system

Abstract

We provide the system. [Solution] A terminal device for inputting educational data, A server means for receiving the educational data, A generation model means that operates on the server means and automatically generates lesson plans, A means for transmitting the lesson plan generated by the generation model means to a terminal means, An analytical method for analyzing student data and creating student personality profiles, A proposal means that suggests an educational policy optimized for the student based on the personality profile, A system that includes this.

Description

Technical Field

[0005]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional educational field, teachers often spend a lot of time and effort on lesson preparation, so there is a problem that the quality of education varies. Also, it is difficult to provide education tailored to each student's individuality, and there is a problem that individual support is limited in large classes. It is necessary to solve such problems and improve the quality of education while reducing the burden on educators.

Means for Solving the Problems

[0005] This invention provides a system comprising a terminal means for inputting educational data and a server means for receiving said educational data, and a generative model means operating on the server means for automatically generating educational lesson plans. It also includes an analysis means for analyzing student data and creating student personality profiles. Furthermore, it provides a system equipped with a suggestion means that proposes an optimized educational policy for each student based on the personality profile, thereby reducing the time educators spend preparing lessons and enabling education tailored to each individual student.

[0006] "Educational data" is a general term for information related to educational activities, such as lesson content, curriculum, and learning objectives.

[0007] "Terminal means" refers to electronic devices used for inputting educational and student data, and for data communication with servers.

[0008] "Server system" refers to a computer system that receives, processes, and transmits data over a network.

[0009] "Generative modeling means" refers to algorithms and systems that automatically generate lesson plans based on educational data.

[0010] "Analysis methods" refer to systems and tools used to analyze student data in order to create student personality profiles.

[0011] A "personality profile" refers to a document or dataset that compiles information about a student's personality traits and learning style.

[0012] "Suggestion method" refers to a system that proposes educational policies and activities optimized for students based on the generated lesson plans and personality profiles. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0014] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0019] In the following embodiments, a numbered communication I / F (Interface) is an interface including a communication processor and 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), or Bluetooth (registered trademark), and the like.

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] As an embodiment of this invention, a system is described that enables educators to efficiently prepare lessons and provide education optimized for each individual student.

[0035] Educators first use a terminal to send educational data, such as lesson content, curriculum, and learning objectives, to a server. The server receives this data and uses a generative model to automatically generate lesson plans based on the relevant information. These lesson plans include structured lesson plans designed to efficiently achieve the set educational objectives. The generated lesson plans are sent from the server to the terminal, where educators can review and adjust them. This automated generation process significantly reduces the time educators spend preparing lessons.

[0036] Next, data such as past academic performance and learning questionnaires about the student are entered into the terminal and sent to the server. The server analyzes this data and creates a personality profile for each student. This profile comprehensively covers information about the student's personality traits, learning style, and interests. This allows educators to develop educational strategies that meet the individual needs of each student.

[0037] Furthermore, the server integrates the generated lesson plans with personality profiles to propose the most suitable educational approach for each student. For example, if a student's profile indicates a strong visual learning tendency, the server will propose a teaching style that heavily utilizes graphics and diagrams. In this way, the proposed teaching methods are sent to the user via the terminal, allowing educators to further refine their lesson content based on this feedback.

[0038] Systems implemented in this form are useful in supporting educators in conducting lessons effectively and efficiently, improving the quality of education, and addressing the individual needs of students.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] Users input information about the lesson content, curriculum, and learning objectives into their devices. This information is then sent to the server as educational data.

[0042] Step 2:

[0043] The server automatically generates lesson plans using a generative model based on the received educational data. The generative model refers to existing educational databases to construct the optimal lesson structure and teaching material proposals.

[0044] Step 3:

[0045] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0046] Step 4:

[0047] Users input student data, such as each student's past grades and survey results, into their terminals. This data is then sent to a server for personality analysis of the students.

[0048] Step 5:

[0049] The server analyzes the received student data and uses AI to create a student personality profile. This profile includes the student's learning style, interests, and other factors.

[0050] Step 6:

[0051] The server combines the generated lesson plans with the students' personality profiles to propose an educational approach optimized for each student.

[0052] Step 7:

[0053] The server sends the proposed teaching method to the terminal. The terminal then presents this information to the user, who can then plan their lessons based on it.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] There is a need for an efficient platform that allows educators to provide personalized education without spending a great deal of time and effort on lesson preparation. Traditional methods require time to organize educational information and understand the characteristics of individual learners, making individualized support difficult.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes an information input means for inputting educational information, a generation mechanism means for automatically generating educational plans, and an analysis means for creating learner characteristic profiles. This enables educators to efficiently generate structured lesson plans and provide education tailored to the individual needs of each learner.

[0059] "Information input means" refers to devices or systems for recording or inputting educational information or learner data.

[0060] "Data processing means" refers to computer systems and associated software used to appropriately analyze and manage received educational information.

[0061] "Generation mechanism means" refers to algorithms and programs for automatically generating educational plans and lesson plans based on received information.

[0062] "Communication methods" refer to the communication infrastructure and protocols used to transfer generated educational plans and lesson plans to educators.

[0063] "Analysis tools" refer to tools and methods for analyzing learner data in detail and creating learner characteristic profiles.

[0064] A "trait profile" refers to a collection of data that contains information about a learner's personality traits, learning style, and interests.

[0065] "Proposal mechanism means" refers to a system or algorithm for proposing individually optimized educational strategies to educators based on the generated profile.

[0066] A specific embodiment of this invention is a system primarily used by educators to prepare lessons and provide optimized education for learners. Educators input information such as lesson content, curriculum, and learning objectives using a dedicated terminal. This information is organized and converted into an appropriate format on the terminal. The converted data is sent to a server, which then receives it.

[0067] The server automatically generates lesson plans using a generative AI model based on the received educational information. The generative AI model generates appropriate lesson plans using prompts. A specific example of a prompt might be, "Generate a history lesson plan based on the following educational data and learner information. The learners have a strong auditory learning ability." This generative AI model formulates the optimal plan by referring to existing information and past lesson plans.

[0068] The generated lesson plans are sent back from the server to the terminal and provided to educators in a format that they can review and adjust. This allows educators to significantly reduce the time and effort previously required for lesson preparation.

[0069] Furthermore, educators, as users, input students' past performance data and learning questionnaire responses into their devices. This data is also sent to the server, which uses it to generate learner profile information. This profile includes the learner's personality, learning style, and interests. The server analyzes these profile information and provides an educational strategy optimized for each individual learner.

[0070] Throughout this entire system, educators can efficiently prepare and conduct lessons tailored to the individual needs of learners, thereby improving the quality of education.

[0071] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0072] Step 1:

[0073] The user inputs educational data such as educational content, curriculum, and learning objectives into the terminal. The terminal then organizes this information and converts it into a predetermined format. The input data includes curriculum content in text format and learning objectives as numerical data, and the resulting data is formatted for transmission to the server as output.

[0074] Step 2:

[0075] The terminal sends formatted educational data to the server. The server receives this data and stores it in a database. During this process, the server performs a verification process to check the accuracy and completeness of the data. The input is the data sent from the terminal, and the output is the educational information successfully stored in the database.

[0076] Step 3:

[0077] The server automatically generates lesson plans using a generative AI model based on the received educational data. In this step, the server inputs prompt sentences into the generative AI model, which then creates the optimal lesson plan. Specifically, the AI ​​model refers to existing databases and historical data to generate the most suitable lesson plan. The input is educational data including prompt sentences, and the output is a structured lesson plan.

[0078] Step 4:

[0079] The server sends the generated training plan to the terminal. The terminal receives this information and prepares it for display in a user-readable format. On the terminal, each item of the plan is visually organized, making it easy for the user to edit. The input is the training plan from the server, and the output is data that can be displayed on the user interface.

[0080] Step 5:

[0081] The user inputs information such as students' past academic performance data and responses to learning questionnaires into the terminal. The terminal organizes this information and converts it into a format for transmission to the server. The input consists of various data about the learner, and the output is data in a unified format that can be transmitted.

[0082] Step 6:

[0083] The server analyzes the input learner data and generates learner characteristic profiles. In this process, the server uses machine learning algorithms to identify individual learning styles and interests. The input is formatted learner data, and the output is the learner characteristic profile.

[0084] Step 7:

[0085] The server integrates the generated profiles and educational plans to propose individually optimized educational strategies. The inputs here are the trait profiles and educational plans, and the output is a recommended list of optimal teaching materials and methods for each learner.

[0086] (Application Example 1)

[0087] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0088] In today's manufacturing environment, efficiently and effectively managing individual manufacturing tasks is a major challenge. In particular, there is a need to provide real-time work instructions that take into account the skills and experience of each worker, but current systems are unable to adequately address this. As a result, decreased productivity and increased manufacturing errors are becoming problems.

[0089] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0090] In this invention, the server includes terminal means for inputting work data, generation module means for receiving the work data and automatically generating work procedures, and analysis means for analyzing worker data and creating worker capability profiles. This makes it possible to propose optimized work strategies for each worker in real time.

[0091] "Work data" refers to information about various steps and requirements in the manufacturing process, and is data entered to generate efficient work instructions.

[0092] "Terminal means" refers to a device used to input work data and send it to a server, and generally includes computers and tablet devices.

[0093] A "server system" refers to a computer system that receives data via a network and performs the necessary processing, possessing the capability to perform large-scale data processing.

[0094] "Generation module means" refers to a software component that operates on the server means and automatically generates the optimal work procedure based on the input data.

[0095] "Work procedures" refer to the specific steps and procedures necessary to efficiently advance the manufacturing process, and are composed of generation module means.

[0096] "Worker data" refers to information about each worker's skills, experience, past work history, etc., and is collected through analytical methods.

[0097] A "capability profile" refers to information that characterizes the abilities and characteristics of workers, created based on worker data.

[0098] "Analysis tools" refer to software components or algorithms used to analyze worker data and create competency profiles.

[0099] "Suggestion module means" refers to a software component that proposes an optimized work strategy for a specific worker based on the generated capability profile.

[0100] As an embodiment of this invention, an automated work instruction system in a manufacturing environment is described. The system includes terminal means for inputting work data, which workers use to transmit data related to the manufacturing process to a server. The terminals used generally include computers and tablet devices.

[0101] The server uses a generative AI model based on the received data and automatically generates the optimal work procedure using a dedicated generative module. This generated work procedure is then transmitted to the worker via a terminal. As the generative AI model, an advanced natural language processing model such as GPT-4 (registered trademark) is used.

[0102] Furthermore, the server is equipped with analytical tools to analyze worker data that shows the skills and experience of workers and create capability profiles. This profile creation makes it possible to propose work strategies tailored to each individual worker.

[0103] As a concrete example of implementation, in a manufacturing line, a server might instruct a worker via a terminal, saying, "Next, assemble the parts for Step 2. I will show you the optimal procedure tailored to your skill level." In this way, workers can receive personalized instructions in real time. These instructions include the tools to be used and points to pay attention to, improving work accuracy.

[0104] An example of a prompt message is: "Generate the optimal work procedure for product X on manufacturing line ID 123. Provide efficient steps based on worker Y's skill profile." By sending this command to the generation AI model, the optimal work procedure can be obtained.

[0105] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0106] Step 1:

[0107] The user enters work data on a terminal and sends it to the server.

[0108] The data entered includes information about each stage of the manufacturing process. Users input product IDs, required materials for each stage, and step details. The entered data is sent to the server and used as material for generating work procedures.

[0109] Step 2:

[0110] Based on the work data received by the server, the system automatically generates the optimal work procedure using a generation AI model.

[0111] The AI ​​model receives work data as input, and an algorithm within the model analyzes the data to calculate an effective work procedure. The generated work procedure is output and compiled into a procedure document.

[0112] Step 3:

[0113] The server sends the work instructions to the terminal, and the user receives them.

[0114] The generated work procedure is sent back to the user's terminal. On the terminal, the received procedure is displayed as a procedure manual or guideline, allowing the user to prepare to start the next task.

[0115] Step 4:

[0116] The user enters worker data into a terminal and sends it to the server.

[0117] Information on each worker's skills, experience, and past work history is collected and transmitted from the terminal. The entered data is aggregated on a server and becomes the basis for individualized analysis.

