system

Abstract

We provide the system. [Solution] A means of collecting business-related data entered by users, A generation agent means that analyzes the aforementioned business-related data and generates an education plan optimized for the user, A means of monitoring the progress of education based on the aforementioned education plan, A means of providing feedback to users based on the aforementioned progress, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is 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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventionally, it has been difficult to set up an educational plan suitable for individual skills and business proficiency in the training of staff at the sales site, and uniform training has often been carried out. Therefore, it has been a cause of hindering efficient skill improvement and personnel training. In addition, progress management and feedback provision have not been systematized, and the transparency and effectiveness of the education process have been insufficient. In order to solve these problems, a mechanism for providing personalized training for individual staff and effectively managing progress is required.

Means for Solving the Problems

[0005] This invention provides a means for collecting work-related data entered by users, and a generation agent means for analyzing the collected data to generate personalized training plans. This generation agent means proposes and provides the user with an optimal plan considering multiple training formats. Furthermore, this system has means for monitoring progress based on the training plan and providing real-time feedback. As a result, users can receive appropriate training tailored to their learning pace, and the training plan can be adjusted as needed, enabling efficient skill development.

[0006] "User" refers to an individual or person who uses this system to receive educational plans or training for the purpose of improving their own skills.

[0007] "Work-related data" refers to information necessary to customize the training plan, including the user's skill level, work experience, and training goals.

[0008] "Generative agent means" refers to an automated process centered on AI that generates an optimized training plan for the user based on collected business-related data.

[0009] An "educational plan" refers to a collection of training and learning modules that are individually designed according to the user's skills and needs.

[0010] "Progress status" refers to information that represents the level of achievement and proficiency a user has reached in the process of learning according to the educational plan.

[0011] "Means of providing feedback" refers to the process or mechanism of communicating evaluations and advice related to progress and learning outcomes to users. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2]This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the 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.

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention provides a system that offers individually personalized training plans to staff working in sales environments, enabling efficient skill development based on these plans. The following describes embodiments of this system.

[0034] First, the terminal is equipped with an interface for users to input their work-related data. This includes forms where users can enter their past work experience, current skill level, and future skill improvement goals. The terminal converts the entered data into a digital format and sends it to the server.

[0035] The server activates a generation agent based on the received data. The generation agent uses the accumulated database and learning algorithms to generate an optimal educational plan for the user. This educational plan combines various educational methods based on the user's needs, such as online courses, workshops, and e-learning modules.

[0036] The generated learning plan is sent from the server to the user's device and used as a guide as the user progresses through their learning. The user learns according to the learning plan through the device, and their progress is constantly monitored. The device sends learning progress data to the server, which analyzes it and provides real-time feedback. This feedback is designed to highlight key points the user needs to understand and skill areas where further improvement is required.

[0037] Furthermore, the server takes progress into consideration and adjusts the content of the training plan as needed. For example, it adds more advanced content to skills acquired ahead of schedule, providing a plan that matches the user's growth.

[0038] For example, when a new employee uses the system for the first time, a training plan on basic customer service skills is initially generated. As the user learns according to this plan, the server monitors their progress and provides regular feedback. Once the user has mastered a particular skill, the server recommends more advanced training modules as the next step, supporting efficient skill improvement.

[0039] Thus, the present invention enables flexible human resource development tailored to the individual training needs of each staff member, thereby contributing to the overall growth of the company.

[0040] The following describes the processing flow.

[0041] Step 1:

[0042] Users log in to the system via their terminal and enter their skill level, work experience, and training goals into a business-related data entry form. This information forms the basis for understanding the individual needs of each user.

[0043] Step 2:

[0044] The terminal converts the entered work-related data into a digital format and transmits it to the server via the communication network. This data transmission is encrypted and carried out in a secure manner.

[0045] Step 3:

[0046] The server stores the received business-related data and activates a generation agent based on that data. The AI ​​within the server analyzes the data and generates an optimized training plan for the user.

[0047] Step 4:

[0048] The generated lesson plan is sent from the server to the terminal. The terminal displays the lesson plan to the user and prompts them to begin learning.

[0049] Step 5:

[0050] Users use their devices to progress through their learning according to their educational plan. Learning progress is continuously recorded by the device and transmitted to the server at regular intervals.

[0051] Step 6:

[0052] The server analyzes learning progress data and generates feedback tailored to the user's progress. This feedback is immediately returned to the user based on the acquired data.

[0053] Step 7:

[0054] Based on the feedback, the server adjusts the training plan as needed. Specifically, it may add new learning modules or modify existing training content.

[0055] Step 8:

[0056] The server sends the updated educational plan to the user's device, allowing them to continue their learning. This process effectively supports the improvement of the user's skill development.

[0057] (Example 1)

[0058] 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."

[0059] In recent years, the need to efficiently provide personalized training plans for each employee has increased in response to the diversifying needs of companies. However, traditional training systems can only offer standardized plans, making it difficult to provide flexible training tailored to individual skills and learning paces. Furthermore, the inability to provide appropriate feedback in real time based on learning progress has been a factor that has slowed down the skill improvement of users.

[0060] 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.

[0061] In this invention, the server includes a device for collecting work-related information entered by the user, a generative model for analyzing the work-related information and generating an optimized training plan for the user, and a device for dynamically adjusting the training plan according to the user's progress. This enables the provision of individualized training plans tailored to each user and allows for rapid skill improvement through real-time feedback.

[0062] A "user" is an entity that uses the system to input work-related information and receives training plans and feedback.

[0063] "Work-related information" refers to data about the user's past work history, current expertise, and future skill goals.

[0064] A "collection device" is a component within a system that has the function of capturing business-related information entered by users.

[0065] "Analysis" refers to data processing that uses collected business-related information to design educational plans tailored to the users.

[0066] A "generative model" refers to the algorithms and data structures used to create user-optimized educational plans.

[0067] An "educational plan" is a curriculum or training program developed based on the user's current skill level and growth goals.

[0068] "Progress data" refers to information that shows the user's learning progress in their educational plan.

[0069] "Feedback" refers to information provided to users based on their learning progress data, including advice for improvement and information for setting new learning goals.

[0070] A "dynamically adjusting device" is a component within a system that has the function of updating and optimizing the educational plan according to the user's learning progress.

[0071] A "system" refers to a group of devices that have the functions of data collection, analysis, plan generation, progress management, and feedback provision.

[0072] A description of the embodiment for carrying out the invention will be provided.

[0073] This system is composed of three components: a server, a terminal, and a user.

[0074] Users first use a terminal to input information related to their work. This includes past work experience, current skill level, and future skill development goals. The terminal is equipped with a user-friendly graphical user interface (GUI) to efficiently collect this information.

[0075] The terminal converts the input information into digital data and transmits it to the server using a secure communication method. Upon receiving this data, the server stores it in a database and prepares it for analysis. To analyze the information, the server runs a generative AI model that uses machine learning algorithms. This model has the capability to automatically generate an educational plan optimized for the user.

[0076] The generated learning plan is sent from the server to the terminal, where the user can use the terminal to review the plan and proceed with their learning. The terminal continuously records the user's learning progress and sends it back to the server as feedback. Based on this feedback, the server analyzes the learning progress in real time, dynamically adjusts the learning plan as needed, and suggests the next steps to the user.

[0077] A concrete example of this operation is when a new employee receives a training plan to acquire basic customer service skills. The user progresses through the proposed online course, and their device automatically sends progress data to the server. The server analyzes this data and, if the user is progressing ahead of schedule, can provide additional, more advanced training modules.

[0078] An example of a prompt message might be, "Please create a training plan for new employees to learn basic customer service skills."

[0079] This allows for the flexible provision of educational plans tailored to individual user needs, enabling the system to function as one that facilitates efficient skill development.

[0080] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0081] Step 1:

[0082] Users input work-related information using a terminal. Specifically, they fill in their work experience, current skill level, and skill improvement goals in an on-screen form. The entered data is converted into digital data and encoded on the terminal. This encoded data is then output to the server.

[0083] Step 2:

[0084] The terminal sends encoded digital data to the server. A secure communication protocol is used to deliver the data to the server. The server receives this data and prepares to store it in its database. This received data becomes the input for analysis.

[0085] Step 3:

[0086] The server analyzes the received business-related information. Specifically, it uses a generative AI model to analyze the data and generate an optimized training plan for the user. This analysis employs machine learning algorithms to select the most suitable training content. This generated training plan is then output from the server to the terminal.

[0087] Step 4:

[0088] The server sends the generated lesson plan to the terminal. The terminal receives this lesson plan and displays it on the screen. The user reviews the displayed lesson plan and begins learning. This received lesson plan becomes the input for the user's learning activities.

[0089] Step 5:

[0090] Users progress through their learning plan via their devices. Specifically, they take online courses and training modules and manage their learning progress. This progress data is automatically recorded on the device. This recorded progress data is then input to the next server.

[0091] Step 6:

[0092] The device sends user learning progress data to the server. The server receives and analyzes this data in real time to evaluate the user's progress. After analysis, it generates feedback as needed and adjusts the plan. This adjusted educational plan becomes the output for new learning activities.

[0093] Through this step, the system provides users with personalized feedback and plans tailored to their individual progress, maximizing learning effectiveness.

[0094] (Application Example 1)

[0095] 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."

[0096] The present invention aims to provide a system that can efficiently and effectively improve the skills of individual staff members in sales settings. In particular, it is required to support on-the-job learning and provide real-time feedback to maximize the potential of each staff member.

[0097] 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.

[0098] In this invention, the server includes a device for collecting work-related information entered by the user, a generation agent device for analyzing the work-related information and generating an optimized training plan for the user, and a device for presenting the training plan and transmitting progress data via a smartphone application. This makes it possible to individually and efficiently improve the skills of each staff member.

[0099] A "user" is an individual who uses the system to input work-related information and aims to improve their skills.

[0100] "Work-related information" refers to information including the user's work experience, current skills, and goals for skill improvement.

[0101] A "generation agent device" is a processing device that generates an optimized training plan for the user based on the input business-related information.

[0102] A "smartphone application" is software for mobile devices that allows users to access educational plans and send progress data.

[0103] An "educational plan" is a learning program that combines online courses, workshops, and e-learning modules designed to improve users' skills.

[0104] "Progress data" refers to information that shows the learning process and results that users have achieved based on their educational plan.

[0105] "Real-time adjustment" means instantly updating and adapting the content of the educational plan according to the user's progress.

[0106] The system for implementing this invention is designed to support staff skill development in corporate sales environments. Specifically, it involves providing personalized training plans for individual staff members and managing their progress in real time, primarily using a server and mobile information terminals.

[0107] First, the user enters their work-related information through a smartphone application. The hardware used here is a personal digital assistant (PDCA) device, and the software installed is a user interface based on React Native. This entered information is then transmitted to the server via a communication protocol.

[0108] On the server side, a generation agent built with Python runs to analyze the user's work-related information. During this process, an algorithm is executed that uses an AI model to generate an optimized training plan. Specifically, analysis and plan generation are performed using platforms such as Google Cloud Machine Learning.

[0109] The generated educational plan is then transmitted back to the user's mobile device via communication. The user progresses through their studies based on this plan, continuously sending progress data to the server using a smartphone app. The server analyzes this data in real time and provides feedback to the user. This allows for immediate adjustments to the plan as the user progresses.

[0110] As a concrete example, a new employee can use a plan to learn basic customer service skills, tracking their progress little by little each day via their smartphone. Once they become proficient at handling clients, the server recommends the next stage, such as training materials perfectly suited to strengthening their sales pitch.

[0111] Examples of prompts for a generative AI model:

[0112] "User Profile: Work Experience: 1 year, Current Skills: Basic Customer Service, Target Skills: Advanced Sales Strategy. Please generate an optimal training plan."

[0113] Thus, the system of the present invention enables autonomous and efficient skill development in the workplace.

[0114] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0115] Step 1:

[0116] Users input work-related information using a smartphone application. During this process, users enter their work experience, current skills, and improvement goals into a form, which is then processed by the application as JSON data. The entered information is transmitted from the user's mobile device to the server via a communication protocol.

