system

Abstract

We provide the system. [Solution] Means for obtaining data on employee characteristics, A means of analyzing the personality traits and aptitudes of employees based on the aforementioned data, A means for generating an optimal training plan for each employee based on the aforementioned analysis results, Means for providing an interface for displaying and modifying the aforementioned training plan, A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In a company, if training is carried out without fully understanding the personalities and aptitudes of new employees, there is a problem that employees cannot adapt to the workplace and may lead to early resignation. Such resignations cause a loss of human resources for the company and have an adverse impact on long-term organizational growth. The present invention aims to reduce the resignation rate by efficiently understanding the characteristics of new employees and providing training according to individual aptitudes. 【Means for Solving the Problems】 【0005】 This invention first provides a means for obtaining data on employee characteristics, and then provides a means for analyzing employees' personalities and aptitudes using AI technology based on this data. Furthermore, it provides a system that automatically generates an optimal training plan using the analysis results, thereby realizing training tailored to each individual employee. In addition, by appropriately selecting and assigning instructors in the proposed training plan, it provides an environment in which employees can easily adapt to the workplace. Moreover, by having a means for dynamically improving the plan based on feedback from employees, the accuracy of training is enhanced and the risk of employee turnover is effectively reduced. 【0006】 "Data on employee characteristics" refers to information including an employee's personality, aptitude, past experience and history, hobbies, etc., and serves as basic data for evaluating an employee's suitability for their job and their abilities. 【0007】 "Means of analysis" refers to a process that uses collected data to extract and evaluate the individual personality traits and job suitability of employees, employing algorithms and machine learning models. 【0008】 "Methods for generating training plans" refers to technology that automatically creates specific plans for guidance and growth designed to maximize each employee's abilities, based on the analyzed characteristics of the employees. 【0009】 "Means of providing an interface" refers to a function that provides a user-friendly interface that visually displays the contents of the training plan, allowing administrators and instructors to review and modify it. 【0010】 "Means for obtaining feedback information" refers to a system for collecting progress reports and areas for improvement from new employees and instructors after the implementation of a training plan. This allows for monitoring the effectiveness of the plan and making adjustments as needed. [Brief explanation of the drawing] 【0011】 [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 the data processing device and 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] 【0012】 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. 【0013】 First, the terms used in the following description will be explained. 【0014】 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. 【0015】 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. 【0016】 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, etc. 【0017】 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), etc. 【0018】 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." 【0019】 [First Embodiment] 【0020】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0021】 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. 【0022】 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). 【0023】 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. 【0024】 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. 【0025】 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. 【0026】 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. 【0027】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0028】 As shown in Figure 2, in the data processing device 12, 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. 【0029】 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. 【0030】 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. 【0031】 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". 【0032】 The system according to the present invention reduces employee turnover in companies by accurately understanding the characteristics of new employees and providing appropriate training tailored to those characteristics. This system functions through the cooperation of three parties: a server, terminals, and users. 【0033】 The server first collects data related to new employees. As part of the onboarding process, users (new employees) answer personality tests and aptitude surveys. The terminal securely transmits this information to the server, which then stores the data. 【0034】 The server uses machine learning techniques to analyze the stored data. This generates detailed reports on employees' personality traits and aptitudes. The analysis results include each employee's job suitability, potential strengths, and areas for improvement. 【0035】 The server then automatically generates an optimal training plan for each employee based on the analysis results. This plan includes specific guidance, such as assigning tasks that allow employees who are evaluated as having "low stress tolerance and high analytical ability" to work in a calm environment and utilize their analytical skills. 【0036】 The device provides an interface that allows administrators and instructors to review the generated training plans. Through this interface, administrators and instructors can review the training plans and modify them as needed. 【0037】 Furthermore, users (elders and managers) input feedback obtained through their actual work. This feedback is collected and analyzed on the server side and used to improve training plans. As a result, training plans are flexibly modified according to the situation, leading to more effective training. 【0038】 As a concrete example, when the server analyzed the data of new employee Tanaka, it was found that while he had strong communication skills, he had issues with task management. Based on this information, the server proposed a plan for Tanaka to improve his task management skills while taking on the role of a facilitator within the team. The server provided an interface that allowed Tanaka's mentor to easily check appropriate guidance policies and improved the plan based on the feedback. 【0039】 By developing employees in this way, while making the most of each employee's strengths, it is possible to increase employee retention and promote the sustainable development of the company. 【0040】 The following describes the processing flow. 【0041】 Step 1: 【0042】 Users (new employees) participate in personality tests and aptitude assessments using a dedicated terminal or web platform provided upon joining the company. Basic personality information and data regarding job suitability are collected during this process. 【0043】 Step 2: 【0044】 The terminal sends the data entered by the user to the server. The server receives this data and securely stores it in the company's database. 【0045】 Step 3: 【0046】 The server processes the stored data using analytical algorithms to analyze employees' personality traits and job suitability from multiple perspectives. This analysis utilizes machine learning techniques to identify characteristics by comparing them with past data. 【0047】 Step 4: 【0048】 Based on the analysis results, the server generates an individual aptitude assessment report for each employee. This report includes information such as the employee's strengths and weaknesses, and what kind of work environment would be most suitable for them. 【0049】 Step 5: 【0050】 The server uses aptitude assessment reports to automatically create optimal training plans for each employee. These plans specifically detail what guidance policies and tasks would be most effective. 【0051】 Step 6: 【0052】 The device displays the generated training plan through an interface that allows administrators and instructors to review it. Administrators and instructors can use this interface to check the plan information and make modifications as needed. 【0053】 Step 7: 【0054】 Users (elders and managers) input feedback obtained through their work into the server via their terminals. The server collects this feedback and analyzes it as data to improve training plans. 【0055】 Step 8: 【0056】 The server dynamically improves training plans based on feedback, updating them according to employee characteristics and work progress. This enables continuous and more effective training. 【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】 The high turnover rate among new employees in companies is due to a lack of proper training and guidance. While it is necessary to efficiently formulate and implement optimal training plans tailored to the individual characteristics of each employee, traditional systems struggle with individualized support, and dynamic plan improvements based on feedback are insufficient. Therefore, a new system is needed that accurately understands employee characteristics and enables training based on those characteristics. 【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 means for collecting employee characteristic data, means for securely storing the data in data storage, means for analyzing the employee's personality traits and aptitudes using a machine learning algorithm, means for automatically generating an optimal training plan based on the analysis results, means for providing an interface that displays and modifies the training plan, and means for acquiring feedback information and dynamically improving the training plan. This enables the formulation of training plans optimized for each employee and flexible improvement of the plans based on feedback. 【0062】 "Employee characteristic data" refers to data about employees' personalities, abilities, aptitudes, and performance, and is primarily collected through diagnostic tests and aptitude surveys. 【0063】 "Data storage" refers to a digital environment for securely storing collected data, and is an information storage system equipped with advanced security measures. 【0064】 A "machine learning algorithm" refers to a technology that recognizes patterns from data and makes predictions, and is a mathematical model used for analyzing employee characteristics and generating training plans. 【0065】 A "training plan" is a set of specific action guidelines and activity plans designed to improve job skills and abilities, based on the characteristics of each employee. 【0066】 An "interface" refers to the means by which a user interacts with a system, and is a mechanism for user interaction that enables the display and modification of training plans. 【0067】 "Feedback information" refers to evaluations and comments regarding employees' performance and behavior in their actual work, and is useful information for improving training plans. 【0068】 The system according to this invention aims to understand the characteristics of employees and generate appropriate training plans. It functions through the coordinated efforts of a server, terminals, and users, and is implemented by the following means. 【0069】 The server first collects employee characteristic data obtained from users (employees) answering personality tests and aptitude surveys. This data is entered by the user, and the terminal securely transmits it to the server via the internet. Data security is ensured during transmission using the HTTPS encryption protocol. 【0070】 The server stores the received data in secure data storage. This data storage incorporates a database system (e.g., an SQL database) and implements strict access control and data encryption. 【0071】 Next, the server analyzes the stored data using machine learning algorithms. This analysis utilizes Python libraries such as "scikit-learn" and "TENSORFLOW®," enabling a detailed understanding of each employee's personality traits and aptitudes. Based on these analysis results, the server automatically generates an optimal training plan for each employee using an AI model. 【0072】 The terminal provides an interface for displaying the generated training plan. This interface allows administrators and instructors to visually review the training plan and modify it as needed. The modified content is quickly sent to the server and reflected in the database. 【0073】 Users (elders and managers) input feedback obtained through their work on a terminal. The terminal receives the feedback and sends it back to the server. The server collects this feedback information, analyzes it using machine learning technology, and dynamically improves the training plan. 【0074】 As a concrete example, by inputting a text-based prompt message into the generation AI model, such as "Design prompt messages that automatically generate training plans tailored to the aptitudes of each new employee based on their personality assessment data," the system will generate appropriate training plans. By using the system in this way, flexible and effective training tailored to the characteristics of each employee can be achieved. 【0075】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0076】 Step 1: 【0077】 As part of the onboarding process, users (employees) complete personality tests and aptitude assessments. Online forms are used for this process, and user responses are directly recorded as digital data. The terminal transmits this data to a server via the internet. Input is the user's text responses, and output is encrypted data sent to the server. 【0078】 Step 2: 【0079】 The server receives data sent from the terminal and stores it in highly secure data storage. The data is encrypted via the HTTPS protocol upon reception and further encrypted within the database for enhanced security during storage. The input is encrypted data from the terminal, and the output is storage in secure data storage. 【0080】 Step 3: 【0081】 The server extracts the stored data and begins analysis using machine learning algorithms. Specifically, it uses Python libraries such as "scikit-learn" and "TensorFlow" to analyze the input trait data and gain insights into employees' personality traits and aptitudes. In this step, the input is trait data from data storage, and the output is the analysis results. 【0082】 Step 4: 【0083】 The server automatically generates an optimal training plan using an AI model based on the analysis results. This generation process specifically formulates tasks and guidance policies tailored to the characteristics of each employee. The input is the analysis results obtained in the previous step, and the output is the training plan. 【0084】 Step 5: 【0085】 The terminal displays the generated training plan on an interface that administrators and instructors can review. Administrators and instructors can then modify this plan as needed. This modified plan is then sent back to the server. The input is the training plan, and the output is the reviewed and modified plan. 【0086】 Step 6: 【0087】 Users (elders and managers) input feedback obtained through their work into a terminal. This feedback includes information about employees' actual work performance and challenges. The terminal sends this data to a server; the input is the feedback information, and the output is the feedback data sent to the server. 【0088】 Step 7: 【0089】 The server analyzes the received feedback information and uses it to improve the training plan. It then uses machine learning techniques to evaluate the data again and optimize the plan. The input is the feedback data, and the output is the updated training plan. 【0090】 (Application Example 1) 【0091】 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." 【0092】 In modern society, there is a need for technologies that support lifestyle improvements based on individual characteristics. However, providing optimal plans tailored to individual characteristics is difficult, and there is a challenge in providing effective support. In particular, with the increasing diversity of lifestyles and the growing need for support that is tailored to individual characteristics, it is desirable to provide a system that can accurately grasp individual characteristics and provide support accordingly. 【0093】 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. 【0094】 In this invention, the server includes means for acquiring data on an individual's characteristics, means for acquiring data on an individual's behavioral habits and proposing an optimal lifestyle improvement plan, and means for providing an interface for displaying and modifying a growth support plan. This enables optimal lifestyle improvement support and growth promotion tailored to individual characteristics. 【0095】 "Traits" refer to the personality and behavioral characteristics of individual people, and are the information that forms the basis for analysis and adaptation. 【0096】 "Data acquisition means" refers to methods and devices for collecting information about an individual's characteristics and behavior. 【0097】 "Analysis methods" refer to methods for understanding and evaluating an individual's characteristics and aptitudes based on acquired data. 【0098】 A "growth support plan" is a specific activity plan formulated based on analysis results, aimed at growth and improvement tailored to an individual's characteristics. 【0099】 An "interface" refers to a function or screen that displays a growth support plan and serves as a point of contact for users to review or modify its contents. 【0100】 "Behavioral habit data" refers to information that shows an individual's daily behavior and patterns, and is basic data used to suggest improvements to their lifestyle. 【0101】 A "lifestyle improvement plan" is a plan proposed to help individuals achieve better lifestyle habits based on their behavioral data. 【0102】 The system for implementing this invention is designed to provide growth support plans optimized for individual characteristics. The system functions through the coordinated interaction of a server, terminals, and users. 【0103】 The server first acquires data about an individual's characteristics through an internet-connected device. This data may include personality assessment results and behavioral habits. This data is securely transferred to a cloud server, such as Google Cloud. The server then analyzes this data using TensorFlow, a machine learning framework. This analysis allows for a detailed understanding of the individual's characteristics and aptitudes. 【0104】 Based on the analysis results, the server automatically generates a growth support plan optimized for each individual's characteristics. This plan includes suggestions for lifestyle improvements and support tailored to each individual's needs. This plan can be viewed by the user via their terminal, and modifications can be made as needed through the interface. 【0105】 Furthermore, this system incorporates feedback from users in their daily lives, providing a function to more effectively improve the growth support plan. This allows the plan to flexibly adapt to individual changes and progress. 【0106】 As a concrete example, for an individual who wants to improve their daily exercise habits, the server can suggest an optimal early morning jogging schedule and support its implementation. A robotic terminal can announce departure times and provide running routes via voice. 