system

Abstract

We provide the system. [Solution] A data collection method for collecting employee work-related information, A data integration means that integrates the collected information and converts it into a format that can be analyzed, An analytical means that analyzes integrated data to calculate employee performance evaluations, mental health status, and AI collaborative scores, Based on the analysis results, a proposal method is provided to suggest the optimization of personnel allocation, recruitment, side job placement, and training programs. A system that includes this.

Description

【Technical Field】 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 companies affected by the shortage of human resources due to the declining birthrate and aging population, an effective personnel strategy is required to address the situation where one employee is required to be active in multiple roles and fields. However, in order to formulate and implement such a strategy, a system that can effectively collect and analyze various data of employees and propose optimal personnel allocation, training, and part-time job mediation is required. In addition, building an environment where AI and employees work in cooperation to improve performance is also an important issue for modern companies. 【Means for Solving the Problems】 【0005】 This invention provides a data collection means for collecting employee work-related information, and a data integration means for integrating each employee's skills, qualifications, achievements, mental health status, etc. Furthermore, it uses an analysis means to analyze this integrated data, evaluates job suitability and potential abilities using a machine learning algorithm, and calculates an AI collaborative score. Based on the analysis results, it provides a system that comprehensively solves a company's human resources challenges by using a proposal means to suggest optimizations for personnel placement, recruitment, side job placement, and training programs. 【0006】 "Data collection means" refers to devices or programs equipped with functions for efficiently collecting work-related information such as employees' skills, qualifications, work performance, and mental health status. 【0007】 A "data integration means" is a device or program that has the function of integrating information from multiple collected data sources and converting it into a format that can be analyzed. 【0008】 "Analysis means" refers to devices or programs that have the function of analyzing integrated data, evaluating employees' potential abilities and job suitability, and calculating an AI collaborative score. 【0009】 A "proposal tool" refers to a device or program that has the function of proposing optimal personnel placement, recruitment, side job placement, and training programs based on the results of data analysis. 【0010】 The "AI Collaboration Score" is an index that quantifies an employee's ability and aptitude to work collaboratively with AI, and is calculated using analytical methods. [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 a data processing device and a smart device according to the first embodiment. [Figure 3]This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【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, let's explain the terminology used in the following explanation. 【0014】 In the following embodiments, the labeled 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 labeled 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 labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0017】 In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. 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). 【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, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【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】 This invention provides a system for optimizing human resources strategies within a company using diverse employee data. The system consists of a server, terminals, and users (HR personnel), and is primarily implemented through the following process. 【0033】 The server first collects information such as employees' skills, qualifications, work performance, and mental health status using data collection methods. During this process, PC logs and activity logs are also acquired as needed to record various activities of employees during work hours. 【0034】 The collected data is centralized and converted into an analyzable format using data integration tools. This unifies data stored in different formats, improving the accuracy and efficiency of analysis. 【0035】 Next, the AI ​​agent uses analytical tools to analyze the integrated data. Using machine learning algorithms, it evaluates employees' job suitability, potential, and AI collaboration scores, generating detailed reports. This allows HR personnel to accurately understand the capabilities of individual employees. 【0036】 Based on its analysis, the AI ​​agent uses suggestion tools to propose optimal personnel placement plans, recruitment strategies, potential side job placements, and training program selections. The suggestions are displayed on the terminal in a dashboard format, making them easy for HR personnel to understand intuitively. 【0037】 As a concrete example of this system, consider a case where an employee is deemed suitable to be a project leader. The server collects the employee's past project performance and leadership evaluations, and an AI agent analyzes this data. If the evaluation concludes that the employee is suitable for leadership, the AI ​​agent presents a placement plan to the terminal, including suggestions for appropriate training and necessary skill development. 【0038】 As described above, this invention provides an effective means to support a company's human resources strategy and strengthen the collaborative relationship between employees and AI. This enables companies to utilize human resources more effectively and maximize operational efficiency. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 The server collects each employee's skills, qualifications, work performance, self-reported information, PC logs, and activity logs from internal systems and external databases. This data is acquired intermittently or continuously and stored in a database for centralized management. 【0042】 Step 2: 【0043】 The server integrates the collected data and converts it into a format suitable for analysis. Specifically, it standardizes different data formats into a standard format and performs preprocessing such as noise reduction and missing value imputation. This enables accurate analysis. 【0044】 Step 3: 【0045】 The AI ​​agent receives integrated data provided by the server and analyzes it using analytical tools. It applies machine learning algorithms to evaluate each employee's job suitability, potential, mental health status, and calculate an AI collaborative score. 【0046】 Step 4: 【0047】 Based on the analysis results, the AI ​​agent utilizes various suggestion tools to generate proposals regarding optimal personnel placement, recruitment, side job placement, and training program selection. These proposals are then developed into concrete action plans tailored to the individual characteristics of each employee. 【0048】 Step 5: 【0049】 The user (HR representative) reviews the suggestions presented on the terminal and adds feedback as needed. Based on the suggestions, they decide on specific actions, such as conducting interviews with employees or implementing placement plans. 【0050】 Step 6: 【0051】 Through the terminal, users can review feedback from the AI ​​agent and take follow-up measures for their employees. This process is expected to help employees grow appropriately and contribute to improving the company's productivity. 【0052】 (Example 1) 【0053】 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." 【0054】 Modern companies face challenges in effectively utilizing diverse employee data to achieve appropriate talent allocation and skill development. Traditional systems are time-consuming to collect and analyze data, and lack consistency and usefulness, making it difficult to formulate optimal human resource strategies. 【0055】 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. 【0056】 In this invention, the server includes information gathering means for collecting work-related data concerning employees, data processing means for integrating and converting the collected data into an analyzable format, and analysis means for evaluating employees' work suitability, potential, and teamwork skills using the integrated data. This enables efficient collection, integration, and analysis of employee data, and optimizes human resource strategies. 【0057】 "Information gathering means" refers to devices or software that have the function of acquiring work-related data about employees from various systems and databases within a company. 【0058】 "Data processing means" refers to a device or software that has the function of converting collected data in various formats into a unified, analyzable format. 【0059】 "Analysis tools" refer to devices or software that have the function of evaluating employees' job suitability and potential based on integrated data. This process utilizes machine learning algorithms. 【0060】 "Proposal means" refers to a device or software that has the function of optimally proposing personnel placement plans and capacity development programs based on analysis results. 【0061】 "Display means" refers to a device or software that has an interface for visually providing users with optimized personnel allocation plans and analysis results. 【0062】 This system is designed to collect employee data within a company and optimize its human resources strategy. The following outlines the specific implementation of this system. 【0063】 The server uses specialized software to collect information on employees' skills, qualifications, work performance, and mental health status from various company systems and databases. Furthermore, client software is installed on employees' computers to record their activities during work hours, thereby acquiring PC logs and activity logs. The collected data is converted from different formats to a unified format using data processing tools and managed in a data warehouse. At this stage, ETL tools are used to filter and normalize the data. 【0064】 Next, the analysis tools running on the server evaluate employees' abilities and aptitudes using the integrated data. Specifically, they use machine learning libraries such as TENSORFLOW® and PyTorch to build models and extract job suitability and potential from the data. This analysis applies either supervised or unsupervised learning methods. 【0065】 Based on the resulting analysis data, the server's suggestion system automatically generates personnel placement plans and training programs. Here, the generation AI model is used, and specific suggestions are elicited using prompt statements. For example, by using the prompt statement, "Suggest the most suitable training program for this employee," the AI ​​model generates suggestions that are appropriate to the situation. 【0066】 Ultimately, the terminal's display method presents these suggestions to the user, the HR manager, in a dashboard format. Through an interactive interface, the user can intuitively understand each suggestion and analysis result and formulate appropriate HR strategies. A concrete example is the process in which AI analyzes the past data of an employee to assess their suitability for a project leader position. 【0067】 As described above, this system provides a concrete form for efficiently utilizing employee data and enabling the optimization of human resources strategies. 【0068】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0069】 Step 1: 【0070】 The server uses information gathering tools to collect data such as employee skills, qualifications, and work performance from various databases. It receives employee information extracted from HR systems and business management systems as input, and generates a raw dataset for temporary storage as output. In this process, automated scripts pick up data via APIs and save it to the server's data storage. 【0071】 Step 2: 【0072】 The server uses data processing tools to integrate the collected datasets and transform them into an analyzable format. It accepts temporarily stored raw datasets as input and generates a refined, integrated dataset as output. This process utilizes ETL (Extract, Transform, Load) tools to perform tasks such as data format conversion, missing value imputation, and data normalization. 【0073】 Step 3: 【0074】 The analysis tools on the server evaluate employees' job suitability and potential using an integrated dataset. It accepts a categorized integrated dataset as input and generates evaluation reports and scores as output. This step utilizes machine learning models, training them using tools like TensorFlow or PyTorch to analyze, classify, and predict patterns within the data. 【0075】 Step 4: 【0076】 The server's suggestion mechanism utilizes a generative AI model to generate employee placement plans and training program proposals. It receives analyzed evaluation data as input and generates specific personnel placement plans and training program proposals as output. In this process, prompts such as "Suggest the most suitable training program for this employee" are input to the generative AI model, which then generates proposals based on the analysis results. 【0077】 Step 5: 【0078】 The terminal provides users with suggestions received from the server through a display mechanism. It receives personnel placement proposals and training program suggestions from the server as input, and presents the results to the user in a visualized dashboard format as output. This step is designed to allow users to intuitively manipulate and understand the information through an interactive interface. 【0079】 (Application Example 1) 【0080】 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." 【0081】 In production processes, collaboration between workers and automated equipment is required, but optimizing the division of roles is difficult. Furthermore, it is challenging to appropriately evaluate each worker's potential and suitability for the job, and to efficiently allocate them to those roles. This can result in decreased production efficiency and worker burnout. 【0082】 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. 【0083】 In this invention, the server includes data collection means for collecting employee work-related information, data integration means for integrating the collected information and converting it into a format that allows for data analysis, analysis means for analyzing the integrated data and calculating employee work performance, mental health status, and artificial intelligence collaborative score, proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results, and placement planning means for optimizing the division of roles between human workers and automated equipment within a manufacturing facility. This enables efficient division of roles between workers and automated equipment and the formulation of an optimal placement plan. 【0084】 "Employee work-related information" refers to all data related to an employee's work, including their skills, qualifications, work performance, and mental health status. 【0085】 "Data collection means" refers to a device or program used to acquire work-related information of employees. 