[0117] Step 2:

[0118] The server analyzes the received business-related information in JSON format. Using a generative agent built in Python, the server generates an optimal training plan based on the received data. In this process, a generative AI model is used to determine the appropriate combination of online courses and workshops based on the user's skill level and goals. The output is a training plan tailored to the user.

[0119] Step 3:

[0120] The server converts the generated educational plan back into JSON format and sends it to the user's mobile device. On the device, the received educational plan is displayed within the application, functioning as a user interface to encourage learning. The output here is the details of the educational plan presented to the user.

[0121] Step 4:

[0122] The user progresses through their daily learning based on the provided educational plan. The device continuously acquires the user's learning progress data and sends it to the server in JSON format. The acquired progress data includes information such as which tasks the user has completed and what content they have spent time on.

[0123] Step 5:

[0124] The server performs real-time analysis based on the received progress data. Based on the acquired data, it evaluates each user's progress rate and skill development, and adjusts the training plan as needed. As a result, the server generates feedback information and sends it to the user's device in JSON format. This shows users the skill areas they should focus on strengthening next.

[0125] Step 6:

[0126] Based on the user's configured learning plan, the next learning steps are suggested. The server selects the appropriate next learning module for that user and incorporates it into the plan. The output of this process is notified to the user's mobile device as an updated learning plan.

[0127] 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.

[0128] This invention provides a system for efficiently developing the individual skills of sales staff, and also enables the recognition of users' emotional states, allowing for the adjustment of feedback and training plans based on those states. The system incorporates an emotion engine to detect users' emotional states in real time, aiming to improve the quality of training.

[0129] First, the user accesses the system using a terminal and inputs their work-related data. This data includes skill levels, work experience, and training goals, and is sent to the server via the terminal. The server analyzes the received data, and a generation agent generates an optimized training plan for the user.

[0130] The generated educational plan is sent from the server to the terminal, and the user can proceed with learning according to the plan on the terminal. At this time, the terminal is equipped with a camera and sensors that analyze the user's facial expressions and voice data in real time. This allows the emotion engine to infer the user's emotional state.

[0131] Along with progress updates, emotional data is periodically sent to the server, which uses this data to evaluate the user's learning experience. If the emotional data indicates stress or dissatisfaction with learning, the server adjusts the learning plan and improves the content to help the user learn more effectively. For example, it may adjust the difficulty level of the learning or suggest changing the learning style.

[0132] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills, and the emotion engine detects an emotion indicating a decrease in concentration. In this case, the server updates the training plan and recommends training modules, such as interactive exercises and visual materials, to help the user regain their focus.

[0133] Thus, the system of the present invention maximizes learning effectiveness by understanding the user's emotional state through an emotion engine and providing flexible plans tailored to individual educational needs. This enables companies to efficiently support staff skill development and contribute to overall productivity improvement.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] The user logs into the terminal and enters work-related data. The terminal converts this data into a digital format and sends it to the server. The data includes skill level, work experience, and training goals.

[0137] Step 2:

[0138] The server analyzes the received business-related data and generates an optimized training plan using a generation agent. This generation process selects the most suitable training methods based on the user's skill gaps and needs.

[0139] Step 3:

[0140] The server sends the generated lesson plan to the terminal, which the user receives. The user then uses the terminal to begin learning according to the displayed lesson plan.

[0141] Step 4:

[0142] Cameras and sensors built into the device collect the user's facial expressions and voice data, which are then analyzed in real time by an emotion engine. The user's emotional state is understood and recorded as emotion data.

[0143] Step 5:

[0144] Emotional data and learning progress are periodically sent to the server. The server uses this data to evaluate the user's learning experience and provide learning support if necessary.

[0145] Step 6:

[0146] If the server determines from emotional data that a user is experiencing stress or dissatisfaction, it will adjust the educational plan. For example, it might consider lowering the difficulty level or changing the learning method.

[0147] Step 7:

[0148] The adjusted learning plan is sent back to the device, and the user continues learning based on the new plan. This allows the user to experience more effective learning.

[0149] Step 8:

[0150] The server compiles the final learning outcomes and provides detailed feedback to the user. This feedback includes achievement levels and areas for improvement, guiding the user through the next learning step.

[0151] (Example 2)

[0152] 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".

[0153] Traditional training systems often provide uniform educational plans without considering the individual circumstances and emotions of users, resulting in a failure to maximize learning effectiveness. Furthermore, the lack of systems capable of quickly responding to changes in users' emotions and motivations makes it difficult to provide an optimal learning environment.

[0154] 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.

[0155] In this invention, the server includes a device for receiving business-related data entered by the user, a generation device for analyzing the business-related data and generating a training plan tailored to the user, and a device for detecting the user's emotional state in real time. This enables the provision of flexible training plans that meet the individual learning needs of the user and the optimization of the learning environment in real time according to the user's emotional state.

[0156] "User" refers to anyone who uses this system for training or learning.

[0157] "Work-related data" refers to data that includes information necessary for creating a training plan, such as the user's skill level, work experience, and training goals.

[0158] "Receiving device" refers to the equipment or software used by the server to acquire business-related data entered by the user.

[0159] A "generation device" refers to a program or function that runs on a server and creates a user-specific training plan based on the acquired data.

[0160] A "training plan" refers to a set of instructional guidelines that combine learning steps and materials designed to improve the user's skills.

[0161] "Progress" refers to the process by which a user learns according to their training plan, and the results of that learning.

[0162] "Emotional state" refers to the psychological and emotional situations and moods that users express as they progress through training or learning.

[0163] "Feedback" refers to advice and information provided to users for improvement or modification, taking into account their training progress and emotional state.

[0164] The system for implementing this invention consists primarily of a user, a terminal, and a server. The user uses the terminal to receive training to improve their skills. The terminal is equipped with a camera and sensors that can collect the user's facial expressions and voice data. This makes it possible to infer the user's emotional state in real time.

[0165] The server receives business-related data sent by users and then analyzes this data. Statistical software and database management systems are used for data analysis. Based on the information obtained from the analysis, a generative AI model generates a user-specific training plan. This generative AI model learns from past examples of effective training plans, enabling it to select the optimal training plan.

[0166] The generated training plan is sent from the server to the terminal, and the user proceeds with learning based on that plan. Progress and sentiment data acquired in real time are also periodically sent to the server. Based on this data, the server evaluates the user's learning progress and adjusts the plan as needed. Feedback is also provided during the adjustment process, allowing the user to learn more effectively.

[0167] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills and a decrease in concentration is detected. In this case, the server can modify the training plan, incorporating game-based exercises and visual aids to re-engage the user.

[0168] To make more effective use of generative AI models, it is important to set appropriate prompts. For example, a possible prompt might be, "Please tell me how to maximize learning effectiveness by analyzing the level of concentration of users during customer service skills training and modifying the plan if necessary." This prompt allows the generative AI model to make suggestions that address specific learning needs.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] Users input work-related data through a terminal. This data includes skill level, work experience, and training goals. The terminal receives this data, formats it, and sends it to the server. The input data is used as information for developing training plans.

[0172] Step 2:

[0173] The server analyzes business-related data received from terminals. The analysis process uses a database management system to compare the data with historical data and extract user-specific characteristics. This generates user-optimized attribute information, which is then provided to the AI ​​model.

[0174] Step 3:

[0175] The server-generated AI model creates a training plan based on user-specific attribute information. The AI ​​model utilizes machine learning algorithms to learn from past successful training plans, selecting efficient and effective training content. As output, an optimal training plan for the user is generated and sent back to the terminal.

[0176] Step 4:

[0177] The device displays the received training plan on its user interface. The user begins learning according to this plan, sequentially completing the learning materials and assignments designed according to the plan. The learning materials used here include video materials and interactive exercises.

[0178] Step 5:

[0179] The device uses its built-in camera and sensors to collect real-time data on facial expressions and voice during the user's learning process, and to infer their emotional state. This emotional data is analyzed to understand the user's emotional state and is periodically sent to a server.

[0180] Step 6:

[0181] The server evaluates the user's learning state based on received emotional and learning progress data. If the emotional data indicates stress or decreased concentration, the server generates feedback and modifies the training plan as needed. The modified plan is immediately sent to the terminal to improve the user's learning experience.

[0182] (Application Example 2)

[0183] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0184] Providing individualized educational plans that take into account the emotional state of users in real time is a challenging task in educational settings. In particular, it is necessary to provide prompt and appropriate feedback and adjust educational content in response to changes in users' concentration and interests.

[0185] 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.

[0186] In this invention, the server includes means for collecting business-related information entered by the user, a generation agent means for analyzing the business-related information and generating an educational policy optimized for the user, and an emotion analysis means for detecting the user's emotional state in real time. This makes it possible to flexibly adjust the educational policy according to the user's emotional state.

[0187] "Job-related information" refers to data entered by users in relation to their work, including skill levels, work experience, and training goals.

[0188] The "generation agent means" is a means of executing a process to generate optimized educational policies based on the user's work-related information.

[0189] An "educational policy" is a plan that specifically outlines the content and format of education for users.

[0190] "Progress status" refers to data that shows the progress of the user's educational activities.

[0191] "Means of providing responses" refers to functions for conveying feedback or instructions to users.

[0192] "Emotion analysis methods" are technologies that estimate a user's emotional state in real time based on their facial expressions, voice, and other factors.

[0193] "Interaction means" are methods for realizing interaction between the user and the system.

[0194] The system for realizing this invention includes software that runs on hardware platforms such as Raspberry Pi and Jetson Nano. When a user inputs work-related information into a terminal, that information is sent to a server for analysis. The server uses a generation agent to analyze the information and generate an optimized educational policy for each user.

[0195] The device incorporates a camera and voice sensors to detect the user's facial expressions and voice, acquiring data in real time. This data is then analyzed using emotion analysis techniques to infer the user's emotional state. Image processing techniques using the OpenCV library and voice analysis techniques using TENSORFLOW® are employed in this analysis.

[0196] The server dynamically adjusts the learning approach based on the user's emotional state and learning progress. If the server detects a decrease in the user's concentration or lack of interest during learning, it suggests new learning styles and activities through interactive means. This helps users continue learning more effectively.

[0197] As a concrete example, when a user is learning a language, if the system detects a decline in interest during conversation practice, it provides educational content incorporating interactive game elements. This content includes a visual interface and audio navigation to attract the user's attention.

[0198] An example of a prompt for a generative AI model would be, "Based on the user's current emotional state, suggest a new learning method to rekindle their interest." This allows the system to quickly provide a learning strategy that is appropriate for the user.

[0199] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0200] Step 1:

[0201] The terminal inputs work-related information. Users input their skill level, work experience, and training goals into the terminal and send this information to the system. This input data is transferred to the server as work-related information.

[0202] Step 2:

[0203] The server analyzes the business-related information. The server analyzes the received business-related information and generates the optimal training policy using a generation agent. Information analysis includes database matching and comparison with historical data. As output, a training policy tailored to the user is generated.

[0204] Step 3:

[0205] The device acquires emotional data. The camera and voice sensor built into the device capture the user's facial expressions and voice in real time. This input data is processed by an emotion analysis system and sent to a server as information to infer the emotional state.

[0206] Step 4:

[0207] The server analyzes the emotional data. The server uses emotional analysis tools to infer the user's emotional state. Data analysis employs image processing techniques using OpenCV and speech recognition technology using TensorFlow. As a result, the user's emotional state is output as numerical or categorical data.

[0208] Step 5:

[0209] The server adjusts the educational policy. The server dynamically adjusts the educational policy based on the student's learning progress and emotional data. It utilizes a generative AI model to generate improvement strategies to enhance user learning satisfaction and concentration. The output of this step is the adjusted educational policy.

[0210] Step 6:

[0211] The device provides responses to the user. Based on a tailored educational policy, the device provides visual or audible feedback to the user. Specific actions include suggesting new learning activities or styles, and presenting interactive content. This output brings about changes in the user's learning environment.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] [Second Embodiment]

[0216] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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).