【0107】 An example of a prompt to the generating AI model is as follows: "Please suggest an appropriate exercise plan and its implementation method for a user aiming to improve their exercise habits." Using this prompt, a more specific improvement plan tailored to the user's characteristics can be generated more effectively. 【0108】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0109】 Step 1: 【0110】 Users use their devices to complete personality assessments and questionnaires about their daily behaviors. This generates data about the user's characteristics. This data is then securely transmitted from the device to the server. 【0111】 Step 2: 【0112】 The server receives data, stores it on a cloud server, and performs analysis using a machine learning model (TensorFlow is used). This analysis outputs user personality traits and behavioral patterns as numerical data. Based on this data, a detailed report on individual characteristics and aptitudes is generated. 【0113】 Step 3: 【0114】 The server utilizes a generation AI model based on the generated report to automatically create a user-specific growth support plan. Prompt statements (e.g., "Please suggest an appropriate exercise plan and implementation method for a user aiming to improve their exercise habits.") are used to specify the plan's content. The generated plan is customized for each user. 【0115】 Step 4: 【0116】 The server sends the growth support plan to the terminal, which the user can then view on the interface. On the interface, the user can browse the plan's contents and modify options as needed. This includes actions such as setting task priorities and adding new goals. 【0117】 Step 5: 【0118】 The user sends feedback from their device to the server during their daily activities. This feedback includes the user's progress and newly emerging needs, and the server dynamically improves the growth support plan based on this feedback. Data calculations incorporating the feedback are performed to generate an updated version of the plan. 【0119】 Step 6: 【0120】 The device then provides the user with an updated growth support plan and offers specific support for the next steps. For example, it might notify the user of the start time for exercise or visualize their progress. This allows the user to continuously improve their lifestyle in accordance with the plan. 【0121】 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. 【0122】 The system of this invention not only analyzes the personality and aptitude of new employees and provides the optimal training method, but also, by combining it with an emotion engine, evaluates the mental state of employees in real time, enabling further individualized support. This system consists of a server, terminals, and users. 【0123】 The server first stores employee characteristic data based on the results of personality assessments and aptitude tests collected from users (new employees). Next, it uses machine learning models with this data to analyze personality and aptitude, and generates an aptitude assessment report for each employee. 【0124】 Based on the generated report, the server automatically creates an optimal training plan. Furthermore, it uses an emotion engine to collect user emotional data, which the server analyzes to evaluate the employee's mental state. The training plan is dynamically adjusted according to this evaluation. 【0125】 The device displays the generated training plan through an interface accessible to managers and instructors. This interface also displays real-time information about the trainee's emotional state, allowing for plan modifications as needed. For example, if the plan detects a high workload, it provides features to reduce the difficulty of tasks or implement measures to alleviate the burden. 【0126】 As a user (instructor), you observe employees' work and input the feedback you receive into a terminal. Based on this feedback, the server combines the feedback with evaluations from the emotion engine to improve the training plan. For example, if the server detects emotional data indicating that an employee is experiencing stress, it provides real-time advice to the instructor to enhance support for the employee. 【0127】 As a concrete example, when the server analyzed employee Yamada's data, it determined that while he possessed high social skills, he tended to be vulnerable to pressure. Based on this personality analysis and the stress levels detected by the emotion engine, the server proposed a plan to reduce Yamada's stress. This plan is available on the terminal for the supervisor to review, allowing the supervisor to create a better training environment by providing guidance tailored to Yamada's emotional state. 【0128】 In this way, this system, which incorporates an emotional engine, enables flexible responses tailored to the mental state of employees, providing a solution to promote employee retention and growth within companies. 【0129】 The following describes the processing flow. 【0130】 Step 1: 【0131】 Users (new employees) collect data about their characteristics by answering personality tests and aptitude surveys using a dedicated terminal or web platform provided upon joining the company. At this stage, certain emotional data is also recorded by the terminal. 【0132】 Step 2: 【0133】 The device transmits user-entered data and sentiment data to the server. The transmitted data is securely stored in a company-specific database. 【0134】 Step 3: 【0135】 The server uses machine learning models to analyze personality traits and job suitability based on stored employee characteristic data. Based on the analysis results, an aptitude assessment report is generated for each individual employee. 【0136】 Step 4: 【0137】 The server integrates information from aptitude assessment reports with emotional data to automatically create an optimal training plan for each employee. Here, an emotional engine is used to plan individualized support based on each employee's mental state. 【0138】 Step 5: 【0139】 The terminal provides an interface that allows managers and instructors to review the generated training plans. This interface also allows real-time viewing of information regarding employees' emotional states. 【0140】 Step 6: 【0141】 Users (instructors) input observations from actual work and feedback from employees into the server via their terminals. The server analyzes this feedback and data obtained from the emotion engine to further adjust training plans. 【0142】 Step 7: 【0143】 The server monitors changes in the user's mental state, as captured by the emotion engine, and uses this information to provide real-time advice to the instructor. For example, if a user's emotional data indicates stress, the server will suggest countermeasures and ways to improve the instruction. 【0144】 Step 8: 【0145】 Based on the user's emotional state and work progress, the server dynamically improves the training plan, providing optimal guidance that is always up-to-date. This process makes it possible to create an effective training environment while supporting employees' mental health. 【0146】 (Example 2) 【0147】 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". 【0148】 Traditional employee training systems typically provide a uniform training plan, making it difficult to provide personalized support that takes into account the individual characteristics and emotional states of each employee. This resulted in challenges in maximizing employee growth and retention rates. Furthermore, real-time emotional assessment and dynamic adjustment of plans were difficult, leading to insufficient responses to stress and pressure. 【0149】 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. 【0150】 In this invention, the server includes means for acquiring data on employee characteristics; means for analyzing employee personality traits and aptitudes based on the data and performing aptitude evaluations using machine learning models; means for generating an optimal training plan for each employee based on the analysis results and creating an individualized plan considering the company's training policies and success story data; means for using emotion recognition technology to evaluate emotional states in real time; and means for providing an interface for displaying and modifying the training plan, and for allowing instructors to input feedback. This enables the provision of flexible and adaptive training plans for individual employees, facilitating rapid mental support and efficient talent development. 【0151】 "Data on employee characteristics" refers to information that indicates an individual employee's personality, aptitude, behavioral patterns, and emotional state. 【0152】 A "machine learning model" is a collection of algorithms and mathematical models that use data to automatically find specific patterns or rules and perform predictions and classifications. 【0153】 "Aptitude assessment" is a process of analyzing an employee's personality, skills, and suitability for the job, and quantitatively determining those characteristics. 【0154】 A "training plan" is a systematically structured plan that includes individualized training content and schedules to promote the improvement and growth of employees' skills. 【0155】 "Emotion recognition technology" is a technology that automatically analyzes and identifies human emotions from data such as voice, text, and facial expressions. 【0156】 An "interface" is a platform or means of connection provided for a user to interact with a system and to display and manipulate information. 【0157】 "Feedback" is the process of providing information such as evaluations and suggestions regarding employees' work performance and behavior, which is used for subsequent improvement and adjustments. 【0158】 The system of this invention analyzes the characteristics and emotional state of employees and provides individually optimized training plans. This system consists of a server, terminals, and users. 【0159】 The server collects personality assessment and aptitude survey data provided by users (employees). This includes online questionnaires and tests. The collected data is stored in a database (e.g., MySQL®). Based on this data, the server uses machine learning models (e.g., scikit-learn or TensorFlow) to analyze employees' personalities and aptitudes and perform aptitude assessments. 【0160】 Based on the analysis results, the server generates an optimal training plan for each employee. This plan is individually optimized using an AI model, taking into account the company's training policies and past success stories. Furthermore, emotion recognition technology (e.g., natural language processing tools) is used to collect and evaluate user emotion data in real time, allowing for an understanding of employees' mental state. 【0161】 The terminal features an interface that displays training plans generated by the server to the user (instructor). In addition to displaying the training plan, this interface provides real-time information on the employee's emotional state, allowing instructors to adjust the plan as needed. For example, if emotional data indicating stress is detected, it becomes possible to adjust the difficulty level of the tasks assigned. 【0162】 The user (instructor) enters feedback on the employee's work into the terminal. This feedback information is sent to the server and further analyzed in combination with other sentiment data to help improve training plans. 【0163】 A concrete example of a prompt statement would be: "Based on the personality assessment results and aptitude survey data of new employees, create an optimal training plan, and adjust that plan in real time based on the employees' emotional state, then report the results." 【0164】 This system aims to provide flexible and adaptive training plans tailored to each employee's individual characteristics and emotional state, thereby promoting employee satisfaction and growth. 【0165】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0166】 Step 1: 【0167】 Users (employees) undergo personality assessments and aptitude tests. This includes online questionnaires and tests. The data provided by the users is sent to the server. 【0168】 Step 2: 【0169】 The server receives personality assessment results and aptitude survey data submitted by users. The input data includes information about an individual's personality traits and skill set. The server manages employee characteristics by storing this data in a database. 【0170】 Step 3: 【0171】 The server performs analysis using machine learning models based on the stored data. It uses scikit-learn and TensorFlow to process and perform calculations on data to evaluate employees' personality traits and aptitudes. The output is the employee aptitude evaluation results. 【0172】 Step 4: 【0173】 The server generates an optimal training plan for each employee based on the aptitude assessment results obtained. It references the company's training policies and success story data, and uses an AI model to create individually optimized training plans. The output is a specific training plan. 【0174】 Step 5: 【0175】 The terminal displays training plans sent from the server to managers and instructors through an interface. Users (instructors) can view the plan's content and real-time information on employees' emotional states. This information serves as a guide for users in implementing the training plans. 【0176】 Step 6: 【0177】 The user (instructor) observes employees during work and inputs the feedback information obtained into a terminal. This feedback includes specific comments about the employee's work performance and emotional state. The input feedback is sent to the server. 【0178】 Step 7: 【0179】 The server combines user feedback information with emotional data collected using emotion recognition technology to improve the training plan. This allows the training plan to be dynamically adjusted in real time. The output is the latest training plan. 【0180】 Through the steps outlined above, this system provides each employee with an appropriate and adaptive training plan, supporting their growth and success within the company. 【0181】 (Application Example 2) 【0182】 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". 【0183】 Traditional new employee training systems can analyze individual personalities and aptitudes to provide optimal training plans, but they have the challenge of dynamically adjusting plans to take into account employees' real-time emotional states. Furthermore, reflecting the societal trend towards prioritizing mental health, there is a growing need to provide training environments that also consider the mental well-being of employees. 【0184】 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. 【0185】 In this invention, the server includes means for acquiring data on employee characteristics, means for analyzing employee personality traits and aptitudes based on the data, means for generating an optimal training plan for each employee based on the analysis results, means for collecting and evaluating emotional information in real time, and means for dynamically adjusting the training plan based on the evaluation. This makes it possible to provide flexible and individualized training plans based on the employee's personality and emotional state. 【0186】 "Data related to characteristics" refers to information that indicates the personality and aptitude of individual employees, and includes the results of personality assessments and aptitude tests. 【0187】 "Analysis" is the process of thoroughly analyzing the obtained data to understand the personality traits and aptitudes of employees. 【0188】 A "training plan" is a plan or guideline created to optimally develop employees' abilities based on analysis results. 【0189】 An "interface" is a computer-based input and output mechanism that allows users to review training plans and make modifications as needed. 【0190】 "Emotional information" refers to data that indicates employees' mental state and mood, including real-time emotional fluctuations. 【0191】 "Methods for real-time evaluation" refer to technologies and methods for analyzing emotional information immediately to assess a person's mental state. 【0192】 "Means of dynamic adjustment" refers to methods for immediately adapting training plans based on changing data. 【0193】 In embodiments of this invention, a server, terminals, and users constitute the system. The server collects data on employee characteristics and uses machine learning algorithms to analyze personality traits and aptitudes based on this data. This analysis automatically generates a training plan tailored to each employee. Furthermore, by using an emotion engine, it is possible to collect employee emotional information in real time and dynamically adjust the training plan based on the analysis results. Here, the evaluation of emotional information is performed using analysis with a cloud service API. The hardware used includes a network-connected server and corresponding terminals. On the software side, cloud service APIs (e.g., AWS®, Google Cloud) are used for analyzing emotional data. 【0194】 The device provides an interface that allows administrators and instructors to review generated training plans and emotional status information. This interface enables users to modify training plans as needed and has the functionality to address employees' mental health issues and stress. 【0195】 The user's role is to input feedback obtained through observation of daily tasks into the terminal. The server receives this feedback and uses a machine learning model to further optimize the training plan. 【0196】 For example, if the server analyzes employee data and determines that a particular employee tends to be susceptible to pressure, it will propose a special stress reduction plan based on this information and the stress levels detected by the emotion engine. This plan can be viewed and modified on the employee's device, and trainers can use this information to provide more effective training. 【0197】 Using a generative AI model, an example of a prompt message could be: "Based on this morning's sentiment analysis, adjust the task plan to provide the best possible support to the user." In this way, the entire system can work together to support employee development. 【0198】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0199】 Step 1: 【0200】 The server acquires data on employee characteristics. Specifically, it collects the results of personality assessments and aptitude tests and stores them in a database. The input for this step is the results of personality assessments and aptitude tests, and the output is the storage of this data in the database used for analysis. 【0201】 Step 2: 【0202】 The server analyzes employees' personality traits and aptitudes based on the acquired data. Specifically, it uses a machine learning model to perform the analysis and generate profiles of personality traits and aptitudes. The input is trait data, and the output is personality traits and aptitude profiles. 【0203】 Step 3: 【0204】 The server generates an optimal training plan for each employee based on the analysis results. This includes a process of creating individualized plans using pre-prepared templates. The input is personality traits and aptitude profiles, and the output is an individual training plan. 【0205】 Step 4: 【0206】 The terminal displays the generated training plan in an interface that allows the user to review it. Here, instructors can review the plan content and make modifications as needed. The input is the training plan, and the output is the information displayed on the user interface. 【0207】 Step 5: 【0208】 The server uses an emotion engine to collect and evaluate employee emotional information in real time. Emotion analysis software is used to process the emotional data and understand the current mental state. The input is real-time emotional data, and the output is an evaluation of the emotional state. 