and the user's answers, and the output is the collected answers. Data calculation involves recording the user's input.

[1292] Step 3:

[1293] The terminal records user behavior data (e.g., work history, machine operation log) for a certain period (e.g., one week) and sends it to the server. The input is daily behavior data, and the output is a summary of the collected behavior data. Data processing involves storing and sending the recorded data.

[1294] Step 4:

[1295] The server analyzes the received psychological test answers and behavioral data to reveal the user's detailed characteristics. The input is the psychological test answers and behavioral data, and the output is the analysis results (user characteristics). Data calculation involves extracting characteristics using statistical analysis and machine learning models.

[1296] Step 5:

[1297] The server generates an optimal individual training program based on the user's goals and aspirations. The input is the user's characteristics, goals, and aspirations, and the output is the training program. Data calculation involves generating the program using a generative AI model.

[1298] Step 6:

[1299] The generated training program is sent to the terminal. The input is the generated training program, and the output is transmission to the terminal. Data processing involves format conversion and communication of the program data.

[1300] Step 7:

[1301] The terminal presents training lessons to the user based on the received training program. The input is the training program, and the output is the presentation to the user. Data processing includes displaying the lessons and providing an interactive user interface.

[1302] Step 8:

[1303] Users take lessons and record their progress on their devices. The input is the user's lesson data, and the output is their progress. Data processing involves recording and periodically sending the progress data.

[1304] Step 9:

[1305] The server analyzes the progress data and adjusts the training program as needed. The input is the progress data and the output is the adjusted training program. Data processing involves monitoring the progress and redesigning the program.

[1306] Step 10:

[1307] The server generates feedback and counseling sessions and provides them to users through terminals. The inputs are progress data and analysis results, and the output is feedback and counseling information. Data processing includes generating reports and scheduling counseling sessions.

[1308] 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.

[1309] This invention relates to a system that uses generative AI to provide an individualized curriculum. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback. This system designs an optimal learning plan based on the user's psychological test answers, behavioral data, and emotional data, provides lessons based on that plan, and manages progress. Below, we will specifically explain the operation of the entire system, focusing on the server, terminal, and user.

[1310] System configuration and operation

[1311] Psychological test submission

[1312] 1. The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list.

[1313] 2. The server sends the generated psychological test question list to the device. The data format is JSON or XML, and it is distributed to the device via API.

[1314] 3. The device presents the user with a list of questions for the psychological test, displaying the questions on the user interface and providing a form for the user to enter their answers.

[1315] 4. The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[1316] 5. The device collects the user's answers and sends them to the server, where they are reformatted and converted into the appropriate data format before being sent.

[1317] Obtaining user behavior data

[1318] 1. The device records user behavior data, including app usage history, location information, and activity logs.

[1319] 2. The device sends the collected behavioral data to the server, which uploads the data to the server periodically or in batches after a certain period of time.

[1320] 3. The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle.

[1321] Acquiring user emotion data

[1322] 1. The device collects user emotional data in real time, including facial recognition and voice analysis using sensors such as cameras and microphones.

[1323] 2. The device collects emotional data and sends it to a server, where it is converted into digital data using image analysis and voice processing technology.

[1324] 3. The server receives and analyzes the emotion data, identifies the user's emotional state, and reflects this in the user profile.

[1325] Data analysis and curriculum generation

[1326] 1. The server integrates the analysis results of the psychological test answers, behavioral data, and emotional data to analyze the user's detailed characteristics.

[1327] 2. The server generates an individual curriculum based on the user's goals, aspirations, and emotional state. This curriculum is designed to allow the user to gradually learn the skills and knowledge they need.

[1328] 3. The server sends the generated curriculum to the terminal.

[1329] Lesson delivery and progress management

[1330] 1. The device presents lessons to the user based on the curriculum received, which includes textbooks and video content.

[1331] 2. The user takes lessons and records their progress on the device. Progress is entered after each lesson, allowing the user to understand their learning status.

[1332] 3. The device records the user's progress data and sends it to the server, where it is uploaded to the server in real time or periodically.

[1333] 4. The server analyzes the progress data and adjusts the curriculum as needed. In addition to the progress data, it also analyzes emotional data and takes into account the state of the user's learning.

[1334] Feedback and Counseling

[1335] 1. The server periodically generates feedback based on the user's progress and emotional data and schedules counseling sessions.

[1336] 2. The server sends feedback and counseling information to the device, including learning evaluation and future advice.

[1337] 3. The device provides feedback and counseling information to the user, allowing the user to self-evaluate and manage their schedule.

[1338] Specific examples

[1339] For example, consider the case where User B uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User B answers each question, and the device collects and sends them to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends it to the server. The server analyzes this data, generates a curriculum based on User B's characteristics and emotional state, and sends it to the device. User B takes lessons based on the received curriculum and records his or her progress on the device. The progress data and emotional data are periodically sent to the server, and feedback and counseling information are generated.

[1340] This allows each user to be provided with the optimal learning plan, leading to efficient self-improvement. The introduction of the emotion engine enables a more precise learning experience.

[1341] The processing flow will be explained below.

[1342] Step 1:

[1343] The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list. The questions include personality assessment, behavioral tendencies, stress levels, etc.

[1344] Step 2:

[1345] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and communication is made to the device via an API.

[1346] Step 3:

[1347] The device presents the user with a list of psychological test questions, displays the questions on a user interface, and provides a form for the user to answer.

[1348] Step 4:

[1349] The user enters answers to each question in the psychological test. The user enters a choice or free response in the form.

[1350] Step 5:

[1351] The device collects the user's answers and sends them to the server, where they are formatted and converted into JSON format before being sent.

[1352] Step 6:

[1353] The device records user behavior data, including app usage history, location information, and activity logs.

[1354] Step 7:

[1355] The device sends the collected behavioral data to a server, either in real time or in batches over a fixed time span.

[1356] Step 8:

[1357] The server receives and analyzes the behavioral data, using machine learning algorithms to reveal the user's activity patterns and daily rhythms.

[1358] Step 9:

[1359] The device collects the user's emotional data in real time using a camera and microphone to recognize facial expressions and analyze voice.

[1360] Step 10:

[1361] The emotional data collected by the device is sent to a server, where it is converted into data using image analysis and voice processing technology.

[1362] Step 11:

[1363] The server receives and analyzes the emotional data, identifying each emotional state (e.g., joy, sadness, anger, etc.) and reflecting it in the user profile.

[1364] Step 12:

[1365] The server integrates the analysis results of the psychological test responses, behavioral data, and emotional data, thereby revealing detailed characteristics of the user.

[1366] Step 13:

[1367] The server generates an individual curriculum based on the user's goals, aspirations, and characteristics. The learning plan is automatically designed using an AI algorithm.

[1368] Step 14:

[1369] The server sends the generated curriculum to the device. The generated curriculum data is sent in JSON or XML format.

[1370] Step 15:

[1371] The device presents lessons to the user based on the received curriculum, which includes textbooks, video content, and practice questions.

[1372] Step 16:

[1373] Users take lessons and record their progress on their devices. Users enter progress data after each lesson.

[1374] Step 17:

[1375] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[1376] Step 18:

[1377] The server analyzes the progress data and adjusts the curriculum as needed. Based on the progress data and emotion data, the lesson plan is updated appropriately.

[1378] Step 19:

[1379] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and emotional state.

[1380] Step 20:

[1381] The server transmits feedback and counseling information to the terminal, and distributes the generated feedback and counseling schedule to the terminal.

[1382] Step 21:

[1383] The terminal presents the feedback and counseling information to the user, and displays the feedback and counseling details on the user interface.

[1384] Step 22:

[1385] Users receive feedback and participate in counseling sessions if necessary. Users complete a self-assessment and book or attend counseling sessions.

[1386] Example 2

[1387] 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."

[1388] Conventional learning systems have had difficulty providing a curriculum that fully takes into account the characteristics and emotional state of each user. Furthermore, they lack the ability to flexibly adjust the curriculum and provide feedback in response to the user's learning progress and emotional changes, which can hinder effective learning.

[1389] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for generating a list of questions for a psychological test, a means for recording user behavioral data and transmitting it to the server, a means for acquiring and analyzing user emotional data, a means for analyzing the collected psychological test answers, behavioral data, and emotional data to clarify the user's characteristics, and a means for generating an individual curriculum based on the user's goals, hopes, and emotional state. This makes it possible to provide an appropriate curriculum and feedback according to the user's characteristics and emotional state.

[1390] A "psychological test question list" is a collection of questions for assessing a user's psychological state and characteristics.