【0086】 A "data integration means" is a device or program that integrates collected information and converts it into a format that can be analyzed. 【0087】 "Analysis means" refers to a device or program for analyzing integrated data and calculating performance evaluations, mental health status, and artificial intelligence collaborative scores. 【0088】 A "proposal means" is a device or program that makes suggestions for optimizing personnel allocation and training programs based on analysis results. 【0089】 "A means for planning the arrangement of roles between human workers and automated equipment within a manufacturing facility" refers to a device or program used in production sites such as factories to optimize the arrangement of workers and automated equipment and to efficiently carry out work. 【0090】 This system consists of a server, terminals, and users (administrators and workers). The server is equipped with data collection means to collect work-related information about employees, including the skills, qualifications, work performance, and mental health status of workers in the factory. This involves the use of sensors, RFID tags, and software to record employees' work history. 【0091】 The collected information is centralized through data integration methods, and data in different formats is integrated into a parseable format. Programming languages ​​such as Python are sometimes used, and databases such as SQL or NoSQL are sometimes employed. 【0092】 The analysis method involves a detailed analysis of integrated data to calculate individual employee performance evaluations, mental health status, and AI collaborative scores. Machine learning algorithms such as TensorFlow and Scikit-Learn are used for this analysis. The analysis results are displayed on a dashboard on the terminal, allowing administrators to understand them intuitively. 【0093】 The proposed solution involves planning the optimal division of labor between workers and automated equipment, and developing a deployment plan to streamline the production process. For example, it involves assigning appropriate workers to processes requiring specific skills, while automated equipment handles the remaining tasks, thereby increasing overall efficiency. This information is also communicated to managers in real time via a dashboard. 【0094】 As a concrete example, if a particular task in the production process is taking longer than usual on a given day, the system will quickly analyze the situation and suggest improvements to appropriate personnel and equipment allocation. By using a prompt such as, "Based on yesterday's work data, please suggest the optimal robot allocation and shift schedule for the future," the generative AI model will generate these suggestions. 【0095】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0096】 Step 1: 【0097】 The server uses sensors and RFID tags within the factory to collect work-related information such as workers' skills, qualifications, and work performance. This information is acquired as raw input data. The server then preprocesses the data and converts it into the required format. At this stage, the acquired data is reviewed to check for any missing or incorrect information. 【0098】 Step 2: 【0099】 The server sends pre-processed information to a data integration system, where it is centralized into an SQL or NoSQL database. This process integrates data with different formats into a consistent data format. The integrated data is output as input data for analysis. Data normalization and aggregation are performed during this integration process. 【0100】 Step 3: 【0101】 The server processes the integrated data using analytical tools and performs analysis using machine learning algorithms. Here, TensorFlow and Scikit-Learn are used to calculate individual worker performance evaluations and AI collaborative scores. The analysis results are output as generated evaluation scores and trend data. This allows for an objective evaluation of worker performance and mental health status. 【0102】 Step 4: 【0103】 Users view the analysis results from the server on a dashboard using their terminal. The dashboard visually displays the optimal personnel allocation plan generated by the proposed methods. Here, users make decisions to improve the current situation using the system's suggestions. The output information is presented in the form of specific allocation proposals and schedules, making it easy to use. 【0104】 Step 5: 【0105】 The server generates instructions to optimize the roles of workers and automated equipment based on the deployment plan. This includes real-time adjustments to deployments and suggestions for new shift schedules. These instructions are communicated to the user via a terminal and evaluated as feasible improvements. In this step, for example, a prompt might be provided such as, "Based on yesterday's work data, please suggest the optimal robot deployment and shift schedule for the future," and the generated AI model optimizes the operation. 【0106】 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. 【0107】 This invention aims to optimize a company's human resources strategy by combining an emotion engine with a system to propose personnel placement and training programs that take into account the emotional state of employees. This system consists of a server, terminals, and users (human resources personnel). 【0108】 The server collects and manages information on employees' skills, qualifications, work performance, and mental health status as integrated data. This includes employee PC logs and activity logs, centralizing information from various data sources. 【0109】 The server uses data integration to consolidate data from different formats into a standard format that can be analyzed, preparing it for transfer to the emotion engine. The emotion engine analyzes the user's voice and facial expressions in real time and quantifies their emotional state. In this process, the emotion engine uses machine learning techniques to recognize emotions and integrates the results with other data. 【0110】 The AI ​​agent uses collected data and emotional information obtained from the emotion engine to calculate employees' job suitability, potential, and AI collaboration score through analytical methods. The analysis results are displayed on the device in a user-friendly dashboard format. 【0111】 The AI ​​agent uses suggestion tools to generate proposals for personnel placement, recruitment, side job placement, and training programs based on analysis results. In particular, it utilizes emotional information obtained from the emotion engine to propose optimal measures that take into account employees' emotional states and motivations. 【0112】 For example, if emotional data is obtained indicating that an employee is experiencing high levels of stress during work, the AI ​​agent can use this data to generate and present suggestions for training or job reassignment aimed at reducing stress. 【0113】 Thus, this system, which incorporates emotion recognition, enables accurate talent utilization that takes into account the emotional state of each employee, leading to increased corporate productivity and maximized operational efficiency. 【0114】 The following describes the processing flow. 【0115】 Step 1: 【0116】 The server collects data such as employees' skills, qualifications, work performance, mental health status, and PC logs from internal systems and external databases. Real-time behavioral data is also acquired from sensors and cameras as needed. 【0117】 Step 2: 【0118】 The server integrates the collected data in various formats and converts it into a format that can be analyzed. By using data integration means to standardize the data into a standard format, it manages information from different data sources in a consistent manner. 【0119】 Step 3: 【0120】 The server uses an emotion engine to analyze the user's voice and facial expression data in real time, quantifying the user's emotional state. This allows for continuous monitoring of emotional changes and the accumulation of this data. 【0121】 Step 4: 【0122】 The AI ​​agent uses integrated data and sentiment data to analyze and calculate employees' job suitability, potential, and AI collaboration scores. This analysis utilizes machine learning algorithms to ensure highly accurate evaluations. 【0123】 Step 5: 【0124】 Based on the analysis results, the AI ​​agent uses suggestion tools to generate proposals for personnel placement, recruitment, side job placement, and training programs. It places particular emphasis on options that improve employees' emotional well-being, taking emotional data into consideration. 【0125】 Step 6: 【0126】 Through the terminal, the user (HR representative) can view a dashboard displaying the AI ​​agent's suggestions and, if necessary, schedule follow-up meetings with employees. They can also provide situation-specific feedback to the AI ​​agent. 【0127】 Step 7: 【0128】 Users implement specific HR strategies based on the suggestions provided. This includes things like reassigning employees or directing them to participate in specific training programs. By implementing the suggestions, the overall improvement in employee performance and satisfaction is achieved. 【0129】 (Example 2) 【0130】 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 will be referred to as the "terminal." 【0131】 In today's business environment, there is a growing need for personnel placement and performance evaluation that take into account the emotional state and mental health of each employee. However, traditional methods rely solely on quantitative performance metrics, making it difficult to optimize personnel utilization by reflecting individual emotional states. This can lead to decreased employee motivation and imbalances in productivity. 【0132】 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. 【0133】 In this invention, the server includes information gathering means, information integration means, and analysis means. This makes it possible to utilize employee sentiment data for performance evaluation and to propose optimal placement, selection, and training programs. 【0134】 "Information gathering means" refers to components that have the function of acquiring employees' work-related information and emotional data. 【0135】 An "information integration means" is a component that has the function of converting collected information in different formats into a standardized format. 【0136】 An "analysis tool" is a component that has the function of quantifying and analyzing employees' work performance and mental state using integrated information and emotional data. 【0137】 The "proposal means" is a component that has the function of generating proposals to optimize employee placement, selection, and training programs based on the analysis results. 【0138】 A "display means" is a component that has the function of visually presenting analysis results and providing information in a format that can be easily understood by the user. 【0139】 A "means of communication" refers to a component that has the function of immediately conveying proposals regarding placement to the human resources department. 【0140】 The embodiments for carrying out the invention are described below. 【0141】 This invention provides a system for companies to optimize personnel placement and training programs by taking into account the individual emotional states of their employees. The system mainly consists of a server, terminals, and HR personnel who act as users. 【0142】 The server is equipped with information gathering mechanisms to collect employee work-related information and real-time emotional data. The server acquires voice, facial expressions, and work data through sensors and digital logs, and stores them in a database. The collected data is converted into a standardized format using information integration mechanisms. ETL tools may be used for this conversion to streamline data processing. 【0143】 The server further analyzes the integrated dataset using machine learning algorithms. This analysis quantifies employees' job suitability, potential, and emotional state, enabling performance evaluations. The analysis results are visually presented to the user via a terminal to support appropriate and timely decision-making. The terminal features a customizable dashboard, allowing users to easily review the analysis results. 【0144】 HR personnel, as users, can refer to the AI's suggestions for optimal personnel placement and training programs based on the displayed analytical information. These suggestions are based on employees' emotional states and include suggestions for job reassignment and training for employees experiencing high stress levels. 【0145】 For example, if an employee shows high stress levels during work, the server can analyze this data and, based on emotional and work data, suggest participation in stress reduction training. This is expected to provide appropriate support and lead to increased productivity. 【0146】 An example of a prompt might be, "Explain how the emotion engine quantifies stress using employee voice data and behavioral logs." This prompt allows the AI ​​model to generate the necessary response, providing the user with the information needed to take appropriate action. 【0147】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0148】 Step 1: 【0149】 The server collects employee work-related information and emotional data. Using data collection methods, it acquires voice, facial expressions, and work data from sensors and digital logs. Inputs include employee PC logs and activity logs, and this data is acquired as output and stored in the database. This allows for the collection of detailed data on employee work and behavior. 【0150】 Step 2: 【0151】 The server performs information integration, converting collected data into a standardized format. It utilizes data integration tools to transform data in different formats into a unified schema. The input consists of collected data in various formats, and the output is standardized data integrated into a database. ETL tools are used during this process to perform data formatting. 【0152】 Step 3: 【0153】 The server performs analysis using integrated data. Using analytical tools and machine learning algorithms, it quantifies and analyzes employees' job suitability, potential, and emotional state. The input includes integrated data, and the output provides individual analysis results for each employee. This allows for a better understanding of each employee's situation. 【0154】 Step 4: 【0155】 The terminal displays the analysis results sent from the server on a dashboard. The terminal displays visually processed data for easy user understanding. Input is analysis data from the server, and output is displayed in a user-viewable format. This display includes graphs and charts to provide information for decision-making. 【0156】 Step 5: 【0157】 The server generates proposals based on the analysis results. Using these proposal tools, it creates optimized plans for personnel allocation and training programs. The input includes analyzed data, and the output is specific proposals presented to the user. The generated proposals take into account the emotional state of employees and include optimal placement and training strategies. 【0158】 Step 6: 【0159】 The terminal presents suggestions from the server to the user. Feedback mechanisms ensure that the suggestions are delivered to the user, supporting their decision-making. The input is the suggestion text from the server, and the output is an action plan that leads to the user's decision-making. This allows for rapid adjustment of organizational strategy. 