【0209】 Step 6: 【0210】 The server dynamically adjusts the training plan based on the results of the emotional state assessment. It may change the difficulty level of the plan or add activities to promote relaxation as needed. The input is the emotional state assessment result, and the output is the adjusted training plan. 【0211】 Step 7: 【0212】 Users observe their daily tasks and input the feedback they receive into a terminal, sending it to the server. The server uses this feedback to further optimize the training plan. The input is feedback information, and the output is the optimized training plan. 【0213】 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. 【0214】 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. 【0215】 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. 【0216】 [Second Embodiment] 【0217】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0218】 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. 【0219】 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). 【0220】 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. 【0221】 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. 【0222】 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). 【0223】 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. 【0224】 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. 【0225】 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. 【0226】 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. 【0227】 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. 【0228】 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". 【0229】 The system according to the present invention reduces employee turnover in companies by accurately understanding the characteristics of new employees and providing appropriate training tailored to those characteristics. This system functions through the cooperation of three parties: a server, terminals, and users. 【0230】 The server first collects data related to new employees. As part of the onboarding process, users (new employees) answer personality tests and aptitude surveys. The terminal securely transmits this information to the server, which then stores the data. 【0231】 The server uses machine learning techniques to analyze the stored data. This generates detailed reports on employees' personality traits and aptitudes. The analysis results include each employee's job suitability, potential strengths, and areas for improvement. 【0232】 The server then automatically generates an optimal training plan for each employee based on the analysis results. This plan includes specific guidance, such as assigning tasks that allow employees who are evaluated as having "low stress tolerance and high analytical ability" to work in a calm environment and utilize their analytical skills. 【0233】 The device provides an interface that allows administrators and instructors to review the generated training plans. Through this interface, administrators and instructors can review the training plans and modify them as needed. 【0234】 Furthermore, users (elders and managers) input feedback obtained through their actual work. This feedback is collected and analyzed on the server side and used to improve training plans. As a result, training plans are flexibly modified according to the situation, leading to more effective training. 【0235】 As a concrete example, when the server analyzed the data of new employee Tanaka, it was found that while he had strong communication skills, he had issues with task management. Based on this information, the server proposed a plan for Tanaka to improve his task management skills while taking on the role of a facilitator within the team. The server provided an interface that allowed Tanaka's mentor to easily check appropriate guidance policies and improved the plan based on the feedback. 【0236】 By developing employees in this way, while making the most of each employee's strengths, it is possible to increase employee retention and promote the sustainable development of the company. 【0237】 The following describes the processing flow. 【0238】 Step 1: 【0239】 Users (new employees) participate in personality tests and aptitude assessments using a dedicated terminal or web platform provided upon joining the company. Basic personality information and data regarding job suitability are collected during this process. 【0240】 Step 2: 【0241】 The terminal sends the data entered by the user to the server. The server receives this data and securely stores it in the company's database. 【0242】 Step 3: 【0243】 The server processes the stored data using analytical algorithms to analyze employees' personality traits and job suitability from multiple perspectives. This analysis utilizes machine learning techniques to identify characteristics by comparing them with past data. 【0244】 Step 4: 【0245】 Based on the analysis results, the server generates an individual aptitude assessment report for each employee. This report includes information such as the employee's strengths and weaknesses, and what kind of work environment would be most suitable for them. 【0246】 Step 5: 【0247】 The server uses aptitude assessment reports to automatically create optimal training plans for each employee. These plans specifically detail what guidance policies and tasks would be most effective. 【0248】 Step 6: 【0249】 The device displays the generated training plan through an interface that allows administrators and instructors to review it. Administrators and instructors can use this interface to check the plan information and make modifications as needed. 【0250】 Step 7: 【0251】 Users (elders and managers) input feedback obtained through their work into the server via their terminals. The server collects this feedback and analyzes it as data to improve training plans. 【0252】 Step 8: 【0253】 The server dynamically improves training plans based on feedback, updating them according to employee characteristics and work progress. This enables continuous and more effective training. 【0254】 (Example 1) 【0255】 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". 【0256】 The high turnover rate among new employees in companies is due to a lack of proper training and guidance. While it is necessary to efficiently formulate and implement optimal training plans tailored to the individual characteristics of each employee, traditional systems struggle with individualized support, and dynamic plan improvements based on feedback are insufficient. Therefore, a new system is needed that accurately understands employee characteristics and enables training based on those characteristics. 【0257】 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. 【0258】 In this invention, the server includes means for collecting employee characteristic data, means for securely storing the data in data storage, means for analyzing the employee's personality traits and aptitudes using a machine learning algorithm, means for automatically generating an optimal training plan based on the analysis results, means for providing an interface that displays and modifies the training plan, and means for acquiring feedback information and dynamically improving the training plan. This enables the formulation of training plans optimized for each employee and flexible improvement of the plans based on feedback. 【0259】 "Employee characteristic data" refers to data about employees' personalities, abilities, aptitudes, and performance, and is primarily collected through diagnostic tests and aptitude surveys. 【0260】 "Data storage" refers to a digital environment for securely storing collected data, and is an information storage system equipped with advanced security measures. 【0261】 A "machine learning algorithm" refers to a technology that recognizes patterns from data and makes predictions, and is a mathematical model used for analyzing employee characteristics and generating training plans. 【0262】 A "training plan" is a set of specific action guidelines and activity plans designed to improve job skills and abilities, based on the characteristics of each employee. 【0263】 An "interface" refers to the means by which a user interacts with a system, and is a mechanism for user interaction that enables the display and modification of training plans. 【0264】 "Feedback information" refers to evaluations and comments regarding employees' performance and behavior in their actual work, and is useful information for improving training plans. 【0265】 The system according to this invention aims to understand the characteristics of employees and generate appropriate training plans. It functions through the coordinated efforts of a server, terminals, and users, and is implemented by the following means. 【0266】 The server first collects employee characteristic data obtained from users (employees) answering personality tests and aptitude surveys. This data is entered by the user, and the terminal securely transmits it to the server via the internet. Data security is ensured during transmission using the HTTPS encryption protocol. 【0267】 The server stores the received data in secure data storage. This data storage incorporates a database system (e.g., an SQL database) and implements strict access control and data encryption. 【0268】 Next, the server analyzes the stored data using machine learning algorithms. This analysis utilizes Python libraries such as "scikit-learn" and "TensorFlow," enabling a detailed understanding of each employee's personality traits and aptitudes. Based on these analysis results, the server automatically generates an optimal training plan for each employee using an AI model. 【0269】 The terminal provides an interface for displaying the generated training plan. This interface allows administrators and instructors to visually review the training plan and modify it as needed. The modified content is quickly sent to the server and reflected in the database. 【0270】 Users (elders and managers) input feedback obtained through their work on a terminal. The terminal receives the feedback and sends it back to the server. The server collects this feedback information, analyzes it using machine learning technology, and dynamically improves the training plan. 【0271】 As a concrete example, by inputting a text-based prompt message into the generation AI model, such as "Design prompt messages that automatically generate training plans tailored to the aptitudes of each new employee based on their personality assessment data," the system will generate appropriate training plans. By using the system in this way, flexible and effective training tailored to the characteristics of each employee can be achieved. 【0272】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0273】 Step 1: 【0274】 As part of the onboarding process, users (employees) complete personality tests and aptitude assessments. Online forms are used for this process, and user responses are directly recorded as digital data. The terminal transmits this data to a server via the internet. Input is the user's text responses, and output is encrypted data sent to the server. 【0275】 Step 2: 【0276】 The server receives data sent from the terminal and stores it in highly secure data storage. The data is encrypted via the HTTPS protocol upon reception and further encrypted within the database for enhanced security during storage. The input is encrypted data from the terminal, and the output is storage in secure data storage. 【0277】 Step 3: 【0278】 The server extracts the stored data and begins analysis using machine learning algorithms. Specifically, it uses Python libraries such as "scikit-learn" and "TensorFlow" to analyze the input trait data and gain insights into employees' personality traits and aptitudes. In this step, the input is trait data from data storage, and the output is the analysis results. 【0279】 Step 4: 【0280】 The server automatically generates an optimal training plan based on the analysis results using an AI model. In this generation process, tasks and guidelines suitable for the characteristics of employees are specifically formulated. The input is the analysis result obtained in the previous step, and the output is the training plan. 【0281】 Step 5: 【0282】 The terminal displays the generated training plan on an interface where administrators and instructors can view it. Based on this plan, administrators and instructors can modify the content as needed. This modified plan is sent back to the server. The input is the training plan, and the output is the confirmed and modified plan. 【0283】 Step 6: 【0284】 Users (elders and management) input the feedback obtained through their work into the terminal. This feedback includes information on the actual work performance and issues of employees. The terminal sends this data to the server. The input is the feedback information, and the output is the feedback data sent to the server. 【0285】 Step 7: 【0286】 The server analyzes the received feedback information and utilizes it to improve the training plan. The data is evaluated again using machine learning techniques to optimize the plan. The input is the feedback data, and the output is the updated training plan. 【0287】 (Application Example 1) 【0288】 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". 【0289】 In modern society, there is a need for technologies that support lifestyle improvements based on individual characteristics. However, providing optimal plans tailored to individual characteristics is difficult, and there is a challenge in providing effective support. In particular, with the increasing diversity of lifestyles and the growing need for support that is tailored to individual characteristics, it is desirable to provide a system that can accurately grasp individual characteristics and provide support accordingly. 【0290】 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. 【0291】 In this invention, the server includes means for acquiring data on an individual's characteristics, means for acquiring data on an individual's behavioral habits and proposing an optimal lifestyle improvement plan, and means for providing an interface for displaying and modifying a growth support plan. This enables optimal lifestyle improvement support and growth promotion tailored to individual characteristics. 【0292】 "Traits" refer to the personality and behavioral characteristics of individual people, and are the information that forms the basis for analysis and adaptation. 【0293】 "Data acquisition means" refers to methods and devices for collecting information about an individual's characteristics and behavior. 【0294】 "Analysis methods" refer to methods for understanding and evaluating an individual's characteristics and aptitudes based on acquired data. 【0295】 A "growth support plan" is a specific activity plan formulated based on analysis results, aimed at growth and improvement tailored to an individual's characteristics. 【0296】 An "interface" refers to a function or screen that displays a growth support plan and serves as a point of contact for users to review or modify its contents. 【0297】 "Behavioral habit data" refers to information that shows an individual's daily behavior and patterns, and is basic data used to suggest improvements to their lifestyle. 【0298】 A "lifestyle improvement plan" is a plan proposed to help individuals achieve better lifestyle habits based on their behavioral data. 【0299】 The system for implementing this invention is designed to provide growth support plans optimized for individual characteristics. The system functions through the coordinated interaction of a server, terminals, and users. 【0300】 The server first acquires data about an individual's characteristics through an internet-connected device. This data may include personality assessment results and behavioral habits. This data is securely transferred to a cloud server, such as Google Cloud. The server then analyzes this data using TensorFlow, a machine learning framework. This analysis allows for a detailed understanding of the individual's characteristics and aptitudes. 【0301】 Based on the analysis results, the server automatically generates a growth support plan optimized for each individual's characteristics. This plan includes suggestions for lifestyle improvements and support tailored to each individual's needs. This plan can be viewed by the user via their terminal, and modifications can be made as needed through the interface. 【0302】 Furthermore, this system incorporates feedback from users in their daily lives, providing a function to more effectively improve the growth support plan. This allows the plan to flexibly adapt to individual changes and progress. 【0303】 As a concrete example, for an individual who wants to improve their daily exercise habits, the server can suggest an optimal early morning jogging schedule and support its implementation. A robotic terminal can announce departure times and provide running routes via voice. 【0304】 Examples of prompt texts for the generative AI model are as follows. "Please propose an appropriate exercise plan and its implementation method for users aiming to improve their exercise habits." By using this prompt, a specific improvement plan tailored to the user's characteristics is generated more effectively. 【0305】 The flow of the specific process in Application Example 1 will be described using FIG. 12. 【0306】 Step 1: 【0307】 The user uses the terminal to input a questionnaire regarding personality diagnosis and daily behavior. As a result, data regarding the user's characteristics is generated. This data is securely transmitted from the terminal to the server. 【0308】 Step 2: 【0309】 The server stores the received data in the cloud server and performs analysis using a machine learning model (TensorFlow is used). Through this analysis, the user's personality characteristics and behavior patterns are output as numerical data. Based on these data, a detailed report regarding an individual's characteristics and suitability is generated. 【0310】 Step 3: 【0311】 The server utilizes the generative AI model based on the generated report and automatically generates a growth support plan tailored to the user. The content of the plan is specified using a prompt text (example: "Please propose an appropriate exercise plan and its implementation method for users aiming to improve their exercise habits."). The generated plan is customized for each user. 【0312】 Step 4: 【0313】 The server sends the growth support plan to the terminal, which the user can then view on the interface. On the interface, the user can browse the plan's contents and modify options as needed. This includes actions such as setting task priorities and adding new goals. 【0314】 Step 5: 【0315】 The user sends feedback from their device to the server during their daily activities. This feedback includes the user's progress and newly emerging needs, and the server dynamically improves the growth support plan based on this feedback. Data calculations incorporating the feedback are performed to generate an updated version of the plan. 【0316】 Step 6: 【0317】 The device then provides the user with an updated growth support plan and offers specific support for the next steps. For example, it might notify the user of the start time for exercise or visualize their progress. This allows the user to continuously improve their lifestyle in accordance with the plan. 【0318】 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. 【0319】 The system of this invention not only analyzes the personality and aptitude of new employees and provides the optimal training method, but also, by combining it with an emotion engine, evaluates the mental state of employees in real time, enabling further individualized support. This system consists of a server, terminals, and users. 