[0118] Step 5:

[0119] The server analyzes worker data and creates individual capability profiles.

[0120] Based on the received worker data, the analysis system processes the data and generates profiles related to skill sets and performance indicators. The generated competency profiles are output and serve as the basis for subsequent work strategy planning.

[0121] Step 6:

[0122] The server references the capability profile and uses a generated AI model to propose an optimized work strategy for the worker.

[0123] The AI ​​model calculates a work strategy by referring to the capability profile generated as input. The generated strategy is output as an optimized work procedure suggestion and presented to the user from the terminal.

[0124] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0125] This invention provides a system for educators to efficiently prepare lessons and provide optimal education to students, further incorporating a function to recognize user emotions. This system includes terminal means, server means, generative model means, analysis means, suggestion means, and emotion engine.

[0126] First, the user uses their device to input information about the educational curriculum and learning objectives, and sends this as educational data to the server. Based on the received educational data, the server automatically generates lesson plans using a generative model. These lesson plans include a lesson framework and specific teaching material suggestions, which the user can review and edit through their device.

[0127] Next, the user inputs student data, such as past academic performance and behavioral patterns, into the terminal and sends it to the server. The server analyzes this data and uses AI technology to create a personality profile of the student. Furthermore, the server integrates the generated lesson plan with the student's personality profile to propose the most suitable educational approach for the student. For example, if a student's personality profile determines that they prefer visual learning, the server will propose lesson content that makes extensive use of illustrations for that student.

[0128] The emotion engine recognizes the user's emotional state in real time and uses this information to adjust the content of lesson plans. For example, if the server detects that the user is under high stress, it will suggest a more relaxed lesson structure in the lesson plan, supporting the smoothest possible lesson delivery. This emotion engine allows users to choose an educational approach that suits their emotional state, making the educational environment more creative and effective.

[0129] In this way, the system takes into account the user's emotional state and provides education optimized for each individual student, enabling improvements in the quality of education and addressing individual needs.

[0130] The following describes the processing flow.

[0131] Step 1:

[0132] Users input educational data, such as curriculum information and learning objectives, into their devices. This data is then sent to the server.

[0133] Step 2:

[0134] The server runs a generative model based on the received educational data and automatically generates lesson plans. This generative model utilizes existing educational databases to design optimal teaching content.

[0135] Step 3:

[0136] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0137] Step 4:

[0138] Users input student data, such as academic performance and behavioral patterns, into their devices. This data is then sent to a server for individual student analysis.

[0139] Step 5:

[0140] The server uses student data to apply analytical methods and build a personality profile for each student. This profile includes the student's learning tendencies and areas of interest.

[0141] Step 6:

[0142] The server combines the generated lesson plans with the students' personality profiles to create the most suitable teaching approach for each student. For example, for students who prefer visual learning, the server designs lesson content that includes many diagrams and visual materials.

[0143] Step 7:

[0144] The server uses an emotion engine that analyzes the user's emotions in real time to recognize the user's current emotional state.

[0145] Step 8:

[0146] The server adjusts the content of the lesson plan according to the user's emotional state, as recognized by the emotion engine. For example, if high stress is detected, it will suggest a relaxing teaching method.

[0147] Step 9:

[0148] The server sends the adjusted educational policy to the terminal. The terminal then allows the user to view the optimized lesson plan.

[0149] In this way, the system realizes a mechanism that provides education tailored to the individual circumstances of users and students through a series of steps.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] In today's educational environment, educators are required to efficiently prepare educational plans and provide education tailored to the individual characteristics and emotional states of each learner. However, achieving this within limited time and resources is difficult and a challenge faced by many educators. Furthermore, there is a lack of appropriate technical tools to accurately understand learners' personality profiles and adjust educational activities accordingly.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes a communication device means for users to input educational plan information, an information processing device means for receiving educational plan information, and an emotion analysis means for identifying emotional states and dynamically adjusting the content of the educational plan. This enables educators to efficiently provide optimal education that corresponds to the individual characteristics and emotional states of each learner by utilizing automatically generated educational plans.

[0155] "Communication device means" refers to an electronic device that provides an interface for users to input educational plan information and transmit that information to a server in digital format.

[0156] The "information processing device means" is a computing platform for analyzing educational plan information received from a user and automatically generating educational plans using a generation algorithm.

[0157] "Generative algorithm means" refers to a set of procedures or processes designed to automatically generate educational plans based on educational plan information.

[0158] "Analysis tools" are data analysis devices used to evaluate learners' past data and behavioral tendencies, and to extract and construct learners' characteristic profiles.

[0159] "Proposed means" refers to technical means of planning and presenting an educational policy optimized for each individual learner, based on the learner's characteristic profile constructed by the analytical means.

[0160] "Emotion analysis means" refers to technology that detects a user's emotional state in real time and supports the adjustment of educational plans accordingly.

[0161] This invention provides a system that enables educators to effectively and efficiently prepare educational activities and provide optimal education to individual learners. This system includes communication devices, information processing devices, generation algorithms, analysis devices, proposal devices, and sentiment analysis devices.

[0162] The user inputs educational plan information using a communication device. This information includes details such as the lesson theme, selection of teaching materials, and learning objectives, and is sent to the server. Based on the received educational plan information, the server automatically generates an educational plan using a generative AI model. In this process, a widely used natural language processing model (e.g., OpenAI® GPT-3®) is used as the specific generation algorithm. The generated educational plan includes specific details of the lesson procedure and the teaching materials to be used.

[0163] The user further inputs the learner's performance data and behavioral tendencies into a communication device and sends it to the server. The server uses specialized analytical tools to construct a personality profile of the learner in order to analyze this data. Based on this profile, the server proposes the most suitable educational approach for each individual learner. For example, for a learner who prefers visual learning, the server will suggest teaching materials that make extensive use of illustrations.

[0164] This system further uses emotion analysis to assess the user's emotional state (e.g., stress level) in real time. If the user is experiencing high stress levels, the system adds relaxation elements to the educational plan to help provide a smoother and more creative learning environment.

[0165] For example, if a user inputs "I want to create a science lesson plan for elementary school students," the server will use a generative AI model to create a lesson plan that includes experimental activities. Furthermore, if the system determines that the user is feeling stressed, it will suggest incorporating relaxation time into the lesson.

[0166] Examples of prompts include, "Use a generative AI model to generate a curriculum plan for a specific grade level," and "Adjust the curriculum plan based on the current emotional state."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The user enters educational plan information using a communication device. This educational plan information includes the lesson theme, objectives, and types of teaching materials to be used. This input data is transmitted digitally to the information processing device. The server analyzes the received data and converts it into an internal format that it can understand.

[0170] Step 2:

[0171] The server automatically generates lesson plans using a generation algorithm. Here, the input lesson plan information is fed into the generation AI model as prompts, and the generation AI model generates the lesson plan (including lesson procedures and specific teaching materials) based on these prompts. This process is achieved by selecting and constructing the most suitable plan proposed by the generation AI model according to a defined algorithm. This generated lesson plan is then sent back to the communication device.

[0172] Step 3:

[0173] Users input learners' past performance data and behavioral patterns using a communication device. This information is sent to a server, which uses specialized analytical tools to create a learner's characteristic profile based on the input data. This profile creation process involves statistical analysis of the data and understanding of trends, resulting in a profile that identifies the learner's educational needs.

[0174] Step 4:

[0175] The server integrates these learner profiles with the generated teaching plans and proposes optimized teaching strategies. Specifically, it generates suggestions based on different learning styles and characteristics, and for learners who prefer visual learning, it develops strategies that make extensive use of diagrams and visual aids. These optimized strategies are then presented to the educators.

[0176] Step 5:

[0177] The device detects the user's emotional state in real time and sends this data to the server. The server uses emotion analysis tools to interpret the received emotional data and adjust the educational plan. If a high level of stress is detected, it generates an adjusted plan incorporating relaxation elements and stress reduction techniques and presents it to the user. This adjusted plan is then provided as the final output.

[0178] (Application Example 2)

[0179] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0180] Traditional education systems have struggled to adequately consider the individual personalities and emotional states of students, hindering the provision of effective educational policies. Furthermore, educational plans that disregard the emotional state of educators often hindered effective lesson delivery. Additionally, employee training has been difficult to tailor to individual needs, often resulting in manual-based instruction. To address these challenges, there is a need for methods that enable education and training optimized for each individual.

[0181] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0182] In this invention, the server includes an information processing device for inputting educational information, an analysis device for analyzing personality information and creating personality traits, and an emotion management device for recognizing emotional states and adjusting content. This makes it possible to provide optimal educational and training content tailored to each individual's personality traits and real-time emotional state.

[0183] "Educational information" refers to data related to educational programs and learning objectives, and forms the basis for educational policies and the creation of teaching materials.

[0184] "Information processing device means" refers to electronic devices used to receive and transmit educational information and personality information, and functions as servers or terminals.

[0185] "Product generation means" refers to a system or algorithm that has the function of automatically generating lesson plans based on collected data.

[0186] "Personality information" refers to data used to analyze individual personality traits, including behavior, attitudes, and learning tendencies.

[0187] "Personality traits" refer to profiles of personality generated by analytical methods, and are data used to optimize educational policies.

[0188] "Analysis means" refers to technical means for analyzing personality information and creating personality traits, and includes data analysis algorithms.

[0189] "Emotional state" refers to an individual's real-time emotions, including psychological states such as stress, excitement, and boredom.

[0190] "Emotional management tools" refer to systems or technologies that recognize emotional states and adjust their content based on that information.

[0191] "Display means" refers to means for displaying information through a visual device, and includes visual devices.

[0192] The system for realizing this invention comprises an information processing device for inputting and receiving educational information, a product creation device, an analysis device, an emotion management device, and a display device. In this system, the information processing device plays the role of receiving educational information from an educator's terminal and transmitting it to a server. The server generates a lesson plan based on the received educational information using the product creation device. This lesson plan includes specific implementation methods and educational policies. The generated lesson plan is provided to the educator's terminal via the information processing device, where it can be reviewed and edited.

[0193] Furthermore, users input personality information from their devices and send it to the server. The server uses analytical tools to create personality traits and supplements the educational plan based on them. The emotion management system analyzes the emotional state of the user and the target group in real time and adjusts the educational plan as needed. This adjustment is made in the form of alleviating stress levels or adding engaging content. The final educational plan is visually displayed to the target group through a display device. Devices such as smart glasses can be used for display, improving the interactivity of the education.

[0194] As a concrete example, in employee training at physical stores, work procedures and customer service methods are displayed through smart glasses, and the training content is dynamically adjusted according to the employee's emotional state. An example of a prompt to the generating AI model in this case is: "Suggest employee training content based on the user's current emotional state. If the emotional state is 'stressed,' suggest relaxation techniques; if it is 'bored,' present an interactive challenge." This allows for more effective and considerate training.

[0195] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0196] Step 1:

[0197] Users input educational and personality information using a terminal and send it to the server. This process includes educational programs and learning objectives as educational information, and past student behavior data and survey results as personality information. The entered data is stored in a database and used for subsequent processing.

[0198] Step 2:

[0199] The server analyzes the received educational information using a product creation mechanism and automatically generates lesson plans using a generation AI model. At this stage, the educational information is analyzed by the AI ​​model, and lesson plans that include optimal teaching materials and teaching policies are output. This ensures that the content is designed taking into account the expertise of educators and the learning styles of students.

[0200] Step 3:

[0201] The analysis tool processes personality information to create personality traits. The server executes a data analysis algorithm based on the input personality information to derive the student's personality traits. This results in the output of an individualized educational plan based on the student's personality.

[0202] Step 4:

[0203] The server uses emotion management tools to recognize the user's emotional state in real time. It analyzes sensor information from input devices and the user's facial expressions to determine emotional states such as stress and anxiety. Based on the emotional state, it adjusts the specific progression and content of the educational plan. This adjustment is supported by a generative AI model.

[0204] Step 5:

[0205] The finalized lesson plan is presented visually to the user or target audience through display means. Using smart glasses or mobile devices, the lesson plan and emotionally responsive content are displayed, allowing users to conduct education and training based on that information. This displayed content is dynamically updated based on the adjustments made in step 4.