【0160】 (Application Example 2) 【0161】 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". 【0162】 Traditional methods for optimizing worker placement and training programs rely solely on work-related information, failing to adequately consider workers' emotional states. This can lead to mental health issues and decreased work efficiency, ultimately limiting overall company productivity. In smart cities, in particular, there is a need for methods to understand workers' emotional states in real time and optimize the work environment accordingly. 【0163】 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. 【0164】 In this invention, the server includes data collection means for collecting work-related information of workers; data integration means for integrating the collected information and converting it into a format that allows for data analysis; analysis means for analyzing the integrated data and the emotional state of workers to calculate the work performance evaluation, mental health status, and AI collaborative score of workers; proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results; and environment optimization means for proposing optimization of the work environment based on the emotional state of workers. This makes it possible to provide appropriate placement and training programs based on the emotional state of workers, enabling optimization of the work environment and improvement of productivity in smart cities. 【0165】 "Data collection means for collecting work-related information of workers" refers to the functions of devices and software that aggregate information such as workers' skills, qualifications, work performance, and mental health status from various data sources. 【0166】 "Data integration means that integrate collected information and convert it into a format that can be analyzed" refers to processes and tools for standardizing data in different formats and preparing it in a form that can be used for analysis. 【0167】 "Analysis methods for analyzing workers' emotional states and calculating their work performance, mental health status, and AI collaboration score" refers to algorithms and technologies for understanding workers' emotions and, based on that understanding, evaluating their suitability for work, mental health status, and ability to collaborate with AI. 【0168】 "A proposal tool for optimizing personnel placement, recruitment, side job placement, and training programs" refers to a system that generates recommendations to provide workers with the most suitable jobs and training. 【0169】 "Environmental optimization means that proposes the optimization of the work environment based on the emotional state of workers" is a technology that uses emotional data acquired in real time to provide a comfortable working environment and present improvement measures to reduce stress. 【0170】 The system for realizing this invention consists of a server, a terminal, and a user. The server is composed of several main means for comprehensively analyzing the worker's work-related information and emotional state. 【0171】 First, data collection systems are activated to gather information such as workers' skills, qualifications, work performance, and mental health status from various data sources. This information includes computer logs and behavioral logs. The collected data is then standardized by data integration systems into a parseable format, regardless of the format. 【0172】 Next, the analysis tool calculates work performance evaluations, mental health status, and AI collaborative scores based on the worker's work-related information and emotional data acquired in real time. In this process, an analysis engine such as EmotionEngine is used, and emotion recognition is performed by machine learning algorithms. 【0173】 Subsequently, the proposed method generates recommendations for optimal personnel placement, recruitment, and training programs based on the analysis results. Furthermore, the environment optimization method proposes measures to improve the work environment based on the emotional state of the workers. 【0174】 The terminal displays proposed placements and training programs in a dashboard format, providing real-time feedback to the user (e.g., HR personnel). This allows the user to optimize the work environment while taking into account the emotional state of the workers. 【0175】 For example, if emotional analysis reveals a high level of stress, the server will suggest an appropriate stress reduction program to the worker. Using a generative AI model, prompts such as "Please check your current emotional state and suggest measures to reduce stress" are provided. 【0176】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0177】 Step 1: 【0178】 The server collects work-related information about workers from various data sources. Inputs include workers' PC logs, activity logs, skills, and qualifications. This information is collected and organized using data collection methods. The output is the collected raw data. 【0179】 Step 2: 【0180】 The server converts the collected information into a format that can be analyzed by the data integration mechanism. The raw data obtained in step 1 is used as input. Specifically, data in different formats is standardized and unified. This process generates integrated data as output. 【0181】 Step 3: 【0182】 The server analyzes integrated data and real-time data on workers' emotional states using analytical tools. Inputs include integrated data and emotional data obtained from voice and facial expressions. Using an analysis engine such as EmotionEngine, performance evaluations, mental health status, and AI collaborative scores are calculated. The output is the analysis results. 【0183】 Step 4: 【0184】 The server generates suggestions based on the analysis results. It uses the analysis results obtained in step 3 as input. Using the suggested methods, it generates optimal personnel placement, recruitment, and training programs for workers. The output is the recommended programs and placement plans. 【0185】 Step 5: 【0186】 The server proposes environmental optimizations based on the emotional state of the workers. The input is based on the emotional data included in the analysis results of step 3. Using environmental optimization methods, it proposes stress reduction and a more comfortable working environment. The output is a proposal for an optimized work environment. 【0187】 Step 6: 【0188】 The terminal displays suggestions from the server to the user (HR personnel) in a dashboard format. The input is the output from steps 4 and 5. The user can decide on actual actions based on the presented placement plans and training programs. The output is a visually displayed program proposal. 【0189】 Step 7: 【0190】 The user adjusts the actual work environment of workers based on prompts provided by a generated AI model. The input is the instruction in the prompt. Specific actions include adjusting HR strategies and implementing training programs. The output is the optimized work environment. 【0191】 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. 【0192】 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. 【0193】 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. 【0194】 [Second Embodiment] 【0195】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0196】 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. 【0197】 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). 【0198】 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. 【0199】 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. 【0200】 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). 【0201】 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. 【0202】 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. 【0203】 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. 【0204】 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. 【0205】 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. 【0206】 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". 【0207】 This invention provides a system for optimizing human resources strategies within a company using diverse employee data. The system consists of a server, terminals, and users (HR personnel), and is primarily implemented through the following process. 【0208】 The server first collects information such as employees' skills, qualifications, work performance, and mental health status using data collection methods. During this process, PC logs and activity logs are also acquired as needed to record various activities of employees during work hours. 【0209】 The collected data is centralized and converted into an analyzable format using data integration tools. This unifies data stored in different formats, improving the accuracy and efficiency of analysis. 【0210】 Next, the AI ​​agent uses analytical tools to analyze the integrated data. Using machine learning algorithms, it evaluates employees' job suitability, potential, and AI collaboration scores, generating detailed reports. This allows HR personnel to accurately understand the capabilities of individual employees. 【0211】 Based on its analysis, the AI ​​agent uses suggestion tools to propose optimal personnel placement plans, recruitment strategies, potential side job placements, and training program selections. The suggestions are displayed on the terminal in a dashboard format, making them easy for HR personnel to understand intuitively. 【0212】 As a concrete example of this system, consider a case where an employee is deemed suitable to be a project leader. The server collects the employee's past project performance and leadership evaluations, and an AI agent analyzes this data. If the evaluation concludes that the employee is suitable for leadership, the AI ​​agent presents a placement plan to the terminal, including suggestions for appropriate training and necessary skill development. 【0213】 As described above, this invention provides an effective means to support a company's human resources strategy and strengthen the collaborative relationship between employees and AI. This enables companies to utilize human resources more effectively and maximize operational efficiency. 【0214】 The following describes the processing flow. 【0215】 Step 1: 【0216】 The server collects each employee's skills, qualifications, work performance, self-reported information, PC logs, and activity logs from internal systems and external databases. This data is acquired intermittently or continuously and stored in a database for centralized management. 【0217】 Step 2: 【0218】 The server integrates the collected data and converts it into a format suitable for analysis. Specifically, it standardizes different data formats into a standard format and performs preprocessing such as noise reduction and missing value imputation. This enables accurate analysis. 【0219】 Step 3: 【0220】 The AI ​​agent receives integrated data provided by the server and analyzes it using analytical tools. It applies machine learning algorithms to evaluate each employee's job suitability, potential, mental health status, and calculate an AI collaborative score. 【0221】 Step 4: 【0222】 Based on the analysis results, the AI ​​agent utilizes various suggestion tools to generate proposals regarding optimal personnel placement, recruitment, side job placement, and training program selection. These proposals are then developed into concrete action plans tailored to the individual characteristics of each employee. 【0223】 Step 5: 【0224】 The user (HR representative) reviews the suggestions presented on the terminal and adds feedback as needed. Based on the suggestions, they decide on specific actions, such as conducting interviews with employees or implementing placement plans. 【0225】 Step 6: 【0226】 Through the terminal, users can review feedback from the AI ​​agent and take follow-up measures for their employees. This process is expected to help employees grow appropriately and contribute to improving the company's productivity. 【0227】 (Example 1) 【0228】 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." 【0229】 Modern companies face challenges in effectively utilizing diverse employee data to achieve appropriate talent allocation and skill development. Traditional systems are time-consuming to collect and analyze data, and lack consistency and usefulness, making it difficult to formulate optimal human resource strategies. 【0230】 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. 【0231】 In this invention, the server includes information gathering means for collecting work-related data concerning employees, data processing means for integrating and converting the collected data into an analyzable format, and analysis means for evaluating employees' work suitability, potential, and teamwork skills using the integrated data. This enables efficient collection, integration, and analysis of employee data, and optimizes human resource strategies. 【0232】 "Information gathering means" refers to devices or software that have the function of acquiring work-related data about employees from various systems and databases within a company. 【0233】 "Data processing means" refers to a device or software that has the function of converting collected data in various formats into a unified, analyzable format. 【0234】 "Analysis tools" refer to devices or software that have the function of evaluating employees' job suitability and potential based on integrated data. This process utilizes machine learning algorithms. 【0235】 "Proposal means" refers to a device or software that has the function of optimally proposing personnel placement plans and capacity development programs based on analysis results. 【0236】 "Display means" refers to a device or software that has an interface for visually providing users with optimized personnel allocation plans and analysis results. 【0237】 This system is designed to collect employee data within a company and optimize its human resources strategy. The following outlines the specific implementation of this system. 【0238】 The server uses specialized software to collect information on employees' skills, qualifications, work performance, and mental health status from various company systems and databases. Furthermore, client software is installed on employees' computers to record their activities during work hours, thereby acquiring PC logs and activity logs. The collected data is converted from different formats to a unified format using data processing tools and managed in a data warehouse. At this stage, ETL tools are used to filter and normalize the data. 【0239】 Next, the analysis tools running on the server evaluate employees' abilities and aptitudes using the integrated data. Specifically, they use machine learning libraries such as TensorFlow and PyTorch to build models and extract job suitability and potential from the data. This analysis applies either supervised or unsupervised learning methods. 【0240】 Based on the resulting analysis data, the server's suggestion system automatically generates personnel placement plans and training programs. Here, the generation AI model is used, and specific suggestions are elicited using prompt statements. For example, by using the prompt statement, "Suggest the most suitable training program for this employee," the AI ​​model generates suggestions that are appropriate to the situation. 