【0320】 The server first stores employee characteristic data based on the results of personality assessments and aptitude tests collected from users (new employees). Next, it uses machine learning models with this data to analyze personality and aptitude, and generates an aptitude assessment report for each employee. 【0321】 Based on the generated report, the server automatically creates an optimal training plan. Furthermore, it uses an emotion engine to collect user emotional data, which the server analyzes to evaluate the employee's mental state. The training plan is dynamically adjusted according to this evaluation. 【0322】 The device displays the generated training plan through an interface accessible to managers and instructors. This interface also displays real-time information about the trainee's emotional state, allowing for plan modifications as needed. For example, if the plan detects a high workload, it provides features to reduce the difficulty of tasks or implement measures to alleviate the burden. 【0323】 As a user (instructor), you observe employees' work and input the feedback you receive into a terminal. Based on this feedback, the server combines the feedback with evaluations from the emotion engine to improve the training plan. For example, if the server detects emotional data indicating that an employee is experiencing stress, it provides real-time advice to the instructor to enhance support for the employee. 【0324】 As a concrete example, when the server analyzed employee Yamada's data, it determined that while he possessed high social skills, he tended to be vulnerable to pressure. Based on this personality analysis and the stress levels detected by the emotion engine, the server proposed a plan to reduce Yamada's stress. This plan is available on the terminal for the supervisor to review, allowing the supervisor to create a better training environment by providing guidance tailored to Yamada's emotional state. 【0325】 In this way, this system, which incorporates an emotional engine, enables flexible responses tailored to the mental state of employees, providing a solution to promote employee retention and growth within companies. 【0326】 The following describes the processing flow. 【0327】 Step 1: 【0328】 Users (new employees) collect data about their characteristics by answering personality tests and aptitude surveys using a dedicated terminal or web platform provided upon joining the company. At this stage, certain emotional data is also recorded by the terminal. 【0329】 Step 2: 【0330】 The device transmits user-entered data and sentiment data to the server. The transmitted data is securely stored in a company-specific database. 【0331】 Step 3: 【0332】 The server uses machine learning models to analyze personality traits and job suitability based on stored employee characteristic data. Based on the analysis results, an aptitude assessment report is generated for each individual employee. 【0333】 Step 4: 【0334】 The server integrates information from aptitude assessment reports with emotional data to automatically create an optimal training plan for each employee. Here, an emotional engine is used to plan individualized support based on each employee's mental state. 【0335】 Step 5: 【0336】 The terminal provides an interface that allows managers and instructors to review the generated training plans. This interface also allows real-time viewing of information regarding employees' emotional states. 【0337】 Step 6: 【0338】 Users (instructors) input observations from actual work and feedback from employees into the server via their terminals. The server analyzes this feedback and data obtained from the emotion engine to further adjust training plans. 【0339】 Step 7: 【0340】 The server monitors changes in the user's mental state, as captured by the emotion engine, and uses this information to provide real-time advice to the instructor. For example, if a user's emotional data indicates stress, the server will suggest countermeasures and ways to improve the instruction. 【0341】 Step 8: 【0342】 Based on the user's emotional state and work progress, the server dynamically improves the training plan, providing optimal guidance that is always up-to-date. This process makes it possible to create an effective training environment while supporting employees' mental health. 【0343】 (Example 2) 【0344】 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". 【0345】 Traditional employee training systems typically provide a uniform training plan, making it difficult to provide personalized support that takes into account the individual characteristics and emotional states of each employee. This resulted in challenges in maximizing employee growth and retention rates. Furthermore, real-time emotional assessment and dynamic adjustment of plans were difficult, leading to insufficient responses to stress and pressure. 【0346】 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. 【0347】 In this invention, the server includes means for acquiring data on employee characteristics; means for analyzing employee personality traits and aptitudes based on the data and performing aptitude evaluations using machine learning models; means for generating an optimal training plan for each employee based on the analysis results and creating an individualized plan considering the company's training policies and success story data; means for using emotion recognition technology to evaluate emotional states in real time; and means for providing an interface for displaying and modifying the training plan, and for allowing instructors to input feedback. This enables the provision of flexible and adaptive training plans for individual employees, facilitating rapid mental support and efficient talent development. 【0348】 "Data on employee characteristics" refers to information that indicates an individual employee's personality, aptitude, behavioral patterns, and emotional state. 【0349】 A "machine learning model" is a collection of algorithms and mathematical models that use data to automatically find specific patterns or rules and perform predictions and classifications. 【0350】 "Aptitude assessment" is a process of analyzing an employee's personality, skills, and suitability for the job, and quantitatively determining those characteristics. 【0351】 A "training plan" is a systematically structured plan that includes individualized training content and schedules to promote the improvement and growth of employees' skills. 【0352】 "Emotion recognition technology" is a technology that automatically analyzes and identifies human emotions from data such as voice, text, and facial expressions. 【0353】 An "interface" is a platform or means of connection provided for a user to interact with a system and to display and manipulate information. 【0354】 "Feedback" is the process of providing information such as evaluations and suggestions regarding employees' work performance and behavior, which is used for subsequent improvement and adjustments. 【0355】 The system of this invention analyzes the characteristics and emotional state of employees and provides individually optimized training plans. This system consists of a server, terminals, and users. 【0356】 The server collects personality assessment and aptitude survey data provided by users (employees). This includes online questionnaires and tests. The collected data is stored in a database (e.g., MySQL). Based on this data, the server uses machine learning models (e.g., scikit-learn or TensorFlow) to analyze employees' personalities and aptitudes and perform aptitude assessments. 【0357】 Based on the analysis results, the server generates an optimal training plan for each employee. This plan is individually optimized using an AI model, taking into account the company's training policies and past success stories. Furthermore, emotion recognition technology (e.g., natural language processing tools) is used to collect and evaluate user emotion data in real time, allowing for an understanding of employees' mental state. 【0358】 The terminal features an interface that displays training plans generated by the server to the user (instructor). In addition to displaying the training plan, this interface provides real-time information on the employee's emotional state, allowing instructors to adjust the plan as needed. For example, if emotional data indicating stress is detected, it becomes possible to adjust the difficulty level of the tasks assigned. 【0359】 The user (instructor) enters feedback on the employee's work into the terminal. This feedback information is sent to the server and further analyzed in combination with other sentiment data to help improve training plans. 【0360】 A concrete example of a prompt statement would be: "Based on the personality assessment results and aptitude survey data of new employees, create an optimal training plan, and adjust that plan in real time based on the employees' emotional state, then report the results." 【0361】 This system aims to provide flexible and adaptive training plans tailored to each employee's individual characteristics and emotional state, thereby promoting employee satisfaction and growth. 【0362】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0363】 Step 1: 【0364】 Users (employees) undergo personality assessments and aptitude tests. This includes online questionnaires and tests. The data provided by the users is sent to the server. 【0365】 Step 2: 【0366】 The server receives personality assessment results and aptitude survey data submitted by users. The input data includes information about an individual's personality traits and skill set. The server manages employee characteristics by storing this data in a database. 【0367】 Step 3: 【0368】 The server performs analysis using machine learning models based on the stored data. It uses scikit-learn and TensorFlow to process and perform calculations on data to evaluate employees' personality traits and aptitudes. The output is the employee aptitude evaluation results. 【0369】 Step 4: 【0370】 The server generates an optimal training plan for each employee based on the aptitude assessment results obtained. It references the company's training policies and success story data, and uses an AI model to create individually optimized training plans. The output is a specific training plan. 【0371】 Step 5: 【0372】 The terminal displays training plans sent from the server to managers and instructors through an interface. Users (instructors) can view the plan's content and real-time information on employees' emotional states. This information serves as a guide for users in implementing the training plans. 【0373】 Step 6: 【0374】 The user (instructor) observes employees during work and inputs the feedback information obtained into a terminal. This feedback includes specific comments about the employee's work performance and emotional state. The input feedback is sent to the server. 【0375】 Step 7: 【0376】 The server combines user feedback information with emotional data collected using emotion recognition technology to improve the training plan. This allows the training plan to be dynamically adjusted in real time. The output is the latest training plan. 【0377】 Through the steps outlined above, this system provides each employee with an appropriate and adaptive training plan, supporting their growth and success within the company. 【0378】 (Application Example 2) 【0379】 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". 【0380】 Traditional new employee training systems can analyze individual personalities and aptitudes to provide optimal training plans, but they have the challenge of dynamically adjusting plans to take into account employees' real-time emotional states. Furthermore, reflecting the societal trend towards prioritizing mental health, there is a growing need to provide training environments that also consider the mental well-being of employees. 【0381】 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. 【0382】 In this invention, the server includes means for acquiring data on employee characteristics, means for analyzing employee personality traits and aptitudes based on the data, means for generating an optimal training plan for each employee based on the analysis results, means for collecting and evaluating emotional information in real time, and means for dynamically adjusting the training plan based on the evaluation. This makes it possible to provide flexible and individualized training plans based on the employee's personality and emotional state. 【0383】 "Data related to characteristics" refers to information that indicates the personality and aptitude of individual employees, and includes the results of personality assessments and aptitude tests. 【0384】 "Analysis" is the process of thoroughly analyzing the obtained data to understand the personality traits and aptitudes of employees. 【0385】 A "training plan" is a plan or guideline created to optimally develop employees' abilities based on analysis results. 【0386】 An "interface" is a computer-based input and output mechanism that allows users to review training plans and make modifications as needed. 【0387】 "Emotional information" refers to data that indicates employees' mental state and mood, including real-time emotional fluctuations. 【0388】 "Methods for real-time evaluation" refer to technologies and methods for analyzing emotional information immediately to assess a person's mental state. 【0389】 "Means of dynamic adjustment" refers to methods for immediately adapting training plans based on changing data. 【0390】 In embodiments of this invention, a server, terminals, and users constitute the system. The server collects data on employee characteristics and uses machine learning algorithms to analyze personality traits and aptitudes based on this data. This analysis automatically generates a training plan tailored to each employee. Furthermore, by using an emotion engine, it is possible to collect employee emotional information in real time and dynamically adjust the training plan based on the analysis results. Here, the evaluation of emotional information is performed using analysis with a cloud service API. The hardware used includes a network-connected server and corresponding terminals. On the software side, a cloud service API (e.g., AWS, Google Cloud) is used for analyzing emotional data. 【0391】 The device provides an interface that allows administrators and instructors to review generated training plans and emotional status information. This interface enables users to modify training plans as needed and has the functionality to address employees' mental health issues and stress. 【0392】 The user's role is to input feedback obtained through observation of daily tasks into the terminal. The server receives this feedback and uses a machine learning model to further optimize the training plan. 【0393】 For example, if the server analyzes employee data and determines that a particular employee tends to be susceptible to pressure, it will propose a special stress reduction plan based on this information and the stress levels detected by the emotion engine. This plan can be viewed and modified on the employee's device, and trainers can use this information to provide more effective training. 【0394】 Using a generative AI model, an example of a prompt message could be: "Based on this morning's sentiment analysis, adjust the task plan to provide the best possible support to the user." In this way, the entire system can work together to support employee development. 【0395】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0396】 Step 1: 【0397】 The server acquires data on employee characteristics. Specifically, it collects the results of personality assessments and aptitude tests and stores them in a database. The input for this step is the results of personality assessments and aptitude tests, and the output is the storage of this data in the database used for analysis. 【0398】 Step 2: 【0399】 The server analyzes employees' personality traits and aptitudes based on the acquired data. Specifically, it uses a machine learning model to perform the analysis and generate profiles of personality traits and aptitudes. The input is trait data, and the output is personality traits and aptitude profiles. 【0400】 Step 3: 【0401】 The server generates an optimal training plan for each employee based on the analysis results. This includes a process of creating individualized plans using pre-prepared templates. The input is personality traits and aptitude profiles, and the output is an individual training plan. 【0402】 Step 4: 【0403】 The terminal displays the generated training plan in an interface that allows the user to review it. Here, instructors can review the plan content and make modifications as needed. The input is the training plan, and the output is the information displayed on the user interface. 【0404】 Step 5: 【0405】 The server uses an emotion engine to collect and evaluate employee emotional information in real time. Emotion analysis software is used to process the emotional data and understand the current mental state. The input is real-time emotional data, and the output is an evaluation of the emotional state. 【0406】 Step 6: 【0407】 The server dynamically adjusts the training plan based on the results of the emotional state assessment. It may change the difficulty level of the plan or add activities to promote relaxation as needed. The input is the emotional state assessment result, and the output is the adjusted training plan. 【0408】 Step 7: 【0409】 Users observe their daily tasks and input the feedback they receive into a terminal, sending it to the server. The server uses this feedback to further optimize the training plan. The input is feedback information, and the output is the optimized training plan. 【0410】 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. 【0411】 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. 【0412】 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. 【0413】 [Third Embodiment] 【0414】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0415】 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. 【0416】 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). 【0417】 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. 【0418】 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. 【0419】 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). 【0420】 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. 【0421】 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. 【0422】 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. 【0423】 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. 【0424】 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. 【0425】 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". 【0426】 The system according to the present invention reduces employee turnover in companies by accurately understanding the characteristics of new employees and providing appropriate training tailored to those characteristics. This system functions through the cooperation of three parties: a server, terminals, and users. 【0427】 The server first collects data related to new employees. As part of the onboarding process, users (new employees) answer personality tests and aptitude surveys. The terminal securely transmits this information to the server, which then stores the data. 【0428】 The server uses machine learning techniques to analyze the stored data. This generates detailed reports on employees' personality traits and aptitudes. The analysis results include each employee's job suitability, potential strengths, and areas for improvement. 