[1391] "User behavior data" refers to information including a user's application usage history, location information, and other activity logs.

[1392] "User emotion data" is information obtained by analyzing the user's emotional state obtained from facial expressions, voice, etc.

[1393] A "server" is a computer system for processing and analyzing data.

[1394] A "terminal" is a device such as a computer device or a smartphone that is directly operated by a user.

[1395] A "curriculum" is a collection of learning plans and teaching materials designed to meet a user's learning objectives.

[1396] "Feedback" refers to evaluations and advice provided based on a user's learning progress and achievements.

[1397] A "counseling session" is an interactive session to provide learning and psychological support to a user.

[1398] "Analysis" is the process of extracting and understanding useful information from collected data.

[1399] A "lesson" is a set of materials or activities designed to teach a specific skill or knowledge.

[1400] "Progress data" is data that indicates the progress of a user's learning activities.

[1401] This invention relates to a learning support system that provides an individualized curriculum using a generative AI model. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback.

[1402] System configuration

[1403] server

[1404] The server is a centralized computer system responsible for generating psychological test questions, analyzing behavioral and emotional data, generating personalized curriculum, and analyzing progress and generating feedback. The server includes software components such as the main database, AI models, analysis algorithms, and APIs (Application Programming Interfaces).

[1405] Terminal

[1406] The terminal is a device that the user directly operates, and is mainly composed of a smartphone or computer. The terminal has the functions of presenting a list of psychological test questions, recording the user's answers and behavioral data, acquiring emotional data, and presenting lessons based on an individual curriculum. The terminal also transmits the collected data to a server.

[1407] User

[1408] Users are individuals who use this system, answering psychological tests and providing daily behavioral and emotional data. Users also take lessons based on the provided curriculum and record their progress on their devices.

[1409] Example of operation

[1410] For example, when User B begins using this system, the server generates a list of psychological test questions and sends them to the device. User B answers the questions displayed on the device, and the answers are collected and sent to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends this data to the server. The server analyzes this data and generates an individual curriculum tailored to User B's characteristics and emotional state, which is then sent to the device.

[1411] User B takes lessons based on the received curriculum and records their progress on their device. Progress and emotion data are then periodically sent to the server, which analyzes the data and generates feedback and counseling information. The device then presents this information to User B, helping with self-evaluation and schedule management.

[1412] Specific hardware and software names used

[1413] The server configuration uses MySQL as the database, OpenAI GPT-3 for the generative AI model, Microsoft Azure Face API and Google Cloud Speech-to-Text for emotion analysis, etc. The terminal application is developed for iOS and Android smartphones, and frameworks such as React Native are used for real-time data collection and display.

[1414] Prompt Sentence Examples

[1415] Here is an example of a prompt for curriculum generation using a generative AI model:

[1416] "User B began using the system. He took a psychological test generated by the server, and an individual curriculum was generated based on the results. After that, the device continued to record behavioral and emotional data, and feedback was provided based on the analysis results."

[1417] In this way, an effective learning experience is achieved by providing optimal learning plans and feedback to each individual user.

[1418] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1419] Program processing flow

[1420] Step 1:

[1421] The server generates a list of questions for the psychological test. It selects template questions from the database and customizes the question set according to the user's characteristics. For example, it executes an SQL query such as "select from question_templates where category='Psychology'" and formats the retrieved questions in JSON format. The input is the template question database, and the output is the generated JSON-formatted question list.

[1422] Step 2:

[1423] The psychological test question list generated by the server is sent to the terminal. It is sent via API using an HTTP request. To ensure communication stability, error handling and resend functions are provided during transmission. The input is a JSON-formatted question list, and the output is the transmission status (success / failure).

[1424] Step 3:

[1425] The device presents the user with a list of questions for a psychological test. The questions are displayed on a user interface and options are provided. For example, React Native is used to render a question form for the user to answer. The input is a list of questions in JSON format, and the output is displayed on the screen.

[1426] Step 4:

[1427] The user answers questions in a psychological test. The user enters their answer in the displayed form and presses the submit button. For example, in response to the question "What are your hobbies?", the user enters "reading and watching movies" in the text box. The input is the user's free description, and the output is the user's answer data.

[1428] Step 5:

[1429] The device sends the user's answers to the psychological test to the server. The answer data is formatted in the appropriate JSON format and sent using an HTTP POST request. The input is the user's answer data, and the output is the submission status (success / failure).

[1430] Step 6:

[1431] The device records user behavior data, collecting application usage history and GPS location information. For example, it accumulates log information such as "App A used for 10 minutes, location information 35.6895, 139.6917." The input is the device's sensor and usage data, and the output is the recorded behavior data.

[1432] Step 7:

[1433] The behavioral data collected by the device is sent to the server. The data is formatted in batches at regular intervals and uploaded to the server using an HTTP POST request. The input is the recorded behavioral data, and the output is the transmission status (success / failure).

[1434] Step 8:

[1435] The server analyzes the behavioral data. It uses the Python pandas library to integrate it into a data frame and extract specific behavioral patterns. For example, it identifies behavioral characteristics such as "the user plays sports at least three times a week." The input is the behavioral data, and the output is the analysis results and behavioral patterns.

[1436] Step 9:

[1437] The device acquires the user's emotional data. It uses a camera and microphone to capture facial expressions and voice in real time and uses an emotion analysis API. For example, "detect happiness from facial expressions and determine low stress levels from voice." The input is real-time video and audio data, and the output is emotional data.

[1438] Step 10:

[1439] The emotion data acquired by the device is sent to the server. The data acquired from the emotion analysis API is formatted in JSON and sent via an HTTP POST request. The input is the emotion data, and the output is the transmission status (success / failure).

[1440] Step 11:

[1441] The server analyzes the emotional data. Using image and audio analysis technology, the server reflects the emotional state in a profile. For example, it might determine that "stress levels are high on Mondays, and emotions of joy are strong on Fridays." The input is the emotional data, and the output is the analysis results and the emotional profile.

[1442] Step 12:

[1443] The server integrates and analyzes psychological test responses, behavioral data, and emotional data. It then links the data stored in the database to extract insights for creating individualized curricula. For example, "The user is sociable and has a high level of concentration." The input is the integrated data, and the output is a detailed user profile.

[1444] Step 13:

[1445] The server generates an individual curriculum that is optimal for the user. Using a generative AI model, an appropriate curriculum is designed based on the analysis results. For example, "Week 1: Basic concepts, Week 2: Applied tasks." The input is a detailed user profile, and the output is the generated curriculum.

[1446] Step 14:

[1447] The server sends the generated curriculum to the terminal. The curriculum is formatted in JSON format and sent via an HTTP POST request. The input is the generated curriculum, and the output is the sending status (success / failure).

[1448] Step 15:

[1449] The terminal presents lessons to the user based on the curriculum. The lesson content is displayed on the user interface, providing an environment in which the user can learn. For example, "Today's lesson: Basic Python grammar." The input is the received curriculum, and the output is the display on the screen.

[1450] Step 16:

[1451] The user takes lessons and records their progress on the device. After each lesson, the progress is entered. For example, "Lesson completed" is recorded on the device. The input is the user's progress, and the output is the progress data.

[1452] Step 17:

[1453] The device sends the recorded progress data to the server. The progress data is formatted in JSON and sent via an HTTP POST request. The input is the progress data, and the output is the sending status (success / failure).

[1454] Step 18:

[1455] The server analyzes the progress data and adjusts the curriculum as needed. It analyzes the progress data and emotion data together and updates the curriculum to make it more adaptable for the user. For example, "Add supplementary lessons before moving on to applied tasks." The input is progress and emotion data, and the output is the adjusted curriculum.

[1456] Step 19:

[1457] The server generates feedback based on the progress and emotion data and suggests a counseling session. The feedback suggests, "You're doing well, let's move on to the applied task next." The input is the analyzed data, and the output is feedback and counseling information.

[1458] Step 20:

[1459] The server sends the generated feedback and counseling information to the terminal. The feedback information is formatted in JSON format and sent via an HTTP POST request. The input is the generated feedback, and the output is the sending status (success / failure).

[1460] Step 21:

[1461] The device presents feedback and counseling information to the user. The feedback and counseling suggestions are displayed on the screen. For example, "You're making good progress. Let's take the next step." The input is the feedback received, and the output is the display on the screen.

[1462] (Application example 2)

[1463] 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."