【0241】 Ultimately, the terminal's display method presents these suggestions to the user, the HR manager, in a dashboard format. Through an interactive interface, the user can intuitively understand each suggestion and analysis result and formulate appropriate HR strategies. A concrete example is the process in which AI analyzes the past data of an employee to assess their suitability for a project leader position. 【0242】 As described above, this system provides a concrete form for efficiently utilizing employee data and enabling the optimization of human resources strategies. 【0243】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0244】 Step 1: 【0245】 The server uses information gathering tools to collect data such as employee skills, qualifications, and work performance from various databases. It receives employee information extracted from HR systems and business management systems as input, and generates a raw dataset for temporary storage as output. In this process, automated scripts pick up data via APIs and save it to the server's data storage. 【0246】 Step 2: 【0247】 The server uses data processing tools to integrate the collected datasets and transform them into an analyzable format. It accepts temporarily stored raw datasets as input and generates a refined, integrated dataset as output. This process utilizes ETL (Extract, Transform, Load) tools to perform tasks such as data format conversion, missing value imputation, and data normalization. 【0248】 Step 3: 【0249】 The analysis tools on the server evaluate employees' job suitability and potential using an integrated dataset. It accepts a categorized integrated dataset as input and generates evaluation reports and scores as output. This step utilizes machine learning models, training them using tools like TensorFlow or PyTorch to analyze, classify, and predict patterns within the data. 【0250】 Step 4: 【0251】 The server's suggestion mechanism utilizes a generative AI model to generate employee placement plans and training program proposals. It receives analyzed evaluation data as input and generates specific personnel placement plans and training program proposals as output. In this process, prompts such as "Suggest the most suitable training program for this employee" are input to the generative AI model, which then generates proposals based on the analysis results. 【0252】 Step 5: 【0253】 The terminal provides users with suggestions received from the server through a display mechanism. It receives personnel placement proposals and training program suggestions from the server as input, and presents the results to the user in a visualized dashboard format as output. This step is designed to allow users to intuitively manipulate and understand the information through an interactive interface. 【0254】 (Application Example 1) 【0255】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0256】 In production processes, collaboration between workers and automated equipment is required, but optimizing the division of roles is difficult. Furthermore, it is challenging to appropriately evaluate each worker's potential and suitability for the job, and to efficiently allocate them to those roles. This can result in decreased production efficiency and worker burnout. 【0257】 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. 【0258】 In this invention, the server includes data collection means for collecting employee work-related information, data integration means for integrating the collected information and converting it into a format that allows for data analysis, analysis means for analyzing the integrated data and calculating employee work performance, mental health status, and artificial intelligence collaborative score, proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results, and placement planning means for optimizing the division of roles between human workers and automated equipment within a manufacturing facility. This enables efficient division of roles between workers and automated equipment and the formulation of an optimal placement plan. 【0259】 "Employee work-related information" refers to all data related to an employee's work, including their skills, qualifications, work performance, and mental health status. 【0260】 "Data collection means" refers to a device or program used to acquire work-related information of employees. 【0261】 A "data integration means" is a device or program that integrates collected information and converts it into a format that can be analyzed. 【0262】 "Analysis means" refers to a device or program for analyzing integrated data and calculating performance evaluations, mental health status, and artificial intelligence collaborative scores. 【0263】 A "proposal means" is a device or program that makes suggestions for optimizing personnel allocation and training programs based on analysis results. 【0264】 "A means for planning the arrangement of roles between human workers and automated equipment within a manufacturing facility" refers to a device or program used in production sites such as factories to optimize the arrangement of workers and automated equipment and to efficiently carry out work. 【0265】 This system consists of a server, terminals, and users (administrators and workers). The server is equipped with data collection means to collect work-related information about employees, including the skills, qualifications, work performance, and mental health status of workers in the factory. This involves the use of sensors, RFID tags, and software to record employees' work history. 【0266】 The collected information is centralized through data integration methods, and data in different formats is integrated into a parseable format. Programming languages ​​such as Python are sometimes used, and databases such as SQL or NoSQL are sometimes employed. 【0267】 The analysis method involves a detailed analysis of integrated data to calculate individual employee performance evaluations, mental health status, and AI collaborative scores. Machine learning algorithms such as TensorFlow and Scikit-Learn are used for this analysis. The analysis results are displayed on a dashboard on the terminal, allowing administrators to understand them intuitively. 【0268】 The proposed solution involves planning the optimal division of labor between workers and automated equipment, and developing a deployment plan to streamline the production process. For example, it involves assigning appropriate workers to processes requiring specific skills, while automated equipment handles the remaining tasks, thereby increasing overall efficiency. This information is also communicated to managers in real time via a dashboard. 【0269】 As a concrete example, if a particular task in the production process is taking longer than usual on a given day, the system will quickly analyze the situation and suggest improvements to appropriate personnel and equipment allocation. By using a prompt such as, "Based on yesterday's work data, please suggest the optimal robot allocation and shift schedule for the future," the generative AI model will generate these suggestions. 【0270】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0271】 Step 1: 【0272】 The server uses sensors and RFID tags within the factory to collect work-related information such as workers' skills, qualifications, and work performance. This information is acquired as raw input data. The server then preprocesses the data and converts it into the required format. At this stage, the acquired data is reviewed to check for any missing or incorrect information. 【0273】 Step 2: 【0274】 The server sends pre-processed information to a data integration system, where it is centralized into an SQL or NoSQL database. This process integrates data with different formats into a consistent data format. The integrated data is output as input data for analysis. Data normalization and aggregation are performed during this integration process. 【0275】 Step 3: 【0276】 The server processes the integrated data using analytical tools and performs analysis using machine learning algorithms. Here, TensorFlow and Scikit-Learn are used to calculate individual worker performance evaluations and AI collaborative scores. The analysis results are output as generated evaluation scores and trend data. This allows for an objective evaluation of worker performance and mental health status. 【0277】 Step 4: 【0278】 Users view the analysis results from the server on a dashboard using their terminal. The dashboard visually displays the optimal personnel allocation plan generated by the proposed methods. Here, users make decisions to improve the current situation using the system's suggestions. The output information is presented in the form of specific allocation proposals and schedules, making it easy to use. 【0279】 Step 5: 【0280】 The server generates instructions to optimize the roles of workers and automated equipment based on the deployment plan. This includes real-time adjustments to deployments and suggestions for new shift schedules. These instructions are communicated to the user via a terminal and evaluated as feasible improvements. In this step, for example, a prompt might be provided such as, "Based on yesterday's work data, please suggest the optimal robot deployment and shift schedule for the future," and the generated AI model optimizes the operation. 【0281】 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. 【0282】 The purpose of this invention is to propose personnel placement and training programs that take into account the emotional states of employees by combining an emotional engine with a system for optimizing a company's personnel strategy. This system is composed of a server, terminals, and users (personnel staff). 【0283】 The server collects information on employees' skills, qualifications, work performance, and mental health status, and manages it as integrated data. This includes employees' PC logs and behavior logs, and unifies information obtained from various data sources. 【0284】 The server uses data integration means to align data in different formats into a standard format that can be analyzed, and prepares to pass it to the emotional engine. The emotional engine analyzes the user's voice and expression in real time and quantifies the emotional state. At this time, the emotional engine uses machine learning technology to recognize emotions and integrates the results with other data. 【0285】 The AI agent uses the collected data and the emotional information obtained from the emotional engine to calculate the work suitability, potential ability, and AI cooperation score of employees by means of analysis. The results of the analysis are displayed on the terminal in a dashboard format that is easy for the user to understand. 【0286】 The AI agent uses proposal means to generate proposals for personnel placement, recruitment, part-time job mediation, and training programs based on the analysis results. Here, in particular, the emotional information obtained from the emotional engine is utilized to propose optimal measures considering the emotional states and motivations of employees. 【0287】 As a specific example, when emotional data indicating that a certain employee shows high stress during work is obtained, the AI agent can generate proposals for training or job transfer to reduce stress based on this and present them to the user. 【0288】 Thus, this system, which incorporates emotion recognition, enables accurate talent utilization that takes into account the emotional state of each employee, leading to increased corporate productivity and maximized operational efficiency. 【0289】 The following describes the processing flow. 【0290】 Step 1: 【0291】 The server collects data such as employees' skills, qualifications, work performance, mental health status, and PC logs from internal systems and external databases. Real-time behavioral data is also acquired from sensors and cameras as needed. 【0292】 Step 2: 【0293】 The server integrates the collected data in various formats and converts it into a format that can be analyzed. By using data integration means to standardize the data into a standard format, it manages information from different data sources in a consistent manner. 【0294】 Step 3: 【0295】 The server uses an emotion engine to analyze the user's voice and facial expression data in real time, quantifying the user's emotional state. This allows for continuous monitoring of emotional changes and the accumulation of this data. 【0296】 Step 4: 【0297】 The AI ​​agent uses integrated data and sentiment data to analyze and calculate employees' job suitability, potential, and AI collaboration scores. This analysis utilizes machine learning algorithms to ensure highly accurate evaluations. 【0298】 Step 5: 【0299】 Based on the analysis results, the AI agent uses the proposed means to generate proposals for personnel allocation, recruitment, part-time job mediation, and training programs. With consideration of emotional data, special emphasis is placed on options for improving the emotional well-being of employees. 【0300】 Step 6: 【0301】 Through the terminal, the user (personnel staff) checks the dashboard on which the proposal results from the AI agent are displayed and sets up follow-up interviews with employees as needed. It is also possible to input situation-dependent feedback to the AI agent. 【0302】 Step 7: 【0303】 The user executes a specific personnel strategy based on the presented proposal. This includes changes in employee placement and instructions to participate in specific training programs. By implementing the proposal, the performance and satisfaction of employees are comprehensively improved. 【0304】 (Example 2) 【0305】 Next, Example 2 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". 【0306】 In the modern corporate environment, there is a demand for personnel allocation and business evaluation that take into account the emotional state and mental health of each employee. However, conventional methods have the problem that they only depend on quantitative business achievements and it is difficult to optimally utilize personnel reflecting individual emotional states. As a result, there is a possibility of a decrease in employee motivation and an imbalance in productivity. 【0307】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means. 【0308】 In this invention, the server includes information gathering means, information integration means, and analysis means. This makes it possible to utilize employee sentiment data for performance evaluation and to propose optimal placement, selection, and training programs. 【0309】 "Information gathering means" refers to components that have the function of acquiring employees' work-related information and emotional data. 【0310】 An "information integration means" is a component that has the function of converting collected information in different formats into a standardized format. 【0311】 An "analysis tool" is a component that has the function of quantifying and analyzing employees' work performance and mental state using integrated information and emotional data. 【0312】 The "proposal means" is a component that has the function of generating proposals to optimize employee placement, selection, and training programs based on the analysis results. 【0313】 A "display means" is a component that has the function of visually presenting analysis results and providing information in a format that can be easily understood by the user. 【0314】 A "means of communication" refers to a component that has the function of immediately conveying proposals regarding placement to the human resources department. 