【0429】 The server then automatically generates an optimal training plan for each employee based on the analysis results. This plan includes specific guidance, such as assigning tasks that allow employees who are evaluated as having "low stress tolerance and high analytical ability" to work in a calm environment and utilize their analytical skills. 【0430】 The device provides an interface that allows administrators and instructors to review the generated training plans. Through this interface, administrators and instructors can review the training plans and modify them as needed. 【0431】 Furthermore, users (elders and managers) input feedback obtained through their actual work. This feedback is collected and analyzed on the server side and used to improve training plans. As a result, training plans are flexibly modified according to the situation, leading to more effective training. 【0432】 As a concrete example, when the server analyzed the data of new employee Tanaka, it was found that while he had strong communication skills, he had issues with task management. Based on this information, the server proposed a plan for Tanaka to improve his task management skills while taking on the role of a facilitator within the team. The server provided an interface that allowed Tanaka's mentor to easily check appropriate guidance policies and improved the plan based on the feedback. 【0433】 By developing employees in this way, while making the most of each employee's strengths, it is possible to increase employee retention and promote the sustainable development of the company. 【0434】 The following describes the processing flow. 【0435】 Step 1: 【0436】 Users (new employees) participate in personality tests and aptitude assessments using a dedicated terminal or web platform provided upon joining the company. Basic personality information and data regarding job suitability are collected during this process. 【0437】 Step 2: 【0438】 The terminal sends the data entered by the user to the server. The server receives this data and securely stores it in the company's database. 【0439】 Step 3: 【0440】 The server processes the stored data using analytical algorithms to analyze employees' personality traits and job suitability from multiple perspectives. This analysis utilizes machine learning techniques to identify characteristics by comparing them with past data. 【0441】 Step 4: 【0442】 Based on the analysis results, the server generates an individual aptitude assessment report for each employee. This report includes information such as the employee's strengths and weaknesses, and what kind of work environment would be most suitable for them. 【0443】 Step 5: 【0444】 The server uses aptitude assessment reports to automatically create optimal training plans for each employee. These plans specifically detail what guidance policies and tasks would be most effective. 【0445】 Step 6: 【0446】 The device displays the generated training plan through an interface that allows administrators and instructors to review it. Administrators and instructors can use this interface to check the plan information and make modifications as needed. 【0447】 Step 7: 【0448】 Users (elders and managers) input feedback obtained through their work into the server via their terminals. The server collects this feedback and analyzes it as data to improve training plans. 【0449】 Step 8: 【0450】 The server dynamically improves training plans based on feedback, updating them according to employee characteristics and work progress. This enables continuous and more effective training. 【0451】 (Example 1) 【0452】 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." 【0453】 The high turnover rate among new employees in companies is due to a lack of proper training and guidance. While it is necessary to efficiently formulate and implement optimal training plans tailored to the individual characteristics of each employee, traditional systems struggle with individualized support, and dynamic plan improvements based on feedback are insufficient. Therefore, a new system is needed that accurately understands employee characteristics and enables training based on those characteristics. 【0454】 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. 【0455】 In this invention, the server includes means for collecting employee characteristic data, means for securely storing the data in data storage, means for analyzing the employee's personality traits and aptitudes using a machine learning algorithm, means for automatically generating an optimal training plan based on the analysis results, means for providing an interface that displays and modifies the training plan, and means for acquiring feedback information and dynamically improving the training plan. This enables the formulation of training plans optimized for each employee and flexible improvement of the plans based on feedback. 【0456】 "Employee characteristic data" refers to data about employees' personalities, abilities, aptitudes, and performance, and is primarily collected through diagnostic tests and aptitude surveys. 【0457】 "Data storage" refers to a digital environment for securely storing collected data, and is an information storage system equipped with advanced security measures. 【0458】 A "machine learning algorithm" refers to a technology that recognizes patterns from data and makes predictions, and is a mathematical model used for analyzing employee characteristics and generating training plans. 【0459】 A "training plan" is a set of specific action guidelines and activity plans designed to improve job skills and abilities, based on the characteristics of each employee. 【0460】 An "interface" refers to the means by which a user interacts with a system, and is a mechanism for user interaction that enables the display and modification of training plans. 【0461】 "Feedback information" refers to evaluations and comments regarding employees' performance and behavior in their actual work, and is useful information for improving training plans. 【0462】 The system according to this invention aims to understand the characteristics of employees and generate appropriate training plans. It functions through the coordinated efforts of a server, terminals, and users, and is implemented by the following means. 【0463】 The server first collects employee characteristic data obtained from users (employees) answering personality tests and aptitude surveys. This data is entered by the user, and the terminal securely transmits it to the server via the internet. Data security is ensured during transmission using the HTTPS encryption protocol. 【0464】 The server stores the received data in secure data storage. This data storage incorporates a database system (e.g., an SQL database) and implements strict access control and data encryption. 【0465】 Next, the server analyzes the stored data using machine learning algorithms. This analysis utilizes Python libraries such as "scikit-learn" and "TensorFlow," enabling a detailed understanding of each employee's personality traits and aptitudes. Based on these analysis results, the server automatically generates an optimal training plan for each employee using an AI model. 【0466】 The terminal provides an interface for displaying the generated training plan. This interface allows administrators and instructors to visually review the training plan and modify it as needed. The modified content is quickly sent to the server and reflected in the database. 【0467】 Users (elders and managers) input feedback obtained through their work on a terminal. The terminal receives the feedback and sends it back to the server. The server collects this feedback information, analyzes it using machine learning technology, and dynamically improves the training plan. 【0468】 As a concrete example, by inputting a text-based prompt message into the generation AI model, such as "Design prompt messages that automatically generate training plans tailored to the aptitudes of each new employee based on their personality assessment data," the system will generate appropriate training plans. By using the system in this way, flexible and effective training tailored to the characteristics of each employee can be achieved. 【0469】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0470】 Step 1: 【0471】 As part of the onboarding process, users (employees) complete personality tests and aptitude assessments. Online forms are used for this process, and user responses are directly recorded as digital data. The terminal transmits this data to a server via the internet. Input is the user's text responses, and output is encrypted data sent to the server. 【0472】 Step 2: 【0473】 The server receives data sent from the terminal and stores it in highly secure data storage. The data is encrypted via the HTTPS protocol upon reception and further encrypted within the database for enhanced security during storage. The input is encrypted data from the terminal, and the output is storage in secure data storage. 【0474】 Step 3: 【0475】 The server extracts the stored data and begins analysis using machine learning algorithms. Specifically, it uses Python libraries such as "scikit-learn" and "TensorFlow" to analyze the input trait data and gain insights into employees' personality traits and aptitudes. In this step, the input is trait data from data storage, and the output is the analysis results. 【0476】 Step 4: 【0477】 The server automatically generates an optimal training plan using an AI model based on the analysis results. This generation process specifically formulates tasks and guidance policies tailored to the characteristics of each employee. The input is the analysis results obtained in the previous step, and the output is the training plan. 【0478】 Step 5: 【0479】 The terminal displays the generated training plan on an interface that administrators and instructors can review. Administrators and instructors can then modify this plan as needed. This modified plan is then sent back to the server. The input is the training plan, and the output is the reviewed and modified plan. 【0480】 Step 6: 【0481】 Users (elders and managers) input feedback obtained through their work into a terminal. This feedback includes information about employees' actual work performance and challenges. The terminal sends this data to a server; the input is the feedback information, and the output is the feedback data sent to the server. 【0482】 Step 7: 【0483】 The server analyzes the received feedback information and uses it to improve the training plan. It then uses machine learning techniques to evaluate the data again and optimize the plan. The input is the feedback data, and the output is the updated training plan. 【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】 In modern society, there is a need for technologies that support lifestyle improvements based on individual characteristics. However, providing optimal plans tailored to individual characteristics is difficult, and there is a challenge in providing effective support. In particular, with the increasing diversity of lifestyles and the growing need for support that is tailored to individual characteristics, it is desirable to provide a system that can accurately grasp individual characteristics and provide support accordingly. 【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 means for acquiring data on an individual's characteristics, means for acquiring data on an individual's behavioral habits and proposing an optimal lifestyle improvement plan, and means for providing an interface for displaying and modifying a growth support plan. This enables optimal lifestyle improvement support and growth promotion tailored to individual characteristics. 【0489】 "Traits" refer to the personality and behavioral characteristics of individual people, and are the information that forms the basis for analysis and adaptation. 【0490】 "Data acquisition means" refers to methods and devices for collecting information about an individual's characteristics and behavior. 【0491】 "Analysis methods" refer to methods for understanding and evaluating an individual's characteristics and aptitudes based on acquired data. 【0492】 A "growth support plan" is a specific activity plan formulated based on analysis results, aimed at growth and improvement tailored to an individual's characteristics. 【0493】 An "interface" refers to a function or screen that displays a growth support plan and serves as a point of contact for users to review or modify its contents. 【0494】 "Behavioral habit data" refers to information that shows an individual's daily behavior and patterns, and is basic data used to suggest improvements to their lifestyle. 【0495】 A "lifestyle improvement plan" is a plan proposed to help individuals achieve better lifestyle habits based on their behavioral data. 【0496】 The system for implementing this invention is designed to provide growth support plans optimized for individual characteristics. The system functions through the coordinated interaction of a server, terminals, and users. 【0497】 The server first acquires data about an individual's characteristics through an internet-connected device. This data may include personality assessment results and behavioral habits. This data is securely transferred to a cloud server, such as Google Cloud. The server then analyzes this data using TensorFlow, a machine learning framework. This analysis allows for a detailed understanding of the individual's characteristics and aptitudes. 【0498】 Based on the analysis results, the server automatically generates a growth support plan optimized for each individual's characteristics. This plan includes suggestions for lifestyle improvements and support tailored to each individual's needs. This plan can be viewed by the user via their terminal, and modifications can be made as needed through the interface. 【0499】 Furthermore, this system incorporates feedback from users in their daily lives, providing a function to more effectively improve the growth support plan. This allows the plan to flexibly adapt to individual changes and progress. 【0500】 As a concrete example, for an individual who wants to improve their daily exercise habits, the server can suggest an optimal early morning jogging schedule and support its implementation. A robotic terminal can announce departure times and provide running routes via voice. 【0501】 An example of a prompt to the generating AI model is as follows: "Please suggest an appropriate exercise plan and its implementation method for a user aiming to improve their exercise habits." Using this prompt, a more specific improvement plan tailored to the user's characteristics can be generated more effectively. 【0502】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0503】 Step 1: 【0504】 Users use their devices to complete personality assessments and questionnaires about their daily behaviors. This generates data about the user's characteristics. This data is then securely transmitted from the device to the server. 【0505】 Step 2: 【0506】 The server receives data, stores it on a cloud server, and performs analysis using a machine learning model (TensorFlow is used). This analysis outputs user personality traits and behavioral patterns as numerical data. Based on this data, a detailed report on individual characteristics and aptitudes is generated. 【0507】 Step 3: 【0508】 The server utilizes a generation AI model based on the generated report to automatically create a user-specific growth support plan. Prompt statements (e.g., "Please suggest an appropriate exercise plan and implementation method for a user aiming to improve their exercise habits.") are used to specify the plan's content. The generated plan is customized for each user. 【0509】 Step 4: 【0510】 The server sends the growth support plan to the terminal, which the user can then view on the interface. On the interface, the user can browse the plan's contents and modify options as needed. This includes actions such as setting task priorities and adding new goals. 【0511】 Step 5: 【0512】 The user sends feedback from their device to the server during their daily activities. This feedback includes the user's progress and newly emerging needs, and the server dynamically improves the growth support plan based on this feedback. Data calculations incorporating the feedback are performed to generate an updated version of the plan. 【0513】 Step 6: 【0514】 The device then provides the user with an updated growth support plan and offers specific support for the next steps. For example, it might notify the user of the start time for exercise or visualize their progress. This allows the user to continuously improve their lifestyle in accordance with the plan. 【0515】 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. 【0516】 The system of this invention not only analyzes the personality and aptitude of new employees and provides the optimal training method, but also, by combining it with an emotion engine, evaluates the mental state of employees in real time, enabling further individualized support. This system consists of a server, terminals, and users. 【0517】 The server first stores employee characteristic data based on the results of personality assessments and aptitude tests collected from users (new employees). Next, it uses machine learning models with this data to analyze personality and aptitude, and generates an aptitude assessment report for each employee. 【0518】 Based on the generated report, the server automatically creates an optimal training plan. Furthermore, it uses an emotion engine to collect user emotional data, which the server analyzes to evaluate the employee's mental state. The training plan is dynamically adjusted according to this evaluation. 【0519】 The device displays the generated training plan through an interface accessible to managers and instructors. This interface also displays real-time information about the trainee's emotional state, allowing for plan modifications as needed. For example, if the plan detects a high workload, it provides features to reduce the difficulty of tasks or implement measures to alleviate the burden. 【0520】 As a user (instructor), you observe employees' work and input the feedback you receive into a terminal. Based on this feedback, the server combines the feedback with evaluations from the emotion engine to improve the training plan. For example, if the server detects emotional data indicating that an employee is experiencing stress, it provides real-time advice to the instructor to enhance support for the employee. 【0521】 As a concrete example, when the server analyzed employee Yamada's data, it determined that while he possessed high social skills, he tended to be vulnerable to pressure. Based on this personality analysis and the stress levels detected by the emotion engine, the server proposed a plan to reduce Yamada's stress. This plan is available on the terminal for the supervisor to review, allowing the supervisor to create a better training environment by providing guidance tailored to Yamada's emotional state. 【0522】 In this way, this system, which incorporates an emotional engine, enables flexible responses tailored to the mental state of employees, providing a solution to promote employee retention and growth within companies. 【0523】 The following describes the processing flow. 【0524】 Step 1: 【0525】 Users (new employees) collect data about their characteristics by answering personality tests and aptitude surveys using a dedicated terminal or web platform provided upon joining the company. At this stage, certain emotional data is also recorded by the terminal. 【0526】 Step 2: 【0527】 The device transmits user-entered data and sentiment data to the server. The transmitted data is securely stored in a company-specific database. 