[0206] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0207] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0208] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0209] [Second Embodiment]

[0210] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0211] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0212] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0213] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0214] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0215] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0216] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0217] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0218] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0219] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0220] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0221] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0222] As an embodiment of this invention, a system is described that enables educators to efficiently prepare lessons and provide education optimized for each individual student.

[0223] Educators first use a terminal to send educational data, such as lesson content, curriculum, and learning objectives, to a server. The server receives this data and uses a generative model to automatically generate lesson plans based on the relevant information. These lesson plans include structured lesson plans designed to efficiently achieve the set educational objectives. The generated lesson plans are sent from the server to the terminal, where educators can review and adjust them. This automated generation process significantly reduces the time educators spend preparing lessons.

[0224] Next, data such as past academic performance and learning questionnaires about the student are entered into the terminal and sent to the server. The server analyzes this data and creates a personality profile for each student. This profile comprehensively covers information about the student's personality traits, learning style, and interests. This allows educators to develop educational strategies that meet the individual needs of each student.

[0225] Furthermore, the server integrates the generated lesson plans with personality profiles to propose the most suitable educational approach for each student. For example, if a student's profile indicates a strong visual learning tendency, the server will propose a teaching style that heavily utilizes graphics and diagrams. In this way, the proposed teaching methods are sent to the user via the terminal, allowing educators to further refine their lesson content based on this feedback.

[0226] Systems implemented in this form are useful in supporting educators in conducting lessons effectively and efficiently, improving the quality of education, and addressing the individual needs of students.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] Users input information about the lesson content, curriculum, and learning objectives into their devices. This information is then sent to the server as educational data.

[0230] Step 2:

[0231] The server automatically generates lesson plans using a generative model based on the received educational data. The generative model refers to existing educational databases to construct the optimal lesson structure and teaching material proposals.

[0232] Step 3:

[0233] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0234] Step 4:

[0235] Users input student data, such as each student's past grades and survey results, into their terminals. This data is then sent to a server for personality analysis of the students.

[0236] Step 5:

[0237] The server analyzes the received student data and uses AI to create a student personality profile. This profile includes the student's learning style, interests, and other factors.

[0238] Step 6:

[0239] The server combines the generated lesson plans with the students' personality profiles to propose an educational approach optimized for each student.

[0240] Step 7:

[0241] The server sends the proposed teaching method to the terminal. The terminal then presents this information to the user, who can then plan their lessons based on it.

[0242] (Example 1)

[0243] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0244] There is a need for an efficient platform that allows educators to provide personalized education without spending a great deal of time and effort on lesson preparation. Traditional methods require time to organize educational information and understand the characteristics of individual learners, making individualized support difficult.

[0245] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0246] In this invention, the server includes an information input means for inputting educational information, a generation mechanism means for automatically generating educational plans, and an analysis means for creating learner characteristic profiles. This enables educators to efficiently generate structured lesson plans and provide education tailored to the individual needs of each learner.

[0247] "Information input means" refers to devices or systems for recording or inputting educational information or learner data.

[0248] "Data processing means" refers to computer systems and associated software used to appropriately analyze and manage received educational information.

[0249] "Generation mechanism means" refers to algorithms and programs for automatically generating educational plans and lesson plans based on received information.

[0250] "Communication methods" refer to the communication infrastructure and protocols used to transfer generated educational plans and lesson plans to educators.

[0251] "Analysis tools" refer to tools and methods for analyzing learner data in detail and creating learner characteristic profiles.

[0252] A "trait profile" refers to a collection of data that contains information about a learner's personality traits, learning style, and interests.

[0253] "Proposal mechanism means" refers to a system or algorithm for proposing individually optimized educational strategies to educators based on the generated profile.

[0254] A specific embodiment of this invention is a system primarily used by educators to prepare lessons and provide optimized education for learners. Educators input information such as lesson content, curriculum, and learning objectives using a dedicated terminal. This information is organized and converted into an appropriate format on the terminal. The converted data is sent to a server, which then receives it.

[0255] The server automatically generates lesson plans using a generative AI model based on the received educational information. The generative AI model generates appropriate lesson plans using prompts. A specific example of a prompt might be, "Generate a history lesson plan based on the following educational data and learner information. The learners have a strong auditory learning ability." This generative AI model formulates the optimal plan by referring to existing information and past lesson plans.

[0256] The generated lesson plans are sent back from the server to the terminal and provided to educators in a format that they can review and adjust. This allows educators to significantly reduce the time and effort previously required for lesson preparation.

[0257] Furthermore, educators, as users, input students' past performance data and learning questionnaire responses into their devices. This data is also sent to the server, which uses it to generate learner profile information. This profile includes the learner's personality, learning style, and interests. The server analyzes these profile information and provides an educational strategy optimized for each individual learner.

[0258] Throughout this entire system, educators can efficiently prepare and conduct lessons tailored to the individual needs of learners, thereby improving the quality of education.

[0259] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0260] Step 1:

[0261] The user inputs educational data such as educational content, curriculum, and learning objectives into the terminal. The terminal then organizes this information and converts it into a predetermined format. The input data includes curriculum content in text format and learning objectives as numerical data, and the resulting data is formatted for transmission to the server as output.

[0262] Step 2:

[0263] The terminal sends formatted educational data to the server. The server receives this data and stores it in a database. During this process, the server performs a verification process to check the accuracy and completeness of the data. The input is the data sent from the terminal, and the output is the educational information successfully stored in the database.

[0264] Step 3:

[0265] The server automatically generates lesson plans using a generative AI model based on the received educational data. In this step, the server inputs prompt sentences into the generative AI model, which then creates the optimal lesson plan. Specifically, the AI ​​model refers to existing databases and historical data to generate the most suitable lesson plan. The input is educational data including prompt sentences, and the output is a structured lesson plan.

[0266] Step 4:

[0267] The server sends the generated training plan to the terminal. The terminal receives this information and prepares it for display in a user-readable format. On the terminal, each item of the plan is visually organized, making it easy for the user to edit. The input is the training plan from the server, and the output is data that can be displayed on the user interface.

[0268] Step 5:

[0269] The user inputs information such as students' past academic performance data and responses to learning questionnaires into the terminal. The terminal organizes this information and converts it into a format for transmission to the server. The input consists of various data about the learner, and the output is data in a unified format that can be transmitted.

[0270] Step 6:

[0271] The server analyzes the input learner data and generates learner characteristic profiles. In this process, the server uses machine learning algorithms to identify individual learning styles and interests. The input is formatted learner data, and the output is the learner characteristic profile.

[0272] Step 7:

[0273] The server integrates the generated profiles and educational plans to propose individually optimized educational strategies. The inputs here are the trait profiles and educational plans, and the output is a recommended list of optimal teaching materials and methods for each learner.

[0274] (Application Example 1)

[0275] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0276] In today's manufacturing environment, efficiently and effectively managing individual manufacturing tasks is a major challenge. In particular, there is a need to provide real-time work instructions that take into account the skills and experience of each worker, but current systems are unable to adequately address this. As a result, decreased productivity and increased manufacturing errors are becoming problems.

[0277] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0278] In this invention, the server includes terminal means for inputting work data, generation module means for receiving the work data and automatically generating work procedures, and analysis means for analyzing worker data and creating worker capability profiles. This makes it possible to propose optimized work strategies for each worker in real time.

[0279] "Work data" refers to information about various steps and requirements in the manufacturing process, and is data entered to generate efficient work instructions.

[0280] "Terminal means" refers to a device used to input work data and send it to a server, and generally includes computers and tablet devices.

[0281] A "server system" refers to a computer system that receives data via a network and performs the necessary processing, possessing the capability to perform large-scale data processing.

[0282] "Generation module means" refers to a software component that operates on the server means and automatically generates the optimal work procedure based on the input data.

[0283] "Work procedures" refer to the specific steps and procedures necessary to efficiently advance the manufacturing process, and are composed of generation module means.

[0284] "Operator data" refers to information related to the skills, experience, past work history, etc. of each operator, and is collected by analysis means.

[0285] "Capability profile" refers to information that characterizes the capabilities and characteristics of an operator, created based on operator data.

[0286] "Analysis means" refers to software components or algorithms for analyzing operator data to create a capability profile.

[0287] "Proposal module means" refers to software components for proposing work strategies optimized for specific operators based on the generated capability profile.

[0288] As a form of implementing this invention, an automated work instruction system in a manufacturing environment will be described. The system includes terminal means for inputting work data, and operators use this terminal to transmit data related to the manufacturing process to the server. The terminals used generally include computers and tablet-type terminals.

[0289] The server utilizes a generated AI model based on the received data and automatically generates an optimal work procedure using a dedicated generation module means. This generated work procedure is transmitted to the operator through the terminal means. As the generated AI model, for example, an advanced natural language processing model such as GPT-4 is used.

[0290] Furthermore, the server has analysis means for analyzing operator data indicating the skills and experience of the operator and creating a capability profile. By creating this profile, it becomes possible to propose work strategies tailored to individual operators.

[0291] As a concrete example of implementation, in a manufacturing line, a server might instruct a worker via a terminal, saying, "Next, assemble the parts for Step 2. I will show you the optimal procedure tailored to your skill level." In this way, workers can receive personalized instructions in real time. These instructions include the tools to be used and points to pay attention to, improving work accuracy.

[0292] An example of a prompt message is: "Generate the optimal work procedure for product X on manufacturing line ID 123. Provide efficient steps based on worker Y's skill profile." By sending this command to the generation AI model, the optimal work procedure can be obtained.

[0293] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0294] Step 1:

[0295] The user enters work data on a terminal and sends it to the server.

[0296] The data entered includes information about each stage of the manufacturing process. Users input product IDs, required materials for each stage, and step details. The entered data is sent to the server and used as material for generating work procedures.

[0297] Step 2:

[0298] Based on the work data received by the server, the system automatically generates the optimal work procedure using a generation AI model.

[0299] The AI ​​model receives work data as input, and an algorithm within the model analyzes the data to calculate an effective work procedure. The generated work procedure is output and compiled into a procedure document.

[0300] Step 3:

[0301] The server sends the work procedure to the terminal, and the user receives it.

[0302] Send back the generated work procedure to the terminal used by the user. On the terminal, the received procedure is displayed as a procedure manual or guideline, and the user prepares to start the next work.

[0303] Step 4:

[0304] The user inputs the worker data into the terminal and sends it to the server.

[0305] Collect information on the skills, experience, and past work history of each worker and send it from the terminal. The input data is aggregated on the server and becomes the basic information for individualized analysis.

[0306] Step 5:

[0307] The server analyzes the worker data and creates an individual ability profile.

[0308] Based on the received worker data, the analysis means processes the data and generates a profile regarding the skill set and performance indicators. The generated ability profile is output and becomes the basis for formulating subsequent work strategies.

[0309] Step 6:

[0310] The server refers to the ability profile and uses the generated AI model to propose a work strategy optimized for the worker.

[0311] Referring to the generated ability profile as input, the AI model calculates the work strategy. The generated strategy is output as a proposal for optimizing the work procedure and presented to the user from the terminal.

[0312] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0313] This invention provides a system for educators to efficiently prepare lessons and provide optimal education to students, further incorporating a function to recognize user emotions. This system includes terminal means, server means, generative model means, analysis means, suggestion means, and emotion engine.

[0314] First, the user uses their device to input information about the educational curriculum and learning objectives, and sends this as educational data to the server. Based on the received educational data, the server automatically generates lesson plans using a generative model. These lesson plans include a lesson framework and specific teaching material suggestions, which the user can review and edit through their device.

[0315] Next, the user inputs student data, such as past academic performance and behavioral patterns, into the terminal and sends it to the server. The server analyzes this data and uses AI technology to create a personality profile of the student. Furthermore, the server integrates the generated lesson plan with the student's personality profile to propose the most suitable educational approach for the student. For example, if a student's personality profile determines that they prefer visual learning, the server will propose lesson content that makes extensive use of illustrations for that student.

[0316] The emotion engine recognizes the user's emotional state in real time and uses this information to adjust the content of lesson plans. For example, if the server detects that the user is under high stress, it will suggest a more relaxed lesson structure in the lesson plan, supporting the smoothest possible lesson delivery. This emotion engine allows users to choose an educational approach that suits their emotional state, making the educational environment more creative and effective.