[0222] 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.

[0223] 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.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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".

[0228] This invention provides a system that offers individually personalized training plans to staff working in sales environments, enabling efficient skill development based on these plans. The following describes embodiments of this system.

[0229] First, the terminal is equipped with an interface for users to input their work-related data. This includes forms where users can enter their past work experience, current skill level, and future skill improvement goals. The terminal converts the entered data into a digital format and sends it to the server.

[0230] The server activates a generation agent based on the received data. The generation agent uses the accumulated database and learning algorithms to generate an optimal educational plan for the user. This educational plan combines various educational methods based on the user's needs, such as online courses, workshops, and e-learning modules.

[0231] The generated learning plan is sent from the server to the user's device and used as a guide as the user progresses through their learning. The user learns according to the learning plan through the device, and their progress is constantly monitored. The device sends learning progress data to the server, which analyzes it and provides real-time feedback. This feedback is designed to highlight key points the user needs to understand and skill areas where further improvement is required.

[0232] Furthermore, the server takes progress into consideration and adjusts the content of the training plan as needed. For example, it adds more advanced content to skills acquired ahead of schedule, providing a plan that matches the user's growth.

[0233] For example, when a new employee uses the system for the first time, a training plan on basic customer service skills is initially generated. As the user learns according to this plan, the server monitors their progress and provides regular feedback. Once the user has mastered a particular skill, the server recommends more advanced training modules as the next step, supporting efficient skill improvement.

[0234] Thus, the present invention enables flexible human resource development tailored to the individual training needs of each staff member, thereby contributing to the overall growth of the company.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users log in to the system via their terminal and enter their skill level, work experience, and training goals into a business-related data entry form. This information forms the basis for understanding the individual needs of each user.

[0238] Step 2:

[0239] The terminal converts the entered work-related data into a digital format and transmits it to the server via the communication network. This data transmission is encrypted and carried out in a secure manner.

[0240] Step 3:

[0241] The server stores the received business-related data and activates a generation agent based on that data. The AI ​​within the server analyzes the data and generates an optimized training plan for the user.

[0242] Step 4:

[0243] The generated lesson plan is sent from the server to the terminal. The terminal displays the lesson plan to the user and prompts them to begin learning.

[0244] Step 5:

[0245] Users use their devices to progress through their learning according to their educational plan. Learning progress is continuously recorded by the device and transmitted to the server at regular intervals.

[0246] Step 6:

[0247] The server analyzes learning progress data and generates feedback tailored to the user's progress. This feedback is immediately returned to the user based on the acquired data.

[0248] Step 7:

[0249] Based on the feedback, the server adjusts the training plan as needed. Specifically, it may add new learning modules or modify existing training content.

[0250] Step 8:

[0251] The server sends the updated educational plan to the user's device, allowing them to continue their learning. This process effectively supports the improvement of the user's skill development.

[0252] (Example 1)

[0253] 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."

[0254] In recent years, the need to efficiently provide personalized training plans for each employee has increased in response to the diversifying needs of companies. However, traditional training systems can only offer standardized plans, making it difficult to provide flexible training tailored to individual skills and learning paces. Furthermore, the inability to provide appropriate feedback in real time based on learning progress has been a factor that has slowed down the skill improvement of users.

[0255] 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.

[0256] In this invention, the server includes a device for collecting work-related information entered by the user, a generative model for analyzing the work-related information and generating an optimized training plan for the user, and a device for dynamically adjusting the training plan according to the user's progress. This enables the provision of individualized training plans tailored to each user and allows for rapid skill improvement through real-time feedback.

[0257] A "user" is an entity that uses the system to input work-related information and receives training plans and feedback.

[0258] "Work-related information" refers to data about the user's past work history, current expertise, and future skill goals.

[0259] A "collection device" is a component within a system that has the function of capturing business-related information entered by users.

[0260] "Analysis" refers to data processing that uses collected business-related information to design educational plans tailored to the users.

[0261] A "generative model" refers to the algorithms and data structures used to create user-optimized educational plans.

[0262] An "educational plan" is a curriculum or training program developed based on the user's current skill level and growth goals.

[0263] "Progress data" refers to information that shows the user's learning progress in their educational plan.

[0264] "Feedback" refers to information provided to users based on their learning progress data, including advice for improvement and information for setting new learning goals.

[0265] A "dynamically adjusting device" is a component within a system that has the function of updating and optimizing the educational plan according to the user's learning progress.

[0266] A "system" refers to a group of devices that have the functions of data collection, analysis, plan generation, progress management, and feedback provision.

[0267] A description of the embodiment for carrying out the invention will be provided.

[0268] This system is composed of three components: a server, a terminal, and a user.

[0269] Users first use a terminal to input information related to their work. This includes past work experience, current skill level, and future skill development goals. The terminal is equipped with a user-friendly graphical user interface (GUI) to efficiently collect this information.

[0270] The terminal converts the input information into digital data and transmits it to the server using a secure communication method. Upon receiving this data, the server stores it in a database and prepares it for analysis. To analyze the information, the server runs a generative AI model that uses machine learning algorithms. This model has the capability to automatically generate an educational plan optimized for the user.

[0271] The generated learning plan is sent from the server to the terminal, where the user can use the terminal to review the plan and proceed with their learning. The terminal continuously records the user's learning progress and sends it back to the server as feedback. Based on this feedback, the server analyzes the learning progress in real time, dynamically adjusts the learning plan as needed, and suggests the next steps to the user.

[0272] A concrete example of this operation is when a new employee receives a training plan to acquire basic customer service skills. The user progresses through the proposed online course, and their device automatically sends progress data to the server. The server analyzes this data and, if the user is progressing ahead of schedule, can provide additional, more advanced training modules.

[0273] An example of a prompt message might be, "Please create a training plan for new employees to learn basic customer service skills."

[0274] This allows for the flexible provision of educational plans tailored to individual user needs, enabling the system to function as one that facilitates efficient skill development.

[0275] The flow of the specific process in Example 1 will be described using FIG. 11.

[0276] Step 1:

[0277] The user uses the terminal to input business-related information. Specifically, the user's work experience, current skill level, and skill improvement goals are described in the form on the screen. The input data is converted into digital data by the terminal and encoded. This encoded data becomes the output to the server.

[0278] Step 2:

[0279] The terminal sends the encoded digital data to the server. Here, a secure communication protocol is used to deliver the data to the server. The server receives this data and prepares to store it in the database. This received data becomes the input for analysis.

[0280] Step 3: [[ID=二十一]]

[0281] The server analyzes the received business-related information. Specifically, a generated AI model is used to analyze the data and generate an optimized education plan for the user. A machine learning algorithm is used in this analysis to select the optimal educational content. This generated education plan becomes the output from the server to the terminal.

[0282] Step 4:

[0283] The server sends the generated education plan to the terminal. The terminal receives this education plan and displays it on the screen. The user checks the displayed education plan and starts learning. This received education plan becomes the input for the user's learning activities.

[0284] Step 5:

[0285] The user advances learning according to an educational plan via a terminal. Specifically, the user takes online courses and training modules and manages the learning progress. This progress data is automatically recorded on the terminal. The recorded progress data serves as the input to the next server.

[0286] Step 6:

[0287] The terminal sends the user's learning progress data to the server. The server receives and analyzes this data in real time, evaluates the user's progress, generates feedback as needed, and adjusts the plan. This adjusted educational plan becomes the output of new learning activities.

[0288] Through this step, the system provides the user with individualized feedback and plans according to their progress, maximizing the learning effect.

[0289] (Application Example 1)

[0290] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0291] An object of the present invention is to provide a system that can efficiently and effectively improve the skills of individual staff at the point of sale. In particular, it is required to support on-site learning and provide real-time feedback to maximize the potential of each staff member.

[0292] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0293] In this invention, the server includes a device for collecting work-related information entered by the user, a generation agent device for analyzing the work-related information and generating an optimized training plan for the user, and a device for presenting the training plan and transmitting progress data via a smartphone application. This makes it possible to individually and efficiently improve the skills of each staff member.

[0294] A "user" is an individual who uses the system to input work-related information and aims to improve their skills.

[0295] "Work-related information" refers to information including the user's work experience, current skills, and goals for skill improvement.

[0296] A "generation agent device" is a processing device that generates an optimized training plan for the user based on the input business-related information.

[0297] A "smartphone application" is software for mobile devices that allows users to access educational plans and send progress data.

[0298] An "educational plan" is a learning program that combines online courses, workshops, and e-learning modules designed to improve users' skills.

[0299] "Progress data" refers to information that shows the learning process and results that users have achieved based on their educational plan.

[0300] "Real-time adjustment" means instantly updating and adapting the content of the educational plan according to the user's progress.

[0301] The system for implementing this invention is designed to support staff skill development in corporate sales environments. Specifically, it involves providing personalized training plans for individual staff members and managing their progress in real time, primarily using a server and mobile information terminals.

[0302] First, the user inputs their business-related information through a smartphone application. The hardware used here is a mobile information terminal, and as software, a user interface using React Native is installed. The input information is sent to the server via a communication protocol.

[0303] On the server side, a generation agent device built with Python operates to analyze the user's business-related information. At that time, an algorithm that generates an optimized education plan using an AI model is executed. Specifically, analysis and plan generation are performed using platforms such as Google Cloud Machine Learning.

[0304] The generated education plan is sent back to the user's mobile information terminal through communication again. The user proceeds with learning based on this plan and continuously sends progress data to the server using the smartphone app. The server analyzes this data in real-time and provides feedback to the user. Thereby, it becomes possible to immediately adjust the plan according to the user's growth.

[0305] As a specific example, while a new employee user utilizes a plan to learn basic customer service skills, they can track their progress little by little every day through the smartphone. When the correspondence with customers becomes smooth, the server recommends teaching materials that are perfect for the next stage, for example, strengthening the sales talk.

[0306] Examples of prompt sentences for the generation AI model:

[0307] "User profile: Job experience: 1 year, current skills: basic customer service, target skills: advanced sales strategy. Please generate an optimal education plan."

[0308] In this way, through the system of the present invention, autonomous and efficient skill improvement is realized on-site.

[0309] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0310] Step 1:

[0311] Users input work-related information using a smartphone application. During this process, users enter their work experience, current skills, and improvement goals into a form, which is then processed by the application as JSON data. The entered information is transmitted from the user's mobile device to the server via a communication protocol.

[0312] Step 2:

[0313] The server analyzes the received business-related information in JSON format. Using a generative agent built in Python, the server generates an optimal training plan based on the received data. In this process, a generative AI model is used to determine the appropriate combination of online courses and workshops based on the user's skill level and goals. The output is a training plan tailored to the user.

[0314] Step 3:

[0315] The server converts the generated educational plan back into JSON format and sends it to the user's mobile device. On the device, the received educational plan is displayed within the application, functioning as a user interface to encourage learning. The output here is the details of the educational plan presented to the user.

[0316] Step 4:

[0317] The user progresses through their daily learning based on the provided educational plan. The device continuously acquires the user's learning progress data and sends it to the server in JSON format. The acquired progress data includes information such as which tasks the user has completed and what content they have spent time on.

[0318] Step 5:

[0319] The server performs real-time analysis based on the received progress data. Based on the acquired data, it evaluates each user's progress rate and skill development, and adjusts the training plan as needed. As a result, the server generates feedback information and sends it to the user's device in JSON format. This shows users the skill areas they should focus on strengthening next.

[0320] Step 6:

[0321] Based on the user's configured learning plan, the next learning steps are suggested. The server selects the appropriate next learning module for that user and incorporates it into the plan. The output of this process is notified to the user's mobile device as an updated learning plan.

[0322] 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.

[0323] This invention provides a system for efficiently developing the individual skills of sales staff, and also enables the recognition of users' emotional states, allowing for the adjustment of feedback and training plans based on those states. The system incorporates an emotion engine to detect users' emotional states in real time, aiming to improve the quality of training.