【0528】 Step 3: 【0529】 The server uses machine learning models to analyze personality traits and job suitability based on stored employee characteristic data. Based on the analysis results, an aptitude assessment report is generated for each individual employee. 【0530】 Step 4: 【0531】 The server integrates information from aptitude assessment reports with emotional data to automatically create an optimal training plan for each employee. Here, an emotional engine is used to plan individualized support based on each employee's mental state. 【0532】 Step 5: 【0533】 The terminal provides an interface that allows managers and instructors to review the generated training plans. This interface also allows real-time viewing of information regarding employees' emotional states. 【0534】 Step 6: 【0535】 Users (instructors) input observations from actual work and feedback from employees into the server via their terminals. The server analyzes this feedback and data obtained from the emotion engine to further adjust training plans. 【0536】 Step 7: 【0537】 The server monitors changes in the user's mental state, as captured by the emotion engine, and uses this information to provide real-time advice to the instructor. For example, if a user's emotional data indicates stress, the server will suggest countermeasures and ways to improve the instruction. 【0538】 Step 8: 【0539】 Based on the user's emotional state and work progress, the server dynamically improves the training plan, providing optimal guidance that is always up-to-date. This process makes it possible to create an effective training environment while supporting employees' mental health. 【0540】 (Example 2) 【0541】 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." 【0542】 Traditional employee training systems typically provide a uniform training plan, making it difficult to provide personalized support that takes into account the individual characteristics and emotional states of each employee. This resulted in challenges in maximizing employee growth and retention rates. Furthermore, real-time emotional assessment and dynamic adjustment of plans were difficult, leading to insufficient responses to stress and pressure. 【0543】 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. 【0544】 In this invention, the server includes means for acquiring data on employee characteristics; means for analyzing employee personality traits and aptitudes based on the data and performing aptitude evaluations using machine learning models; means for generating an optimal training plan for each employee based on the analysis results and creating an individualized plan considering the company's training policies and success story data; means for using emotion recognition technology to evaluate emotional states in real time; and means for providing an interface for displaying and modifying the training plan, and for allowing instructors to input feedback. This enables the provision of flexible and adaptive training plans for individual employees, facilitating rapid mental support and efficient talent development. 【0545】 "Data on employee characteristics" refers to information that indicates an individual employee's personality, aptitude, behavioral patterns, and emotional state. 【0546】 A "machine learning model" is a collection of algorithms and mathematical models that use data to automatically find specific patterns or rules and perform predictions and classifications. 【0547】 "Aptitude assessment" is a process of analyzing an employee's personality, skills, and suitability for the job, and quantitatively determining those characteristics. 【0548】 A "training plan" is a systematically structured plan that includes individualized training content and schedules to promote the improvement and growth of employees' skills. 【0549】 "Emotion recognition technology" is a technology that automatically analyzes and identifies human emotions from data such as voice, text, and facial expressions. 【0550】 An "interface" is a platform or means of connection provided for a user to interact with a system and to display and manipulate information. 【0551】 "Feedback" is the process of providing information such as evaluations and suggestions regarding employees' work performance and behavior, which is used for subsequent improvement and adjustments. 【0552】 The system of this invention analyzes the characteristics and emotional state of employees and provides individually optimized training plans. This system consists of a server, terminals, and users. 【0553】 The server collects personality assessment and aptitude survey data provided by users (employees). This includes online questionnaires and tests. The collected data is stored in a database (e.g., MySQL). Based on this data, the server uses machine learning models (e.g., scikit-learn or TensorFlow) to analyze employees' personalities and aptitudes and perform aptitude assessments. 【0554】 Based on the analysis results, the server generates an optimal training plan for each employee. This plan is individually optimized using an AI model, taking into account the company's training policies and past success stories. Furthermore, emotion recognition technology (e.g., natural language processing tools) is used to collect and evaluate user emotion data in real time, allowing for an understanding of employees' mental state. 【0555】 The terminal features an interface that displays training plans generated by the server to the user (instructor). In addition to displaying the training plan, this interface provides real-time information on the employee's emotional state, allowing instructors to adjust the plan as needed. For example, if emotional data indicating stress is detected, it becomes possible to adjust the difficulty level of the tasks assigned. 【0556】 The user (instructor) enters feedback on the employee's work into the terminal. This feedback information is sent to the server and further analyzed in combination with other sentiment data to help improve training plans. 【0557】 A concrete example of a prompt statement would be: "Based on the personality assessment results and aptitude survey data of new employees, create an optimal training plan, and adjust that plan in real time based on the employees' emotional state, then report the results." 【0558】 This system aims to provide flexible and adaptive training plans tailored to each employee's individual characteristics and emotional state, thereby promoting employee satisfaction and growth. 【0559】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0560】 Step 1: 【0561】 Users (employees) undergo personality assessments and aptitude tests. This includes online questionnaires and tests. The data provided by the users is sent to the server. 【0562】 Step 2: 【0563】 The server receives personality assessment results and aptitude survey data submitted by users. The input data includes information about an individual's personality traits and skill set. The server manages employee characteristics by storing this data in a database. 【0564】 Step 3: 【0565】 The server performs analysis using machine learning models based on the stored data. It uses scikit-learn and TensorFlow to process and perform calculations on data to evaluate employees' personality traits and aptitudes. The output is the employee aptitude evaluation results. 【0566】 Step 4: 【0567】 The server generates an optimal training plan for each employee based on the aptitude assessment results obtained. It references the company's training policies and success story data, and uses an AI model to create individually optimized training plans. The output is a specific training plan. 【0568】 Step 5: 【0569】 The terminal displays training plans sent from the server to managers and instructors through an interface. Users (instructors) can view the plan's content and real-time information on employees' emotional states. This information serves as a guide for users in implementing the training plans. 【0570】 Step 6: 【0571】 The user (instructor) observes employees during work and inputs the feedback information obtained into a terminal. This feedback includes specific comments about the employee's work performance and emotional state. The input feedback is sent to the server. 【0572】 Step 7: 【0573】 The server combines user feedback information with emotional data collected using emotion recognition technology to improve the training plan. This allows the training plan to be dynamically adjusted in real time. The output is the latest training plan. 【0574】 Through the steps outlined above, this system provides each employee with an appropriate and adaptive training plan, supporting their growth and success within the company. 【0575】 (Application Example 2) 【0576】 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." 【0577】 Traditional new employee training systems can analyze individual personalities and aptitudes to provide optimal training plans, but they have the challenge of dynamically adjusting plans to take into account employees' real-time emotional states. Furthermore, reflecting the societal trend towards prioritizing mental health, there is a growing need to provide training environments that also consider the mental well-being of employees. 【0578】 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. 【0579】 In this invention, the server includes means for acquiring data on employee characteristics, means for analyzing employee personality traits and aptitudes based on the data, means for generating an optimal training plan for each employee based on the analysis results, means for collecting and evaluating emotional information in real time, and means for dynamically adjusting the training plan based on the evaluation. This makes it possible to provide flexible and individualized training plans based on the employee's personality and emotional state. 【0580】 "Data related to characteristics" refers to information that indicates the personality and aptitude of individual employees, and includes the results of personality assessments and aptitude tests. 【0581】 "Analysis" is the process of thoroughly analyzing the obtained data to understand the personality traits and aptitudes of employees. 【0582】 A "training plan" is a plan or guideline created to optimally develop employees' abilities based on analysis results. 【0583】 An "interface" is a computer-based input and output mechanism that allows users to review training plans and make modifications as needed. 【0584】 "Emotional information" refers to data that indicates employees' mental state and mood, including real-time emotional fluctuations. 【0585】 "Methods for real-time evaluation" refer to technologies and methods for analyzing emotional information immediately to assess a person's mental state. 【0586】 "Means of dynamic adjustment" refers to methods for immediately adapting training plans based on changing data. 【0587】 In embodiments of this invention, a server, terminals, and users constitute the system. The server collects data on employee characteristics and uses machine learning algorithms to analyze personality traits and aptitudes based on this data. This analysis automatically generates a training plan tailored to each employee. Furthermore, by using an emotion engine, it is possible to collect employee emotional information in real time and dynamically adjust the training plan based on the analysis results. Here, the evaluation of emotional information is performed using analysis with a cloud service API. The hardware used includes a network-connected server and corresponding terminals. On the software side, a cloud service API (e.g., AWS, Google Cloud) is used for analyzing emotional data. 【0588】 The device provides an interface that allows administrators and instructors to review generated training plans and emotional status information. This interface enables users to modify training plans as needed and has the functionality to address employees' mental health issues and stress. 【0589】 The user's role is to input feedback obtained through observation of daily tasks into the terminal. The server receives this feedback and uses a machine learning model to further optimize the training plan. 【0590】 For example, if the server analyzes employee data and determines that a particular employee tends to be susceptible to pressure, it will propose a special stress reduction plan based on this information and the stress levels detected by the emotion engine. This plan can be viewed and modified on the employee's device, and trainers can use this information to provide more effective training. 【0591】 Using a generative AI model, an example of a prompt message could be: "Based on this morning's sentiment analysis, adjust the task plan to provide the best possible support to the user." In this way, the entire system can work together to support employee development. 【0592】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0593】 Step 1: 【0594】 The server acquires data on employee characteristics. Specifically, it collects the results of personality assessments and aptitude tests and stores them in a database. The input for this step is the results of personality assessments and aptitude tests, and the output is the storage of this data in the database used for analysis. 【0595】 Step 2: 【0596】 The server analyzes employees' personality traits and aptitudes based on the acquired data. Specifically, it uses a machine learning model to perform the analysis and generate profiles of personality traits and aptitudes. The input is trait data, and the output is personality traits and aptitude profiles. 【0597】 Step 3: 【0598】 The server generates an optimal training plan for each employee based on the analysis results. This includes a process of creating individualized plans using pre-prepared templates. The input is personality traits and aptitude profiles, and the output is an individual training plan. 【0599】 Step 4: 【0600】 The terminal displays the generated training plan in an interface that allows the user to review it. Here, instructors can review the plan content and make modifications as needed. The input is the training plan, and the output is the information displayed on the user interface. 【0601】 Step 5: 【0602】 The server uses an emotion engine to collect and evaluate employee emotional information in real time. Emotion analysis software is used to process the emotional data and understand the current mental state. The input is real-time emotional data, and the output is an evaluation of the emotional state. 【0603】 Step 6: 【0604】 The server dynamically adjusts the training plan based on the results of the emotional state assessment. It may change the difficulty level of the plan or add activities to promote relaxation as needed. The input is the emotional state assessment result, and the output is the adjusted training plan. 【0605】 Step 7: 【0606】 Users observe their daily tasks and input the feedback they receive into a terminal, sending it to the server. The server uses this feedback to further optimize the training plan. The input is feedback information, and the output is the optimized training plan. 【0607】 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. 【0608】 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. 【0609】 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. 【0610】 [Fourth Embodiment] 【0611】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0612】 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. 【0613】 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). 【0614】 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. 【0615】 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. 【0616】 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). 【0617】 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. 【0618】 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. 【0619】 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. 【0620】 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. 【0621】 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. 【0622】 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. 【0623】 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". 【0624】 The system according to the present invention reduces employee turnover in companies by accurately understanding the characteristics of new employees and providing appropriate training tailored to those characteristics. This system functions through the cooperation of three parties: a server, terminals, and users. 【0625】 The server first collects data related to new employees. As part of the onboarding process, users (new employees) answer personality tests and aptitude surveys. The terminal securely transmits this information to the server, which then stores the data. 【0626】 The server uses machine learning techniques to analyze the stored data. This generates detailed reports on employees' personality traits and aptitudes. The analysis results include each employee's job suitability, potential strengths, and areas for improvement. 【0627】 The server then automatically generates an optimal training plan for each employee based on the analysis results. This plan includes specific guidance, such as assigning tasks that allow employees who are evaluated as having "low stress tolerance and high analytical ability" to work in a calm environment and utilize their analytical skills. 【0628】 The device provides an interface that allows administrators and instructors to review the generated training plans. Through this interface, administrators and instructors can review the training plans and modify them as needed. 【0629】 Furthermore, users (elders and managers) input feedback obtained through their actual work. This feedback is collected and analyzed on the server side and used to improve training plans. As a result, training plans are flexibly modified according to the situation, leading to more effective training. 【0630】 As a concrete example, when the server analyzed the data of new employee Tanaka, it was found that while he had strong communication skills, he had issues with task management. Based on this information, the server proposed a plan for Tanaka to improve his task management skills while taking on the role of a facilitator within the team. The server provided an interface that allowed Tanaka's mentor to easily check appropriate guidance policies and improved the plan based on the feedback. 【0631】 By developing employees in this way, while making the most of each employee's strengths, it is possible to increase employee retention and promote the sustainable development of the company. 【0632】 The following describes the processing flow. 【0633】 Step 1: 【0634】 Users (new employees) participate in personality tests and aptitude assessments using a dedicated terminal or web platform provided upon joining the company. Basic personality information and data regarding job suitability are collected during this process. 【0635】 Step 2: 【0636】 The terminal sends the data entered by the user to the server. The server receives this data and securely stores it in the company's database. 【0637】 Step 3: 【0638】 The server processes the stored data using analytical algorithms to analyze employees' personality traits and job suitability from multiple perspectives. This analysis utilizes machine learning techniques to identify characteristics by comparing them with past data. 【0639】 Step 4: 【0640】 Based on the analysis results, the server generates an individual aptitude assessment report for each employee. This report includes information such as the employee's strengths and weaknesses, and what kind of work environment would be most suitable for them. 【0641】 Step 5: 【0642】 The server uses aptitude assessment reports to automatically create optimal training plans for each employee. These plans specifically detail what guidance policies and tasks would be most effective. 