[0317] In this way, the system takes into account the user's emotional state and provides education optimized for each individual student, enabling improvements in the quality of education and addressing individual needs.

[0318] The following describes the processing flow.

[0319] Step 1:

[0320] Users input educational data, such as curriculum information and learning objectives, into their devices. This data is then sent to the server.

[0321] Step 2:

[0322] The server runs a generative model based on the received educational data and automatically generates lesson plans. This generative model utilizes existing educational databases to design optimal teaching content.

[0323] Step 3:

[0324] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0325] Step 4:

[0326] Users input student data, such as academic performance and behavioral patterns, into their devices. This data is then sent to a server for individual student analysis.

[0327] Step 5:

[0328] The server uses student data to apply analytical methods and build a personality profile for each student. This profile includes the student's learning tendencies and areas of interest.

[0329] Step 6:

[0330] The server combines the generated lesson plans with the students' personality profiles to create the most suitable teaching approach for each student. For example, for students who prefer visual learning, the server designs lesson content that includes many diagrams and visual materials.

[0331] Step 7:

[0332] The server uses an emotion engine that analyzes the user's emotions in real time to recognize the user's current emotional state.

[0333] Step 8:

[0334] The server adjusts the content of the lesson plan according to the user's emotional state, as recognized by the emotion engine. For example, if high stress is detected, it will suggest a relaxing teaching method.

[0335] Step 9:

[0336] The server sends the adjusted educational policy to the terminal. The terminal then allows the user to view the optimized lesson plan.

[0337] In this way, the system realizes a mechanism that provides education tailored to the individual circumstances of users and students through a series of steps.

[0338] (Example 2)

[0339] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0340] In today's educational environment, educators are required to efficiently prepare educational plans and provide education tailored to the individual characteristics and emotional states of each learner. However, achieving this within limited time and resources is difficult and a challenge faced by many educators. Furthermore, there is a lack of appropriate technical tools to accurately understand learners' personality profiles and adjust educational activities accordingly.

[0341] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0342] In this invention, the server includes a communication device means for users to input educational plan information, an information processing device means for receiving educational plan information, and an emotion analysis means for identifying emotional states and dynamically adjusting the content of the educational plan. This enables educators to efficiently provide optimal education that corresponds to the individual characteristics and emotional states of each learner by utilizing automatically generated educational plans.

[0343] "Communication device means" refers to an electronic device that provides an interface for users to input educational plan information and transmit that information to a server in digital format.

[0344] The "information processing device means" is a computing platform for analyzing educational plan information received from a user and automatically generating educational plans using a generation algorithm.

[0345] "Generative algorithm means" refers to a set of procedures or processes designed to automatically generate educational plans based on educational plan information.

[0346] "Analysis tools" are data analysis devices used to evaluate learners' past data and behavioral tendencies, and to extract and construct learners' characteristic profiles.

[0347] "Proposed means" refers to technical means of planning and presenting an educational policy optimized for each individual learner, based on the learner's characteristic profile constructed by the analytical means.

[0348] "Emotion analysis means" refers to technology that detects a user's emotional state in real time and supports the adjustment of educational plans accordingly.

[0349] This invention provides a system that enables educators to effectively and efficiently prepare educational activities and provide optimal education to individual learners. This system includes communication devices, information processing devices, generation algorithms, analysis devices, proposal devices, and sentiment analysis devices.

[0350] The user inputs educational plan information using a communication device. This information includes details such as the lesson theme, selection of teaching materials, and learning objectives, and is sent to the server. Based on the received educational plan information, the server automatically generates an educational plan using a generative AI model. In this process, a widely used natural language processing model (e.g., OpenAI GPT-3) is used as the specific generation algorithm. The generated educational plan includes specific details about the lesson procedure and the teaching materials to be used.

[0351] The user further inputs the learner's performance data and behavioral tendencies into a communication device and sends it to the server. The server uses specialized analytical tools to construct a personality profile of the learner in order to analyze this data. Based on this profile, the server proposes the most suitable educational approach for each individual learner. For example, for a learner who prefers visual learning, the server will suggest teaching materials that make extensive use of illustrations.

[0352] This system further uses emotion analysis to assess the user's emotional state (e.g., stress level) in real time. If the user is experiencing high stress levels, the system adds relaxation elements to the educational plan to help provide a smoother and more creative learning environment.

[0353] For example, if a user inputs "I want to create a science lesson plan for elementary school students," the server will use a generative AI model to create a lesson plan that includes experimental activities. Furthermore, if the system determines that the user is feeling stressed, it will suggest incorporating relaxation time into the lesson.

[0354] Examples of prompts include, "Use a generative AI model to generate a curriculum plan for a specific grade level," and "Adjust the curriculum plan based on the current emotional state."

[0355] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0356] Step 1:

[0357] The user enters educational plan information using a communication device. This educational plan information includes the lesson theme, objectives, and types of teaching materials to be used. This input data is transmitted digitally to the information processing device. The server analyzes the received data and converts it into an internal format that it can understand.

[0358] Step 2:

[0359] The server automatically generates lesson plans using a generation algorithm. Here, the input lesson plan information is fed into the generation AI model as prompts, and the generation AI model generates the lesson plan (including lesson procedures and specific teaching materials) based on these prompts. This process is achieved by selecting and constructing the most suitable plan proposed by the generation AI model according to a defined algorithm. This generated lesson plan is then sent back to the communication device.

[0360] Step 3:

[0361] Users input learners' past performance data and behavioral patterns using a communication device. This information is sent to a server, which uses specialized analytical tools to create a learner's characteristic profile based on the input data. This profile creation process involves statistical analysis of the data and understanding of trends, resulting in a profile that identifies the learner's educational needs.

[0362] Step 4:

[0363] The server integrates these learner profiles with the generated teaching plans and proposes optimized teaching strategies. Specifically, it generates suggestions based on different learning styles and characteristics, and for learners who prefer visual learning, it develops strategies that make extensive use of diagrams and visual aids. These optimized strategies are then presented to the educators.

[0364] Step 5:

[0365] The device detects the user's emotional state in real time and sends this data to the server. The server uses emotion analysis tools to interpret the received emotional data and adjust the educational plan. If a high level of stress is detected, it generates an adjusted plan incorporating relaxation elements and stress reduction techniques and presents it to the user. This adjusted plan is then provided as the final output.

[0366] (Application Example 2)

[0367] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0368] Traditional education systems have struggled to adequately consider the individual personalities and emotional states of students, hindering the provision of effective educational policies. Furthermore, educational plans that disregard the emotional state of educators often hindered effective lesson delivery. Additionally, employee training has been difficult to tailor to individual needs, often resulting in manual-based instruction. To address these challenges, there is a need for methods that enable education and training optimized for each individual.

[0369] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0370] In this invention, the server includes an information processing device for inputting educational information, an analysis device for analyzing personality information and creating personality traits, and an emotion management device for recognizing emotional states and adjusting content. This makes it possible to provide optimal educational and training content tailored to each individual's personality traits and real-time emotional state.

[0371] "Educational information" refers to data related to educational programs and learning objectives, and forms the basis for educational policies and the creation of teaching materials.

[0372] "Information processing device means" refers to electronic devices used to receive and transmit educational information and personality information, and functions as servers or terminals.

[0373] "Product generation means" refers to a system or algorithm that has the function of automatically generating lesson plans based on collected data.

[0374] "Personality information" refers to data used to analyze individual personality traits, including behavior, attitudes, and learning tendencies.

[0375] "Personality traits" refer to profiles of personality generated by analytical methods, and are data used to optimize educational policies.

[0376] "Analysis means" refers to technical means for analyzing personality information and creating personality traits, and includes data analysis algorithms.

[0377] "Emotional state" refers to an individual's real-time emotions, including psychological states such as stress, excitement, and boredom.

[0378] "Emotional management tools" refer to systems or technologies that recognize emotional states and adjust their content based on that information.

[0379] "Display means" refers to means for displaying information through a visual device, and includes visual devices.

[0380] The system for realizing this invention comprises an information processing device for inputting and receiving educational information, a product creation device, an analysis device, an emotion management device, and a display device. In this system, the information processing device plays the role of receiving educational information from an educator's terminal and transmitting it to a server. The server generates a lesson plan based on the received educational information using the product creation device. This lesson plan includes specific implementation methods and educational policies. The generated lesson plan is provided to the educator's terminal via the information processing device, where it can be reviewed and edited.

[0381] Furthermore, users input personality information from their devices and send it to the server. The server uses analytical tools to create personality traits and supplements the educational plan based on them. The emotion management system analyzes the emotional state of the user and the target group in real time and adjusts the educational plan as needed. This adjustment is made in the form of alleviating stress levels or adding engaging content. The final educational plan is visually displayed to the target group through a display device. Devices such as smart glasses can be used for display, improving the interactivity of the education.

[0382] As a concrete example, in employee training at physical stores, work procedures and customer service methods are displayed through smart glasses, and the training content is dynamically adjusted according to the employee's emotional state. An example of a prompt to the generating AI model in this case is: "Suggest employee training content based on the user's current emotional state. If the emotional state is 'stressed,' suggest relaxation techniques; if it is 'bored,' present an interactive challenge." This allows for more effective and considerate training.

[0383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0384] Step 1:

[0385] Users input educational and personality information using a terminal and send it to the server. This process includes educational programs and learning objectives as educational information, and past student behavior data and survey results as personality information. The entered data is stored in a database and used for subsequent processing.

[0386] Step 2:

[0387] The server analyzes the received educational information using a product creation mechanism and automatically generates lesson plans using a generation AI model. At this stage, the educational information is analyzed by the AI ​​model, and lesson plans that include optimal teaching materials and teaching policies are output. This ensures that the content is designed taking into account the expertise of educators and the learning styles of students.

[0388] Step 3:

[0389] The analysis tool processes personality information to create personality traits. The server executes a data analysis algorithm based on the input personality information to derive the student's personality traits. This results in the output of an individualized educational plan based on the student's personality.

[0390] Step 4:

[0391] The server uses emotion management tools to recognize the user's emotional state in real time. It analyzes sensor information from input devices and the user's facial expressions to determine emotional states such as stress and anxiety. Based on the emotional state, it adjusts the specific progression and content of the educational plan. This adjustment is supported by a generative AI model.

[0392] Step 5:

[0393] The finalized lesson plan is presented visually to the user or target audience through display means. Using smart glasses or mobile devices, the lesson plan and emotionally responsive content are displayed, allowing users to conduct education and training based on that information. This displayed content is dynamically updated based on the adjustments made in step 4.

[0394] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0395] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0396] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0397] [Third Embodiment]

[0398] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0399] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0400] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0401] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0402] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0403] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0404] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0405] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0406] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0407] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0408] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0409] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0410] As an embodiment of this invention, a system is described that enables educators to efficiently prepare lessons and provide education optimized for each individual student.

[0411] Educators first use a terminal to send educational data, such as lesson content, curriculum, and learning objectives, to a server. The server receives this data and uses a generative model to automatically generate lesson plans based on the relevant information. These lesson plans include structured lesson plans designed to efficiently achieve the set educational objectives. The generated lesson plans are sent from the server to the terminal, where educators can review and adjust them. This automated generation process significantly reduces the time educators spend preparing lessons.

[0412] Next, data such as past academic performance and learning questionnaires about the student are entered into the terminal and sent to the server. The server analyzes this data and creates a personality profile for each student. This profile comprehensively covers information about the student's personality traits, learning style, and interests. This allows educators to develop educational strategies that meet the individual needs of each student.

[0413] Furthermore, the server integrates the generated lesson plans with personality profiles to propose the most suitable educational approach for each student. For example, if a student's profile indicates a strong visual learning tendency, the server will propose a teaching style that heavily utilizes graphics and diagrams. In this way, the proposed teaching methods are sent to the user via the terminal, allowing educators to further refine their lesson content based on this feedback.

[0414] Systems implemented in this form are useful in supporting educators in conducting lessons effectively and efficiently, improving the quality of education, and addressing the individual needs of students.

[0415] The following describes the processing flow.

[0416] Step 1:

[0417] Users input information about the lesson content, curriculum, and learning objectives into their devices. This information is then sent to the server as educational data.