[1464] Conventional skill training systems struggle to fully understand and individually respond to the characteristics, goals, and aspirations of individual employees. Furthermore, they often provide a uniform curriculum without considering the emotional state of employees, resulting in ineffective training. Furthermore, they are unable to integrate and analyze work data and emotional data from industrial equipment, leaving challenges in improving employee productivity and work efficiency. This invention aims to solve these challenges by comprehensively analyzing the characteristics and emotional state of individual employees and providing an optimal training curriculum.

[1465] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to the user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, means for acquiring and analyzing the user's emotional data, means for generating an individual curriculum that reflects the user's emotional state, and means for recording employee work data on industrial equipment. This makes it possible to provide an optimal curriculum that takes into account the characteristics and emotional state of employees.

[1466] A "psychological test questionnaire" is a set of questions designed to measure a user's psychological state or characteristics.

[1467] "User" refers to an individual employee or other target person who uses this system for skill training or learning.

[1468] "Behavioral data" refers to data such as a user's daily behavioral patterns, activity logs, working hours, and movement patterns.

[1469] "Collected psychological test answers" refers to the answers given by the user to the psychological test.

[1470] "Emotion data" refers to data on the emotional state obtained by analyzing the user's facial expressions and voice.

[1471] "Individualized curriculum" refers to a learning or skills training plan that is customized based on a user's characteristics and goals.

[1472] "Lesson" refers to a specific learning content or training session provided to a User.

[1473] "Progress data" refers to data that indicates the progress of a user's learning or training.

[1474] "Feedback" refers to evaluations and advice provided based on a user's learning status and training results.

[1475] A "counseling session" refers to a time spent interviewing or consulting with a user, taking into account their characteristics and emotional state.

[1476] "Industrial equipment" refers to production facilities and machinery used in factories, etc.

[1477] "Work data" refers to recorded data regarding employees' operations and work when using industrial equipment.

[1478] This invention is a system that supports skill training for factory employees, generating individualized curricula using psychological test answers, behavioral data, and emotional data, and providing progress management and feedback. The system consists of a server, terminals (smartphones and industrial tablets), and industrial equipment.

[1479] First, the server generates a list of psychological test questions. To do this, it selects an appropriate set of questions from a question template database and creates a list in JSON or XML format. Next, the server sends the generated psychological test question list to the terminal. The terminal presents the psychological test questions to the user, and the answers are collected as the user answers them. These answers are sent to the server for data analysis.

[1480] In addition, the device records the user's behavioral data, including work status, movement patterns, and work time, and uploads the data to a server periodically or in batches. At the same time, the device also acquires the user's emotional data using sensors such as a camera and microphone. The server analyzes the emotional data using facial recognition and voice analysis technologies (e.g., OpenCV and Google Cloud Speech-to-Text) to identify the user's emotional state.

[1481] This data is analyzed comprehensively to generate an individual curriculum based on the user's characteristics and goals. The curriculum is designed as a learning plan specific to the user and sent to the device. The device then presents lessons to the user based on this curriculum and records the user's progress as they take the lessons. Progress data is also periodically sent to the server for analysis. This allows the server to adjust the curriculum as needed, providing the user with the optimal learning experience.

[1482] In addition, the server periodically generates feedback and counseling sessions and sends them to the terminal, allowing the user to self-evaluate and manage their schedule based on this information, resulting in efficient skill training.

[1483] As a concrete example, consider the case where Employee A is learning a new welding technique in a factory. The server generates a psychological test such as "work stress level" and sends it to a terminal. Employee A answers the questions on their smartphone and sends the answers to the server. Factory robots and smartphones monitor Employee A's working hours and facial expressions, collecting behavioral and emotional data. The server analyzes this data, generates a "welding training curriculum" optimal for Employee A, and sends it to the terminal. Employee A progresses through the training according to the curriculum and records his or her progress, and the server provides feedback based on the progress data and emotional data.

[1484] An example of a prompt is:

[1485] AI prompts to generate optimal curriculum based on user behavioral and emotional data:

[1486] The user's emotional state is "{emotion}" and their current behavior pattern is "{behavior}". Based on this, please generate the optimal curriculum for the user.

[1487] Examples include:

[1488] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1489] Step 1:

[1490] The server generates a list of psychological test questions, which involves selecting an appropriate set of questions from a database, creating a list in JSON or XML format, and sending the list to the device.

[1491] Input: Question template database

[1492] Data processing / calculation: Select appropriate questions and generate lists

[1493] Output: Psychological test question list (JSON or XML format)

[1494] Step 2:

[1495] The terminal presents the user with a list of psychological test questions received from the server, and the user answers the questions, which are then collected and stored on the terminal.

[1496] Input: Psychological test question list

[1497] Data processing / calculation: Present psychological test questions to users

[1498] Output: User's answer

[1499] Step 3:

[1500] The device formats the user's answers, converts them into JSON or XML format, and sends them to the server.

[1501] Input: User's answer

[1502] Data processing / calculation: Formatting answers and converting them to data formats

[1503] Output: Formatted response data (JSON or XML format)

[1504] Step 4:

[1505] The device records user behavior data, including work status, movement patterns, work time, etc. The data is uploaded to a server periodically or in batches.

[1506] Input: User behavior data

[1507] Data processing / calculation: Data recording, periodic uploading

[1508] Output: Uploaded behavioral data

[1509] Step 5:

[1510] The device uses sensors such as a camera and microphone to capture the user's emotional data in real time, including facial expression recognition and voice analysis, and transmits the emotional data to a server.

[1511] Input: Real-time video and audio data of the user

[1512] Data processing / calculation: facial expression recognition, voice analysis

[1513] Output: Emotion data

[1514] Step 6:

[1515] The server analyzes the collected psychological test answers, behavioral data, and emotional data, and generates an individual curriculum based on the user's characteristics and goals.

[1516] Input: Psychological test answers, behavioral data, emotional data

[1517] Data processing / calculation: Data integration, analysis, and creation of individual curriculum

[1518] Output: Individual curriculum

[1519] Step 7:

[1520] The terminal presents lessons to the user based on the curriculum received from the server, records the user's progress as they take the lessons, and periodically transmits the progress data to the server.

[1521] Input: Individualized curriculum

[1522] Data processing / calculation: Presenting lessons and recording progress

[1523] Output: Progress data

[1524] Step 8:

[1525] The server generates feedback and counseling sessions based on the progress data and emotion data and transmits them to the terminal.

[1526] Input: Progress data, emotion data

[1527] Data processing / calculation: Data analysis, feedback and generation of counseling sessions

[1528] Output: Feedback, counseling sessions

[1529] Step 9:

[1530] The terminal provides the user with feedback from the server and counseling sessions, allowing the user to self-evaluate and manage their schedule.

[1531] Input: Feedback, counseling sessions

[1532] Data processing / calculation: Presentation to the user

[1533] Output: Self-evaluation, schedule management

[1534] 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.

[1535] 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.

[1536] 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.

[1537] [Fourth embodiment]

[1538] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1539] 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.

[1540] 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).

[1541] 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.

[1542] 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.

[1543] 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).

[1544] 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.

[1545] 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.

[1546] 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.

[1547] 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.

[1548] 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.

[1549] 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.

[1550] 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."

[1551] This invention relates to a system that uses generative AI to provide an individualized curriculum. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[1552] System configuration and operation

[1553] Psychological test submission

[1554] 1. The server generates a list of psychological test questions, which involves selecting appropriate questions from a number of templates and creating the list.

[1555] 2. The server sends the generated psychological test question list to the terminal.

[1556] 3. The device presents the received list of questions to the user. The device displays the questions through a user interface and prompts the user to answer them.

[1557] 4. The user enters an answer to each question.

[1558] 5. The device collects the user's answers and sends them to the server.

[1559] Obtaining user behavior data

[1560] 1. The device records user behavioral data, including app usage history and daily activity logs.

[1561] 2. The device sends the behavioral data collected over a certain period of time (for example, one week) to the server.

[1562] 3. The server analyzes the received behavioral data to identify the user's characteristics. This analysis is carried out to identify the user's daily rhythm and behavioral patterns.

[1563] Data analysis and curriculum generation

[1564] 1. The server integrates the psychological test answers and the analysis results of the behavioral data to analyze the user's detailed characteristics.

[1565] 2. The server takes into account the user's goals and aspirations and generates an optimal individual curriculum. This curriculum is designed to enable the user to gradually acquire the skills and knowledge they need.

[1566] 3. The server sends the generated curriculum to the terminal.

[1567] Lesson delivery and progress management

[1568] 1. The device presents lessons to the user based on the received curriculum, which includes textbooks and video content.