[0324] First, the user accesses the system using a terminal and inputs their work-related data. This data includes skill levels, work experience, and training goals, and is sent to the server via the terminal. The server analyzes the received data, and a generation agent generates an optimized training plan for the user.

[0325] The generated educational plan is sent from the server to the terminal, and the user can proceed with learning according to the plan on the terminal. At this time, the terminal is equipped with a camera and sensors that analyze the user's facial expressions and voice data in real time. This allows the emotion engine to infer the user's emotional state.

[0326] Along with progress updates, emotional data is periodically sent to the server, which uses this data to evaluate the user's learning experience. If the emotional data indicates stress or dissatisfaction with learning, the server adjusts the learning plan and improves the content to help the user learn more effectively. For example, it may adjust the difficulty level of the learning or suggest changing the learning style.

[0327] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills, and the emotion engine detects an emotion indicating a decrease in concentration. In this case, the server updates the training plan and recommends training modules, such as interactive exercises and visual materials, to help the user regain their focus.

[0328] Thus, the system of the present invention maximizes learning effectiveness by understanding the user's emotional state through an emotion engine and providing flexible plans tailored to individual educational needs. This enables companies to efficiently support staff skill development and contribute to overall productivity improvement.

[0329] The following describes the processing flow.

[0330] Step 1:

[0331] The user logs into the terminal and enters work-related data. The terminal converts this data into a digital format and sends it to the server. The data includes skill level, work experience, and training goals.

[0332] Step 2:

[0333] The server analyzes the received business-related data and generates an optimized training plan using a generation agent. This generation process selects the most suitable training methods based on the user's skill gaps and needs.

[0334] Step 3:

[0335] The server sends the generated lesson plan to the terminal, which the user receives. The user then uses the terminal to begin learning according to the displayed lesson plan.

[0336] Step 4:

[0337] Cameras and sensors built into the device collect the user's facial expressions and voice data, which are then analyzed in real time by an emotion engine. The user's emotional state is understood and recorded as emotion data.

[0338] Step 5:

[0339] Emotional data and learning progress are periodically sent to the server. The server uses this data to evaluate the user's learning experience and provide learning support if necessary.

[0340] Step 6:

[0341] If the server determines from emotional data that a user is experiencing stress or dissatisfaction, it will adjust the educational plan. For example, it might consider lowering the difficulty level or changing the learning method.

[0342] Step 7:

[0343] The adjusted learning plan is sent back to the device, and the user continues learning based on the new plan. This allows the user to experience more effective learning.

[0344] Step 8:

[0345] The server compiles the final learning outcomes and provides detailed feedback to the user. This feedback includes achievement levels and areas for improvement, guiding the user through the next learning step.

[0346] (Example 2)

[0347] 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".

[0348] Traditional training systems often provide uniform educational plans without considering the individual circumstances and emotions of users, resulting in a failure to maximize learning effectiveness. Furthermore, the lack of systems capable of quickly responding to changes in users' emotions and motivations makes it difficult to provide an optimal learning environment.

[0349] 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.

[0350] In this invention, the server includes a device for receiving business-related data entered by the user, a generation device for analyzing the business-related data and generating a training plan tailored to the user, and a device for detecting the user's emotional state in real time. This enables the provision of flexible training plans that meet the individual learning needs of the user and the optimization of the learning environment in real time according to the user's emotional state.

[0351] "User" refers to anyone who uses this system for training or learning.

[0352] "Work-related data" refers to data that includes information necessary for creating a training plan, such as the user's skill level, work experience, and training goals.

[0353] "Receiving device" refers to the equipment or software used by the server to acquire business-related data entered by the user.

[0354] A "generation device" refers to a program or function that runs on a server and creates a user-specific training plan based on the acquired data.

[0355] A "training plan" refers to a set of instructional guidelines that combine learning steps and materials designed to improve the user's skills.

[0356] "Progress" refers to the process by which a user learns according to their training plan, and the results of that learning.

[0357] "Emotional state" refers to the psychological and emotional situations and moods that users express as they progress through training or learning.

[0358] "Feedback" refers to advice and information provided to users for improvement or modification, taking into account their training progress and emotional state.

[0359] The system for implementing this invention consists primarily of a user, a terminal, and a server. The user uses the terminal to receive training to improve their skills. The terminal is equipped with a camera and sensors that can collect the user's facial expressions and voice data. This makes it possible to infer the user's emotional state in real time.

[0360] The server receives business-related data sent by users and then analyzes this data. Statistical software and database management systems are used for data analysis. Based on the information obtained from the analysis, a generative AI model generates a user-specific training plan. This generative AI model learns from past examples of effective training plans, enabling it to select the optimal training plan.

[0361] The generated training plan is sent from the server to the terminal, and the user proceeds with learning based on that plan. Progress and sentiment data acquired in real time are also periodically sent to the server. Based on this data, the server evaluates the user's learning progress and adjusts the plan as needed. Feedback is also provided during the adjustment process, allowing the user to learn more effectively.

[0362] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills and a decrease in concentration is detected. In this case, the server can modify the training plan, incorporating game-based exercises and visual aids to re-engage the user.

[0363] To make more effective use of generative AI models, it is important to set appropriate prompts. For example, a possible prompt might be, "Please tell me how to maximize learning effectiveness by analyzing the level of concentration of users during customer service skills training and modifying the plan if necessary." This prompt allows the generative AI model to make suggestions that address specific learning needs.

[0364] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0365] Step 1:

[0366] Users input work-related data through a terminal. This data includes skill level, work experience, and training goals. The terminal receives this data, formats it, and sends it to the server. The input data is used as information for developing training plans.

[0367] Step 2:

[0368] The server analyzes business-related data received from terminals. The analysis process uses a database management system to compare the data with historical data and extract user-specific characteristics. This generates user-optimized attribute information, which is then provided to the AI ​​model.

[0369] Step 3:

[0370] The server-generated AI model creates a training plan based on user-specific attribute information. The AI ​​model utilizes machine learning algorithms to learn from past successful training plans, selecting efficient and effective training content. As output, an optimal training plan for the user is generated and sent back to the terminal.

[0371] Step 4:

[0372] The device displays the received training plan on its user interface. The user begins learning according to this plan, sequentially completing the learning materials and assignments designed according to the plan. The learning materials used here include video materials and interactive exercises.

[0373] Step 5:

[0374] The device uses its built-in camera and sensors to collect real-time data on facial expressions and voice during the user's learning process, and to infer their emotional state. This emotional data is analyzed to understand the user's emotional state and is periodically sent to a server.

[0375] Step 6:

[0376] The server evaluates the user's learning state based on received emotional and learning progress data. If the emotional data indicates stress or decreased concentration, the server generates feedback and modifies the training plan as needed. The modified plan is immediately sent to the terminal to improve the user's learning experience.

[0377] (Application Example 2)

[0378] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0379] Providing individualized educational plans that take into account the emotional state of users in real time is a challenging task in educational settings. In particular, it is necessary to provide prompt and appropriate feedback and adjust educational content in response to changes in users' concentration and interests.

[0380] 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.

[0381] In this invention, the server includes means for collecting business-related information entered by the user, a generation agent means for analyzing the business-related information and generating an educational policy optimized for the user, and an emotion analysis means for detecting the user's emotional state in real time. This makes it possible to flexibly adjust the educational policy according to the user's emotional state.

[0382] "Job-related information" refers to data entered by users in relation to their work, including skill levels, work experience, and training goals.

[0383] The "generation agent means" is a means of executing a process to generate optimized educational policies based on the user's work-related information.

[0384] An "educational policy" is a plan that specifically outlines the content and format of education for users.

[0385] "Progress status" refers to data that shows the progress of the user's educational activities.

[0386] "Means of providing responses" refers to functions for conveying feedback or instructions to users.

[0387] "Emotion analysis methods" are technologies that estimate a user's emotional state in real time based on their facial expressions, voice, and other factors.

[0388] "Interaction means" are methods for realizing interaction between the user and the system.

[0389] The system for realizing this invention includes software that runs on hardware platforms such as Raspberry Pi and Jetson Nano. When a user inputs work-related information into a terminal, that information is sent to a server for analysis. The server uses a generation agent to analyze the information and generate an optimized educational policy for each user.

[0390] The device incorporates a camera and voice sensors, which detect the user's facial expressions and voice, acquiring data in real time. This data is then analyzed using emotion analysis techniques to infer the user's emotional state. Image processing techniques using the OpenCV library and voice analysis techniques using TensorFlow are employed in this analysis.

[0391] The server dynamically adjusts the learning approach based on the user's emotional state and learning progress. If the server detects a decrease in the user's concentration or lack of interest during learning, it suggests new learning styles and activities through interactive means. This helps users continue learning more effectively.

[0392] As a concrete example, when a user is learning a language, if the system detects a decline in interest during conversation practice, it provides educational content incorporating interactive game elements. This content includes a visual interface and audio navigation to attract the user's attention.

[0393] An example of a prompt for a generative AI model would be, "Based on the user's current emotional state, suggest a new learning method to rekindle their interest." This allows the system to quickly provide a learning strategy that is appropriate for the user.

[0394] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0395] Step 1:

[0396] The terminal inputs work-related information. Users input their skill level, work experience, and training goals into the terminal and send this information to the system. This input data is transferred to the server as work-related information.

[0397] Step 2:

[0398] The server analyzes the business-related information. The server analyzes the received business-related information and generates the optimal training policy using a generation agent. Information analysis includes database matching and comparison with historical data. As output, a training policy tailored to the user is generated.

[0399] Step 3:

[0400] The device acquires emotional data. The camera and voice sensor built into the device capture the user's facial expressions and voice in real time. This input data is processed by an emotion analysis system and sent to a server as information to infer the emotional state.

[0401] Step 4:

[0402] The server analyzes the emotional data. The server uses emotional analysis tools to infer the user's emotional state. Data analysis employs image processing techniques using OpenCV and speech recognition technology using TensorFlow. As a result, the user's emotional state is output as numerical or categorical data.

[0403] Step 5:

[0404] The server adjusts the educational policy. The server dynamically adjusts the educational policy based on the student's learning progress and emotional data. It utilizes a generative AI model to generate improvement strategies to enhance user learning satisfaction and concentration. The output of this step is the adjusted educational policy.

[0405] Step 6:

[0406] The device provides responses to the user. Based on a tailored educational policy, the device provides visual or audible feedback to the user. Specific actions include suggesting new learning activities or styles, and presenting interactive content. This output brings about changes in the user's learning environment.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] 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.

[0413] 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).

[0414] 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.

[0415] 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.

[0416] 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).

[0417] 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.

[0418] 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.

[0419] 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.

[0420] 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.

[0421] 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.

[0422] 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".

[0423] This invention provides a system that offers individually personalized training plans to staff working in sales environments, enabling efficient skill development based on these plans. The following describes embodiments of this system.

[0424] First, the terminal is equipped with an interface for users to input their work-related data. This includes forms where users can enter their past work experience, current skill level, and future skill improvement goals. The terminal converts the entered data into a digital format and sends it to the server.

[0425] The server activates a generation agent based on the received data. The generation agent uses the accumulated database and learning algorithms to generate an optimal educational plan for the user. This educational plan combines various educational methods based on the user's needs, such as online courses, workshops, and e-learning modules.

[0426] The generated learning plan is sent from the server to the user's device and used as a guide as the user progresses through their learning. The user learns according to the learning plan through the device, and their progress is constantly monitored. The device sends learning progress data to the server, which analyzes it and provides real-time feedback. This feedback is designed to highlight key points the user needs to understand and skill areas where further improvement is required.

[0427] Furthermore, the server takes progress into consideration and adjusts the content of the training plan as needed. For example, it adds more advanced content to skills acquired ahead of schedule, providing a plan that matches the user's growth.

[0428] For example, when a new employee uses the system for the first time, a training plan on basic customer service skills is initially generated. As the user learns according to this plan, the server monitors their progress and provides regular feedback. Once the user has mastered a particular skill, the server recommends more advanced training modules as the next step, supporting efficient skill improvement.