[0418] Step 2:

[0419] The server automatically generates lesson plans using a generative model based on the received educational data. The generative model refers to existing educational databases to construct the optimal lesson structure and teaching material proposals.

[0420] Step 3:

[0421] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0422] Step 4:

[0423] Users input student data, such as each student's past grades and survey results, into their terminals. This data is then sent to a server for personality analysis of the students.

[0424] Step 5:

[0425] The server analyzes the received student data and uses AI to create a student personality profile. This profile includes the student's learning style, interests, and other factors.

[0426] Step 6:

[0427] The server combines the generated lesson plans with the students' personality profiles to propose an educational approach optimized for each student.

[0428] Step 7:

[0429] The server sends the proposed teaching method to the terminal. The terminal then presents this information to the user, who can then plan their lessons based on it.

[0430] (Example 1)

[0431] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0432] There is a need for an efficient platform that allows educators to provide personalized education without spending a great deal of time and effort on lesson preparation. Traditional methods require time to organize educational information and understand the characteristics of learners, making individualized support difficult.

[0433] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0434] In this invention, the server includes an information input means for inputting educational information, a generation mechanism means for automatically generating educational plans, and an analysis means for creating learner characteristic profiles. This enables educators to efficiently generate structured lesson plans and provide education tailored to the individual needs of each learner.

[0435] "Information input means" refers to devices or systems for recording or inputting educational information or learner data.

[0436] "Data processing means" refers to computer systems and associated software used to appropriately analyze and manage received educational information.

[0437] "Generation mechanism means" refers to algorithms and programs for automatically generating educational plans and lesson plans based on received information.

[0438] "Communication methods" refer to the communication infrastructure and protocols used to transfer generated educational plans and lesson plans to educators.

[0439] "Analysis tools" refer to tools and methods for analyzing learner data in detail and creating learner characteristic profiles.

[0440] A "trait profile" refers to a collection of data that contains information about a learner's personality traits, learning style, and interests.

[0441] "Proposal mechanism means" refers to a system or algorithm for proposing individually optimized educational strategies to educators based on the generated profile.

[0442] A specific embodiment of this invention is a system primarily used by educators to prepare lessons and provide optimized education for learners. Educators input information such as lesson content, curriculum, and learning objectives using a dedicated terminal. This information is organized and converted into an appropriate format on the terminal. The converted data is sent to a server, which then receives it.

[0443] The server automatically generates lesson plans using a generative AI model based on the received educational information. The generative AI model generates appropriate lesson plans using prompts. A specific example of a prompt might be, "Generate a history lesson plan based on the following educational data and learner information. The learners have a strong auditory learning ability." This generative AI model formulates the optimal plan by referring to existing information and past lesson plans.

[0444] The generated lesson plans are sent back from the server to the terminal and provided to educators in a format that they can review and adjust. This allows educators to significantly reduce the time and effort previously required for lesson preparation.

[0445] Furthermore, educators, as users, input students' past performance data and learning questionnaire responses into their devices. This data is also sent to the server, which uses it to generate learner profile information. This profile includes the learner's personality, learning style, and interests. The server analyzes these profile information and provides an educational strategy optimized for each individual learner.

[0446] Throughout this entire system, educators can efficiently prepare and conduct lessons tailored to the individual needs of learners, thereby improving the quality of education.

[0447] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0448] Step 1:

[0449] The user inputs educational data such as educational content, curriculum, and learning objectives into the terminal. The terminal then organizes this information and converts it into a predetermined format. The input data includes curriculum content in text format and learning objectives as numerical data, and the resulting data is formatted for transmission to the server as output.

[0450] Step 2:

[0451] The terminal sends formatted educational data to the server. The server receives this data and stores it in a database. During this process, the server performs a verification process to check the accuracy and completeness of the data. The input is the data sent from the terminal, and the output is the educational information successfully stored in the database.

[0452] Step 3:

[0453] The server automatically generates lesson plans using a generative AI model based on the received educational data. In this step, the server inputs prompt sentences into the generative AI model, which then creates the optimal lesson plan. Specifically, the AI ​​model refers to existing databases and historical data to generate the most suitable lesson plan. The input is educational data including prompt sentences, and the output is a structured lesson plan.

[0454] Step 4:

[0455] The server sends the generated training plan to the terminal. The terminal receives this information and prepares it for display in a user-readable format. On the terminal, each item of the plan is visually organized, making it easy for the user to edit. The input is the training plan from the server, and the output is data that can be displayed on the user interface.

[0456] Step 5:

[0457] The user inputs information such as students' past academic performance data and responses to learning questionnaires into the terminal. The terminal organizes this information and converts it into a format for transmission to the server. The input consists of various data about the learner, and the output is data in a unified format that can be transmitted.

[0458] Step 6:

[0459] The server analyzes the input learner data and generates learner characteristic profiles. In this process, the server uses machine learning algorithms to identify individual learning styles and interests. The input is formatted learner data, and the output is the learner characteristic profile.

[0460] Step 7:

[0461] The server integrates the generated profiles and educational plans to propose individually optimized educational strategies. The inputs here are the trait profiles and educational plans, and the output is a recommended list of optimal teaching materials and methods for each learner.

[0462] (Application Example 1)

[0463] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0464] In today's manufacturing environment, efficiently and effectively managing individual manufacturing tasks is a major challenge. In particular, there is a need to provide real-time work instructions that take into account the skills and experience of each worker, but current systems are unable to adequately address this. As a result, decreased productivity and increased manufacturing errors are becoming problems.

[0465] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0466] In this invention, the server includes terminal means for inputting work data, generation module means for receiving the work data and automatically generating work procedures, and analysis means for analyzing worker data and creating worker capability profiles. This makes it possible to propose optimized work strategies for each worker in real time.

[0467] "Work data" refers to information about various steps and requirements in the manufacturing process, and is data entered to generate efficient work instructions.

[0468] "Terminal means" refers to a device used to input work data and send it to a server, and generally includes computers and tablet devices.

[0469] A "server system" refers to a computer system that receives data via a network and performs the necessary processing, possessing the capability to perform large-scale data processing.

[0470] "Generation module means" refers to a software component that operates on the server means and automatically generates the optimal work procedure based on the input data.

[0471] "Work procedures" refer to the specific steps and procedures necessary to efficiently advance the manufacturing process, and are composed of generation module means.

[0472] "Worker data" refers to information about each worker's skills, experience, past work history, etc., and is collected through analytical methods.

[0473] A "capability profile" refers to information that characterizes the abilities and characteristics of workers, created based on worker data.

[0474] "Analysis tools" refer to software components or algorithms used to analyze worker data and create competency profiles.

[0475] "Suggestion module means" refers to a software component that proposes an optimized work strategy for a specific worker based on the generated capability profile.

[0476] As an embodiment of this invention, an automated work instruction system in a manufacturing environment is described. The system includes terminal means for inputting work data, which workers use to transmit data related to the manufacturing process to a server. The terminals used generally include computers and tablet devices.

[0477] The server uses a generative AI model based on the received data and automatically generates the optimal work procedure using a dedicated generative module. This generated work procedure is then transmitted to the worker via a terminal. For example, an advanced natural language processing model such as GPT-4 is used as the generative AI model.

[0478] Furthermore, the server is equipped with analytical tools to analyze worker data that shows the skills and experience of workers and create capability profiles. This profile creation makes it possible to propose work strategies tailored to each individual worker.

[0479] As a concrete example of implementation, in a manufacturing line, a server might instruct a worker via a terminal, saying, "Next, assemble the parts for Step 2. I will show you the optimal procedure tailored to your skill level." In this way, workers can receive personalized instructions in real time. These instructions include the tools to be used and points to pay attention to, improving work accuracy.

[0480] An example of a prompt message is: "Generate the optimal work procedure for product X on manufacturing line ID 123. Provide efficient steps based on worker Y's skill profile." By sending this command to the generation AI model, the optimal work procedure can be obtained.

[0481] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0482] Step 1:

[0483] The user enters work data on a terminal and sends it to the server.

[0484] The data entered includes information about each stage of the manufacturing process. Users input product IDs, required materials for each stage, and step details. The entered data is sent to the server and used as material for generating work procedures.

[0485] Step 2:

[0486] Based on the work data received by the server, the system automatically generates the optimal work procedure using a generation AI model.

[0487] The AI ​​model receives work data as input, and an algorithm within the model analyzes the data to calculate an effective work procedure. The generated work procedure is output and compiled into a procedure document.

[0488] Step 3:

[0489] The server sends the work instructions to the terminal, and the user receives them.

[0490] The generated work procedure is sent back to the user's terminal. On the terminal, the received procedure is displayed as a procedure manual or guideline, allowing the user to prepare to start the next task.

[0491] Step 4:

[0492] The user enters worker data into a terminal and sends it to the server.

[0493] Information on each worker's skills, experience, and past work history is collected and transmitted from the terminal. The entered data is aggregated on a server and becomes the basis for individualized analysis.

[0494] Step 5:

[0495] The server analyzes worker data and creates individual capability profiles.

[0496] Based on the received worker data, the analysis system processes the data and generates profiles related to skill sets and performance indicators. The generated competency profiles are output and serve as the basis for subsequent work strategy planning.

[0497] Step 6:

[0498] The server references the capability profile and uses a generated AI model to propose an optimized work strategy for the worker.

[0499] The AI ​​model calculates a work strategy by referring to the capability profile generated as input. The generated strategy is output as an optimized work procedure suggestion and presented to the user from the terminal.

[0500] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0501] This invention provides a system for educators to efficiently prepare lessons and provide optimal education to students, further incorporating a function to recognize user emotions. This system includes terminal means, server means, generative model means, analysis means, suggestion means, and emotion engine.

[0502] First, the user uses their device to input information about the educational curriculum and learning objectives, and sends this as educational data to the server. Based on the received educational data, the server automatically generates lesson plans using a generative model. These lesson plans include a lesson framework and specific teaching material suggestions, which the user can review and edit through their device.

[0503] Next, the user inputs student data, such as past academic performance and behavioral patterns, into the terminal and sends it to the server. The server analyzes this data and uses AI technology to create a personality profile of the student. Furthermore, the server integrates the generated lesson plan with the student's personality profile to propose the most suitable educational approach for the student. For example, if a student's personality profile determines that they prefer visual learning, the server will propose lesson content that makes extensive use of illustrations for that student.

[0504] The emotion engine recognizes the user's emotional state in real time and uses this information to adjust the content of lesson plans. For example, if the server detects that the user is under high stress, it will suggest a more relaxed lesson structure in the lesson plan, supporting the smoothest possible lesson delivery. This emotion engine allows users to choose an educational approach that suits their emotional state, making the educational environment more creative and effective.

[0505] In this way, the system takes into account the user's emotional state and provides education optimized for each individual student, thereby improving the quality of education and addressing individual needs.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] Users input educational data, such as curriculum information and learning objectives, into their devices. This data is then sent to the server.

[0509] Step 2:

[0510] The server runs a generative model based on the received educational data and automatically generates lesson plans. This generative model utilizes existing educational databases to design optimal teaching content.

[0511] Step 3:

[0512] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0513] Step 4:

[0514] Users input student data, such as academic performance and behavioral patterns, into their devices. This data is then sent to a server for individual student analysis.

[0515] Step 5:

[0516] The server uses student data to apply analytical methods and build a personality profile for each student. This profile includes the student's learning tendencies and areas of interest.

[0517] Step 6:

[0518] The server combines the generated lesson plans with the students' personality profiles to create the most suitable teaching approach for each student. For example, for students who prefer visual learning, the server designs lesson content that includes many diagrams and visual materials.

[0519] Step 7:

[0520] The server uses an emotion engine that analyzes the user's emotions in real time to recognize the user's current emotional state.

[0521] Step 8:

[0522] The server adjusts the content of the lesson plan according to the user's emotional state, as recognized by the emotion engine. For example, if high stress is detected, it will suggest a relaxing teaching method.

[0523] Step 9:

[0524] The server sends the adjusted educational policy to the terminal. The terminal then allows the user to view the optimized lesson plan.

[0525] In this way, the system realizes a mechanism that provides education tailored to the individual circumstances of users and students through a series of steps.