[1569] 2. Users take lessons and record their progress on their device, including by completing quizzes after each lesson.

[1570] 3. The device records the user's progress data and periodically sends it to the server.

[1571] 4. The server analyzes the progress data and adjusts the curriculum as needed.

[1572] Feedback and Counseling

[1573] 1. The server periodically generates feedback based on the user's progress and schedules counseling sessions.

[1574] 2. The server sends feedback and counseling information to the terminal.

[1575] 3. The device presents feedback and counseling information to the user, allowing them to self-assess and participate in a counseling session if necessary.

[1576] Specific examples

[1577] For example, consider the case where User A uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User A answers each question, and the device collects and sends the answers to the server. Next, the device records User A's behavioral data for one week and sends it to the server. The server analyzes this data, generates a curriculum tailored to User A's characteristics, and sends it to the device. User A takes lessons based on the received curriculum and records his or her progress on the device. Progress data is periodically sent to the server, and feedback and counseling information is generated.

[1578] This allows each user to be provided with the optimal learning plan, enabling efficient self-development.

[1579] The processing flow will be explained below.

[1580] Step 1:

[1581] The server generates a list of questions for the psychological test by selecting an appropriate set of questions from multiple question template databases and creating the list.

[1582] Step 2:

[1583] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and the questions are delivered to the device via an API.

[1584] Step 3:

[1585] The terminal presents the user with a list of questions for the psychological test, displays the questions on a user interface, and provides a form for the user to enter their answers.

[1586] Step 4:

[1587] The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[1588] Step 5:

[1589] The device collects the user's answers and sends them to the server, which formats the data before sending it to the server.

[1590] Step 6:

[1591] The device will record user behavior data over a one-week period, including app usage history, location information, and activity logs.

[1592] Step 7:

[1593] The device sends the collected behavioral data to a server, which uploads the data to the server periodically or in batches after a week.

[1594] Step 8:

[1595] The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle rhythms.

[1596] Step 9:

[1597] The server combines the results of the psychological test and the behavioral data analysis to perform a detailed analysis of the user's characteristics, and reflects the resulting characteristic data in the user profile.

[1598] Step 10:

[1599] The server generates a personalized curriculum based on the user's goals and preferences, and uses algorithms to create an optimal learning plan.

[1600] Step 11:

[1601] The server sends the generated curriculum to the device, converts the curriculum data into JSON or XML format, and distributes it to the device via API.

[1602] Step 12:

[1603] The device presents lessons to the user based on the received curriculum, displaying teaching materials, exercises, video content, and other information and notifying the user.

[1604] Step 13:

[1605] Users take lessons and enter their progress into the device. After completing the lesson, they enter progress data and record their evaluation and impressions.

[1606] Step 14:

[1607] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[1608] Step 15:

[1609] The server analyzes the progress data and adjusts the curriculum as needed, rebuilding lesson plans based on the user's progress.

[1610] Step 16:

[1611] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and analysis results.

[1612] Step 17:

[1613] The server transmits feedback and counseling information to the terminal, and distributes the created feedback and counseling schedule to the terminal.

[1614] Step 18:

[1615] The terminal presents the feedback and counseling information to the user, and displays and notifies the details of the feedback and counseling on the user interface.

[1616] Step 19:

[1617] Users receive feedback and participate in counseling sessions if necessary. Users complete self-assessments and schedule or attend counseling sessions.

[1618] Example 1

[1619] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1620] Conventional learning systems have difficulty providing an optimal learning curriculum based on the individual characteristics and behavioral patterns of each user, resulting in the inability to provide efficient learning support. Furthermore, conventional methods have difficulty dynamically adjusting the curriculum according to the user's progress, making it impossible to maximize learning effectiveness. Furthermore, the lack of regular feedback and counseling means that users are unable to effectively realize their own personal growth.

[1621] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1622] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individualized curriculum based on the user's goals and aspirations, means for generating an individualized curriculum using a generative AI model, means for inputting prompts into the generative AI model to obtain optimal instructions, means for transmitting the generated curriculum to a terminal, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, and means for periodically generating and providing feedback and counseling sessions to the user. This allows for the provision of an optimal learning curriculum based on the user's individual characteristics, dynamic adjustment of the curriculum according to the user's progress, and effective learning support through regular feedback and counseling.

[1623]

[1624] A "psychological test question list" is a list containing a series of questions used to assess a user's psychological state, learning style, etc.

[1625] The "means for presenting questions for a psychological test" is a means for displaying questions for a psychological test to a user via a terminal.

[1626] The "means for collecting user responses" refers to a means for recording the responses entered by the user to the questions in the psychological test.

[1627] "User behavior data" refers to data including a user's app usage history and daily activity log.

[1628] The "means for recording user behavior data" refers to a means for collecting and storing user behavior data.

[1629] The "means for analyzing psychological test answers and behavioral data" refers to a means for analyzing the collected psychological test answers and behavioral data to identify the characteristics of the user.

[1630] "User characteristics" are the individual characteristics and features of the user that are revealed based on the responses to the psychological test and behavioral data.

[1631] The "means for generating an individual curriculum" is a means for creating an optimal learning plan for an individual user based on the characteristics and goals of the user.

[1632] A "generative AI model" is a model that uses artificial intelligence to generate a response from an input prompt sentence.

[1633] A "prompt" is an instruction or question given to a generative AI model.

[1634] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet.

[1635] A "lesson" is content or learning material for users to study.

[1636] "Progress data" is data that indicates the learning progress of a user that is recorded as a result of the user taking a lesson.

[1637] "Feedback" is a response that includes evaluation and advice regarding the user's learning progress.

[1638] A "counseling session" is a session in which a user receives advice from an expert regarding their learning progress or concerns.

[1639]

[1640] This invention relates to a system that provides an individualized curriculum using generative AI. This system designs an optimal learning plan based on the user's psychological test answers and behavioral data, provides lessons based on that plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[1641] System configuration and operation

[1642] Psychological test submission

[1643] The server randomly selects appropriate questions from a number of templates and generates a list of psychological test questions. This process is performed using a Python script. The generated list of questions is sent to the device via an HTTP POST request. The device then presents the psychological test questions to the user through a user interface built using React Native. The user then answers each question, and the answers are collected by the device and sent to the server again via an HTTP POST request.

[1644] For example, the server generates a question such as "What time of day do you study? (Morning, afternoon, or evening)" and the device presents this to the user. The user answers "Morning," and the device sends the answer data to the server.

[1645] Obtaining user behavior data

[1646] The device records the user's behavioral data, such as app usage history and daily activity logs. This data is recorded using the Google Analytics SDK. The behavioral data is collected over a certain period (for example, one week) and sent to the server in batch processing. The server analyzes the sent behavioral data using the Pandas library to identify the user's characteristics.

[1647] As a specific example, data is collected on how many times and at what times a user uses an app over the course of a week, and this data is used to identify daily rhythms and behavioral patterns.

[1648] Data analysis and curriculum generation

[1649] The server integrates the psychological test responses and behavioral data analysis results to reveal the user's detailed characteristics. Next, OpenAI's generative AI model is used to generate an optimal individualized curriculum that takes into account the user's goals and aspirations. The prompt sentence is then input into the generative AI model to obtain the expected curriculum proposal.

[1650] An example of a prompt sentence is, "User characteristics: morning person, prefers visual learning materials. Please generate the optimal curriculum." A curriculum is generated based on the response of the generative AI model and sent to the device via an HTTP POST request.

[1651] Lesson delivery and progress management

[1652] Based on the received curriculum, the device presents lessons to the user using textbooks, video content, etc. For example, this includes using YouTube's API to retrieve and display appropriate educational videos. The user takes the lessons and records their progress on the device. Progress data is stored locally using SQLite and periodically sent to the server via batch processing.

[1653] Feedback and Counseling

[1654] The server analyzes the progress data and generates regular feedback using a generative AI model. Additionally, it schedules necessary counseling sessions. The generated feedback and counseling information are sent to the device via an HTTP POST request. The device then presents the feedback to the user, who can then self-evaluate and participate in counseling sessions as needed.

[1655] In this way, a system using generative AI can provide an optimal learning curriculum based on each user's individual characteristics, dynamically adjust the curriculum according to the user's progress, and provide effective learning support through regular feedback and counseling.

[1656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1657]

[1658] Step 1:

[1659] The server randomly selects appropriate questions from a large number of templates and generates a list of psychological test questions using a Python script. The generated list of questions is sent to the terminal via an HTTP POST request in JSON format.