【0315】 The embodiments for carrying out the invention are described below. 【0316】 This invention provides a system for companies to optimize personnel placement and training programs by taking into account the individual emotional states of their employees. The system mainly consists of a server, terminals, and HR personnel who act as users. 【0317】 The server is equipped with information gathering mechanisms to collect employee work-related information and real-time emotional data. The server acquires voice, facial expressions, and work data through sensors and digital logs, and stores them in a database. The collected data is converted into a standardized format using information integration mechanisms. ETL tools may be used for this conversion to streamline data processing. 【0318】 The server further analyzes the integrated dataset using machine learning algorithms. This analysis quantifies employees' job suitability, potential, and emotional state, enabling performance evaluations. The analysis results are visually presented to the user via a terminal to support appropriate and timely decision-making. The terminal features a customizable dashboard, allowing users to easily review the analysis results. 【0319】 HR personnel, as users, can refer to the AI's suggestions for optimal personnel placement and training programs based on the displayed analytical information. These suggestions are based on employees' emotional states and include suggestions for job reassignment and training for employees experiencing high stress levels. 【0320】 For example, if an employee shows high stress levels during work, the server can analyze this data and, based on emotional and work data, suggest participation in stress reduction training. This is expected to provide appropriate support and lead to increased productivity. 【0321】 An example of a prompt might be, "Explain how the emotion engine quantifies stress using employee voice data and behavioral logs." This prompt allows the AI ​​model to generate the necessary response, providing the user with the information needed to take appropriate action. 【0322】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0323】 Step 1: 【0324】 The server collects employee work-related information and emotional data. Using data collection methods, it acquires voice, facial expressions, and work data from sensors and digital logs. Inputs include employee PC logs and activity logs, and this data is acquired as output and stored in the database. This allows for the collection of detailed data on employee work and behavior. 【0325】 Step 2: 【0326】 The server performs information integration, converting collected data into a standardized format. It utilizes data integration tools to transform data in different formats into a unified schema. The input consists of collected data in various formats, and the output is standardized data integrated into a database. ETL tools are used during this process to perform data formatting. 【0327】 Step 3: 【0328】 The server performs analysis using integrated data. Using analytical tools and machine learning algorithms, it quantifies and analyzes employees' job suitability, potential, and emotional state. The input includes integrated data, and the output provides individual analysis results for each employee. This allows for a better understanding of each employee's situation. 【0329】 Step 4: 【0330】 The terminal displays the analysis results sent from the server on a dashboard. The terminal displays visually processed data for easy user understanding. Input is analysis data from the server, and output is displayed in a user-viewable format. This display includes graphs and charts to provide information for decision-making. 【0331】 Step 5: 【0332】 The server generates proposals based on the analysis results. Using these proposal tools, it creates optimized plans for personnel allocation and training programs. The input includes analyzed data, and the output is specific proposals presented to the user. The generated proposals take into account the emotional state of employees and include optimal placement and training strategies. 【0333】 Step 6: 【0334】 The terminal presents suggestions from the server to the user. Feedback mechanisms ensure that the suggestions are delivered to the user, supporting their decision-making. The input is the suggestion text from the server, and the output is an action plan that leads to the user's decision-making. This allows for rapid adjustment of organizational strategy. 【0335】 (Application Example 2) 【0336】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0337】 Traditional methods for optimizing worker placement and training programs rely solely on work-related information, failing to adequately consider workers' emotional states. This can lead to mental health issues and decreased work efficiency, ultimately limiting overall company productivity. In smart cities, in particular, there is a need for methods to understand workers' emotional states in real time and optimize the work environment accordingly. 【0338】 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. 【0339】 In this invention, the server includes data collection means for collecting work-related information of workers; data integration means for integrating the collected information and converting it into a format that allows for data analysis; analysis means for analyzing the integrated data and the emotional state of workers to calculate the work performance evaluation, mental health status, and AI collaborative score of workers; proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results; and environment optimization means for proposing optimization of the work environment based on the emotional state of workers. This makes it possible to provide appropriate placement and training programs based on the emotional state of workers, enabling optimization of the work environment and improvement of productivity in smart cities. 【0340】 "Data collection means for collecting work-related information of workers" refers to the functions of devices and software that aggregate information such as workers' skills, qualifications, work performance, and mental health status from various data sources. 【0341】 "Data integration means that integrate collected information and convert it into a format that can be analyzed" refers to processes and tools for standardizing data in different formats and preparing it in a form that can be used for analysis. 【0342】 "Analysis methods for analyzing workers' emotional states and calculating their work performance, mental health status, and AI collaboration score" refers to algorithms and technologies for understanding workers' emotions and, based on that understanding, evaluating their suitability for work, mental health status, and ability to collaborate with AI. 【0343】 "A proposal tool for optimizing personnel placement, recruitment, side job placement, and training programs" refers to a system that generates recommendations to provide workers with the most suitable jobs and training. 【0344】 "Environmental optimization means that proposes the optimization of the work environment based on the emotional state of workers" is a technology that uses emotional data acquired in real time to provide a comfortable working environment and present improvement measures to reduce stress. 【0345】 The system for realizing this invention consists of a server, a terminal, and a user. The server is composed of several main means for comprehensively analyzing the worker's work-related information and emotional state. 【0346】 First, data collection systems are activated to gather information such as workers' skills, qualifications, work performance, and mental health status from various data sources. This information includes computer logs and behavioral logs. The collected data is then standardized by data integration systems into a parseable format, regardless of the format. 【0347】 Next, the analysis tool calculates work performance evaluations, mental health status, and AI collaborative scores based on the worker's work-related information and emotional data acquired in real time. In this process, an analysis engine such as EmotionEngine is used, and emotion recognition is performed by machine learning algorithms. 【0348】 Subsequently, the proposed method generates recommendations for optimal personnel placement, recruitment, and training programs based on the analysis results. Furthermore, the environment optimization method proposes measures to improve the work environment based on the emotional state of the workers. 【0349】 The terminal displays proposed placements and training programs in a dashboard format, providing real-time feedback to the user (e.g., HR personnel). This allows the user to optimize the work environment while taking into account the emotional state of the workers. 【0350】 For example, if emotional analysis reveals a high level of stress, the server will suggest an appropriate stress reduction program to the worker. Using a generative AI model, prompts such as "Please check your current emotional state and suggest measures to reduce stress" are provided. 【0351】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0352】 Step 1: 【0353】 The server collects work-related information about workers from various data sources. Inputs include workers' PC logs, activity logs, skills, and qualifications. This information is collected and organized using data collection methods. The output is the collected raw data. 【0354】 Step 2: 【0355】 The server converts the collected information into a format that can be analyzed by the data integration mechanism. The raw data obtained in step 1 is used as input. Specifically, data in different formats is standardized and unified. This process generates integrated data as output. 【0356】 Step 3: 【0357】 The server analyzes integrated data and real-time data on workers' emotional states using analytical tools. Inputs include integrated data and emotional data obtained from voice and facial expressions. Using an analysis engine such as EmotionEngine, performance evaluations, mental health status, and AI collaborative scores are calculated. The output is the analysis results. 【0358】 Step 4: 【0359】 The server generates suggestions based on the analysis results. It uses the analysis results obtained in step 3 as input. Using the suggested methods, it generates optimal personnel placement, recruitment, and training programs for workers. The output is the recommended programs and placement plans. 【0360】 Step 5: 【0361】 The server proposes environmental optimizations based on the emotional state of the workers. The input is based on the emotional data included in the analysis results of step 3. Using environmental optimization methods, it proposes stress reduction and a more comfortable working environment. The output is a proposal for an optimized work environment. 【0362】 Step 6: 【0363】 The terminal displays suggestions from the server to the user (HR personnel) in a dashboard format. The input is the output from steps 4 and 5. The user can decide on actual actions based on the presented placement plans and training programs. The output is a visually displayed program proposal. 【0364】 Step 7: 【0365】 The user adjusts the actual work environment of workers based on prompts provided by a generated AI model. The input is the instruction in the prompt. Specific actions include adjusting HR strategies and implementing training programs. The output is the optimized work environment. 【0366】 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. 【0367】 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. 【0368】 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. 【0369】 [Third Embodiment] 【0370】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0371】 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. 【0372】 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). 【0373】 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. 【0374】 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. 【0375】 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). 【0376】 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. 【0377】 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. 【0378】 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. 【0379】 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. 【0380】 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. 【0381】 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". 【0382】 This invention provides a system for optimizing human resources strategies within a company using diverse employee data. The system consists of a server, terminals, and users (HR personnel), and is primarily implemented through the following process. 【0383】 The server first collects information such as employees' skills, qualifications, work performance, and mental health status using data collection methods. During this process, PC logs and activity logs are also acquired as needed to record various activities of employees during work hours. 【0384】 The collected data is centralized and converted into an analyzable format using data integration tools. This unifies data stored in different formats, improving the accuracy and efficiency of analysis. 【0385】 Next, the AI ​​agent uses analytical tools to analyze the integrated data. Using machine learning algorithms, it evaluates employees' job suitability, potential, and AI collaboration scores, generating detailed reports. This allows HR personnel to accurately understand the capabilities of individual employees. 【0386】 Based on its analysis, the AI ​​agent uses suggestion tools to propose optimal personnel placement plans, recruitment strategies, potential side job placements, and training program selections. The suggestions are displayed on the terminal in a dashboard format, making them easy for HR personnel to understand intuitively. 【0387】 As a concrete example of this system, consider a case where an employee is deemed suitable to be a project leader. The server collects the employee's past project performance and leadership evaluations, and an AI agent analyzes this data. If the evaluation concludes that the employee is suitable for leadership, the AI ​​agent presents a placement plan to the terminal, including suggestions for appropriate training and necessary skill development. 【0388】 As described above, this invention provides an effective means to support a company's human resources strategy and strengthen the collaborative relationship between employees and AI. This enables companies to utilize human resources more effectively and maximize operational efficiency. 【0389】 The following describes the processing flow. 【0390】 Step 1: 【0391】 The server collects each employee's skills, qualifications, work performance, self-reported information, PC logs, and activity logs from internal systems and external databases. This data is acquired intermittently or continuously and stored in a database for centralized management. 【0392】 Step 2: 【0393】 The server integrates the collected data and converts it into a format suitable for analysis. Specifically, it standardizes different data formats into a standard format and performs preprocessing such as noise reduction and missing value imputation. This enables accurate analysis. 【0394】 Step 3: 【0395】 The AI ​​agent receives integrated data provided by the server and analyzes it using analytical tools. It applies machine learning algorithms to evaluate each employee's job suitability, potential, mental health status, and calculate an AI collaborative score. 