[0526] (Example 2)

[0527] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0528] In today's educational environment, educators are required to efficiently prepare educational plans and provide education tailored to the individual characteristics and emotional states of each learner. However, achieving this within limited time and resources is difficult and a challenge faced by many educators. Furthermore, there is a lack of appropriate technical tools to accurately understand learners' personality profiles and adjust educational activities accordingly.

[0529] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0530] In this invention, the server includes a communication device means for users to input educational plan information, an information processing device means for receiving educational plan information, and an emotion analysis means for identifying emotional states and dynamically adjusting the content of the educational plan. This enables educators to efficiently provide optimal education that corresponds to the individual characteristics and emotional states of each learner by utilizing automatically generated educational plans.

[0531] "Communication device means" refers to an electronic device that provides an interface for users to input educational plan information and transmit that information to a server in digital format.

[0532] The "information processing device" is a computing platform that analyzes educational plan information received from a user and automatically generates educational plans using a generation algorithm.

[0533] A "generation algorithm means" is a set of procedures or processes designed to automatically generate an educational plan based on educational plan information.

[0534] "Analysis tools" are data analysis devices used to evaluate learners' past data and behavioral tendencies, and to extract and construct learners' characteristic profiles.

[0535] "Proposed means" refers to technical means that plan and present an educational policy optimized for each individual learner, based on the learner's characteristic profile constructed by the analytical means.

[0536] "Emotion analysis means" refers to technology that detects a user's emotional state in real time and supports the adjustment of educational plans accordingly.

[0537] This invention provides a system that enables educators to effectively and efficiently prepare educational activities and provide optimal education to individual learners. This system includes communication devices, information processing devices, generation algorithms, analysis devices, proposal devices, and sentiment analysis devices.

[0538] The user inputs educational plan information using a communication device. This information includes details such as the lesson theme, selection of teaching materials, and learning objectives, and is sent to the server. Based on the received educational plan information, the server automatically generates an educational plan using a generative AI model. In this process, a widely used natural language processing model (e.g., OpenAI GPT-3) is used as the specific generation algorithm. The generated educational plan includes specific details about the lesson procedure and the teaching materials to be used.

[0539] The user further inputs the learner's performance data and behavioral tendencies into a communication device and sends it to the server. The server uses specialized analytical tools to construct a personality profile of the learner in order to analyze this data. Based on this profile, the server proposes the most suitable educational approach for each individual learner. For example, for a learner who prefers visual learning, the server will suggest teaching materials that make extensive use of illustrations.

[0540] This system further uses emotion analysis to assess the user's emotional state (e.g., stress level) in real time. If the user is experiencing high stress levels, the system adds relaxation elements to the educational plan to help provide a smoother and more creative learning environment.

[0541] For example, if a user inputs "I want to create a science lesson plan for elementary school students," the server will use a generative AI model to create a lesson plan that includes experimental activities. Furthermore, if the system determines that the user is feeling stressed, it will suggest incorporating relaxation time into the lesson.

[0542] Examples of prompts include, "Use a generative AI model to generate a curriculum plan for a specific grade level," and "Adjust the curriculum plan based on the current emotional state."

[0543] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0544] Step 1:

[0545] The user enters educational plan information using a communication device. This educational plan information includes the lesson theme, objectives, and types of teaching materials to be used. This input data is transmitted digitally to the information processing device. The server analyzes the received data and converts it into an internal format that it can understand.

[0546] Step 2:

[0547] The server automatically generates lesson plans using a generation algorithm. Here, the input lesson plan information is fed into the generation AI model as prompts, and the generation AI model generates the lesson plan (including lesson procedures and specific teaching materials) based on these prompts. This process is achieved by selecting and constructing the most suitable plan proposed by the generation AI model according to a defined algorithm. This generated lesson plan is then sent back to the communication device.

[0548] Step 3:

[0549] Users input learners' past performance data and behavioral patterns using a communication device. This information is sent to a server, which uses specialized analytical tools to create a learner's characteristic profile based on the input data. This profile creation process involves statistical analysis of the data and understanding of trends, resulting in a profile that identifies the learner's educational needs.

[0550] Step 4:

[0551] The server integrates these learner profiles with the generated teaching plans and proposes optimized teaching strategies. Specifically, it generates suggestions based on different learning styles and characteristics, and for learners who prefer visual learning, it develops strategies that make extensive use of diagrams and visual aids. These optimized strategies are then presented to the educators.

[0552] Step 5:

[0553] The device detects the user's emotional state in real time and sends this data to the server. The server uses emotion analysis tools to interpret the received emotional data and adjust the educational plan. If a high level of stress is detected, it generates an adjusted plan incorporating relaxation elements and stress reduction techniques and presents it to the user. This adjusted plan is then provided as the final output.

[0554] (Application Example 2)

[0555] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0556] Traditional education systems have struggled to adequately consider the individual personalities and emotional states of students, hindering the provision of effective educational policies. Furthermore, educational plans that disregard the emotional state of educators often hindered effective lesson delivery. Additionally, employee training has been difficult to tailor to individual needs, often resulting in manual-based instruction. To address these challenges, there is a need for methods that enable education and training optimized for each individual.

[0557] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0558] In this invention, the server includes an information processing device for inputting educational information, an analysis device for analyzing personality information and creating personality traits, and an emotion management device for recognizing emotional states and adjusting content. This makes it possible to provide optimal educational and training content tailored to each individual's personality traits and real-time emotional state.

[0559] "Educational information" refers to data related to educational programs and learning objectives, and forms the basis for educational policies and the creation of teaching materials.

[0560] "Information processing device means" refers to electronic devices used to receive and transmit educational information and personality information, and functions as servers or terminals.

[0561] "Product generation means" refers to a system or algorithm that has the function of automatically generating lesson plans based on collected data.

[0562] "Personality information" refers to data used to analyze individual personality traits, including behavior, attitudes, and learning tendencies.

[0563] "Personality traits" refer to profiles of personality generated by analytical methods, and are data used to optimize educational policies.

[0564] "Analysis means" refers to technical means for analyzing personality information and creating personality traits, and includes data analysis algorithms.

[0565] "Emotional state" refers to an individual's real-time emotions, including psychological states such as stress, excitement, and boredom.

[0566] "Emotional management tools" refer to systems or technologies that recognize emotional states and adjust their content based on that information.

[0567] "Display means" refers to means for displaying information through a visual device, and includes visual devices.

[0568] The system for realizing this invention comprises an information processing device for inputting and receiving educational information, a product creation device, an analysis device, an emotion management device, and a display device. In this system, the information processing device plays the role of receiving educational information from an educator's terminal and transmitting it to a server. The server generates a lesson plan based on the received educational information using the product creation device. This lesson plan includes specific implementation methods and educational policies. The generated lesson plan is provided to the educator's terminal via the information processing device, where it can be reviewed and edited.

[0569] Furthermore, users input personality information from their devices and send it to the server. The server uses analytical tools to create personality traits and supplements the educational plan based on them. The emotion management system analyzes the emotional state of the user and the target group in real time and adjusts the educational plan as needed. This adjustment is made in the form of alleviating stress levels or adding engaging content. The final educational plan is visually displayed to the target group through a display device. Devices such as smart glasses can be used for display, improving the interactivity of the education.

[0570] As a concrete example, in employee training at physical stores, work procedures and customer service methods are displayed through smart glasses, and the training content is dynamically adjusted according to the employee's emotional state. An example of a prompt to the generating AI model in this case is: "Suggest employee training content based on the user's current emotional state. If the emotional state is 'stressed,' suggest relaxation techniques; if it is 'bored,' present an interactive challenge." This allows for more effective and considerate training.

[0571] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0572] Step 1:

[0573] Users input educational and personality information using a terminal and send it to the server. This process includes educational programs and learning objectives as educational information, and past student behavior data and survey results as personality information. The entered data is stored in a database and used for subsequent processing.

[0574] Step 2:

[0575] The server analyzes the received educational information using a product creation mechanism and automatically generates lesson plans using a generation AI model. At this stage, the educational information is analyzed by the AI ​​model, and lesson plans that include optimal teaching materials and teaching policies are output. This ensures that the content is designed taking into account the expertise of educators and the learning styles of students.

[0576] Step 3:

[0577] The analysis tool processes personality information to create personality traits. The server executes a data analysis algorithm based on the input personality information to derive the student's personality traits. This results in the output of an individualized educational plan based on the student's personality.

[0578] Step 4:

[0579] The server uses emotion management tools to recognize the user's emotional state in real time. It analyzes sensor information from input devices and the user's facial expressions to determine emotional states such as stress and anxiety. Based on the emotional state, it adjusts the specific progression and content of the educational plan. This adjustment is supported by a generative AI model.

[0580] Step 5:

[0581] The finalized lesson plan is presented visually to the user or target audience through display means. Using smart glasses or mobile devices, the lesson plan and emotionally responsive content are displayed, allowing users to conduct education and training based on that information. This displayed content is dynamically updated based on the adjustments made in step 4.

[0582] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0583] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0584] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0585] [Fourth Embodiment]

[0586] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0587] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0588] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0589] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0590] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0591] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0592] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0593] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0594] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0595] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0596] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0597] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0598] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0599] As an embodiment of this invention, a system is described that enables educators to efficiently prepare lessons and provide education optimized for each individual student.

[0600] Educators first use a terminal to send educational data, such as lesson content, curriculum, and learning objectives, to a server. The server receives this data and uses a generative model to automatically generate lesson plans based on the relevant information. These lesson plans include structured lesson plans designed to efficiently achieve the set educational objectives. The generated lesson plans are sent from the server to the terminal, where educators can review and adjust them. This automated generation process significantly reduces the time educators spend preparing lessons.

[0601] Next, data such as past academic performance and learning questionnaires about the student are entered into the terminal and sent to the server. The server analyzes this data and creates a personality profile for each student. This profile comprehensively covers information about the student's personality traits, learning style, and interests. This allows educators to develop educational strategies that meet the individual needs of each student.

[0602] Furthermore, the server integrates the generated lesson plans with personality profiles to propose the most suitable educational approach for each student. For example, if a student's profile indicates a strong visual learning tendency, the server will propose a teaching style that heavily utilizes graphics and diagrams. In this way, the proposed teaching methods are sent to the user via the terminal, allowing educators to further refine their lesson content based on this feedback.

[0603] Systems implemented in this form are useful in supporting educators in conducting lessons effectively and efficiently, improving the quality of education, and addressing the individual needs of students.

[0604] The following describes the processing flow.

[0605] Step 1:

[0606] Users input information about the lesson content, curriculum, and learning objectives into their devices. This information is then sent to the server as educational data.

[0607] Step 2:

[0608] The server automatically generates lesson plans using a generative model based on the received educational data. The generative model refers to existing educational databases to construct the optimal lesson structure and teaching material proposals.

[0609] Step 3:

[0610] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0611] Step 4:

[0612] Users input student data, such as each student's past grades and survey results, into their terminals. This data is then sent to a server for personality analysis of the students.

[0613] Step 5:

[0614] The server analyzes the received student data and uses AI to create a student personality profile. This profile includes the student's learning style, interests, and other factors.

[0615] Step 6:

[0616] The server combines the generated lesson plans with the students' personality profiles to propose an educational approach optimized for each student.

[0617] Step 7:

[0618] The server sends the proposed teaching method to the terminal. The terminal then presents this information to the user, who can then plan their lessons based on it.

[0619] (Example 1)

[0620] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0621] There is a need for an efficient platform that allows educators to provide personalized education without spending a great deal of time and effort on lesson preparation. Traditional methods require time to organize educational information and understand the characteristics of learners, making individualized support difficult.

[0622] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0623] In this invention, the server includes an information input means for inputting educational information, a generation mechanism means for automatically generating educational plans, and an analysis means for creating learner characteristic profiles. This enables educators to efficiently generate structured lesson plans and provide education tailored to the individual needs of each learner.

[0624] "Information input means" refers to devices or systems for recording or inputting educational information or learner data.

[0625] "Data processing means" refers to computer systems and associated software used to appropriately analyze and manage received educational information.

[0626] "Generation mechanism means" refers to algorithms and programs for automatically generating educational plans and lesson plans based on received information.