[1660] Input: Template data

[1661] Output: Question list

[1662] Data processing: Randomly select questions from the template data and create a list

[1663] Specific behavior: questions = random.sample(all_templates, number_of_questions)

[1664] Step 2:

[1665] The device presents the received psychological test question list to the user. The questions are displayed through a user interface built using React Native, and the user inputs their answers.

[1666] Input: Question List

[1667] Output: User response data

[1668] Data calculation: None (display and input collection)

[1669] Specific operation: renderQuestions(questions)

[1670] Step 3:

[1671] The user inputs answers to each question, and the input answers are recorded on the terminal.

[1672] Input: Question display and answer input form

[1673] Output: Response data

[1674] Data processing: None (user input)

[1675] Specific behavior: The user answers "Morning" to the question "What time of day do you study? (Morning, afternoon, evening)"

[1676] Step 4:

[1677] The device collects the user's response data and sends it to the server in JSON format via an HTTP POST request.

[1678] Input: Answer data

[1679] Output: Response data to the server

[1680] Data calculation: None (data transmission)

[1681] Specific operation: response = requests.post(endpoint_url, json=user_answers)

[1682] Step 5:

[1683] The device uses the Google Analytics SDK to record user behavior data, such as app usage history and daily activity logs, which are collected periodically.

[1684] Input: User action

[1685] Output: Behavioral data

[1686] Data processing: recording behavioral data

[1687] Specific behavior: analytics.logEvent('page_view', {'page': currentPage})

[1688] Step 6:

[1689] The device sends behavioral data collected over a period of time (e.g., one week) to the server, where the data is compiled in batch processing and sent in JSON format.

[1690] Input: Collected behavioral data

[1691] Output: Behavioral data to the server

[1692] Data Computation: Data Batching

[1693] Specific operation: response = requests.post(endpoint_url, json=collected_data)

[1694] Step 7:

[1695] The server analyzes the received behavioral data using Python's Pandas library to identify user characteristics, and the analysis results are stored in a database.

[1696] Input: Behavioral data

[1697] Output: User characteristics data

[1698] Data operations: transformation into data frames and statistical analysis

[1699] Specific behavior: df = pd.DataFrame(json_data), user_traits = df.describe()

[1700] Step 8:

[1701] The server integrates the psychological test response data with the analyzed behavioral data to reveal detailed user characteristics. It retrieves the data from the database using SQL queries and performs integrated analysis.

[1702] Input: Psychological test response data and behavioral data

[1703] Output: Integrated property data

[1704] Data Calculation: Joining Data with SQL Queries

[1705] Specific operation: SELECT FROM user_data WHERE userid = user_id

[1706] Step 9:

[1707] The server uses OpenAI's generative AI model to generate an individual curriculum based on the characteristic data. It inputs prompt statements into the generative AI model to obtain appropriate instructions.

[1708] Input: User characteristics data, prompt text

[1709] Output: Individual curriculum

[1710] Data Computation: Curriculum Generation with Generative AI Models

[1711] Specific behavior: response = openai.Completion.create(engine="davinci", prompt=prompt)

[1712] Step 10:

[1713] The server sends the generated curriculum to the device, again using an HTTP POST request.

[1714] Input: Individualized curriculum

[1715] Output: Curriculum to terminal

[1716] Data calculation: None (data transmission)

[1717] Specific operation: response = requests.post(endpoint_url, json=generated_curriculum)

[1718] Step 11:

[1719] Based on the received curriculum, the device presents the user with lessons using textbooks, video content, etc. It uses the YouTube API to retrieve and display appropriate educational videos.

[1720] Input: Individualized curriculum

[1721] Output:Lesson display

[1722] Data Computing: Acquiring and displaying video content

[1723] Specific operation: renderLessonContent(lesson_content)

[1724] Step 12:

[1725] Users take lessons and record their progress on their device, updating it by answering quizzes, for example.

[1726] Input: Lesson content

[1727] Output: Progress data

[1728] Data calculation: None (user input and recording)

[1729] Specific action: User answers a quiz

[1730] Step 13:

[1731] The device records progress data and periodically sends it to the server. The data is stored locally using SQLite and sent in batches.

[1732] Input: Progress data

[1733] Output: Progress data to the server

[1734] Data calculation: None (data transmission)

[1735] Specific behavior: db.insert('progress', progress_data), response = requests.post(endpoint_url, json=progress_data)

[1736] Step 14:

[1737] The server analyzes the progress data and generates feedback using a generative AI model, often using Python's Scikit-learn, to adjust the curriculum as needed.

[1738] Input: Progress data

[1739] Output: Feedback and adjusted curriculum

[1740] Data Computing: Progress in Data Analysis and Prediction

[1741] Specific behavior: model.predict(new_progress_data)

[1742] Step 15:

[1743] The server sends the generated feedback and counseling information to the device using an HTTP POST request.

[1744] Input: Feedback and Counseling Information

[1745] Output: Feedback and counseling information to the terminal

[1746] Data calculation: None (data transmission)

[1747] Specific operation: response = requests.post(endpoint_url, json=feedback_data)

[1748] Step 16:

[1749] The device provides feedback and counseling information to the user, displayed using a React Native UI.

[1750] Input: Feedback and Counseling Information

[1751] Output: Displaying information to the user

[1752] Data calculation: None (display)

[1753] Specific operation: displayFeedback(feedback_data)

[1754] (Application example 1)

[1755] 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."

[1756] Conventional learning support systems have difficulty providing an optimal curriculum based on the characteristics and behavioral data of each user, and have had limitations in providing effective training, particularly to factory workers. This has led to issues such as insufficient achievement of individual skill improvement and efficient work support.

[1757] 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.

[1758] In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to a user and collecting the user's answers, means for recording user behavioral data, means for analyzing the collected psychological test answers and behavioral data to clarify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, and means for generating a training program to be executed by a robot terminal in the factory, thereby making it possible to provide factory workers with an optimal training curriculum tailored to their individual characteristics.

[1759] A "psychological test" is an evaluation method consisting of questions and tasks to measure a user's psychological characteristics and personality.

[1760] A "questionnaire" is a set of questions prepared for a psychological test.

[1761] "User" refers to an individual or employee who uses the System.

[1762] "Answer" refers to information entered by a user in response to a question in a psychological test.

[1763] "Behavioral data" is recorded data including the user's daily behavior, application usage history, work history, and the like.

[1764] A "curriculum" is a set of learning or training plans designed based on a user's characteristics and goals.

[1765] "Lesson" means a curriculum-based learning module or training session.

[1766] "Progress data" is data that shows the results of a user's learning or training in accordance with a curriculum.

[1767] "Feedback" refers to evaluation and advice regarding the user's learning or training progress.

[1768] A "counseling session" is an interactive support session to support a user's learning or training.

[1769] A "robot terminal" is a machine used in a factory or work environment, which has an interface with the user and provides training programs.

[1770] A "training program" is a series of learning activities or assignments designed to improve a user's skills.

[1771] This invention relates to a system that provides personalized training programs using a generative AI model. This system designs an optimal training plan based on the user's psychological test answers and behavioral data, provides lessons based on the plan, and manages progress. The following describes the operation of the entire system, focusing on the server, terminal, and user.

[1772] System configuration and operation

[1773] Psychological test submission

[1774] The server generates a list of questions for the psychological test. This generation process involves selecting appropriate questions from a number of templates and creating a list. The generated list of questions for the psychological test is sent to the robot terminal. The terminal displays the questions through a user interface and prompts the user to answer them. The user enters answers to each question, and the terminal collects and sends them to the server.

[1775] Obtaining user behavior data

[1776] The device records the user's behavioral data, including factory activities, work history, and machine operation logs. The device then sends the behavioral data collected over a period of time (e.g., one week) to a server. The server analyzes the received behavioral data to identify the user's work characteristics. This analysis is performed to identify the user's work rhythm and behavioral patterns.

[1777] Data analysis and training program generation

[1778] The server combines the answers to the psychological test and the results of the behavioral data analysis to analyze the user's detailed characteristics. The server then generates an optimal individual training program, taking into account the user's work goals and wishes. This training program is designed to allow the user to gradually learn the skills and knowledge they need. The generated training program is then sent to the robot terminal.

[1779] Lesson delivery and progress management

[1780] The device presents training lessons to the user based on the received curriculum, which may include textbook materials and video content. The user takes the lessons and records their progress on the device. The device records the user's progress data and periodically sends it to the server. The server analyzes the progress data and adjusts the training program as needed.