【0643】 Step 6: 【0644】 The device displays the generated training plan through an interface that allows administrators and instructors to review it. Administrators and instructors can use this interface to check the plan information and make modifications as needed. 【0645】 Step 7: 【0646】 Users (elders and managers) input feedback obtained through their work into the server via their terminals. The server collects this feedback and analyzes it as data to improve training plans. 【0647】 Step 8: 【0648】 The server dynamically improves training plans based on feedback, updating them according to employee characteristics and work progress. This enables continuous and more effective training. 【0649】 (Example 1) 【0650】 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". 【0651】 The high turnover rate among new employees in companies is due to a lack of proper training and guidance. While it is necessary to efficiently formulate and implement optimal training plans tailored to the individual characteristics of each employee, traditional systems struggle with individualized support, and dynamic plan improvements based on feedback are insufficient. Therefore, a new system is needed that accurately understands employee characteristics and enables training based on those characteristics. 【0652】 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. 【0653】 In this invention, the server includes means for collecting employee characteristic data, means for securely storing the data in data storage, means for analyzing the employee's personality traits and aptitudes using a machine learning algorithm, means for automatically generating an optimal training plan based on the analysis results, means for providing an interface that displays and modifies the training plan, and means for acquiring feedback information and dynamically improving the training plan. This enables the formulation of training plans optimized for each employee and flexible improvement of the plans based on feedback. 【0654】 "Employee characteristic data" refers to data about employees' personalities, abilities, aptitudes, and performance, and is primarily collected through diagnostic tests and aptitude surveys. 【0655】 "Data storage" refers to a digital environment for securely storing collected data, and is an information storage system equipped with advanced security measures. 【0656】 A "machine learning algorithm" refers to a technology that recognizes patterns from data and makes predictions, and is a mathematical model used for analyzing employee characteristics and generating training plans. 【0657】 A "training plan" is a set of specific action guidelines and activity plans designed to improve job skills and abilities, based on the characteristics of each employee. 【0658】 An "interface" refers to the means by which a user interacts with a system, and is a mechanism for user interaction that enables the display and modification of training plans. 【0659】 "Feedback information" refers to evaluations and comments regarding employees' performance and behavior in their actual work, and is useful information for improving training plans. 【0660】 The system according to this invention aims to understand the characteristics of employees and generate appropriate training plans. It functions through the coordinated efforts of a server, terminals, and users, and is implemented by the following means. 【0661】 The server first collects employee characteristic data obtained from users (employees) answering personality tests and aptitude surveys. This data is entered by the user, and the terminal securely transmits it to the server via the internet. Data security is ensured during transmission using the HTTPS encryption protocol. 【0662】 The server stores the received data in secure data storage. This data storage incorporates a database system (e.g., an SQL database) and implements strict access control and data encryption. 【0663】 Next, the server analyzes the stored data using machine learning algorithms. This analysis utilizes Python libraries such as "scikit-learn" and "TensorFlow," enabling a detailed understanding of each employee's personality traits and aptitudes. Based on these analysis results, the server automatically generates an optimal training plan for each employee using an AI model. 【0664】 The terminal provides an interface for displaying the generated training plan. This interface allows administrators and instructors to visually review the training plan and modify it as needed. The modified content is quickly sent to the server and reflected in the database. 【0665】 Users (elders and managers) input feedback obtained through their work on a terminal. The terminal receives the feedback and sends it back to the server. The server collects this feedback information, analyzes it using machine learning technology, and dynamically improves the training plan. 【0666】 As a concrete example, by inputting a text-based prompt message into the generation AI model, such as "Design prompt messages that automatically generate training plans tailored to the aptitudes of each new employee based on their personality assessment data," the system will generate appropriate training plans. By using the system in this way, flexible and effective training tailored to the characteristics of each employee can be achieved. 【0667】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0668】 Step 1: 【0669】 As part of the onboarding process, users (employees) complete personality tests and aptitude assessments. Online forms are used for this process, and user responses are directly recorded as digital data. The terminal transmits this data to a server via the internet. Input is the user's text responses, and output is encrypted data sent to the server. 【0670】 Step 2: 【0671】 The server receives data sent from the terminal and stores it in highly secure data storage. The data is encrypted via the HTTPS protocol upon reception and further encrypted within the database for enhanced security during storage. The input is encrypted data from the terminal, and the output is storage in secure data storage. 【0672】 Step 3: 【0673】 The server extracts the stored data and begins analysis using machine learning algorithms. Specifically, it uses Python libraries such as "scikit-learn" and "TensorFlow" to analyze the input trait data and gain insights into employees' personality traits and aptitudes. In this step, the input is trait data from data storage, and the output is the analysis results. 【0674】 Step 4: 【0675】 The server automatically generates an optimal training plan using an AI model based on the analysis results. This generation process specifically formulates tasks and guidance policies tailored to the characteristics of each employee. The input is the analysis results obtained in the previous step, and the output is the training plan. 【0676】 Step 5: 【0677】 The terminal displays the generated training plan on an interface that administrators and instructors can review. Administrators and instructors can then modify this plan as needed. This modified plan is then sent back to the server. The input is the training plan, and the output is the reviewed and modified plan. 【0678】 Step 6: 【0679】 Users (elders and managers) input feedback obtained through their work into a terminal. This feedback includes information about employees' actual work performance and challenges. The terminal sends this data to a server; the input is the feedback information, and the output is the feedback data sent to the server. 【0680】 Step 7: 【0681】 The server analyzes the received feedback information and uses it to improve the training plan. It then uses machine learning techniques to evaluate the data again and optimize the plan. The input is the feedback data, and the output is the updated training plan. 【0682】 (Application Example 1) 【0683】 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". 【0684】 In modern society, there is a need for technologies that support lifestyle improvements based on individual characteristics. However, providing optimal plans tailored to individual characteristics is difficult, and there is a challenge in providing effective support. In particular, with the increasing diversity of lifestyles and the growing need for support that is tailored to individual characteristics, it is desirable to provide a system that can accurately grasp individual characteristics and provide support accordingly. 【0685】 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. 【0686】 In this invention, the server includes means for acquiring data on an individual's characteristics, means for acquiring data on an individual's behavioral habits and proposing an optimal lifestyle improvement plan, and means for providing an interface for displaying and modifying a growth support plan. This enables optimal lifestyle improvement support and growth promotion tailored to individual characteristics. 【0687】 "Traits" refer to the personality and behavioral characteristics of individual people, and are the information that forms the basis for analysis and adaptation. 【0688】 "Data acquisition means" refers to methods and devices for collecting information about an individual's characteristics and behavior. 【0689】 "Analysis methods" refer to methods for understanding and evaluating an individual's characteristics and aptitudes based on acquired data. 【0690】 A "growth support plan" is a specific activity plan formulated based on analysis results, aimed at growth and improvement tailored to an individual's characteristics. 【0691】 An "interface" refers to a function or screen that displays a growth support plan and serves as a point of contact for users to review or modify its contents. 【0692】 "Behavioral habit data" refers to information that shows an individual's daily behavior and patterns, and is basic data used to suggest improvements to their lifestyle. 【0693】 A "lifestyle improvement plan" is a plan proposed to help individuals achieve better lifestyle habits based on their behavioral data. 【0694】 The system for implementing this invention is designed to provide growth support plans optimized for individual characteristics. The system functions through the coordinated interaction of a server, terminals, and users. 【0695】 The server first acquires data about an individual's characteristics through an internet-connected device. This data may include personality assessment results and behavioral habits. This data is securely transferred to a cloud server, such as Google Cloud. The server then analyzes this data using TensorFlow, a machine learning framework. This analysis allows for a detailed understanding of the individual's characteristics and aptitudes. 【0696】 Based on the analysis results, the server automatically generates a growth support plan optimized for each individual's characteristics. This plan includes suggestions for lifestyle improvements and support tailored to each individual's needs. This plan can be viewed by the user via their terminal, and modifications can be made as needed through the interface. 【0697】 Furthermore, this system incorporates feedback from users in their daily lives, providing a function to more effectively improve the growth support plan. This allows the plan to flexibly adapt to individual changes and progress. 【0698】 As a concrete example, for an individual who wants to improve their daily exercise habits, the server can suggest an optimal early morning jogging schedule and support its implementation. A robotic terminal can announce departure times and provide running routes via voice. 【0699】 An example of a prompt to the generating AI model is as follows: "Please suggest an appropriate exercise plan and its implementation method for a user aiming to improve their exercise habits." Using this prompt, a more specific improvement plan tailored to the user's characteristics can be generated more effectively. 【0700】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0701】 Step 1: 【0702】 Users use their devices to complete personality assessments and questionnaires about their daily behaviors. This generates data about the user's characteristics. This data is then securely transmitted from the device to the server. 【0703】 Step 2: 【0704】 The server receives data, stores it on a cloud server, and performs analysis using a machine learning model (TensorFlow is used). This analysis outputs user personality traits and behavioral patterns as numerical data. Based on this data, a detailed report on individual characteristics and aptitudes is generated. 【0705】 Step 3: 【0706】 The server utilizes a generation AI model based on the generated report to automatically create a user-specific growth support plan. Prompt statements (e.g., "Please suggest an appropriate exercise plan and implementation method for a user aiming to improve their exercise habits.") are used to specify the plan's content. The generated plan is customized for each user. 【0707】 Step 4: 【0708】 The server sends the growth support plan to the terminal, which the user can then view on the interface. On the interface, the user can browse the plan's contents and modify options as needed. This includes actions such as setting task priorities and adding new goals. 【0709】 Step 5: 【0710】 The user sends feedback from their device to the server during their daily activities. This feedback includes the user's progress and newly emerging needs, and the server dynamically improves the growth support plan based on this feedback. Data calculations incorporating the feedback are performed to generate an updated version of the plan. 【0711】 Step 6: 【0712】 The device then provides the user with an updated growth support plan and offers specific support for the next steps. For example, it might notify the user of the start time for exercise or visualize their progress. This allows the user to continuously improve their lifestyle in accordance with the 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】 The system of this invention not only analyzes the personality and aptitude of new employees and provides the optimal training method, but also, by combining it with an emotion engine, evaluates the mental state of employees in real time, enabling further individualized support. This system consists of a server, terminals, and users. 【0715】 The server first stores employee characteristic data based on the results of personality assessments and aptitude tests collected from users (new employees). Next, it uses machine learning models with this data to analyze personality and aptitude, and generates an aptitude assessment report for each employee. 【0716】 Based on the generated report, the server automatically creates an optimal training plan. Furthermore, it uses an emotion engine to collect user emotional data, which the server analyzes to evaluate the employee's mental state. The training plan is dynamically adjusted according to this evaluation. 【0717】 The device displays the generated training plan through an interface accessible to managers and instructors. This interface also displays real-time information about the trainee's emotional state, allowing for plan modifications as needed. For example, if the plan detects a high workload, it provides features to reduce the difficulty of tasks or implement measures to alleviate the burden. 【0718】 As a user (instructor), you observe employees' work and input the feedback you receive into a terminal. Based on this feedback, the server combines the feedback with evaluations from the emotion engine to improve the training plan. For example, if the server detects emotional data indicating that an employee is experiencing stress, it provides real-time advice to the instructor to enhance support for the employee. 【0719】 As a concrete example, when the server analyzed employee Yamada's data, it determined that while he possessed high social skills, he tended to be vulnerable to pressure. Based on this personality analysis and the stress levels detected by the emotion engine, the server proposed a plan to reduce Yamada's stress. This plan is available on the terminal for the supervisor to review, allowing the supervisor to create a better training environment by providing guidance tailored to Yamada's emotional state. 【0720】 In this way, this system, which incorporates an emotional engine, enables flexible responses tailored to the mental state of employees, providing a solution to promote employee retention and growth within companies. 【0721】 The following describes the processing flow. 【0722】 Step 1: 【0723】 Users (new employees) collect data about their characteristics by answering personality tests and aptitude surveys using a dedicated terminal or web platform provided upon joining the company. At this stage, certain emotional data is also recorded by the terminal. 【0724】 Step 2: 【0725】 The device transmits user-entered data and sentiment data to the server. The transmitted data is securely stored in a company-specific database. 【0726】 Step 3: 【0727】 The server uses machine learning models to analyze personality traits and job suitability based on stored employee characteristic data. Based on the analysis results, an aptitude assessment report is generated for each individual employee. 【0728】 Step 4: 【0729】 The server integrates information from aptitude assessment reports with emotional data to automatically create an optimal training plan for each employee. Here, an emotional engine is used to plan individualized support based on each employee's mental state. 【0730】 Step 5: 【0731】 The terminal provides an interface that allows managers and instructors to review the generated training plans. This interface also allows real-time viewing of information regarding employees' emotional states. 【0732】 Step 6: 【0733】 Users (instructors) input observations from actual work and feedback from employees into the server via their terminals. The server analyzes this feedback and data obtained from the emotion engine to further adjust training plans. 【0734】 Step 7: 【0735】 The server monitors changes in the user's mental state, as captured by the emotion engine, and uses this information to provide real-time advice to the instructor. For example, if a user's emotional data indicates stress, the server will suggest countermeasures and ways to improve the instruction. 【0736】 Step 8: 【0737】 Based on the user's emotional state and work progress, the server dynamically improves the training plan, providing optimal guidance that is always up-to-date. This process makes it possible to create an effective training environment while supporting employees' mental health. 【0738】 (Example 2) 【0739】 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". 【0740】 Traditional employee training systems typically provide a uniform training plan, making it difficult to provide personalized support that takes into account the individual characteristics and emotional states of each employee. This resulted in challenges in maximizing employee growth and retention rates. Furthermore, real-time emotional assessment and dynamic adjustment of plans were difficult, leading to insufficient responses to stress and pressure. 【0741】 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. 【0742】 In this invention, the server includes means for acquiring data on employee characteristics; means for analyzing employee personality traits and aptitudes based on the data and performing aptitude evaluations using machine learning models; means for generating an optimal training plan for each employee based on the analysis results and creating an individualized plan considering the company's training policies and success story data; means for using emotion recognition technology to evaluate emotional states in real time; and means for providing an interface for displaying and modifying the training plan, and for allowing instructors to input feedback. This enables the provision of flexible and adaptive training plans for individual employees, facilitating rapid mental support and efficient talent development. 