[0429] Thus, the present invention enables flexible human resource development tailored to the individual training needs of each staff member, thereby contributing to the overall growth of the company.

[0430] The following describes the processing flow.

[0431] Step 1:

[0432] Users log in to the system via their terminal and enter their skill level, work experience, and training goals into a business-related data entry form. This information forms the basis for understanding the individual needs of each user.

[0433] Step 2:

[0434] The terminal converts the entered work-related data into a digital format and transmits it to the server via the communication network. This data transmission is encrypted and carried out in a secure manner.

[0435] Step 3:

[0436] The server stores the received business-related data and activates a generation agent based on that data. The AI ​​within the server analyzes the data and generates an optimized training plan for the user.

[0437] Step 4:

[0438] The generated lesson plan is sent from the server to the terminal. The terminal displays the lesson plan to the user and prompts them to begin learning.

[0439] Step 5:

[0440] Users use their devices to progress through their learning according to their educational plan. Learning progress is continuously recorded by the device and transmitted to the server at regular intervals.

[0441] Step 6:

[0442] The server analyzes learning progress data and generates feedback tailored to the user's progress. This feedback is immediately returned to the user based on the acquired data.

[0443] Step 7:

[0444] Based on the feedback, the server adjusts the training plan as needed. Specifically, it may add new learning modules or modify existing training content.

[0445] Step 8:

[0446] The server sends the updated educational plan to the user's device, allowing them to continue their learning. This process effectively supports the improvement of the user's skill development.

[0447] (Example 1)

[0448] 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."

[0449] In recent years, the need to efficiently provide personalized training plans for each employee has increased in response to the diversifying needs of companies. However, traditional training systems can only offer standardized plans, making it difficult to provide flexible training tailored to individual skills and learning paces. Furthermore, the inability to provide appropriate feedback in real time based on learning progress has been a factor that has slowed down the skill improvement of users.

[0450] 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.

[0451] In this invention, the server includes a device for collecting work-related information entered by the user, a generative model for analyzing the work-related information and generating an optimized training plan for the user, and a device for dynamically adjusting the training plan according to the user's progress. This enables the provision of individualized training plans tailored to each user and allows for rapid skill improvement through real-time feedback.

[0452] A "user" is an entity that uses the system to input work-related information and receives training plans and feedback.

[0453] "Work-related information" refers to data about the user's past work history, current expertise, and future skill goals.

[0454] A "collection device" is a component within a system that has the function of capturing business-related information entered by users.

[0455] "Analysis" refers to data processing that uses collected business-related information to design educational plans tailored to the users.

[0456] A "generative model" refers to the algorithms and data structures used to create user-optimized educational plans.

[0457] An "educational plan" is a curriculum or training program developed based on the user's current skill level and growth goals.

[0458] "Progress data" refers to information that shows the user's learning progress in their educational plan.

[0459] "Feedback" refers to information provided to users based on their learning progress data, including advice for improvement and information for setting new learning goals.

[0460] A "dynamically adjusting device" is a component within a system that has the function of updating and optimizing the educational plan according to the user's learning progress.

[0461] A "system" refers to a group of devices that have the functions of data collection, analysis, plan generation, progress management, and feedback provision.

[0462] A description of the embodiment for carrying out the invention will be provided.

[0463] This system is composed of three components: a server, a terminal, and a user.

[0464] Users first use a terminal to input information related to their work. This includes past work experience, current skill level, and future skill development goals. The terminal is equipped with a user-friendly graphical user interface (GUI) to efficiently collect this information.

[0465] The terminal converts the input information into digital data and transmits it to the server using a secure communication method. Upon receiving this data, the server stores it in a database and prepares it for analysis. To analyze the information, the server runs a generative AI model that uses machine learning algorithms. This model has the capability to automatically generate an educational plan optimized for the user.

[0466] The generated learning plan is sent from the server to the terminal, where the user can use the terminal to review the plan and proceed with their learning. The terminal continuously records the user's learning progress and sends it back to the server as feedback. Based on this feedback, the server analyzes the learning progress in real time, dynamically adjusts the learning plan as needed, and suggests the next steps to the user.

[0467] A concrete example of this operation is when a new employee receives a training plan to acquire basic customer service skills. The user progresses through the proposed online course, and their device automatically sends progress data to the server. The server analyzes this data and, if the user is progressing ahead of schedule, can provide additional, more advanced training modules.

[0468] An example of a prompt message might be, "Please create a training plan for new employees to learn basic customer service skills."

[0469] This allows for the flexible provision of educational plans tailored to individual user needs, enabling the system to function as one that facilitates efficient skill development.

[0470] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0471] Step 1:

[0472] Users input work-related information using a terminal. Specifically, they fill in their work experience, current skill level, and skill improvement goals in an on-screen form. The entered data is converted into digital data and encoded on the terminal. This encoded data is then output to the server.

[0473] Step 2:

[0474] The terminal sends encoded digital data to the server. A secure communication protocol is used to deliver the data to the server. The server receives this data and prepares to store it in its database. This received data becomes the input for analysis.

[0475] Step 3:

[0476] The server analyzes the received business-related information. Specifically, it uses a generative AI model to analyze the data and generate an optimized training plan for the user. This analysis employs machine learning algorithms to select the most suitable training content. This generated training plan is then output from the server to the terminal.

[0477] Step 4:

[0478] The server sends the generated lesson plan to the terminal. The terminal receives this lesson plan and displays it on the screen. The user reviews the displayed lesson plan and begins learning. This received lesson plan becomes the input for the user's learning activities.

[0479] Step 5:

[0480] Users progress through their learning plan via their devices. Specifically, they take online courses and training modules and manage their learning progress. This progress data is automatically recorded on the device. This recorded progress data is then input to the next server.

[0481] Step 6:

[0482] The device sends user learning progress data to the server. The server receives and analyzes this data in real time to evaluate the user's progress. After analysis, it generates feedback as needed and adjusts the plan. This adjusted educational plan becomes the output for new learning activities.

[0483] Through this step, the system provides users with personalized feedback and plans tailored to their individual progress, maximizing learning effectiveness.

[0484] (Application Example 1)

[0485] 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."

[0486] The present invention aims to provide a system that can efficiently and effectively improve the skills of individual staff members in sales settings. In particular, it is required to support on-the-job learning and provide real-time feedback to maximize the potential of each staff member.

[0487] 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.

[0488] In this invention, the server includes a device for collecting work-related information entered by the user, a generation agent device for analyzing the work-related information and generating an optimized training plan for the user, and a device for presenting the training plan and transmitting progress data via a smartphone application. This makes it possible to individually and efficiently improve the skills of each staff member.

[0489] A "user" is an individual who uses the system to input work-related information and aims to improve their skills.

[0490] "Work-related information" refers to information including the user's work experience, current skills, and goals for skill improvement.

[0491] A "generation agent device" is a processing device that generates an optimized training plan for the user based on the input business-related information.

[0492] A "smartphone application" is software for mobile devices that allows users to access educational plans and send progress data.

[0493] An "educational plan" is a learning program that combines online courses, workshops, and e-learning modules designed to improve users' skills.

[0494] "Progress data" refers to information that shows the learning process and results that users have achieved based on their educational plan.

[0495] "Real-time adjustment" means instantly updating and adapting the content of the educational plan according to the user's progress.

[0496] The system for implementing this invention is designed to support staff skill development in corporate sales environments. Specifically, it involves providing personalized training plans for individual staff members and managing their progress in real time, primarily using a server and mobile information terminals.

[0497] First, the user enters their work-related information through a smartphone application. The hardware used here is a personal digital assistant (PDCA) device, and the software installed is a user interface based on React Native. This entered information is then transmitted to the server via a communication protocol.

[0498] On the server side, a generation agent built with Python runs to analyze the user's work-related information. During this process, an algorithm is executed that uses an AI model to generate an optimized training plan. Specifically, analysis and plan generation are performed using platforms such as Google Cloud Machine Learning.

[0499] The generated educational plan is then transmitted back to the user's mobile device via communication. The user progresses through their studies based on this plan, continuously sending progress data to the server using a smartphone app. The server analyzes this data in real time and provides feedback to the user. This allows for immediate adjustments to the plan as the user progresses.

[0500] As a concrete example, a new employee can use a plan to learn basic customer service skills, tracking their progress little by little each day via their smartphone. Once they become proficient at handling clients, the server recommends the next stage, such as training materials perfectly suited to strengthening their sales pitch.

[0501] Examples of prompts for a generative AI model:

[0502] "User Profile: Work Experience: 1 year, Current Skills: Basic Customer Service, Target Skills: Advanced Sales Strategy. Please generate an optimal training plan."

[0503] Thus, the system of the present invention enables autonomous and efficient skill development in the workplace.

[0504] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0505] Step 1:

[0506] Users input work-related information using a smartphone application. During this process, users enter their work experience, current skills, and improvement goals into a form, which is then processed by the application as JSON data. The entered information is transmitted from the user's mobile device to the server via a communication protocol.

[0507] Step 2:

[0508] The server analyzes the received business-related information in JSON format. Using a generative agent built in Python, the server generates an optimal training plan based on the received data. In this process, a generative AI model is used to determine the appropriate combination of online courses and workshops based on the user's skill level and goals. The output is a training plan tailored to the user.

[0509] Step 3:

[0510] The server converts the generated educational plan back into JSON format and sends it to the user's mobile device. On the device, the received educational plan is displayed within the application, functioning as a user interface to encourage learning. The output here is the details of the educational plan presented to the user.

[0511] Step 4:

[0512] The user progresses through their daily learning based on the provided educational plan. The device continuously acquires the user's learning progress data and sends it to the server in JSON format. The acquired progress data includes information such as which tasks the user has completed and what content they have spent time on.

[0513] Step 5:

[0514] The server performs real-time analysis based on the received progress data. Based on the acquired data, it evaluates each user's progress rate and skill development, and adjusts the training plan as needed. As a result, the server generates feedback information and sends it to the user's device in JSON format. This shows users the skill areas they should focus on strengthening next.

[0515] Step 6:

[0516] Based on the user's configured learning plan, the next learning steps are suggested. The server selects the appropriate next learning module for that user and incorporates it into the plan. The output of this process is notified to the user's mobile device as an updated learning plan.

[0517] 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.

[0518] This invention provides a system for efficiently developing the individual skills of sales staff, and also enables the recognition of users' emotional states, allowing for the adjustment of feedback and training plans based on those states. The system incorporates an emotion engine to detect users' emotional states in real time, aiming to improve the quality of training.

[0519] First, the user accesses the system using a terminal and inputs their work-related data. This data includes skill levels, work experience, and training goals, and is sent to the server via the terminal. The server analyzes the received data, and a generation agent generates an optimized training plan for the user.

[0520] The generated educational plan is sent from the server to the terminal, and the user can proceed with learning according to the plan on the terminal. At this time, the terminal is equipped with a camera and sensors that analyze the user's facial expressions and voice data in real time. This allows the emotion engine to infer the user's emotional state.

[0521] Along with progress updates, emotional data is periodically sent to the server, which uses this data to evaluate the user's learning experience. If the emotional data indicates stress or dissatisfaction with learning, the server adjusts the learning plan and improves the content to help the user learn more effectively. For example, it may adjust the difficulty level of the learning or suggest changing the learning style.

[0522] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills, and the emotion engine detects an emotion indicating a decrease in concentration. In this case, the server updates the training plan and recommends training modules, such as interactive exercises and visual materials, to help the user regain their focus.

[0523] Thus, the system of the present invention maximizes learning effectiveness by understanding the user's emotional state through an emotion engine and providing flexible plans tailored to individual educational needs. This enables companies to efficiently support staff skill development and contribute to overall productivity improvement.

[0524] The following describes the processing flow.

[0525] Step 1:

[0526] The user logs into the terminal and enters work-related data. The terminal converts this data into a digital format and sends it to the server. The data includes skill level, work experience, and training goals.