【0396】 Step 4: 【0397】 Based on the analysis results, the AI ​​agent utilizes various suggestion tools to generate proposals regarding optimal personnel placement, recruitment, side job placement, and training program selection. These proposals are then developed into concrete action plans tailored to the individual characteristics of each employee. 【0398】 Step 5: 【0399】 The user (HR representative) reviews the suggestions presented on the terminal and adds feedback as needed. Based on the suggestions, they decide on specific actions, such as conducting interviews with employees or implementing placement plans. 【0400】 Step 6: 【0401】 Through the terminal, users can review feedback from the AI ​​agent and take follow-up measures for their employees. This process is expected to help employees grow appropriately and contribute to improving the company's productivity. 【0402】 (Example 1) 【0403】 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." 【0404】 Modern companies face challenges in effectively utilizing diverse employee data to achieve appropriate talent allocation and skill development. Traditional systems are time-consuming to collect and analyze data, and lack consistency and usefulness, making it difficult to formulate optimal human resource strategies. 【0405】 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. 【0406】 In this invention, the server includes information gathering means for collecting work-related data concerning employees, data processing means for integrating and converting the collected data into an analyzable format, and analysis means for evaluating employees' work suitability, potential, and teamwork skills using the integrated data. This enables efficient collection, integration, and analysis of employee data, and optimizes human resource strategies. 【0407】 "Information gathering means" refers to devices or software that have the function of acquiring work-related data about employees from various systems and databases within a company. 【0408】 "Data processing means" refers to a device or software that has the function of converting collected data in various formats into a unified, analyzable format. 【0409】 "Analysis tools" refer to devices or software that have the function of evaluating employees' job suitability and potential based on integrated data. This process utilizes machine learning algorithms. 【0410】 "Proposal means" refers to a device or software that has the function of optimally proposing personnel placement plans and capacity development programs based on analysis results. 【0411】 "Display means" refers to a device or software that has an interface for visually providing users with optimized personnel allocation plans and analysis results. 【0412】 This system is designed to collect employee data within a company and optimize its human resources strategy. The following outlines the specific implementation of this system. 【0413】 The server uses specialized software to collect information on employees' skills, qualifications, work performance, and mental health status from various company systems and databases. Furthermore, client software is installed on employees' computers to record their activities during work hours, thereby acquiring PC logs and activity logs. The collected data is converted from different formats to a unified format using data processing tools and managed in a data warehouse. At this stage, ETL tools are used to filter and normalize the data. 【0414】 Next, the analysis tools running on the server evaluate employees' abilities and aptitudes using the integrated data. Specifically, they use machine learning libraries such as TensorFlow and PyTorch to build models and extract job suitability and potential from the data. This analysis applies either supervised or unsupervised learning methods. 【0415】 Based on the resulting analysis data, the server's suggestion system automatically generates personnel placement plans and training programs. Here, the generation AI model is used, and specific suggestions are elicited using prompt statements. For example, by using the prompt statement, "Suggest the most suitable training program for this employee," the AI ​​model generates suggestions that are appropriate to the situation. 【0416】 Ultimately, the terminal's display method presents these suggestions to the user, the HR manager, in a dashboard format. Through an interactive interface, the user can intuitively understand each suggestion and analysis result and formulate appropriate HR strategies. A concrete example is the process in which AI analyzes the past data of an employee to assess their suitability for a project leader position. 【0417】 As described above, this system provides a concrete form for efficiently utilizing employee data and enabling the optimization of human resources strategies. 【0418】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0419】 Step 1: 【0420】 The server uses information gathering tools to collect data such as employee skills, qualifications, and work performance from various databases. It receives employee information extracted from HR systems and business management systems as input, and generates a raw dataset for temporary storage as output. In this process, automated scripts pick up data via APIs and save it to the server's data storage. 【0421】 Step 2: 【0422】 The server uses data processing tools to integrate the collected datasets and transform them into an analyzable format. It accepts temporarily stored raw datasets as input and generates a refined, integrated dataset as output. This process utilizes ETL (Extract, Transform, Load) tools to perform tasks such as data format conversion, missing value imputation, and data normalization. 【0423】 Step 3: 【0424】 The analysis tools on the server evaluate employees' job suitability and potential using an integrated dataset. It accepts a categorized integrated dataset as input and generates evaluation reports and scores as output. This step utilizes machine learning models, training them using tools like TensorFlow or PyTorch to analyze, classify, and predict patterns within the data. 【0425】 Step 4: 【0426】 The server's suggestion mechanism utilizes a generative AI model to generate employee placement plans and training program proposals. It receives analyzed evaluation data as input and generates specific personnel placement plans and training program proposals as output. In this process, prompts such as "Suggest the most suitable training program for this employee" are input to the generative AI model, which then generates proposals based on the analysis results. 【0427】 Step 5: 【0428】 The terminal provides users with suggestions received from the server through a display mechanism. It receives personnel placement proposals and training program suggestions from the server as input, and presents the results to the user in a visualized dashboard format as output. This step is designed to allow users to intuitively manipulate and understand the information through an interactive interface. 【0429】 (Application Example 1) 【0430】 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." 【0431】 In production processes, collaboration between workers and automated equipment is required, but optimizing the division of roles is difficult. Furthermore, it is challenging to appropriately evaluate each worker's potential and suitability for the job, and to efficiently allocate them to those roles. This can result in decreased production efficiency and worker burnout. 【0432】 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. 【0433】 In this invention, the server includes data collection means for collecting employee work-related information, data integration means for integrating the collected information and converting it into a format that allows for data analysis, analysis means for analyzing the integrated data and calculating employee work performance, mental health status, and artificial intelligence collaborative score, proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results, and placement planning means for optimizing the division of roles between human workers and automated equipment within a manufacturing facility. This enables efficient division of roles between workers and automated equipment and the formulation of an optimal placement plan. 【0434】 "Employee work-related information" refers to all data related to an employee's work, including their skills, qualifications, work performance, and mental health status. 【0435】 "Data collection means" refers to a device or program used to acquire work-related information of employees. 【0436】 A "data integration means" is a device or program that integrates collected information and converts it into a format that can be analyzed. 【0437】 "Analysis means" refers to a device or program for analyzing integrated data and calculating performance evaluations, mental health status, and artificial intelligence collaborative scores. 【0438】 A "proposal means" is a device or program that makes suggestions for optimizing personnel allocation and training programs based on analysis results. 【0439】 "A means for planning the arrangement of roles between human workers and automated equipment within a manufacturing facility" refers to a device or program used in production sites such as factories to optimize the arrangement of workers and automated equipment and to efficiently carry out work. 【0440】 This system consists of a server, terminals, and users (administrators and workers). The server is equipped with data collection means to collect work-related information about employees, including the skills, qualifications, work performance, and mental health status of workers in the factory. This involves the use of sensors, RFID tags, and software to record employees' work history. 【0441】 The collected information is centralized through data integration methods, and data in different formats is integrated into a parseable format. Programming languages ​​such as Python are sometimes used, and databases such as SQL or NoSQL are sometimes employed. 【0442】 The analysis method involves a detailed analysis of integrated data to calculate individual employee performance evaluations, mental health status, and AI collaborative scores. Machine learning algorithms such as TensorFlow and Scikit-Learn are used for this analysis. The analysis results are displayed on a dashboard on the terminal, allowing administrators to understand them intuitively. 【0443】 The proposed solution involves planning the optimal division of labor between workers and automated equipment, and developing a deployment plan to streamline the production process. For example, it involves assigning appropriate workers to processes requiring specific skills, while automated equipment handles the remaining tasks, thereby increasing overall efficiency. This information is also communicated to managers in real time via a dashboard. 【0444】 As a concrete example, if a particular task in the production process is taking longer than usual on a given day, the system will quickly analyze the situation and suggest improvements to appropriate personnel and equipment allocation. By using a prompt such as, "Based on yesterday's work data, please suggest the optimal robot allocation and shift schedule for the future," the generative AI model will generate these suggestions. 【0445】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0446】 Step 1: 【0447】 The server uses sensors and RFID tags within the factory to collect work-related information such as workers' skills, qualifications, and work performance. This information is acquired as raw input data. The server then preprocesses the data and converts it into the required format. At this stage, the acquired data is reviewed to check for any missing or incorrect information. 【0448】 Step 2: 【0449】 The server sends pre-processed information to a data integration system, where it is centralized into an SQL or NoSQL database. This process integrates data with different formats into a consistent data format. The integrated data is output as input data for analysis. Data normalization and aggregation are performed during this integration process. 【0450】 Step 3: 【0451】 The server processes the integrated data using analytical tools and performs analysis using machine learning algorithms. Here, TensorFlow and Scikit-Learn are used to calculate individual worker performance evaluations and AI collaborative scores. The analysis results are output as generated evaluation scores and trend data. This allows for an objective evaluation of worker performance and mental health status. 【0452】 Step 4: 【0453】 Users view the analysis results from the server on a dashboard using their terminal. The dashboard visually displays the optimal personnel allocation plan generated by the proposed methods. Here, users make decisions to improve the current situation using the system's suggestions. The output information is presented in the form of specific allocation proposals and schedules, making it easy to use. 【0454】 Step 5: 【0455】 The server generates instructions to optimize the roles of workers and automated equipment based on the deployment plan. This includes real-time adjustments to deployments and suggestions for new shift schedules. These instructions are communicated to the user via a terminal and evaluated as feasible improvements. In this step, for example, a prompt might be provided such as, "Based on yesterday's work data, please suggest the optimal robot deployment and shift schedule for the future," and the generated AI model optimizes the operation. 【0456】 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. 【0457】 This invention aims to optimize a company's human resources strategy by combining an emotion engine with a system to propose personnel placement and training programs that take into account the emotional state of employees. This system consists of a server, terminals, and users (human resources personnel). 【0458】 The server collects and manages information on employees' skills, qualifications, work performance, and mental health status as integrated data. This includes employee PC logs and activity logs, centralizing information from various data sources. 【0459】 The server uses data integration to consolidate data from different formats into a standard format that can be analyzed, preparing it for transfer to the emotion engine. The emotion engine analyzes the user's voice and facial expressions in real time and quantifies their emotional state. In this process, the emotion engine uses machine learning techniques to recognize emotions and integrates the results with other data. 【0460】 The AI ​​agent uses collected data and emotional information obtained from the emotion engine to calculate employees' job suitability, potential, and AI collaboration score through analytical methods. The analysis results are displayed on the device in a user-friendly dashboard format. 【0461】 The AI ​​agent uses suggestion tools to generate proposals for personnel placement, recruitment, side job placement, and training programs based on analysis results. In particular, it utilizes emotional information obtained from the emotion engine to propose optimal measures that take into account employees' emotional states and motivations. 【0462】 For example, if emotional data is obtained indicating that an employee is experiencing high levels of stress during work, the AI ​​agent can use this data to generate and present suggestions for training or job reassignment aimed at reducing stress. 