[1781] Feedback and Counseling

[1782] The server periodically generates feedback based on the user's progress and schedules counseling sessions. The feedback and counseling information are provided to the user via their terminal, allowing the user to self-evaluate and participate in counseling sessions as needed.

[1783] Hardware and software used

[1784] Hardware: Factory robots, servers, IoT devices, etc.

[1785] Software: Python, database (e.g., PostgreSQL), AI library (e.g., TensorFlow)

[1786] Specific examples

[1787] For example, when factory employee A uses this system, the server first generates a list of psychological test questions and sends them to the robot terminal. The questions are presented to factory employee A through the user interface, and the employee enters their answers. These answers are sent to the server. The terminal then records factory employee A's behavioral data for one week and sends it to the server. The server analyzes the data, generates an optimal training program for factory employee A, and sends it to the terminal. Factory employee A takes lessons based on the training plan, and progress reports are periodically sent to the server. The server analyzes progress and schedules and provides feedback and counseling sessions.

[1788] Prompt Sentence Examples

[1789] Questions to ask the generative AI model:

[1790] "Generate an appropriate training curriculum based on the psychological test results and behavioral data of factory employees. Example questions include, 'How motivated are you to work?' and 'Are you interested in new technologies?' Examples of behavioral data include compliance with safety measures and machine maintenance history. The curriculum may include basic technology modules, applied technology modules, etc."

[1791] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1792] Step 1:

[1793] The server generates a list of questions for the psychological test. It selects appropriate questions from many templates and creates a list. This generated list of questions is sent to the terminal. The input is an existing template, and the output is the generated list of questions. Data processing involves selection and combination from templates.

[1794] Step 2:

[1795] The terminal presents the received psychological test question list to the user. The user answers each question, and the terminal collects the answers. The input is the question list sent from the server and the user's answers, and the output is the collected answers. Data calculation involves recording the user's input.

[1796] Step 3:

[1797] The terminal records user behavior data (e.g., work history, machine operation log) for a certain period (e.g., one week) and sends it to the server. The input is daily behavior data, and the output is a summary of the collected behavior data. Data processing involves storing and sending the recorded data.

[1798] Step 4:

[1799] The server analyzes the received psychological test answers and behavioral data to reveal the user's detailed characteristics. The input is the psychological test answers and behavioral data, and the output is the analysis results (user characteristics). Data calculation involves extracting characteristics using statistical analysis and machine learning models.

[1800] Step 5:

[1801] The server generates an optimal individual training program based on the user's goals and aspirations. The input is the user's characteristics, goals, and aspirations, and the output is the training program. Data calculation involves generating the program using a generative AI model.

[1802] Step 6:

[1803] The generated training program is sent to the terminal. The input is the generated training program, and the output is transmission to the terminal. Data processing involves format conversion and communication of the program data.

[1804] Step 7:

[1805] The terminal presents training lessons to the user based on the received training program. The input is the training program, and the output is the presentation to the user. Data processing includes displaying the lessons and providing an interactive user interface.

[1806] Step 8:

[1807] Users take lessons and record their progress on their devices. The input is the user's lesson data, and the output is their progress. Data processing involves recording and periodically sending the progress data.

[1808] Step 9:

[1809] The server analyzes the progress data and adjusts the training program as needed. The input is the progress data and the output is the adjusted training program. Data processing involves monitoring the progress and redesigning the program.

[1810] Step 10:

[1811] The server generates feedback and counseling sessions and provides them to users through terminals. The inputs are progress data and analysis results, and the output is feedback and counseling information. Data processing includes generating reports and scheduling counseling sessions.

[1812] 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.

[1813] This invention relates to a system that uses generative AI to provide an individualized curriculum. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback. This system designs an optimal learning plan based on the user's psychological test answers, behavioral data, and emotional data, provides lessons based on that plan, and manages progress. Below, we will specifically explain the operation of the entire system, focusing on the server, terminal, and user.

[1814] System configuration and operation

[1815] Psychological test submission

[1816] 1. The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list.

[1817] 2. The server sends the generated psychological test question list to the device. The data format is JSON or XML, and it is distributed to the device via API.

[1818] 3. The device presents the user with a list of questions for the psychological test, displaying the questions on the user interface and providing a form for the user to enter their answers.

[1819] 4. The user enters answers to each question in the psychological test. The user selects or fills in the answers in the form.

[1820] 5. The device collects the user's answers and sends them to the server, where they are reformatted and converted into the appropriate data format before being sent.

[1821] Obtaining user behavior data

[1822] 1. The device records user behavior data, including app usage history, location information, and activity logs.

[1823] 2. The device sends the collected behavioral data to the server, which uploads the data to the server periodically or in batches after a certain period of time.

[1824] 3. The server receives and analyzes the behavioral data, applying algorithms to identify and characterize the user's behavioral patterns and lifestyle.

[1825] Acquiring user emotion data

[1826] 1. The device collects user emotional data in real time, including facial recognition and voice analysis using sensors such as cameras and microphones.

[1827] 2. The device collects emotional data and sends it to a server, where it is converted into digital data using image analysis and voice processing technology.

[1828] 3. The server receives and analyzes the emotion data, identifies the user's emotional state, and reflects this in the user profile.

[1829] Data analysis and curriculum generation

[1830] 1. The server integrates the analysis results of the psychological test answers, behavioral data, and emotional data to analyze the user's detailed characteristics.

[1831] 2. The server generates an individual curriculum based on the user's goals, aspirations, and emotional state. This curriculum is designed to allow the user to gradually learn the skills and knowledge they need.

[1832] 3. The server sends the generated curriculum to the terminal.

[1833] Lesson delivery and progress management

[1834] 1. The device presents lessons to the user based on the curriculum received, which includes textbooks and video content.

[1835] 2. The user takes lessons and records their progress on the device. Progress is entered after each lesson, allowing the user to understand their learning status.

[1836] 3. The device records the user's progress data and sends it to the server, where it is uploaded to the server in real time or periodically.

[1837] 4. The server analyzes the progress data and adjusts the curriculum as needed. In addition to the progress data, it also analyzes emotional data and takes into account the state of the user's learning.

[1838] Feedback and Counseling

[1839] 1. The server periodically generates feedback based on the user's progress and emotional data and schedules counseling sessions.

[1840] 2. The server sends feedback and counseling information to the device, including learning evaluation and future advice.

[1841] 3. The device provides feedback and counseling information to the user, allowing the user to self-evaluate and manage their schedule.

[1842] Specific examples

[1843] For example, consider the case where User B uses this system. First, the server generates a list of questions for a psychological test and sends it to the device. User B answers each question, and the device collects and sends them to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends it to the server. The server analyzes this data, generates a curriculum based on User B's characteristics and emotional state, and sends it to the device. User B takes lessons based on the received curriculum and records his or her progress on the device. The progress data and emotional data are periodically sent to the server, and feedback and counseling information are generated.

[1844] This allows each user to be provided with the optimal learning plan, leading to efficient self-improvement. The introduction of the emotion engine enables a more precise learning experience.

[1845] The processing flow will be explained below.

[1846] Step 1:

[1847] The server generates a list of questions for the psychological test. The server selects an appropriate set of questions from multiple question template databases and creates the list. The questions include personality assessment, behavioral tendencies, stress levels, etc.

[1848] Step 2:

[1849] The server generates a list of psychological test questions and sends them to the device. The data format is JSON or XML, and communication is made to the device via an API.

[1850] Step 3:

[1851] The device presents the user with a list of psychological test questions, displays the questions on a user interface, and provides a form for the user to answer.

[1852] Step 4:

[1853] The user enters answers to each question in the psychological test. The user enters a choice or free response in the form.

[1854] Step 5:

[1855] The device collects the user's answers and sends them to the server, where they are formatted and converted into JSON format before being sent.

[1856] Step 6:

[1857] The device records user behavior data, including app usage history, location information, and activity logs.

[1858] Step 7:

[1859] The device sends the collected behavioral data to a server, either in real time or in batches over a fixed time span.

[1860] Step 8:

[1861] The server receives and analyzes the behavioral data, using machine learning algorithms to reveal the user's activity patterns and daily rhythms.

[1862] Step 9:

[1863] The device collects the user's emotional data in real time using a camera and microphone to recognize facial expressions and analyze voice.

[1864] Step 10:

[1865] The emotional data collected by the device is sent to a server, where it is converted into data using image analysis and voice processing technology.

[1866] Step 11:

[1867] The server receives and analyzes the emotional data, identifying each emotional state (e.g., joy, sadness, anger, etc.) and reflecting it in the user profile.