[0527] Step 2:

[0528] The server analyzes the received business-related data and generates an optimized training plan using a generation agent. This generation process selects the most suitable training methods based on the user's skill gaps and needs.

[0529] Step 3:

[0530] The server sends the generated lesson plan to the terminal, which the user receives. The user then uses the terminal to begin learning according to the displayed lesson plan.

[0531] Step 4:

[0532] Cameras and sensors built into the device collect the user's facial expressions and voice data, which are then analyzed in real time by an emotion engine. The user's emotional state is understood and recorded as emotion data.

[0533] Step 5:

[0534] Emotional data and learning progress are periodically sent to the server. The server uses this data to evaluate the user's learning experience and provide learning support if necessary.

[0535] Step 6:

[0536] If the server determines from emotional data that a user is experiencing stress or dissatisfaction, it will adjust the educational plan. For example, it might consider lowering the difficulty level or changing the learning method.

[0537] Step 7:

[0538] The adjusted learning plan is sent back to the device, and the user continues learning based on the new plan. This allows the user to experience more effective learning.

[0539] Step 8:

[0540] The server compiles the final learning outcomes and provides detailed feedback to the user. This feedback includes achievement levels and areas for improvement, guiding the user through the next learning step.

[0541] (Example 2)

[0542] 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."

[0543] Traditional training systems often provide uniform educational plans without considering the individual circumstances and emotions of users, resulting in a failure to maximize learning effectiveness. Furthermore, the lack of systems capable of quickly responding to changes in users' emotions and motivations makes it difficult to provide an optimal learning environment.

[0544] 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.

[0545] In this invention, the server includes a device for receiving business-related data entered by the user, a generation device for analyzing the business-related data and generating a training plan tailored to the user, and a device for detecting the user's emotional state in real time. This enables the provision of flexible training plans that meet the individual learning needs of the user and the optimization of the learning environment in real time according to the user's emotional state.

[0546] "User" refers to anyone who uses this system for training or learning.

[0547] "Work-related data" refers to data that includes information necessary for creating a training plan, such as the user's skill level, work experience, and training goals.

[0548] "Receiving device" refers to the equipment or software used by the server to acquire business-related data entered by the user.

[0549] A "generation device" refers to a program or function that runs on a server and creates a user-specific training plan based on the acquired data.

[0550] A "training plan" refers to a set of instructional guidelines that combine learning steps and materials designed to improve the user's skills.

[0551] "Progress" refers to the process by which a user learns according to their training plan, and the results of that learning.

[0552] "Emotional state" refers to the psychological and emotional situations and moods that users express as they progress through training or learning.

[0553] "Feedback" refers to advice and information provided to users for improvement or modification, taking into account their training progress and emotional state.

[0554] The system for implementing this invention consists primarily of a user, a terminal, and a server. The user uses the terminal to receive training to improve their skills. The terminal is equipped with a camera and sensors that can collect the user's facial expressions and voice data. This makes it possible to infer the user's emotional state in real time.

[0555] The server receives business-related data sent by users and then analyzes this data. Statistical software and database management systems are used for data analysis. Based on the information obtained from the analysis, a generative AI model generates a user-specific training plan. This generative AI model learns from past examples of effective training plans, enabling it to select the optimal training plan.

[0556] The generated training plan is sent from the server to the terminal, and the user proceeds with learning based on that plan. Progress and sentiment data acquired in real time are also periodically sent to the server. Based on this data, the server evaluates the user's learning progress and adjusts the plan as needed. Feedback is also provided during the adjustment process, allowing the user to learn more effectively.

[0557] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills and a decrease in concentration is detected. In this case, the server can modify the training plan, incorporating game-based exercises and visual aids to re-engage the user.

[0558] To make more effective use of generative AI models, it is important to set appropriate prompts. For example, a possible prompt might be, "Please tell me how to maximize learning effectiveness by analyzing the level of concentration of users during customer service skills training and modifying the plan if necessary." This prompt allows the generative AI model to make suggestions that address specific learning needs.

[0559] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0560] Step 1:

[0561] Users input work-related data through a terminal. This data includes skill level, work experience, and training goals. The terminal receives this data, formats it, and sends it to the server. The input data is used as information for developing training plans.

[0562] Step 2:

[0563] The server analyzes business-related data received from terminals. The analysis process uses a database management system to compare the data with historical data and extract user-specific characteristics. This generates user-optimized attribute information, which is then provided to the AI ​​model.

[0564] Step 3:

[0565] The server-generated AI model creates a training plan based on user-specific attribute information. The AI ​​model utilizes machine learning algorithms to learn from past successful training plans, selecting efficient and effective training content. As output, an optimal training plan for the user is generated and sent back to the terminal.

[0566] Step 4:

[0567] The device displays the received training plan on its user interface. The user begins learning according to this plan, sequentially completing the learning materials and assignments designed according to the plan. The learning materials used here include video materials and interactive exercises.

[0568] Step 5:

[0569] The device uses its built-in camera and sensors to collect real-time data on facial expressions and voice during the user's learning process, and to infer their emotional state. This emotional data is analyzed to understand the user's emotional state and is periodically sent to a server.

[0570] Step 6:

[0571] The server evaluates the user's learning state based on received emotional and learning progress data. If the emotional data indicates stress or decreased concentration, the server generates feedback and modifies the training plan as needed. The modified plan is immediately sent to the terminal to improve the user's learning experience.

[0572] (Application Example 2)

[0573] 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."

[0574] Providing individualized educational plans that take into account the emotional state of users in real time is a challenging task in educational settings. In particular, it is necessary to provide prompt and appropriate feedback and adjust educational content in response to changes in users' concentration and interests.

[0575] 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.

[0576] In this invention, the server includes means for collecting business-related information entered by the user, a generation agent means for analyzing the business-related information and generating an educational policy optimized for the user, and an emotion analysis means for detecting the user's emotional state in real time. This makes it possible to flexibly adjust the educational policy according to the user's emotional state.

[0577] "Job-related information" refers to data entered by users in relation to their work, including skill levels, work experience, and training goals.

[0578] The "generation agent means" is a means of executing a process to generate optimized educational policies based on the user's work-related information.

[0579] An "educational policy" is a plan that specifically outlines the content and format of education for users.

[0580] "Progress status" refers to data that shows the progress of the user's educational activities.

[0581] "Means of providing responses" refers to functions for conveying feedback or instructions to users.

[0582] "Emotion analysis methods" are technologies that estimate a user's emotional state in real time based on their facial expressions, voice, and other factors.

[0583] "Interaction means" are methods for realizing interaction between the user and the system.

[0584] The system for realizing this invention includes software that runs on hardware platforms such as Raspberry Pi and Jetson Nano. When a user inputs work-related information into a terminal, that information is sent to a server for analysis. The server uses a generation agent to analyze the information and generate an optimized educational policy for each user.

[0585] The device incorporates a camera and voice sensors, which detect the user's facial expressions and voice, acquiring data in real time. This data is then analyzed using emotion analysis techniques to infer the user's emotional state. Image processing techniques using the OpenCV library and voice analysis techniques using TensorFlow are employed in this analysis.

[0586] The server dynamically adjusts the learning approach based on the user's emotional state and learning progress. If the server detects a decrease in the user's concentration or lack of interest during learning, it suggests new learning styles and activities through interactive means. This helps users continue learning more effectively.

[0587] As a concrete example, when a user is learning a language, if the system detects a decline in interest during conversation practice, it provides educational content incorporating interactive game elements. This content includes a visual interface and audio navigation to attract the user's attention.

[0588] An example of a prompt for a generative AI model would be, "Based on the user's current emotional state, suggest a new learning method to rekindle their interest." This allows the system to quickly provide a learning strategy that is appropriate for the user.

[0589] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0590] Step 1:

[0591] The terminal inputs work-related information. Users input their skill level, work experience, and training goals into the terminal and send this information to the system. This input data is transferred to the server as work-related information.

[0592] Step 2:

[0593] The server analyzes the business-related information. The server analyzes the received business-related information and generates the optimal training policy using a generation agent. Information analysis includes database matching and comparison with historical data. As output, a training policy tailored to the user is generated.

[0594] Step 3:

[0595] The device acquires emotional data. The camera and voice sensor built into the device capture the user's facial expressions and voice in real time. This input data is processed by an emotion analysis system and sent to a server as information to infer the emotional state.

[0596] Step 4:

[0597] The server analyzes the emotional data. The server uses emotional analysis tools to infer the user's emotional state. Data analysis employs image processing techniques using OpenCV and speech recognition technology using TensorFlow. As a result, the user's emotional state is output as numerical or categorical data.

[0598] Step 5:

[0599] The server adjusts the educational policy. The server dynamically adjusts the educational policy based on the student's learning progress and emotional data. It utilizes a generative AI model to generate improvement strategies to enhance user learning satisfaction and concentration. The output of this step is the adjusted educational policy.

[0600] Step 6:

[0601] The device provides responses to the user. Based on a tailored educational policy, the device provides visual or audible feedback to the user. Specific actions include suggesting new learning activities or styles, and presenting interactive content. This output brings about changes in the user's learning environment.

[0602] 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.

[0603] 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.

[0604] 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.

[0605] [Fourth Embodiment]

[0606] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0607] 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.

[0608] 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).

[0609] 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.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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.

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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".

[0619] This invention provides a system that offers individually personalized training plans to staff working in sales environments, enabling efficient skill development based on these plans. The following describes embodiments of this system.

[0620] First, the terminal is equipped with an interface for users to input their work-related data. This includes forms where users can enter their past work experience, current skill level, and future skill improvement goals. The terminal converts the entered data into a digital format and sends it to the server.

[0621] The server activates a generation agent based on the received data. The generation agent uses the accumulated database and learning algorithms to generate an optimal educational plan for the user. This educational plan combines various educational methods based on the user's needs, such as online courses, workshops, and e-learning modules.

[0622] The generated learning plan is sent from the server to the user's device and used as a guide as the user progresses through their learning. The user learns according to the learning plan through the device, and their progress is constantly monitored. The device sends learning progress data to the server, which analyzes it and provides real-time feedback. This feedback is designed to highlight key points the user needs to understand and skill areas where further improvement is required.

[0623] Furthermore, the server takes progress into consideration and adjusts the content of the training plan as needed. For example, it adds more advanced content to skills acquired ahead of schedule, providing a plan that matches the user's growth.

[0624] For example, when a new employee uses the system for the first time, a training plan on basic customer service skills is initially generated. As the user learns according to this plan, the server monitors their progress and provides regular feedback. Once the user has mastered a particular skill, the server recommends more advanced training modules as the next step, supporting efficient skill improvement.

[0625] Thus, the present invention enables flexible human resource development tailored to the individual training needs of each staff member, thereby contributing to the overall growth of the company.

[0626] The following describes the processing flow.

[0627] Step 1:

[0628] Users log in to the system via their terminal and enter their skill level, work experience, and training goals into a business-related data entry form. This information forms the basis for understanding the individual needs of each user.

[0629] Step 2:

[0630] The terminal converts the entered work-related data into a digital format and transmits it to the server via the communication network. This data transmission is encrypted and carried out in a secure manner.

[0631] Step 3:

[0632] The server stores the received business-related data and activates a generation agent based on that data. The AI ​​within the server analyzes the data and generates an optimized training plan for the user.

[0633] Step 4:

[0634] The generated lesson plan is sent from the server to the terminal. The terminal displays the lesson plan to the user and prompts them to begin learning.

[0635] Step 5:

[0636] Users use their devices to progress through their learning according to their educational plan. Learning progress is continuously recorded by the device and transmitted to the server at regular intervals.

[0637] Step 6:

[0638] The server analyzes learning progress data and generates feedback tailored to the user's progress. This feedback is immediately returned to the user based on the acquired data.

[0639] Step 7:

[0640] Based on the feedback, the server adjusts the training plan as needed. Specifically, it may add new learning modules or modify existing training content.

[0641] Step 8:

[0642] The server sends the updated educational plan to the user's device, allowing them to continue their learning. This process effectively supports the improvement of the user's skill development.