【0743】 "Data on employee characteristics" refers to information that indicates an individual employee's personality, aptitude, behavioral patterns, and emotional state. 【0744】 A "machine learning model" is a collection of algorithms and mathematical models that use data to automatically find specific patterns or rules and perform predictions and classifications. 【0745】 "Aptitude assessment" is a process of analyzing an employee's personality, skills, and suitability for the job, and quantitatively determining those characteristics. 【0746】 A "training plan" is a systematically structured plan that includes individualized training content and schedules to promote the improvement and growth of employees' skills. 【0747】 "Emotion recognition technology" is a technology that automatically analyzes and identifies human emotions from data such as voice, text, and facial expressions. 【0748】 An "interface" is a platform or means of connection provided for a user to interact with a system and to display and manipulate information. 【0749】 "Feedback" is the process of providing information such as evaluations and suggestions regarding employees' work performance and behavior, which is used for subsequent improvement and adjustments. 【0750】 The system of this invention analyzes the characteristics and emotional state of employees and provides individually optimized training plans. This system consists of a server, terminals, and users. 【0751】 The server collects personality assessment and aptitude survey data provided by users (employees). This includes online questionnaires and tests. The collected data is stored in a database (e.g., MySQL). Based on this data, the server uses machine learning models (e.g., scikit-learn or TensorFlow) to analyze employees' personalities and aptitudes and perform aptitude assessments. 【0752】 Based on the analysis results, the server generates an optimal training plan for each employee. This plan is individually optimized using an AI model, taking into account the company's training policies and past success stories. Furthermore, emotion recognition technology (e.g., natural language processing tools) is used to collect and evaluate user emotion data in real time, allowing for an understanding of employees' mental state. 【0753】 The terminal features an interface that displays training plans generated by the server to the user (instructor). In addition to displaying the training plan, this interface provides real-time information on the employee's emotional state, allowing instructors to adjust the plan as needed. For example, if emotional data indicating stress is detected, it becomes possible to adjust the difficulty level of the tasks assigned. 【0754】 The user (instructor) enters feedback on the employee's work into the terminal. This feedback information is sent to the server and further analyzed in combination with other sentiment data to help improve training plans. 【0755】 A concrete example of a prompt statement would be: "Based on the personality assessment results and aptitude survey data of new employees, create an optimal training plan, and adjust that plan in real time based on the employees' emotional state, then report the results." 【0756】 This system aims to provide flexible and adaptive training plans tailored to each employee's individual characteristics and emotional state, thereby promoting employee satisfaction and growth. 【0757】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0758】 Step 1: 【0759】 Users (employees) undergo personality assessments and aptitude tests. This includes online questionnaires and tests. The data provided by the users is sent to the server. 【0760】 Step 2: 【0761】 The server receives personality assessment results and aptitude survey data submitted by users. The input data includes information about an individual's personality traits and skill set. The server manages employee characteristics by storing this data in a database. 【0762】 Step 3: 【0763】 The server performs analysis using machine learning models based on the stored data. It uses scikit-learn and TensorFlow to process and perform calculations on data to evaluate employees' personality traits and aptitudes. The output is the employee aptitude evaluation results. 【0764】 Step 4: 【0765】 The server generates an optimal training plan for each employee based on the aptitude assessment results obtained. It references the company's training policies and success story data, and uses an AI model to create individually optimized training plans. The output is a specific training plan. 【0766】 Step 5: 【0767】 The terminal displays training plans sent from the server to managers and instructors through an interface. Users (instructors) can view the plan's content and real-time information on employees' emotional states. This information serves as a guide for users in implementing the training plans. 【0768】 Step 6: 【0769】 The user (instructor) observes employees during work and inputs the feedback information obtained into a terminal. This feedback includes specific comments about the employee's work performance and emotional state. The input feedback is sent to the server. 【0770】 Step 7: 【0771】 The server combines user feedback information with emotional data collected using emotion recognition technology to improve the training plan. This allows the training plan to be dynamically adjusted in real time. The output is the latest training plan. 【0772】 Through the steps outlined above, this system provides each employee with an appropriate and adaptive training plan, supporting their growth and success within the company. 【0773】 (Application Example 2) 【0774】 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". 【0775】 Traditional new employee training systems can analyze individual personalities and aptitudes to provide optimal training plans, but they have the challenge of dynamically adjusting plans to take into account employees' real-time emotional states. Furthermore, reflecting the societal trend towards prioritizing mental health, there is a growing need to provide training environments that also consider the mental well-being of employees. 【0776】 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. 【0777】 In this invention, the server includes means for acquiring data on employee characteristics, means for analyzing employee personality traits and aptitudes based on the data, means for generating an optimal training plan for each employee based on the analysis results, means for collecting and evaluating emotional information in real time, and means for dynamically adjusting the training plan based on the evaluation. This makes it possible to provide flexible and individualized training plans based on the employee's personality and emotional state. 【0778】 "Data related to characteristics" refers to information that indicates the personality and aptitude of individual employees, and includes the results of personality assessments and aptitude tests. 【0779】 "Analysis" is the process of thoroughly analyzing the obtained data to understand the personality traits and aptitudes of employees. 【0780】 A "training plan" is a plan or guideline created to optimally develop employees' abilities based on analysis results. 【0781】 An "interface" is a computer-based input and output mechanism that allows users to review training plans and make modifications as needed. 【0782】 "Emotional information" refers to data that indicates employees' mental state and mood, including real-time emotional fluctuations. 【0783】 "Methods for real-time evaluation" refer to technologies and methods for analyzing emotional information immediately to assess a person's mental state. 【0784】 "Means of dynamic adjustment" refers to methods for immediately adapting training plans based on changing data. 【0785】 In embodiments of this invention, a server, terminals, and users constitute the system. The server collects data on employee characteristics and uses machine learning algorithms to analyze personality traits and aptitudes based on this data. This analysis automatically generates a training plan tailored to each employee. Furthermore, by using an emotion engine, it is possible to collect employee emotional information in real time and dynamically adjust the training plan based on the analysis results. Here, the evaluation of emotional information is performed using analysis with a cloud service API. The hardware used includes a network-connected server and corresponding terminals. On the software side, a cloud service API (e.g., AWS, Google Cloud) is used for analyzing emotional data. 【0786】 The device provides an interface that allows administrators and instructors to review generated training plans and emotional status information. This interface enables users to modify training plans as needed and has the functionality to address employees' mental health issues and stress. 【0787】 The user's role is to input feedback obtained through observation of daily tasks into the terminal. The server receives this feedback and uses a machine learning model to further optimize the training plan. 【0788】 For example, if the server analyzes employee data and determines that a particular employee tends to be susceptible to pressure, it will propose a special stress reduction plan based on this information and the stress levels detected by the emotion engine. This plan can be viewed and modified on the employee's device, and trainers can use this information to provide more effective training. 【0789】 Using a generative AI model, an example of a prompt message could be: "Based on this morning's sentiment analysis, adjust the task plan to provide the best possible support to the user." In this way, the entire system can work together to support employee development. 【0790】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0791】 Step 1: 【0792】 The server acquires data on employee characteristics. Specifically, it collects the results of personality assessments and aptitude tests and stores them in a database. The input for this step is the results of personality assessments and aptitude tests, and the output is the storage of this data in the database used for analysis. 【0793】 Step 2: 【0794】 The server analyzes employees' personality traits and aptitudes based on the acquired data. Specifically, it uses a machine learning model to perform the analysis and generate profiles of personality traits and aptitudes. The input is trait data, and the output is personality traits and aptitude profiles. 【0795】 Step 3: 【0796】 The server generates an optimal training plan for each employee based on the analysis results. This includes a process of creating individualized plans using pre-prepared templates. The input is personality traits and aptitude profiles, and the output is an individual training plan. 【0797】 Step 4: 【0798】 The terminal displays the generated training plan in an interface that allows the user to review it. Here, instructors can review the plan content and make modifications as needed. The input is the training plan, and the output is the information displayed on the user interface. 【0799】 Step 5: 【0800】 The server uses an emotion engine to collect and evaluate employee emotional information in real time. Emotion analysis software is used to process the emotional data and understand the current mental state. The input is real-time emotional data, and the output is an evaluation of the emotional state. 【0801】 Step 6: 【0802】 The server dynamically adjusts the training plan based on the results of the emotional state assessment. It may change the difficulty level of the plan or add activities to promote relaxation as needed. The input is the emotional state assessment result, and the output is the adjusted training plan. 【0803】 Step 7: 【0804】 Users observe their daily tasks and input the feedback they receive into a terminal, sending it to the server. The server uses this feedback to further optimize the training plan. The input is feedback information, and the output is the optimized training plan. 【0805】 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. 【0806】 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. 【0807】 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 robot 414. 【0808】 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. 【0809】 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. 【0810】 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. 【0811】 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. 【0812】 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. 【0813】 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." 【0814】 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. 【0815】 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. 【0816】 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. 【0817】 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. 【0818】 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. 【0819】 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. 【0820】 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 this memory. 【0821】 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. 【0822】 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. 【0823】 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. 【0824】 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. 【0825】 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. 【0826】 The following is further disclosed regarding the embodiments described above. 【0827】 (Claim 1) 【0828】 Means for obtaining data on employee characteristics, 【0829】 A means of analyzing the personality traits and aptitudes of employees based on the aforementioned data, 【0830】 A means for generating an optimal training plan for each employee based on the aforementioned analysis results, 【0831】 Means for providing an interface for displaying and modifying the aforementioned training plan, 【0832】 A system that includes this. 【0833】 (Claim 2) 【0834】 The system according to claim 1, which includes means for assigning an employee to a trainer suited to their characteristics when proposing a training plan. 【0835】 (Claim 3) 【0836】 The system according to claim 1, comprising means for acquiring employee feedback information and dynamically improving the training plan. 【0837】 "Example 1" 【0838】 (Claim 1) 【0839】 Means for collecting employee characteristic data, 【0840】 Means for securely storing the aforementioned data in data storage, 【0841】 A method for analyzing employee personality traits and aptitudes using machine learning algorithms, 【0842】 A means for automatically generating an optimal training plan based on the aforementioned analysis results, 【0843】 Means for providing an interface that displays and allows modification of the aforementioned training plan, 【0844】 A means of acquiring feedback information and dynamically improving the training plan, 【0845】 A system that includes this. 【0846】 (Claim 2) 【0847】 The system according to claim 1, comprising means for assigning an educator suited to the characteristics of an employee. 【0848】 (Claim 3) 【0849】 The system according to claim 1, comprising means for analyzing feedback using machine learning technology and optimizing the training plan. 【0850】 "Application Example 1" 【0851】 (Claim 1) 【0852】 Means of obtaining data on individual characteristics, 【0853】 A means of analyzing individual characteristics and aptitudes based on the aforementioned data, 【0854】 A means for generating an optimal growth support plan for each individual based on the aforementioned analysis results, 【0855】 Means for providing an interface for displaying and modifying the aforementioned growth support plan, 【0856】 A means of acquiring individual behavioral habit data and proposing an optimal lifestyle improvement plan, 【0857】 ... 【0858】 A system that includes this. 【0859】 (Claim 2) 【0860】 The system according to claim 1, which includes means for assigning supporters suited to the individual's characteristics in the proposal of a growth support plan. 【0861】 (Claim 3) 【0862】 The system according to claim 1, comprising means for acquiring individual evaluation information and dynamically improving the growth support plan. 【0863】 "Example 2 of combining an emotion engine" 【0864】 (Claim 1) 【0865】 Means for obtaining data on employee characteristics, 【0866】 Based on the aforementioned data, a means of analyzing the personality traits and aptitudes of employees and performing aptitude evaluation using a machine learning model, 【0867】 A means for generating an optimal training plan for each employee based on the aforementioned analysis results, and creating an individualized plan that takes into account the company's training policies and success story data, 【0868】 Means of using emotion recognition technology to evaluate emotional states in real time, 【0869】 The aforementioned training plan is provided with an interface for displaying and modifying it, and a means for instructors to input feedback. 【0870】 A system that includes this. 【0871】 (Claim 2) 【0872】 The system according to claim 1, which includes means for dynamically adjusting the content of training in accordance with changes in the characteristics of employees and providing feedback based on emotional data in the proposal of training plans. 【0873】 (Claim 3) 【0874】 The system according to claim 1, comprising means for acquiring employee feedback information, dynamically improving the training plan in combination with emotional data, and promoting flexible human resource development. 【0875】 "Application example 2 when combining with an emotional engine" 【0876】 (Claim 1) 【0877】 Means for obtaining data on employee characteristics, 【0878】 A means of analyzing the personality traits and aptitudes of employees based on the aforementioned data, 【0879】 A means for generating an optimal training plan for each employee based on the aforementioned analysis results, 【0880】 Means for providing an interface for displaying and modifying the aforementioned training plan, 【0881】 A means of collecting and evaluating emotional information in real time, 【0882】 A means for dynamically adjusting the training plan based on the aforementioned evaluation, 【0883】 A system that includes this. 【0884】 (Claim 2) 【0885】 The system according to claim 1, which includes means for assigning an employee to a trainer suited to their characteristics when proposing a training plan. 【0886】 (Claim 3) 【0887】 The system according to claim 1, comprising means for acquiring employee feedback information and dynamically improving the training plan. [Explanation of Symbols] 【0888】 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

[Claim 1] Means for obtaining data on employee characteristics, A means of analyzing the personality traits and aptitudes of employees based on the aforementioned data, A means for generating an optimal training plan for each employee based on the aforementioned analysis results, Means for providing an interface for displaying and modifying the aforementioned training plan, A system that includes this. [Claim 2] The system according to claim 1, which includes means for assigning an instructor suited to the characteristics of an employee in the proposal of a training plan. [Claim 3] The system according to claim 1, comprising means for acquiring employee feedback information and dynamically improving the training plan.

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