[1868] Step 12:

[1869] The server integrates the analysis results of the psychological test responses, behavioral data, and emotional data, thereby revealing detailed characteristics of the user.

[1870] Step 13:

[1871] The server generates an individual curriculum based on the user's goals, aspirations, and characteristics. The learning plan is automatically designed using an AI algorithm.

[1872] Step 14:

[1873] The server sends the generated curriculum to the device. The generated curriculum data is sent in JSON or XML format.

[1874] Step 15:

[1875] The device presents lessons to the user based on the received curriculum, which includes textbooks, video content, and practice questions.

[1876] Step 16:

[1877] Users take lessons and record their progress on their devices. Users enter progress data after each lesson.

[1878] Step 17:

[1879] The device records the user's progress data and sends it to the server, which then uploads the data to the server in real time or periodically.

[1880] Step 18:

[1881] The server analyzes the progress data and adjusts the curriculum as needed. Based on the progress data and emotion data, the lesson plan is updated appropriately.

[1882] Step 19:

[1883] The server generates periodic feedback and counseling sessions, creating feedback based on the user's progress and emotional state.

[1884] Step 20:

[1885] The server transmits feedback and counseling information to the terminal, and distributes the generated feedback and counseling schedule to the terminal.

[1886] Step 21:

[1887] The terminal presents the feedback and counseling information to the user, and displays the feedback and counseling details on the user interface.

[1888] Step 22:

[1889] Users receive feedback and participate in counseling sessions if necessary. Users complete a self-assessment and book or attend counseling sessions.

[1890] Example 2

[1891] 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."

[1892] Conventional learning systems have had difficulty providing a curriculum that fully takes into account the characteristics and emotional state of each user. Furthermore, they lack the ability to flexibly adjust the curriculum and provide feedback in response to the user's learning progress and emotional changes, which can hinder effective learning.

[1893] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes a means for generating a list of questions for a psychological test, a means for recording user behavioral data and transmitting it to the server, a means for acquiring and analyzing user emotional data, a means for analyzing the collected psychological test answers, behavioral data, and emotional data to clarify the user's characteristics, and a means for generating an individual curriculum based on the user's goals, hopes, and emotional state. This makes it possible to provide an appropriate curriculum and feedback according to the user's characteristics and emotional state.

[1894] A "psychological test question list" is a collection of questions for assessing a user's psychological state and characteristics.

[1895] "User behavior data" refers to information including a user's application usage history, location information, and other activity logs.

[1896] "User emotion data" is information obtained by analyzing the user's emotional state obtained from facial expressions, voice, etc.

[1897] A "server" is a computer system for processing and analyzing data.

[1898] A "terminal" is a device such as a computer device or a smartphone that is directly operated by a user.

[1899] A "curriculum" is a collection of learning plans and teaching materials designed to meet a user's learning objectives.

[1900] "Feedback" refers to evaluations and advice provided based on a user's learning progress and achievements.

[1901] A "counseling session" is an interactive session to provide learning and psychological support to a user.

[1902] "Analysis" is the process of extracting and understanding useful information from collected data.

[1903] A "lesson" is a set of materials or activities designed to teach a specific skill or knowledge.

[1904] "Progress data" is data that indicates the progress of a user's learning activities.

[1905] This invention relates to a learning support system that provides an individualized curriculum using a generative AI model. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, we aim to provide a more accurate curriculum and feedback.

[1906] System configuration

[1907] server

[1908] The server is a centralized computer system responsible for generating psychological test questions, analyzing behavioral and emotional data, generating personalized curriculum, and analyzing progress and generating feedback. The server includes software components such as the main database, AI models, analysis algorithms, and APIs (Application Programming Interfaces).

[1909] Terminal

[1910] The terminal is a device that the user directly operates, and is mainly composed of a smartphone or computer. The terminal has the functions of presenting a list of psychological test questions, recording the user's answers and behavioral data, acquiring emotional data, and presenting lessons based on an individual curriculum. The terminal also transmits the collected data to a server.

[1911] User

[1912] Users are individuals who use this system, answering psychological tests and providing daily behavioral and emotional data. Users also take lessons based on the provided curriculum and record their progress on their devices.

[1913] Example of operation

[1914] For example, when User B begins using this system, the server generates a list of psychological test questions and sends them to the device. User B answers the questions displayed on the device, and the answers are collected and sent to the server. Next, the device records User B's behavioral and emotional data over a certain period of time and sends this data to the server. The server analyzes this data and generates an individual curriculum tailored to User B's characteristics and emotional state, which is then sent to the device.

[1915] User B takes lessons based on the received curriculum and records their progress on their device. Progress and emotion data are then periodically sent to the server, which analyzes the data and generates feedback and counseling information. The device then presents this information to User B, helping with self-evaluation and schedule management.

[1916] Specific hardware and software names used

[1917] The server configuration uses MySQL as the database, OpenAI GPT-3 for the generative AI model, Microsoft Azure Face API and Google Cloud Speech-to-Text for emotion analysis, etc. The terminal application is developed for iOS and Android smartphones, and frameworks such as React Native are used for real-time data collection and display.

[1918] Prompt Sentence Examples

[1919] Here is an example of a prompt for curriculum generation using a generative AI model:

[1920] "User B began using the system. He took a psychological test generated by the server, and an individual curriculum was generated based on the results. After that, the device continued to record behavioral and emotional data, and feedback was provided based on the analysis results."

[1921] In this way, an effective learning experience is achieved by providing optimal learning plans and feedback to each individual user.

[1922] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1923] Program processing flow

[1924] Step 1:

[1925] The server generates a list of questions for the psychological test. It selects template questions from the database and customizes the question set according to the user's characteristics. For example, it executes an SQL query such as "select from question_templates where category='Psychology'" and formats the retrieved questions in JSON format. The input is the template question database, and the output is the generated JSON-formatted question list.

[1926] Step 2:

[1927] The psychological test question list generated by the server is sent to the terminal. It is sent via API using an HTTP request. To ensure communication stability, error handling and resend functions are provided during transmission. The input is a JSON-formatted question list, and the output is the transmission status (success / failure).

[1928] Step 3:

[1929] The device presents the user with a list of questions for a psychological test. The questions are displayed on a user interface and options are provided. For example, React Native is used to render a question form for the user to answer. The input is a list of questions in JSON format, and the output is displayed on the screen.

[1930] Step 4:

[1931] The user answers questions in a psychological test. The user enters their answer in the displayed form and presses the submit button. For example, in response to the question "What are your hobbies?", the user enters "reading and watching movies" in the text box. The input is the user's free description, and the output is the user's answer data.

[1932] Step 5:

[1933] The device sends the user's answers to the psychological test to the server. The answer data is formatted in the appropriate JSON format and sent using an HTTP POST request. The input is the user's answer data, and the output is the submission status (success / failure).

[1934] Step 6:

[1935] The device records user behavior data, collecting application usage history and GPS location information. For example, it accumulates log information such as "App A used for 10 minutes, location information 35.6895, 139.6917." The input is the device's sensor and usage data, and the output is the recorded behavior data.

[1936] Step 7:

[1937] The behavioral data collected by the device is sent to the server. The data is formatted in batches at regular intervals and uploaded to the server using an HTTP POST request. The input is the recorded behavioral data, and the output is the transmission status (success / failure).

[1938] Step 8:

[1939] The server analyzes the behavioral data. It uses the Python pandas library to integrate it into a data frame and extract specific behavioral patterns. For example, it identifies behavioral characteristics such as "the user plays sports at least three times a week." The input is the behavioral data, and the output is the analysis results and behavioral patterns.

[1940] Step 9:

[1941] The device acquires the user's emotional data. It uses a camera and microphone to capture facial expressions and voice in real time and uses an emotion analysis API. For example, "detect happiness from facial expressions and determine low stress levels from voice." The input is real-time video and audio data, and the output is emotional data.

[1942] Step 10:

[1943] The emotion data acquired by the device is sent to the server. The data acquired from the emotion analysis API is formatted in JSON and sent via an HTTP POST request. The input is the emotion data, and the output is the transmission status (success / failure).

[1944] Step 11:

[1945] The server analyzes the emotional data. Using image and audio analysis technology, the server reflects the emotional state in a profile. For example, it might determine that "stress levels are high on Mondays, and emotions of joy are strong on Fridays." The input is the emotional data, and the output is the analysis results and the emotional profile.