[0643] (Example 1)

[0644] 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".

[0645] In recent years, the need to efficiently provide personalized training plans for each employee has increased in response to the diversifying needs of companies. However, traditional training systems can only offer standardized plans, making it difficult to provide flexible training tailored to individual skills and learning paces. Furthermore, the inability to provide appropriate feedback in real time based on learning progress has been a factor that has slowed down the skill improvement of users.

[0646] 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.

[0647] In this invention, the server includes a device for collecting work-related information entered by the user, a generative model for analyzing the work-related information and generating an optimized training plan for the user, and a device for dynamically adjusting the training plan according to the user's progress. This enables the provision of individualized training plans tailored to each user and allows for rapid skill improvement through real-time feedback.

[0648] A "user" is an entity that uses the system to input work-related information and receives training plans and feedback.

[0649] "Work-related information" refers to data about the user's past work history, current expertise, and future skill goals.

[0650] A "collection device" is a component within a system that has the function of capturing business-related information entered by users.

[0651] "Analysis" refers to data processing that uses collected business-related information to design educational plans tailored to the users.

[0652] A "generative model" refers to the algorithms and data structures used to create user-optimized educational plans.

[0653] An "educational plan" is a curriculum or training program developed based on the user's current skill level and growth goals.

[0654] "Progress data" refers to information that shows the user's learning progress in their educational plan.

[0655] "Feedback" refers to information provided to users based on their learning progress data, including advice for improvement and information for setting new learning goals.

[0656] A "dynamically adjusting device" is a component within a system that has the function of updating and optimizing the educational plan according to the user's learning progress.

[0657] A "system" refers to a group of devices that have the functions of data collection, analysis, plan generation, progress management, and feedback provision.

[0658] A description of the embodiment for carrying out the invention will be provided.

[0659] This system is composed of three components: a server, a terminal, and a user.

[0660] Users first use a terminal to input information related to their work. This includes past work experience, current skill level, and future skill development goals. The terminal is equipped with a user-friendly graphical user interface (GUI) to efficiently collect this information.

[0661] The terminal converts the input information into digital data and transmits it to the server using a secure communication method. Upon receiving this data, the server stores it in a database and prepares it for analysis. To analyze the information, the server runs a generative AI model that uses machine learning algorithms. This model has the capability to automatically generate an educational plan optimized for the user.

[0662] The generated learning plan is sent from the server to the terminal, where the user can use the terminal to review the plan and proceed with their learning. The terminal continuously records the user's learning progress and sends it back to the server as feedback. Based on this feedback, the server analyzes the learning progress in real time, dynamically adjusts the learning plan as needed, and suggests the next steps to the user.

[0663] A concrete example of this operation is when a new employee receives a training plan to acquire basic customer service skills. The user progresses through the proposed online course, and their device automatically sends progress data to the server. The server analyzes this data and, if the user is progressing ahead of schedule, can provide additional, more advanced training modules.

[0664] An example of a prompt message might be, "Please create a training plan for new employees to learn basic customer service skills."

[0665] This allows for the flexible provision of educational plans tailored to individual user needs, enabling the system to function as one that facilitates efficient skill development.

[0666] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0667] Step 1:

[0668] Users input work-related information using a terminal. Specifically, they fill in their work experience, current skill level, and skill improvement goals in an on-screen form. The entered data is converted into digital data and encoded on the terminal. This encoded data is then output to the server.

[0669] Step 2:

[0670] The terminal sends encoded digital data to the server. A secure communication protocol is used to deliver the data to the server. The server receives this data and prepares to store it in its database. This received data becomes the input for analysis.

[0671] Step 3:

[0672] The server analyzes the received business-related information. Specifically, it uses a generative AI model to analyze the data and generate an optimized training plan for the user. This analysis employs machine learning algorithms to select the most suitable training content. This generated training plan is then output from the server to the terminal.

[0673] Step 4:

[0674] The server sends the generated lesson plan to the terminal. The terminal receives this lesson plan and displays it on the screen. The user reviews the displayed lesson plan and begins learning. This received lesson plan becomes the input for the user's learning activities.

[0675] Step 5:

[0676] Users progress through their learning plan via their devices. Specifically, they take online courses and training modules and manage their learning progress. This progress data is automatically recorded on the device. This recorded progress data is then input to the next server.

[0677] Step 6:

[0678] The device sends user learning progress data to the server. The server receives and analyzes this data in real time to evaluate the user's progress. After analysis, it generates feedback as needed and adjusts the plan. This adjusted educational plan becomes the output for new learning activities.

[0679] Through this step, the system provides users with personalized feedback and plans tailored to their individual progress, maximizing learning effectiveness.

[0680] (Application Example 1)

[0681] 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".

[0682] The present invention aims to provide a system that can efficiently and effectively improve the skills of individual staff members in sales settings. In particular, it is required to support on-the-job learning and provide real-time feedback to maximize the potential of each staff member.

[0683] 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.

[0684] In this invention, the server includes a device for collecting work-related information entered by the user, a generation agent device for analyzing the work-related information and generating an optimized training plan for the user, and a device for presenting the training plan and transmitting progress data via a smartphone application. This makes it possible to individually and efficiently improve the skills of each staff member.

[0685] A "user" is an individual who uses the system to input work-related information and aims to improve their skills.

[0686] "Work-related information" refers to information including the user's work experience, current skills, and goals for skill improvement.

[0687] A "generation agent device" is a processing device that generates an optimized training plan for the user based on the input business-related information.

[0688] A "smartphone application" is software for mobile devices that allows users to access educational plans and send progress data.

[0689] An "educational plan" is a learning program that combines online courses, workshops, and e-learning modules designed to improve users' skills.

[0690] "Progress data" refers to information that shows the learning process and results that users have achieved based on their educational plan.

[0691] "Real-time adjustment" means instantly updating and adapting the content of the educational plan according to the user's progress.

[0692] The system for implementing this invention is designed to support staff skill development in corporate sales environments. Specifically, it involves providing personalized training plans for individual staff members and managing their progress in real time, primarily using a server and mobile information terminals.

[0693] First, the user enters their work-related information through a smartphone application. The hardware used here is a personal digital assistant (PDCA) device, and the software installed is a user interface based on React Native. This entered information is then transmitted to the server via a communication protocol.

[0694] On the server side, a generation agent built with Python runs to analyze the user's work-related information. During this process, an algorithm is executed that uses an AI model to generate an optimized training plan. Specifically, analysis and plan generation are performed using platforms such as Google Cloud Machine Learning.

[0695] The generated educational plan is then transmitted back to the user's mobile device via communication. The user progresses through their studies based on this plan, continuously sending progress data to the server using a smartphone app. The server analyzes this data in real time and provides feedback to the user. This allows for immediate adjustments to the plan as the user progresses.

[0696] As a concrete example, a new employee can use a plan to learn basic customer service skills, tracking their progress little by little each day via their smartphone. Once they become proficient at handling clients, the server recommends the next stage, such as training materials perfectly suited to strengthening their sales pitch.

[0697] Examples of prompts for a generative AI model:

[0698] "User Profile: Work Experience: 1 year, Current Skills: Basic Customer Service, Target Skills: Advanced Sales Strategy. Please generate an optimal training plan."

[0699] Thus, the system of the present invention enables autonomous and efficient skill development in the workplace.

[0700] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0701] Step 1:

[0702] Users input work-related information using a smartphone application. During this process, users enter their work experience, current skills, and improvement goals into a form, which is then processed by the application as JSON data. The entered information is transmitted from the user's mobile device to the server via a communication protocol.

[0703] Step 2:

[0704] The server analyzes the received business-related information in JSON format. Using a generative agent built in Python, the server generates an optimal training plan based on the received data. In this process, a generative AI model is used to determine the appropriate combination of online courses and workshops based on the user's skill level and goals. The output is a training plan tailored to the user.

[0705] Step 3:

[0706] The server converts the generated educational plan back into JSON format and sends it to the user's mobile device. On the device, the received educational plan is displayed within the application, functioning as a user interface to encourage learning. The output here is the details of the educational plan presented to the user.

[0707] Step 4:

[0708] The user progresses through their daily learning based on the provided educational plan. The device continuously acquires the user's learning progress data and sends it to the server in JSON format. The acquired progress data includes information such as which tasks the user has completed and what content they have spent time on.

[0709] Step 5:

[0710] The server performs real-time analysis based on the received progress data. Based on the acquired data, it evaluates each user's progress rate and skill development, and adjusts the training plan as needed. As a result, the server generates feedback information and sends it to the user's device in JSON format. This shows users the skill areas they should focus on strengthening next.

[0711] Step 6:

[0712] Based on the user's configured learning plan, the next learning steps are suggested. The server selects the appropriate next learning module for that user and incorporates it into the plan. The output of this process is notified to the user's mobile device as an updated learning plan.

[0713] 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.

[0714] This invention provides a system for efficiently developing the individual skills of sales staff, and also enables the recognition of users' emotional states, allowing for the adjustment of feedback and training plans based on those states. The system incorporates an emotion engine to detect users' emotional states in real time, aiming to improve the quality of training.

[0715] First, the user accesses the system using a terminal and inputs their work-related data. This data includes skill levels, work experience, and training goals, and is sent to the server via the terminal. The server analyzes the received data, and a generation agent generates an optimized training plan for the user.

[0716] The generated educational plan is sent from the server to the terminal, and the user can proceed with learning according to the plan on the terminal. At this time, the terminal is equipped with a camera and sensors that analyze the user's facial expressions and voice data in real time. This allows the emotion engine to infer the user's emotional state.

[0717] Along with progress updates, emotional data is periodically sent to the server, which uses this data to evaluate the user's learning experience. If the emotional data indicates stress or dissatisfaction with learning, the server adjusts the learning plan and improves the content to help the user learn more effectively. For example, it may adjust the difficulty level of the learning or suggest changing the learning style.

[0718] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills, and the emotion engine detects an emotion indicating a decrease in concentration. In this case, the server updates the training plan and recommends training modules, such as interactive exercises and visual materials, to help the user regain their focus.

[0719] Thus, the system of the present invention maximizes learning effectiveness by understanding the user's emotional state through an emotion engine and providing flexible plans tailored to individual educational needs. This enables companies to efficiently support staff skill development and contribute to overall productivity improvement.

[0720] The following describes the processing flow.

[0721] Step 1:

[0722] The user logs into the terminal and enters work-related data. The terminal converts this data into a digital format and sends it to the server. The data includes skill level, work experience, and training goals.

[0723] Step 2:

[0724] The server analyzes the received business-related data and generates an optimized training plan using a generation agent. This generation process selects the most suitable training methods based on the user's skill gaps and needs.

[0725] Step 3:

[0726] The server sends the generated lesson plan to the terminal, which the user receives. The user then uses the terminal to begin learning according to the displayed lesson plan.

[0727] Step 4:

[0728] Cameras and sensors built into the device collect the user's facial expressions and voice data, which are then analyzed in real time by an emotion engine. The user's emotional state is understood and recorded as emotion data.

[0729] Step 5:

[0730] Emotional data and learning progress are periodically sent to the server. The server uses this data to evaluate the user's learning experience and provide learning support if necessary.

[0731] Step 6:

[0732] If the server determines from emotional data that a user is experiencing stress or dissatisfaction, it will adjust the educational plan. For example, it might consider lowering the difficulty level or changing the learning method.

[0733] Step 7:

[0734] The adjusted learning plan is sent back to the device, and the user continues learning based on the new plan. This allows the user to experience more effective learning.

[0735] Step 8:

[0736] The server compiles the final learning outcomes and provides detailed feedback to the user. This feedback includes achievement levels and areas for improvement, guiding the user through the next learning step.

[0737] (Example 2)

[0738] 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".

[0739] Traditional training systems often provide uniform educational plans without considering the individual circumstances and emotions of users, resulting in a failure to maximize learning effectiveness. Furthermore, the lack of systems capable of quickly responding to changes in users' emotions and motivations makes it difficult to provide an optimal learning environment.