[0627] "Communication methods" refer to the communication infrastructure and protocols used to transfer generated educational plans and lesson plans to educators.

[0628] "Analysis tools" refer to tools and methods for analyzing learner data in detail and creating learner characteristic profiles.

[0629] A "trait profile" refers to a collection of data that contains information about a learner's personality traits, learning style, and interests.

[0630] "Proposal mechanism means" refers to a system or algorithm for proposing individually optimized educational strategies to educators based on the generated profile.

[0631] A specific embodiment of this invention is a system primarily used by educators to prepare lessons and provide optimized education for learners. Educators input information such as lesson content, curriculum, and learning objectives using a dedicated terminal. This information is organized and converted into an appropriate format on the terminal. The converted data is sent to a server, which then receives it.

[0632] The server automatically generates lesson plans using a generative AI model based on the received educational information. The generative AI model generates appropriate lesson plans using prompts. A specific example of a prompt might be, "Generate a history lesson plan based on the following educational data and learner information. The learners have a strong auditory learning ability." This generative AI model formulates the optimal plan by referring to existing information and past lesson plans.

[0633] The generated lesson plans are sent back from the server to the terminal and provided to educators in a format that they can review and adjust. This allows educators to significantly reduce the time and effort previously required for lesson preparation.

[0634] Furthermore, educators, as users, input students' past performance data and learning questionnaire responses into their devices. This data is also sent to the server, which uses it to generate learner profile information. This profile includes the learner's personality, learning style, and interests. The server analyzes these profile information and provides an educational strategy optimized for each individual learner.

[0635] Throughout this entire system, educators can efficiently prepare and conduct lessons tailored to the individual needs of learners, thereby improving the quality of education.

[0636] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0637] Step 1:

[0638] The user inputs educational data such as educational content, curriculum, and learning objectives into the terminal. The terminal then organizes this information and converts it into a predetermined format. The input data includes curriculum content in text format and learning objectives as numerical data, and the resulting data is formatted for transmission to the server as output.

[0639] Step 2:

[0640] The terminal sends formatted educational data to the server. The server receives this data and stores it in a database. During this process, the server performs a verification process to check the accuracy and completeness of the data. The input is the data sent from the terminal, and the output is the educational information successfully stored in the database.

[0641] Step 3:

[0642] The server automatically generates lesson plans using a generative AI model based on the received educational data. In this step, the server inputs prompt sentences into the generative AI model, which then creates the optimal lesson plan. Specifically, the AI ​​model refers to existing databases and historical data to generate the most suitable lesson plan. The input is educational data including prompt sentences, and the output is a structured lesson plan.

[0643] Step 4:

[0644] The server sends the generated training plan to the terminal. The terminal receives this information and prepares it for display in a user-readable format. On the terminal, each item of the plan is visually organized, making it easy for the user to edit. The input is the training plan from the server, and the output is data that can be displayed on the user interface.

[0645] Step 5:

[0646] The user inputs information such as students' past academic performance data and responses to learning questionnaires into the terminal. The terminal organizes this information and converts it into a format for transmission to the server. The input consists of various data about the learner, and the output is data in a unified format that can be transmitted.

[0647] Step 6:

[0648] The server analyzes the input learner data and generates learner characteristic profiles. In this process, the server uses machine learning algorithms to identify individual learning styles and interests. The input is formatted learner data, and the output is the learner characteristic profile.

[0649] Step 7:

[0650] The server integrates the generated profiles and educational plans to propose individually optimized educational strategies. The inputs here are the trait profiles and educational plans, and the output is a recommended list of optimal teaching materials and methods for each learner.

[0651] (Application Example 1)

[0652] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0653] In today's manufacturing environment, efficiently and effectively managing individual manufacturing tasks is a major challenge. In particular, there is a need to provide real-time work instructions that take into account the skills and experience of each worker, but current systems are unable to adequately address this. As a result, decreased productivity and increased manufacturing errors are becoming problems.

[0654] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0655] In this invention, the server includes terminal means for inputting work data, generation module means for receiving the work data and automatically generating work procedures, and analysis means for analyzing worker data and creating worker capability profiles. This makes it possible to propose optimized work strategies for each worker in real time.

[0656] "Work data" refers to information about various steps and requirements in the manufacturing process, and is data entered to generate efficient work instructions.

[0657] "Terminal means" refers to a device used to input work data and send it to a server, and generally includes computers and tablet devices.

[0658] A "server system" refers to a computer system that receives data via a network and performs the necessary processing, possessing the capability to perform large-scale data processing.

[0659] "Generation module means" refers to a software component that operates on the server means and automatically generates the optimal work procedure based on the input data.

[0660] "Work procedures" refer to the specific steps and procedures necessary to efficiently advance the manufacturing process, and are composed of generation module means.

[0661] "Worker data" refers to information about each worker's skills, experience, past work history, etc., and is collected through analytical methods.

[0662] A "capability profile" refers to information that characterizes the abilities and characteristics of workers, created based on worker data.

[0663] "Analysis tools" refer to software components or algorithms used to analyze worker data and create competency profiles.

[0664] "Suggestion module means" refers to a software component that proposes an optimized work strategy for a specific worker based on the generated capability profile.

[0665] As an embodiment of this invention, an automated work instruction system in a manufacturing environment is described. The system includes terminal means for inputting work data, which workers use to transmit data related to the manufacturing process to a server. The terminals used generally include computers and tablet devices.

[0666] The server uses a generative AI model based on the received data and automatically generates the optimal work procedure using a dedicated generative module. This generated work procedure is then transmitted to the worker via a terminal. For example, an advanced natural language processing model such as GPT-4 is used as the generative AI model.

[0667] Furthermore, the server is equipped with analytical tools to analyze worker data that shows the skills and experience of workers and create capability profiles. This profile creation makes it possible to propose work strategies tailored to each individual worker.

[0668] As a concrete example of implementation, in a manufacturing line, a server might instruct a worker via a terminal, saying, "Next, assemble the parts for Step 2. I will show you the optimal procedure tailored to your skill level." In this way, workers can receive personalized instructions in real time. These instructions include the tools to be used and points to pay attention to, improving work accuracy.

[0669] An example of a prompt message is: "Generate the optimal work procedure for product X on manufacturing line ID 123. Provide efficient steps based on worker Y's skill profile." By sending this command to the generation AI model, the optimal work procedure can be obtained.

[0670] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0671] Step 1:

[0672] The user enters work data on a terminal and sends it to the server.

[0673] The data entered includes information about each stage of the manufacturing process. Users input product IDs, required materials for each stage, and step details. The entered data is sent to the server and used as material for generating work procedures.

[0674] Step 2:

[0675] Based on the work data received by the server, the system automatically generates the optimal work procedure using a generation AI model.

[0676] The AI ​​model receives work data as input, and an algorithm within the model analyzes the data to calculate an effective work procedure. The generated work procedure is output and compiled into a procedure document.

[0677] Step 3:

[0678] The server sends the work instructions to the terminal, and the user receives them.

[0679] The generated work procedure is sent back to the user's terminal. On the terminal, the received procedure is displayed as a procedure manual or guideline, allowing the user to prepare to start the next task.

[0680] Step 4:

[0681] The user enters worker data into a terminal and sends it to the server.

[0682] Information on each worker's skills, experience, and past work history is collected and transmitted from the terminal. The entered data is aggregated on a server and becomes the basis for individualized analysis.

[0683] Step 5:

[0684] The server analyzes worker data and creates individual capability profiles.

[0685] Based on the received worker data, the analysis system processes the data and generates profiles related to skill sets and performance indicators. The generated competency profiles are output and serve as the basis for subsequent work strategy planning.

[0686] Step 6:

[0687] The server references the capability profile and uses a generated AI model to propose an optimized work strategy for the worker.

[0688] The AI ​​model calculates a work strategy by referring to the capability profile generated as input. The generated strategy is output as an optimized work procedure suggestion and presented to the user from the terminal.

[0689] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0690] This invention provides a system for educators to efficiently prepare lessons and provide optimal education to students, further incorporating a function to recognize user emotions. This system includes terminal means, server means, generative model means, analysis means, suggestion means, and emotion engine.

[0691] First, the user uses their device to input information about the educational curriculum and learning objectives, and sends this as educational data to the server. Based on the received educational data, the server automatically generates lesson plans using a generative model. These lesson plans include a lesson framework and specific teaching material suggestions, which the user can review and edit through their device.

[0692] Next, the user inputs student data, such as past academic performance and behavioral patterns, into the terminal and sends it to the server. The server analyzes this data and uses AI technology to create a personality profile of the student. Furthermore, the server integrates the generated lesson plan with the student's personality profile to propose the most suitable educational approach for the student. For example, if a student's personality profile determines that they prefer visual learning, the server will propose lesson content that makes extensive use of illustrations for that student.

[0693] The emotion engine recognizes the user's emotional state in real time and uses this information to adjust the content of lesson plans. For example, if the server detects that the user is under high stress, it will suggest a more relaxed lesson structure in the lesson plan, supporting the smoothest possible lesson delivery. This emotion engine allows users to choose an educational approach that suits their emotional state, making the educational environment more creative and effective.

[0694] In this way, the system takes into account the user's emotional state and provides education optimized for each individual student, thereby improving the quality of education and addressing individual needs.

[0695] The following describes the processing flow.

[0696] Step 1:

[0697] Users input educational data, such as curriculum information and learning objectives, into their devices. This data is then sent to the server.

[0698] Step 2:

[0699] The server runs a generative model based on the received educational data and automatically generates lesson plans. This generative model utilizes existing educational databases to design optimal teaching content.

[0700] Step 3:

[0701] The server sends the generated lesson plan to the terminal. The terminal displays the received lesson plan to the user, who can then review and modify it.

[0702] Step 4:

[0703] Users input student data, such as academic performance and behavioral patterns, into their devices. This data is then sent to a server for individual student analysis.

[0704] Step 5:

[0705] The server uses student data to apply analytical methods and build a personality profile for each student. This profile includes the student's learning tendencies and areas of interest.

[0706] Step 6:

[0707] The server combines the generated lesson plans with the students' personality profiles to create the most suitable teaching approach for each student. For example, for students who prefer visual learning, the server designs lesson content that includes many diagrams and visual materials.

[0708] Step 7:

[0709] The server uses an emotion engine that analyzes the user's emotions in real time to recognize the user's current emotional state.

[0710] Step 8:

[0711] The server adjusts the content of the lesson plan according to the user's emotional state, as recognized by the emotion engine. For example, if high stress is detected, it will suggest a relaxing teaching method.

[0712] Step 9:

[0713] The server sends the adjusted educational policy to the terminal. The terminal then allows the user to view the optimized lesson plan.

[0714] In this way, the system realizes a mechanism that provides education tailored to the individual circumstances of users and students through a series of steps.

[0715] (Example 2)

[0716] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0717] In today's educational environment, educators are required to efficiently prepare educational plans and provide education tailored to the individual characteristics and emotional states of each learner. However, achieving this within limited time and resources is difficult and a challenge faced by many educators. Furthermore, there is a lack of appropriate technical tools to accurately understand learners' personality profiles and adjust educational activities accordingly.

[0718] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0719] In this invention, the server includes a communication device means for users to input educational plan information, an information processing device means for receiving educational plan information, and an emotion analysis means for identifying emotional states and dynamically adjusting the content of the educational plan. This enables educators to efficiently provide optimal education that corresponds to the individual characteristics and emotional states of each learner by utilizing automatically generated educational plans.

[0720] "Communication device means" refers to an electronic device that provides an interface for users to input educational plan information and transmit that information to a server in digital format.

[0721] The "information processing device" is a computing platform that analyzes educational plan information received from a user and automatically generates educational plans using a generation algorithm.

[0722] A "generation algorithm means" is a set of procedures or processes designed to automatically generate an educational plan based on educational plan information.

[0723] "Analysis tools" are data analysis devices used to evaluate learners' past data and behavioral tendencies, and to extract and construct learners' characteristic profiles.

[0724] "Proposed means" refers to technical means that plan and present an educational policy optimized for each individual learner, based on the learner's characteristic profile constructed by the analytical means.

[0725] "Emotion analysis means" refers to technology that detects a user's emotional state in real time and supports the adjustment of educational plans accordingly.