[1946] Step 12:

[1947] The server integrates and analyzes psychological test responses, behavioral data, and emotional data. It then links the data stored in the database to extract insights for creating individualized curricula. For example, "The user is sociable and has a high level of concentration." The input is the integrated data, and the output is a detailed user profile.

[1948] Step 13:

[1949] The server generates an individual curriculum that is optimal for the user. Using a generative AI model, an appropriate curriculum is designed based on the analysis results. For example, "Week 1: Basic concepts, Week 2: Applied tasks." The input is a detailed user profile, and the output is the generated curriculum.

[1950] Step 14:

[1951] The server sends the generated curriculum to the terminal. The curriculum is formatted in JSON format and sent via an HTTP POST request. The input is the generated curriculum, and the output is the sending status (success / failure).

[1952] Step 15:

[1953] The terminal presents lessons to the user based on the curriculum. The lesson content is displayed on the user interface, providing an environment in which the user can learn. For example, "Today's lesson: Basic Python grammar." The input is the received curriculum, and the output is the display on the screen.

[1954] Step 16:

[1955] The user takes lessons and records their progress on the device. After each lesson, the progress is entered. For example, "Lesson completed" is recorded on the device. The input is the user's progress, and the output is the progress data.

[1956] Step 17:

[1957] The device sends the recorded progress data to the server. The progress data is formatted in JSON and sent via an HTTP POST request. The input is the progress data, and the output is the sending status (success / failure).

[1958] Step 18:

[1959] The server analyzes the progress data and adjusts the curriculum as needed. It analyzes the progress data and emotion data together and updates the curriculum to make it more adaptable for the user. For example, "Add supplementary lessons before moving on to applied tasks." The input is progress and emotion data, and the output is the adjusted curriculum.

[1960] Step 19:

[1961] The server generates feedback based on the progress and emotion data and suggests a counseling session. The feedback suggests, "You're doing well, let's move on to the applied task next." The input is the analyzed data, and the output is feedback and counseling information.

[1962] Step 20:

[1963] The server sends the generated feedback and counseling information to the terminal. The feedback information is formatted in JSON format and sent via an HTTP POST request. The input is the generated feedback, and the output is the sending status (success / failure).

[1964] Step 21:

[1965] The device presents feedback and counseling information to the user. The feedback and counseling suggestions are displayed on the screen. For example, "You're making good progress. Let's take the next step." The input is the feedback received, and the output is the display on the screen.

[1966] (Application example 2)

[1967] 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."

[1968] Conventional skill training systems struggle to fully understand and individually respond to the characteristics, goals, and aspirations of individual employees. Furthermore, they often provide a uniform curriculum without considering the emotional state of employees, resulting in ineffective training. Furthermore, they are unable to integrate and analyze work data and emotional data from industrial equipment, leaving challenges in improving employee productivity and work efficiency. This invention aims to solve these challenges by comprehensively analyzing the characteristics and emotional state of individual employees and providing an optimal training curriculum.

[1969] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for generating a list of psychological test questions, means for presenting psychological test questions to the user and collecting the user's answers, means for recording the user's behavioral data, means for analyzing the collected psychological test answers and behavioral data to identify the user's characteristics, means for generating an individual curriculum based on the user's goals and aspirations, means for presenting lessons to the user based on the generated curriculum, means for recording the user's progress and analyzing the progress data to adjust the curriculum, means for periodically generating and providing feedback and counseling sessions to the user, means for acquiring and analyzing the user's emotional data, means for generating an individual curriculum that reflects the user's emotional state, and means for recording employee work data on industrial equipment. This makes it possible to provide an optimal curriculum that takes into account the characteristics and emotional state of employees.

[1970] A "psychological test questionnaire" is a set of questions designed to measure a user's psychological state or characteristics.

[1971] "User" refers to an individual employee or other target person who uses this system for skill training or learning.

[1972] "Behavioral data" refers to data such as a user's daily behavioral patterns, activity logs, working hours, and movement patterns.

[1973] "Collected psychological test answers" refers to the answers given by the user to the psychological test.

[1974] "Emotion data" refers to data on the emotional state obtained by analyzing the user's facial expressions and voice.

[1975] "Individualized curriculum" refers to a learning or skills training plan that is customized based on a user's characteristics and goals.

[1976] "Lesson" refers to a specific learning content or training session provided to a User.

[1977] "Progress data" refers to data that indicates the progress of a user's learning or training.

[1978] "Feedback" refers to evaluations and advice provided based on a user's learning status and training results.

[1979] A "counseling session" refers to a time spent interviewing or consulting with a user, taking into account their characteristics and emotional state.

[1980] "Industrial equipment" refers to production facilities and machinery used in factories, etc.

[1981] "Work data" refers to recorded data regarding employees' operations and work when using industrial equipment.

[1982] This invention is a system that supports skill training for factory employees, generating individualized curricula using psychological test answers, behavioral data, and emotional data, and providing progress management and feedback. The system consists of a server, terminals (smartphones and industrial tablets), and industrial equipment.

[1983] First, the server generates a list of psychological test questions. To do this, it selects an appropriate set of questions from a question template database and creates a list in JSON or XML format. Next, the server sends the generated psychological test question list to the terminal. The terminal presents the psychological test questions to the user, and the answers are collected as the user answers them. These answers are sent to the server for data analysis.

[1984] In addition, the device records the user's behavioral data, including work status, movement patterns, and work time, and uploads the data to a server periodically or in batches. At the same time, the device also acquires the user's emotional data using sensors such as a camera and microphone. The server analyzes the emotional data using facial recognition and voice analysis technologies (e.g., OpenCV and Google Cloud Speech-to-Text) to identify the user's emotional state.

[1985] This data is analyzed comprehensively to generate an individual curriculum based on the user's characteristics and goals. The curriculum is designed as a learning plan specific to the user and sent to the device. The device then presents lessons to the user based on this curriculum and records the user's progress as they take the lessons. Progress data is also periodically sent to the server for analysis. This allows the server to adjust the curriculum as needed, providing the user with the optimal learning experience.

[1986] In addition, the server periodically generates feedback and counseling sessions and sends them to the terminal, allowing the user to self-evaluate and manage their schedule based on this information, resulting in efficient skill training.

[1987] As a concrete example, consider the case where Employee A is learning a new welding technique in a factory. The server generates a psychological test such as "work stress level" and sends it to a terminal. Employee A answers the questions on their smartphone and sends the answers to the server. Factory robots and smartphones monitor Employee A's working hours and facial expressions, collecting behavioral and emotional data. The server analyzes this data, generates a "welding training curriculum" optimal for Employee A, and sends it to the terminal. Employee A progresses through the training according to the curriculum and records his or her progress, and the server provides feedback based on the progress data and emotional data.

[1988] An example of a prompt is:

[1989] AI prompts to generate optimal curriculum based on user behavioral and emotional data:

[1990] The user's emotional state is "{emotion}" and their current behavior pattern is "{behavior}". Based on this, please generate the optimal curriculum for the user.

[1991] Examples include:

[1992] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1993] Step 1:

[1994] The server generates a list of psychological test questions, which involves selecting an appropriate set of questions from a database, creating a list in JSON or XML format, and sending the list to the device.

[1995] Input: Question template database

[1996] Data processing / calculation: Select appropriate questions and generate lists

[1997] Output: Psychological test question list (JSON or XML format)

[1998] Step 2:

[1999] The terminal presents the user with a list of psychological test questions received from the server, and the user answers the questions, which are then collected and stored on the terminal.

[2000] Input: Psychological test question list

[2001] Data processing / calculation: Present psychological test questions to users

[2002] Output: User's answer

[2003] Step 3:

[2004] The device formats the user's answers, converts them into JSON or XML format, and sends them to the server.

[2005] Input: User's answer

[2006] Data processing / calculation: Formatting answers and converting them to data formats

[2007] Output: Formatted response data (JSON or XML format)

[2008] Step 4:

[2009] The device records user behavior data, including work status, movement patterns, work time, etc. The data is uploade...

Claims

1. a means for generating a list of psychological test questions; means for presenting psychological test questions to a user and collecting responses from the user; means for recording user behavior data; A means for analyzing collected psychological test answers and behavioral data to clarify user characteristics; means for generating a personalized curriculum based on the user's goals and desires; means for presenting lessons to a user based on the generated curriculum; a means for recording user progress and analyzing the progress data to adjust the curriculum; means for generating and providing periodic feedback and counseling sessions to the user; A system including:

2. 10. The system of claim 1, wherein the system records user behavior data over a one-week period.

3. 2. The system according to claim 1, wherein the generated individual curriculum is transmitted to a terminal.

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