[0740] 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.

[0741] In this invention, the server includes a device for receiving business-related data entered by the user, a generation device for analyzing the business-related data and generating a training plan tailored to the user, and a device for detecting the user's emotional state in real time. This enables the provision of flexible training plans that meet the individual learning needs of the user and the optimization of the learning environment in real time according to the user's emotional state.

[0742] "User" refers to anyone who uses this system for training or learning.

[0743] "Work-related data" refers to data that includes information necessary for creating a training plan, such as the user's skill level, work experience, and training goals.

[0744] "Receiving device" refers to the equipment or software used by the server to acquire business-related data entered by the user.

[0745] A "generation device" refers to a program or function that runs on a server and creates a user-specific training plan based on the acquired data.

[0746] A "training plan" refers to a set of instructional guidelines that combine learning steps and materials designed to improve the user's skills.

[0747] "Progress" refers to the process by which a user learns according to their training plan, and the results of that learning.

[0748] "Emotional state" refers to the psychological and emotional situations and moods that users express as they progress through training or learning.

[0749] "Feedback" refers to advice and information provided to users for improvement or modification, taking into account their training progress and emotional state.

[0750] The system for implementing this invention consists primarily of a user, a terminal, and a server. The user uses the terminal to receive training to improve their skills. The terminal is equipped with a camera and sensors that can collect the user's facial expressions and voice data. This makes it possible to infer the user's emotional state in real time.

[0751] The server receives business-related data sent by users and then analyzes this data. Statistical software and database management systems are used for data analysis. Based on the information obtained from the analysis, a generative AI model generates a user-specific training plan. This generative AI model learns from past examples of effective training plans, enabling it to select the optimal training plan.

[0752] The generated training plan is sent from the server to the terminal, and the user proceeds with learning based on that plan. Progress and sentiment data acquired in real time are also periodically sent to the server. Based on this data, the server evaluates the user's learning progress and adjusts the plan as needed. Feedback is also provided during the adjustment process, allowing the user to learn more effectively.

[0753] As a concrete example, consider a scenario where a user is undergoing training to improve their customer service skills and a decrease in concentration is detected. In this case, the server can modify the training plan, incorporating game-based exercises and visual aids to re-engage the user.

[0754] To make more effective use of generative AI models, it is important to set appropriate prompts. For example, a possible prompt might be, "Please tell me how to maximize learning effectiveness by analyzing the level of concentration of users during customer service skills training and modifying the plan if necessary." This prompt allows the generative AI model to make suggestions that address specific learning needs.

[0755] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0756] Step 1:

[0757] Users input work-related data through a terminal. This data includes skill level, work experience, and training goals. The terminal receives this data, formats it, and sends it to the server. The input data is used as information for developing training plans.

[0758] Step 2:

[0759] The server analyzes business-related data received from terminals. The analysis process uses a database management system to compare the data with historical data and extract user-specific characteristics. This generates user-optimized attribute information, which is then provided to the AI ​​model.

[0760] Step 3:

[0761] The server-generated AI model creates a training plan based on user-specific attribute information. The AI ​​model utilizes machine learning algorithms to learn from past successful training plans, selecting efficient and effective training content. As output, an optimal training plan for the user is generated and sent back to the terminal.

[0762] Step 4:

[0763] The device displays the received training plan on its user interface. The user begins learning according to this plan, sequentially completing the learning materials and assignments designed according to the plan. The learning materials used here include video materials and interactive exercises.

[0764] Step 5:

[0765] The device uses its built-in camera and sensors to collect real-time data on facial expressions and voice during the user's learning process, and to infer their emotional state. This emotional data is analyzed to understand the user's emotional state and is periodically sent to a server.

[0766] Step 6:

[0767] The server evaluates the user's learning state based on received emotional and learning progress data. If the emotional data indicates stress or decreased concentration, the server generates feedback and modifies the training plan as needed. The modified plan is immediately sent to the terminal to improve the user's learning experience.

[0768] (Application Example 2)

[0769] 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".

[0770] Providing individualized educational plans that take into account the emotional state of users in real time is a challenging task in educational settings. In particular, it is necessary to provide prompt and appropriate feedback and adjust educational content in response to changes in users' concentration and interests.

[0771] 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.

[0772] In this invention, the server includes means for collecting business-related information entered by the user, a generation agent means for analyzing the business-related information and generating an educational policy optimized for the user, and an emotion analysis means for detecting the user's emotional state in real time. This makes it possible to flexibly adjust the educational policy according to the user's emotional state.

[0773] "Job-related information" refers to data entered by users in relation to their work, including skill levels, work experience, and training goals.

[0774] The "generation agent means" is a means of executing a process to generate optimized educational policies based on the user's work-related information.

[0775] An "educational policy" is a plan that specifically outlines the content and format of education for users.

[0776] "Progress status" refers to data that shows the progress of the user's educational activities.

[0777] "Means of providing responses" refers to functions for conveying feedback or instructions to users.

[0778] "Emotion analysis methods" are technologies that estimate a user's emotional state in real time based on their facial expressions, voice, and other factors.

[0779] "Interaction means" are methods for realizing interaction between the user and the system.

[0780] The system for realizing this invention includes software that runs on hardware platforms such as Raspberry Pi and Jetson Nano. When a user inputs work-related information into a terminal, that information is sent to a server for analysis. The server uses a generation agent to analyze the information and generate an optimized educational policy for each user.

[0781] The device incorporates a camera and voice sensors, which detect the user's facial expressions and voice, acquiring data in real time. This data is then analyzed using emotion analysis techniques to infer the user's emotional state. Image processing techniques using the OpenCV library and voice analysis techniques using TensorFlow are employed in this analysis.

[0782] The server dynamically adjusts the learning approach based on the user's emotional state and learning progress. If the server detects a decrease in the user's concentration or lack of interest during learning, it suggests new learning styles and activities through interactive means. This helps users continue learning more effectively.

[0783] As a concrete example, when a user is learning a language, if the system detects a decline in interest during conversation practice, it provides educational content incorporating interactive game elements. This content includes a visual interface and audio navigation to attract the user's attention.

[0784] An example of a prompt for a generative AI model would be, "Based on the user's current emotional state, suggest a new learning method to rekindle their interest." This allows the system to quickly provide a learning strategy that is appropriate for the user.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The terminal inputs work-related information. Users input their skill level, work experience, and training goals into the terminal and send this information to the system. This input data is transferred to the server as work-related information.

[0788] Step 2:

[0789] The server analyzes the business-related information. The server analyzes the received business-related information and generates the optimal training policy using a generation agent. Information analysis includes database matching and comparison with historical data. As output, a training policy tailored to the user is generated.

[0790] Step 3:

[0791] The device acquires emotional data. The camera and voice sensor built into the device capture the user's facial expressions and voice in real time. This input data is processed by an emotion analysis system and sent to a server as information to infer the emotional state.

[0792] Step 4:

[0793] The server analyzes the emotional data. The server uses emotional analysis tools to infer the user's emotional state. Data analysis employs image processing techniques using OpenCV and speech recognition technology using TensorFlow. As a result, the user's emotional state is output as numerical or categorical data.

[0794] Step 5:

[0795] The server adjusts the educational policy. The server dynamically adjusts the educational policy based on the student's learning progress and emotional data. It utilizes a generative AI model to generate improvement strategies to enhance user learning satisfaction and concentration. The output of this step is the adjusted educational policy.

[0796] Step 6:

[0797] The device provides responses to the user. Based on a tailored educational policy, the device provides visual or audible feedback to the user. Specific actions include suggesting new learning activities or styles, and presenting interactive content. This output brings about changes in the user's learning environment.

[0798] 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.

[0799] 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.

[0800] 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.

[0801] 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.

[0802] 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.

[0803] 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.

[0804] 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.

[0805] 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.

[0806] 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."

[0807] 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.

[0808] 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.

[0809] 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.

[0810] 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.

[0811] 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.

[0812] 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.

[0813] 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.

[0814] 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.

[0815] 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.

[0816] 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.

[0817] 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.

[0818] 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.

[0819] The following is further disclosed regarding the embodiments described above.

[0820] (Claim 1)

[0821] A means of collecting business-related data entered by users,

[0822] A generation agent means that analyzes the aforementioned business-related data and generates an education plan optimized for the user,

[0823] A means of monitoring the progress of education based on the aforementioned education plan,

[0824] A means of providing feedback to users based on the aforementioned progress,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the generating agent means selects a plurality of educational formats.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the feedback provision means adjusts the educational plan in real time.

[0830] "Example 1"

[0831] (Claim 1)

[0832] A device that collects business-related information entered by users,

[0833] A generative model that analyzes the aforementioned business-related information and generates an educational plan optimized for the user,

[0834] A device for monitoring learning progress based on the aforementioned educational plan,

[0835] A device that provides immediate feedback to the user based on the aforementioned progress data,

[0836] A device that dynamically adjusts the aforementioned educational plan according to the user's growth,

[0837] A system that includes this.

[0838] (Claim 2)

[0839] The system according to claim 1, wherein the generative model selects a variety of educational methods.

[0840] (Claim 3)

[0841] The system according to claim 1, wherein the feedback providing device revises the educational plan in real time.

[0842] "Application Example 1"

[0843] (Claim 1)

[0844] A device that collects business-related information entered by users,

[0845] A generation agent device that analyzes the aforementioned business-related information and generates an education plan optimized for the user,

[0846] A device for monitoring the progress of education based on the aforementioned education plan,

[0847] A device that provides information to the user based on the aforementioned progress status,

[0848] A device that presents educational plans and transmits progress data via a smartphone application,

[0849] A device that analyzes the aforementioned progress data and adjusts the content of education,

[0850] A system that includes this.

[0851] (Claim 2)

[0852] The system according to claim 1, wherein the generating agent device selects from a plurality of educational formats and supports the improvement of the user's skills.

[0853] (Claim 3)

[0854] The system according to claim 1, wherein the information providing device adjusts the training plan in real time and proposes further training in accordance with the improvement of user skills.

[0855] "Example 2 of combining an emotion engine"

[0856] (Claim 1)

[0857] A device that receives business-related data entered by users,

[0858] A generation device that analyzes the aforementioned business-related data and generates a training plan tailored to the user,

[0859] A device for tracking the progress of training based on the aforementioned training plan,

[0860] A device that detects the emotional state of users in real time,

[0861] A device that provides users with immediate plan adjustments and feedback based on their emotional state and progress,

[0862] A system that includes this.

[0863] (Claim 2)

[0864] The system according to claim 1, wherein the generating device automatically selects from a plurality of training formats.

[0865] (Claim 3)

[0866] The system according to claim 1, wherein the feedback providing device modifies the educational plan in real time according to the user's emotional state.

[0867] "Application example 2 when combining with an emotional engine"

[0868] (Claim 1)

[0869] A means of collecting business-related information entered by users,

[0870] A generation agent means that analyzes the aforementioned business-related information and generates an educational policy optimized for the user,

[0871] A means of monitoring the progress of education based on the aforementioned educational policy,

[0872] A means for providing a response to the user based on the progress status,

[0873] A means of sentiment analysis that detects the user's emotional state in real time,

[0874] A means of adjusting educational policies based on the aforementioned emotional state,

[0875] A system that includes this.

[0876] (Claim 2)

[0877] The system according to claim 1, wherein the generating agent means selects a plurality of educational formats and includes interaction means for interacting with the user.

[0878] (Claim 3)

[0879] The system according to claim 1, wherein the response provision means adjusts educational policies in real time and proposes educational activities to regain the user's concentration and interest. [Explanation of symbols]

[0880] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting business-related data entered by users, A generation agent means that analyzes the aforementioned business-related data and generates an education plan optimized for the user, A means of monitoring the progress of education based on the aforementioned education plan, A means of providing feedback to users based on the aforementioned progress, A system that includes this.

2. The system according to claim 1, wherein the generating agent means selects a plurality of educational formats.

3. The system according to claim 1, wherein the feedback provision means adjusts the educational plan in real time.

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