【0463】 Thus, this system, which incorporates emotion recognition, enables accurate talent utilization that takes into account the emotional state of each employee, leading to increased corporate productivity and maximized operational efficiency. 【0464】 The following describes the processing flow. 【0465】 Step 1: 【0466】 The server collects data such as employees' skills, qualifications, work performance, mental health status, and PC logs from internal systems and external databases. Real-time behavioral data is also acquired from sensors and cameras as needed. 【0467】 Step 2: 【0468】 The server integrates the collected data in various formats and converts it into a format that can be analyzed. By using data integration means to standardize the data into a standard format, it manages information from different data sources in a consistent manner. 【0469】 Step 3: 【0470】 The server uses an emotion engine to analyze the user's voice and facial expression data in real time, quantifying the user's emotional state. This allows for continuous monitoring of emotional changes and the accumulation of this data. 【0471】 Step 4: 【0472】 The AI ​​agent uses integrated data and sentiment data to analyze and calculate employees' job suitability, potential, and AI collaboration scores. This analysis utilizes machine learning algorithms to ensure highly accurate evaluations. 【0473】 Step 5: 【0474】 Based on the analysis results, the AI ​​agent uses suggestion tools to generate proposals for personnel placement, recruitment, side job placement, and training programs. It places particular emphasis on options that improve employees' emotional well-being, taking emotional data into consideration. 【0475】 Step 6: 【0476】 Through the terminal, the user (HR representative) can view a dashboard displaying the AI ​​agent's suggestions and, if necessary, schedule follow-up meetings with employees. They can also provide situation-specific feedback to the AI ​​agent. 【0477】 Step 7: 【0478】 Users implement specific HR strategies based on the suggestions provided. This includes things like reassigning employees or directing them to participate in specific training programs. By implementing the suggestions, the overall improvement in employee performance and satisfaction is achieved. 【0479】 (Example 2) 【0480】 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." 【0481】 In today's business environment, there is a growing need for personnel placement and performance evaluation that take into account the emotional state and mental health of each employee. However, traditional methods rely solely on quantitative performance metrics, making it difficult to optimize personnel utilization by reflecting individual emotional states. This can lead to decreased employee motivation and imbalances in productivity. 【0482】 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. 【0483】 In this invention, the server includes information gathering means, information integration means, and analysis means. This makes it possible to utilize employee sentiment data for performance evaluation and to propose optimal placement, selection, and training programs. 【0484】 "Information gathering means" refers to components that have the function of acquiring employees' work-related information and emotional data. 【0485】 An "information integration means" is a component that has the function of converting collected information in different formats into a standardized format. 【0486】 An "analysis tool" is a component that has the function of quantifying and analyzing employees' work performance and mental state using integrated information and emotional data. 【0487】 The "proposal means" is a component that has the function of generating proposals to optimize employee placement, selection, and training programs based on the analysis results. 【0488】 A "display means" is a component that has the function of visually presenting analysis results and providing information in a format that can be easily understood by the user. 【0489】 A "means of communication" refers to a component that has the function of immediately conveying proposals regarding placement to the human resources department. 【0490】 The embodiments for carrying out the invention are described below. 【0491】 This invention provides a system for companies to optimize personnel placement and training programs by taking into account the individual emotional states of their employees. The system mainly consists of a server, terminals, and HR personnel who act as users. 【0492】 The server is equipped with information gathering mechanisms to collect employee work-related information and real-time emotional data. The server acquires voice, facial expressions, and work data through sensors and digital logs, and stores them in a database. The collected data is converted into a standardized format using information integration mechanisms. ETL tools may be used for this conversion to streamline data processing. 【0493】 The server further analyzes the integrated dataset using machine learning algorithms. This analysis quantifies employees' job suitability, potential, and emotional state, enabling performance evaluations. The analysis results are visually presented to the user via a terminal to support appropriate and timely decision-making. The terminal features a customizable dashboard, allowing users to easily review the analysis results. 【0494】 HR personnel, as users, can refer to the AI's suggestions for optimal personnel placement and training programs based on the displayed analytical information. These suggestions are based on employees' emotional states and include suggestions for job reassignment and training for employees experiencing high stress levels. 【0495】 For example, if an employee shows high stress levels during work, the server can analyze this data and, based on emotional and work data, suggest participation in stress reduction training. This is expected to provide appropriate support and lead to increased productivity. 【0496】 An example of a prompt might be, "Explain how the emotion engine quantifies stress using employee voice data and behavioral logs." This prompt allows the AI ​​model to generate the necessary response, providing the user with the information needed to take appropriate action. 【0497】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0498】 Step 1: 【0499】 The server collects employee work-related information and emotional data. Using data collection methods, it acquires voice, facial expressions, and work data from sensors and digital logs. Inputs include employee PC logs and activity logs, and this data is acquired as output and stored in the database. This allows for the collection of detailed data on employee work and behavior. 【0500】 Step 2: 【0501】 The server performs information integration, converting collected data into a standardized format. It utilizes data integration tools to transform data in different formats into a unified schema. The input consists of collected data in various formats, and the output is standardized data integrated into a database. ETL tools are used during this process to perform data formatting. 【0502】 Step 3: 【0503】 The server performs analysis using integrated data. Using analytical tools and machine learning algorithms, it quantifies and analyzes employees' job suitability, potential, and emotional state. The input includes integrated data, and the output provides individual analysis results for each employee. This allows for a better understanding of each employee's situation. 【0504】 Step 4: 【0505】 The terminal displays the analysis results sent from the server on a dashboard. The terminal displays visually processed data for easy user understanding. Input is analysis data from the server, and output is displayed in a user-viewable format. This display includes graphs and charts to provide information for decision-making. 【0506】 Step 5: 【0507】 The server generates proposals based on the analysis results. Using these proposal tools, it creates optimized plans for personnel allocation and training programs. The input includes analyzed data, and the output is specific proposals presented to the user. The generated proposals take into account the emotional state of employees and include optimal placement and training strategies. 【0508】 Step 6: 【0509】 The terminal presents suggestions from the server to the user. Feedback mechanisms ensure that the suggestions are delivered to the user, supporting their decision-making. The input is the suggestion text from the server, and the output is an action plan that leads to the user's decision-making. This allows for rapid adjustment of organizational strategy. 【0510】 (Application Example 2) 【0511】 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." 【0512】 Traditional methods for optimizing worker placement and training programs rely solely on work-related information, failing to adequately consider workers' emotional states. This can lead to mental health issues and decreased work efficiency, ultimately limiting overall company productivity. In smart cities, in particular, there is a need for methods to understand workers' emotional states in real time and optimize the work environment accordingly. 【0513】 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. 【0514】 In this invention, the server includes data collection means for collecting work-related information of workers; data integration means for integrating the collected information and converting it into a format that allows for data analysis; analysis means for analyzing the integrated data and the emotional state of workers to calculate the work performance evaluation, mental health status, and AI collaborative score of workers; proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results; and environment optimization means for proposing optimization of the work environment based on the emotional state of workers. This makes it possible to provide appropriate placement and training programs based on the emotional state of workers, enabling optimization of the work environment and improvement of productivity in smart cities. 【0515】 "Data collection means for collecting work-related information of workers" refers to the functions of devices and software that aggregate information such as workers' skills, qualifications, work performance, and mental health status from various data sources. 【0516】 "Data integration means that integrate collected information and convert it into a format that can be analyzed" refers to processes and tools for standardizing data in different formats and preparing it in a form that can be used for analysis. 【0517】 "Analysis methods for analyzing workers' emotional states and calculating their work performance, mental health status, and AI collaboration score" refers to algorithms and technologies for understanding workers' emotions and, based on that understanding, evaluating their suitability for work, mental health status, and ability to collaborate with AI. 【0518】 "A proposal tool for optimizing personnel placement, recruitment, side job placement, and training programs" refers to a system that generates recommendations to provide workers with the most suitable jobs and training. 【0519】 "Environmental optimization means that proposes the optimization of the work environment based on the emotional state of workers" is a technology that uses emotional data acquired in real time to provide a comfortable working environment and present improvement measures to reduce stress. 【0520】 The system for realizing this invention consists of a server, a terminal, and a user. The server is composed of several main means for comprehensively analyzing the worker's work-related information and emotional state. 【0521】 First, data collection systems are activated to gather information such as workers' skills, qualifications, work performance, and mental health status from various data sources. This information includes computer logs and behavioral logs. The collected data is then standardized by data integration systems into a parseable format, regardless of the format. 【0522】 Next, the analysis tool calculates work performance evaluations, mental health status, and AI collaborative scores based on the worker's work-related information and emotional data acquired in real time. In this process, an analysis engine such as EmotionEngine is used, and emotion recognition is performed by machine learning algorithms. 【0523】 Subsequently, the proposed method generates recommendations for optimal personnel placement, recruitment, and training programs based on the analysis results. Furthermore, the environment optimization method proposes measures to improve the work environment based on the emotional state of the workers. 【0524】 The terminal displays proposed placements and training programs in a dashboard format, providing real-time feedback to the user (e.g., HR personnel). This allows the user to optimize the work environment while taking into account the emotional state of the workers. 【0525】 For example, if emotional analysis reveals a high level of stress, the server will suggest an appropriate stress reduction program to the worker. Using a generative AI model, prompts such as "Please check your current emotional state and suggest measures to reduce stress" are provided. 【0526】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0527】 Step 1: 【0528】 The server collects work-related information about workers from various data sources. Inputs include workers' PC logs, activity logs, skills, and qualifications. This information is collected and organized using data collection methods. The output is the collected raw data. 【0529】 Step 2: 【0530】 The server converts the collected information into a format that can be analyzed by the data integration mechanism. The raw data obtained in step 1 is used as input. Specifically, data in different formats is standardized and unified. This process generates integrated data as output. 【0531】 Step 3: 【0532】 The server analyzes integrated data and real-time data on workers' emotional states using analytical tools. Inputs include integrated data and emotional data obtained from voice and facial expressions. Using an analysis engine such as EmotionEngine, performance evaluations, mental health status, and AI collaborative scores are calculated. The output is the analysis results. 【0533】 Step 4: 【0534】 The server generates suggestions based on the analysis results. It uses the analysis results obtained in step 3 as input. Using the suggested methods, it generates optimal personnel placement, recruitment, and training programs for workers. The output is the recommended programs and placement plans. 【0535】 Step 5: 【0536】 The server proposes environmental optimizations based on the emotional state of the workers. The input is based on the emotional data included in the analysis results of step 3. Using environmental optimization methods, it proposes stress reduction and a more comfortable working environment. The output is a proposal for an optimized work environment. 【0537】 Step 6: 【0538】 The terminal displays suggestions from the server to the user (HR personnel) in a dashboard format. The input is the output from steps 4 and 5. The user can decide on actual actions based on the presented placement plans and training programs. The output is a visually displayed program proposal. 