[0726] This invention provides a system that enables educators to effectively and efficiently prepare educational activities and provide optimal education to individual learners. This system includes communication devices, information processing devices, generation algorithms, analysis devices, proposal devices, and sentiment analysis devices.

[0727] The user inputs educational plan information using a communication device. This information includes details such as the lesson theme, selection of teaching materials, and learning objectives, and is sent to the server. Based on the received educational plan information, the server automatically generates an educational plan using a generative AI model. In this process, a widely used natural language processing model (e.g., OpenAI GPT-3) is used as the specific generation algorithm. The generated educational plan includes specific details about the lesson procedure and the teaching materials to be used.

[0728] The user further inputs the learner's performance data and behavioral tendencies into a communication device and sends it to the server. The server uses specialized analytical tools to construct a personality profile of the learner in order to analyze this data. Based on this profile, the server proposes the most suitable educational approach for each individual learner. For example, for a learner who prefers visual learning, the server will suggest teaching materials that make extensive use of illustrations.

[0729] This system further uses emotion analysis to assess the user's emotional state (e.g., stress level) in real time. If the user is experiencing high stress levels, the system adds relaxation elements to the educational plan to help provide a smoother and more creative learning environment.

[0730] For example, if a user inputs "I want to create a science lesson plan for elementary school students," the server will use a generative AI model to create a lesson plan that includes experimental activities. Furthermore, if the system determines that the user is feeling stressed, it will suggest incorporating relaxation time into the lesson.

[0731] Examples of prompts include, "Use a generative AI model to generate a curriculum plan for a specific grade level," and "Adjust the curriculum plan based on the current emotional state."

[0732] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0733] Step 1:

[0734] The user enters educational plan information using a communication device. This educational plan information includes the lesson theme, objectives, and types of teaching materials to be used. This input data is transmitted digitally to the information processing device. The server analyzes the received data and converts it into an internal format that it can understand.

[0735] Step 2:

[0736] The server automatically generates lesson plans using a generation algorithm. Here, the input lesson plan information is fed into the generation AI model as prompts, and the generation AI model generates the lesson plan (including lesson procedures and specific teaching materials) based on these prompts. This process is achieved by selecting and constructing the most suitable plan proposed by the generation AI model according to a defined algorithm. This generated lesson plan is then sent back to the communication device.

[0737] Step 3:

[0738] Users input learners' past performance data and behavioral patterns using a communication device. This information is sent to a server, which uses specialized analytical tools to create a learner's characteristic profile based on the input data. This profile creation process involves statistical analysis of the data and understanding of trends, resulting in a profile that identifies the learner's educational needs.

[0739] Step 4:

[0740] The server integrates these learner profiles with the generated teaching plans and proposes optimized teaching strategies. Specifically, it generates suggestions based on different learning styles and characteristics, and for learners who prefer visual learning, it develops strategies that make extensive use of diagrams and visual aids. These optimized strategies are then presented to the educators.

[0741] Step 5:

[0742] The device detects the user's emotional state in real time and sends this data to the server. The server uses emotion analysis tools to interpret the received emotional data and adjust the educational plan. If a high level of stress is detected, it generates an adjusted plan incorporating relaxation elements and stress reduction techniques and presents it to the user. This adjusted plan is then provided as the final output.

[0743] (Application Example 2)

[0744] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0745] Traditional education systems have struggled to adequately consider the individual personalities and emotional states of students, hindering the provision of effective educational policies. Furthermore, educational plans that disregard the emotional state of educators often hindered effective lesson delivery. Additionally, employee training has been difficult to tailor to individual needs, often resulting in manual-based instruction. To address these challenges, there is a need for methods that enable education and training optimized for each individual.

[0746] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0747] In this invention, the server includes an information processing device for inputting educational information, an analysis device for analyzing personality information and creating personality traits, and an emotion management device for recognizing emotional states and adjusting content. This makes it possible to provide optimal educational and training content tailored to each individual's personality traits and real-time emotional state.

[0748] "Educational information" refers to data related to educational programs and learning objectives, and forms the basis for educational policies and the creation of teaching materials.

[0749] "Information processing device means" refers to electronic devices used to receive and transmit educational information and personality information, and functions as servers or terminals.

[0750] "Product generation means" refers to a system or algorithm that has the function of automatically generating lesson plans based on collected data.

[0751] "Personality information" refers to data used to analyze individual personality traits, including behavior, attitudes, and learning tendencies.

[0752] "Personality traits" refer to profiles of personality generated by analytical methods, and are data used to optimize educational policies.

[0753] "Analysis means" refers to technical means for analyzing personality information and creating personality traits, and includes data analysis algorithms.

[0754] "Emotional state" refers to an individual's real-time emotions, including psychological states such as stress, excitement, and boredom.

[0755] "Emotional management tools" refer to systems or technologies that recognize emotional states and adjust their content based on that information.

[0756] "Display means" refers to means for displaying information through a visual device, and includes visual devices.

[0757] The system for realizing this invention comprises an information processing device for inputting and receiving educational information, a product creation device, an analysis device, an emotion management device, and a display device. In this system, the information processing device plays the role of receiving educational information from an educator's terminal and transmitting it to a server. The server generates a lesson plan based on the received educational information using the product creation device. This lesson plan includes specific implementation methods and educational policies. The generated lesson plan is provided to the educator's terminal via the information processing device, where it can be reviewed and edited.

[0758] Furthermore, users input personality information from their devices and send it to the server. The server uses analytical tools to create personality traits and supplements the educational plan based on them. The emotion management system analyzes the emotional state of the user and the target group in real time and adjusts the educational plan as needed. This adjustment is made in the form of alleviating stress levels or adding engaging content. The final educational plan is visually displayed to the target group through a display device. Devices such as smart glasses can be used for display, improving the interactivity of the education.

[0759] As a concrete example, in employee training at physical stores, work procedures and customer service methods are displayed through smart glasses, and the training content is dynamically adjusted according to the employee's emotional state. An example of a prompt to the generating AI model in this case is: "Suggest employee training content based on the user's current emotional state. If the emotional state is 'stressed,' suggest relaxation techniques; if it is 'bored,' present an interactive challenge." This allows for more effective and considerate training.

[0760] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0761] Step 1:

[0762] Users input educational and personality information using a terminal and send it to the server. This process includes educational programs and learning objectives as educational information, and past student behavior data and survey results as personality information. The entered data is stored in a database and used for subsequent processing.

[0763] Step 2:

[0764] The server analyzes the received educational information using a product creation mechanism and automatically generates lesson plans using a generation AI model. At this stage, the educational information is analyzed by the AI ​​model, and lesson plans that include optimal teaching materials and teaching policies are output. This ensures that the content is designed taking into account the expertise of educators and the learning styles of students.

[0765] Step 3:

[0766] The analysis tool processes personality information to create personality traits. The server executes a data analysis algorithm based on the input personality information to derive the student's personality traits. This results in the output of an individualized educational plan based on the student's personality.

[0767] Step 4:

[0768] The server uses emotion management tools to recognize the user's emotional state in real time. It analyzes sensor information from input devices and the user's facial expressions to determine emotional states such as stress and anxiety. Based on the emotional state, it adjusts the specific progression and content of the educational plan. This adjustment is supported by a generative AI model.

[0769] Step 5:

[0770] The finalized lesson plan is presented visually to the user or target audience through display means. Using smart glasses or mobile devices, the lesson plan and emotionally responsive content are displayed, allowing users to conduct education and training based on that information. This displayed content is dynamically updated based on the adjustments made in step 4.

[0771] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0772] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0773] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0774] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0775] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0776] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0777] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0778] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0779] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0780] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0781] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0782] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0783] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0784] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0785] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0786] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0787] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0788] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0789] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0790] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0791] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A terminal device for inputting educational data,

[0795] A server means for receiving the educational data,

[0796] A generation model means that operates on the server means and automatically generates lesson plans,

[0797] A means for transmitting the lesson plan generated by the generation model means to a terminal means,

[0798] An analytical method for analyzing student data and creating student personality profiles,

[0799] A proposal means that suggests an educational policy optimized for the student based on the personality profile,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, wherein the lesson plan is generated based on educational curriculum information and learning objectives.

[0803] (Claim 3)

[0804] The system according to claim 1, wherein the proposed means designs individually optimized educational activities for each student by integrating the student's personality profile and past educational data.

[0805] "Example 1"

[0806] (Claim 1)

[0807] Information input means for inputting educational information,

[0808] A data processing means for receiving the educational information,

[0809] A generation mechanism means that operates on the data processing means and automatically generates an educational plan,

[0810] A communication means for transmitting the educational plan generated by the generation mechanism means to an information input means,

[0811] An analytical method for analyzing learner data and creating learner characteristic profiles,

[0812] Based on the characteristic profile, a proposal mechanism means proposes an educational strategy optimized for the learner,

[0813] A system that includes this.

[0814] (Claim 2)

[0815] The system according to claim 1, wherein the educational plan is generated based on curriculum information and learning objectives.

[0816] (Claim 3)

[0817] The system according to claim 1, wherein the proposed mechanism integrates the learner's characteristic profile with past educational information to design educational activities optimized for each individual learner.

[0818] "Application Example 1"

[0819] (Claim 1)

[0820] A terminal device for inputting work data,

[0821] A server means for receiving the work data,

[0822] A generation module means that operates on the server means and automatically generates work procedures,

[0823] Means for transmitting the work procedure generated by the generation module means to the terminal means,

[0824] An analytical method for analyzing worker data and creating worker competency profiles,

[0825] A proposal module means that proposes an optimized work strategy for the worker based on the capability profile,

[0826] A system that includes this.

[0827] (Claim 2)

[0828] The system according to claim 1, wherein the work procedure is generated based on manufacturing line information and production targets.

[0829] (Claim 3)

[0830] The system according to claim 1, wherein the proposed module means designs work activities optimized for each individual worker by integrating the worker's ability profile and past work data.

[0831] "Example 2 of combining an emotion engine"

[0832] (Claim 1)

[0833] A communication device means for the user to input educational plan information,

[0834] Information processing device means for receiving the education plan information,

[0835] A generation algorithm means that operates on the information processing device means and automatically generates an educational plan,

[0836] A method means for transmitting the educational plan generated by the generation algorithm means to a communication device means,

[0837] An analytical method for analyzing learner data and creating learner characteristic profiles,

[0838] A proposal means for suggesting an educational strategy adapted to the learner based on the characteristic profile,

[0839] An emotion analysis tool that identifies emotional states and dynamically adjusts the content of the educational plan,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, wherein the educational plan is generated based on educational plan information and learning objectives.

[0843] (Claim 3)

[0844] The system according to claim 1, wherein the proposed means designs educational activities tailored to each individual learner by integrating the learner's characteristic profile and past educational plan information.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] Information processing device means for inputting educational information,

[0848] Information processing device means for receiving the educational information,

[0849] A product creation means that operates on the information processing device and automatically generates educational plans,

[0850] A means for transmitting the educational plan generated by the product creation means to an information processing device means,

[0851] An analytical means for analyzing personality information and creating personality traits,

[0852] A proposal means that proposes an optimized guidance policy for the subject based on the personality traits,

[0853] An emotion management means that recognizes an emotional state and adjusts the content based on that emotional state,

[0854] A display means that displays educational plans and emotion-based adjustments through a visual device,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, wherein the educational plan is generated based on educational program information and learning objectives, and the emotion management means dynamically adjusts the educational plan in response to changes in emotional state.

[0858] (Claim 3)

[0859] The proposed means integrates personality traits with past educational and emotional information to design individually optimized instructional activities, which are then displayed through a visual device, according to claim 1. [Explanation of symbols]

[0860] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal device for inputting educational data, A server means for receiving the educational data, A generation model means that operates on the server means and automatically generates lesson plans, A means for transmitting the lesson plan generated by the generation model means to a terminal means, An analytical method for analyzing student data and creating student personality profiles, A proposal means that suggests an educational policy optimized for the student based on the personality profile, A system that includes this.

2. The system according to claim 1, wherein the lesson plan is generated based on educational curriculum information and learning objectives.

3. The system according to claim 1, wherein the proposed means designs individually optimized educational activities for each student by integrating the student's personality profile and past educational data.

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