【0539】 Step 7: 【0540】 The user adjusts the actual work environment of workers based on prompts provided by a generated AI model. The input is the instruction in the prompt. Specific actions include adjusting HR strategies and implementing training programs. The output is the optimized work environment. 【0541】 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. 【0542】 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. 【0543】 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. 【0544】 [Fourth Embodiment] 【0545】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0546】 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. 【0547】 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). 【0548】 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. 【0549】 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. 【0550】 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). 【0551】 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. 【0552】 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. 【0553】 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. 【0554】 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. 【0555】 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. 【0556】 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. 【0557】 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". 【0558】 This invention provides a system for optimizing human resources strategies within a company using diverse employee data. The system consists of a server, terminals, and users (HR personnel), and is primarily implemented through the following process. 【0559】 The server first collects information such as employees' skills, qualifications, work performance, and mental health status using data collection methods. During this process, PC logs and activity logs are also acquired as needed to record various activities of employees during work hours. 【0560】 The collected data is centralized and converted into an analyzable format using data integration tools. This unifies data stored in different formats, improving the accuracy and efficiency of analysis. 【0561】 Next, the AI ​​agent uses analytical tools to analyze the integrated data. Using machine learning algorithms, it evaluates employees' job suitability, potential, and AI collaboration scores, generating detailed reports. This allows HR personnel to accurately understand the capabilities of individual employees. 【0562】 Based on its analysis, the AI ​​agent uses suggestion tools to propose optimal personnel placement plans, recruitment strategies, potential side job placements, and training program selections. The suggestions are displayed on the terminal in a dashboard format, making them easy for HR personnel to understand intuitively. 【0563】 As a concrete example of this system, consider a case where an employee is deemed suitable to be a project leader. The server collects the employee's past project performance and leadership evaluations, and an AI agent analyzes this data. If the evaluation concludes that the employee is suitable for leadership, the AI ​​agent presents a placement plan to the terminal, including suggestions for appropriate training and necessary skill development. 【0564】 As described above, this invention provides an effective means to support a company's human resources strategy and strengthen the collaborative relationship between employees and AI. This enables companies to utilize human resources more effectively and maximize operational efficiency. 【0565】 The following describes the processing flow. 【0566】 Step 1: 【0567】 The server collects each employee's skills, qualifications, work performance, self-reported information, PC logs, and activity logs from internal systems and external databases. This data is acquired intermittently or continuously and stored in a database for centralized management. 【0568】 Step 2: 【0569】 The server integrates the collected data and converts it into a format suitable for analysis. Specifically, it standardizes different data formats into a standard format and performs preprocessing such as noise reduction and missing value imputation. This enables accurate analysis. 【0570】 Step 3: 【0571】 The AI ​​agent receives integrated data provided by the server and analyzes it using analytical tools. It applies machine learning algorithms to evaluate each employee's job suitability, potential, mental health status, and calculate an AI collaborative score. 【0572】 Step 4: 【0573】 Based on the analysis results, the AI ​​agent utilizes various suggestion tools to generate proposals regarding optimal personnel placement, recruitment, side job placement, and training program selection. These proposals are then developed into concrete action plans tailored to the individual characteristics of each employee. 【0574】 Step 5: 【0575】 The user (HR representative) reviews the suggestions presented on the terminal and adds feedback as needed. Based on the suggestions, they decide on specific actions, such as conducting interviews with employees or implementing placement plans. 【0576】 Step 6: 【0577】 Through the terminal, users can review feedback from the AI ​​agent and take follow-up measures for their employees. This process is expected to help employees grow appropriately and contribute to improving the company's productivity. 【0578】 (Example 1) 【0579】 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". 【0580】 Modern companies face challenges in effectively utilizing diverse employee data to achieve appropriate talent allocation and skill development. Traditional systems are time-consuming to collect and analyze data, and lack consistency and usefulness, making it difficult to formulate optimal human resource strategies. 【0581】 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. 【0582】 In this invention, the server includes information gathering means for collecting work-related data concerning employees, data processing means for integrating and converting the collected data into an analyzable format, and analysis means for evaluating employees' work suitability, potential, and teamwork skills using the integrated data. This enables efficient collection, integration, and analysis of employee data, and optimizes human resource strategies. 【0583】 "Information gathering means" refers to devices or software that have the function of acquiring work-related data about employees from various systems and databases within a company. 【0584】 "Data processing means" refers to a device or software that has the function of converting collected data in various formats into a unified, analyzable format. 【0585】 "Analysis tools" refer to devices or software that have the function of evaluating employees' job suitability and potential based on integrated data. This process utilizes machine learning algorithms. 【0586】 "Proposal means" refers to a device or software that has the function of optimally proposing personnel placement plans and capacity development programs based on analysis results. 【0587】 "Display means" refers to a device or software that has an interface for visually providing users with optimized personnel allocation plans and analysis results. 【0588】 This system is designed to collect employee data within a company and optimize its human resources strategy. The following outlines the specific implementation of this system. 【0589】 The server uses specialized software to collect information on employees' skills, qualifications, work performance, and mental health status from various company systems and databases. Furthermore, client software is installed on employees' computers to record their activities during work hours, thereby acquiring PC logs and activity logs. The collected data is converted from different formats to a unified format using data processing tools and managed in a data warehouse. At this stage, ETL tools are used to filter and normalize the data. 【0590】 Next, the analysis tools running on the server evaluate employees' abilities and aptitudes using the integrated data. Specifically, they use machine learning libraries such as TensorFlow and PyTorch to build models and extract job suitability and potential from the data. This analysis applies either supervised or unsupervised learning methods. 【0591】 Based on the resulting analysis data, the server's suggestion system automatically generates personnel placement plans and training programs. Here, the generation AI model is used, and specific suggestions are elicited using prompt statements. For example, by using the prompt statement, "Suggest the most suitable training program for this employee," the AI ​​model generates suggestions that are appropriate to the situation. 【0592】 Ultimately, the terminal's display method presents these suggestions to the user, the HR manager, in a dashboard format. Through an interactive interface, the user can intuitively understand each suggestion and analysis result and formulate appropriate HR strategies. A concrete example is the process in which AI analyzes the past data of an employee to assess their suitability for a project leader position. 【0593】 As described above, this system provides a concrete form for efficiently utilizing employee data and enabling the optimization of human resources strategies. 【0594】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0595】 Step 1: 【0596】 The server uses information gathering tools to collect data such as employee skills, qualifications, and work performance from various databases. It receives employee information extracted from HR systems and business management systems as input, and generates a raw dataset for temporary storage as output. In this process, automated scripts pick up data via APIs and save it to the server's data storage. 【0597】 Step 2: 【0598】 The server uses data processing tools to integrate the collected datasets and transform them into an analyzable format. It accepts temporarily stored raw datasets as input and generates a refined, integrated dataset as output. This process utilizes ETL (Extract, Transform, Load) tools to perform tasks such as data format conversion, missing value imputation, and data normalization. 【0599】 Step 3: 【0600】 The analysis tools on the server evaluate employees' job suitability and potential using an integrated dataset. It accepts a categorized integrated dataset as input and generates evaluation reports and scores as output. This step utilizes machine learning models, training them using tools like TensorFlow or PyTorch to analyze, classify, and predict patterns within the data. 【0601】 Step 4: 【0602】 The server's suggestion mechanism utilizes a generative AI model to generate employee placement plans and training program proposals. It receives analyzed evaluation data as input and generates specific personnel placement plans and training program proposals as output. In this process, prompts such as "Suggest the most suitable training program for this employee" are input to the generative AI model, which then generates proposals based on the analysis results. 【0603】 Step 5: 【0604】 The terminal provides users with suggestions received from the server through a display mechanism. It receives personnel placement proposals and training program suggestions from the server as input, and presents the results to the user in a visualized dashboard format as output. This step is designed to allow users to intuitively manipulate and understand the information through an interactive interface. 【0605】 (Application Example 1) 【0606】 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". 【0607】 In production processes, collaboration between workers and automated equipment is required, but optimizing the division of roles is difficult. Furthermore, it is challenging to appropriately evaluate each worker's potential and suitability for the job, and to efficiently allocate them to those roles. This can result in decreased production efficiency and worker burnout. 【0608】 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. 【0609】 In this invention, the server includes data collection means for collecting employee work-related information, data integration means for integrating the collected information and converting it into a format that allows for data analysis, analysis means for analyzing the integrated data and calculating employee work performance, mental health status, and artificial intelligence collaborative score, proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results, and placement planning means for optimizing the division of roles between human workers and automated equipment within a manufacturing facility. This enables efficient division of roles between workers and automated equipment and the formulation of an optimal placement plan. 【0610】 "Employee work-related information" refers to all data related to an employee's work, including their skills, qualifications, work performance, and mental health status. 【0611】 "Data collection means" refers to a device or program used to acquire work-related information of employees. 【0612】 A "data integration means" is a device or program that integrates collected information and converts it into a format that can be analyzed. 【0613】 "Analysis means" refers to a device or program for analyzing integrated data and calculating performance evaluations, mental health status, and artificial intelligence collaborative scores. 【0614】 A "proposal means" is a device or program that makes suggestions for optimizing personnel allocation and training programs based on analysis results. 【0615】 "A means for planning the arrangement of roles between human workers and automated equipment within a manufacturing facility" refers to a device or program used in production sites such as factories to optimize the arrangement of workers and automated equipment and to efficiently carry out work. 【0616】 This system consists of a server, terminals, and users (administrators and workers). The server is equipped with data collection means to collect work-related information about employees, including the skills, qualifications, work performance, and mental health status of workers in the factory. This involves the use of sensors, RFID tags, and software to record employees' work history. 【0617】 The collected information is centralized through data integration methods, and data in different formats is integrated into a parseable format. Programming languages ​​such as Python are sometimes used, and databases such as SQL or NoSQL are sometimes employed. 【0618】 The analysis method involves a detailed analysis of integrated data to calculate individual employee performance evaluations, mental health status, and AI collaborative scores. Machine learning algorithms such as TensorFlow and Scikit-Learn are used for this analysis. The analysis results are displayed on a dashboard on the terminal, allowing administrators to understand them intuitively. 【0619】 The proposed solution involves planning the optimal division of labor between workers and automated equipment, and developing a deployment plan to streamline the production process. For example, it involves assigning appropriate workers to processes requiring specific skills, while automated equipment handles the remaining tasks, thereby increasing overall efficiency. This information is also communicated to managers in real time via a dashboard. 【0620】 As a concrete example, if a particular task in the production process is taking longer than usual on a given day, the system will quickly analyze the situation and suggest improvements to appropriate personnel and equipment allocation. By using a prompt such as, "Based on yesterday's work data, please suggest the optimal robot allocation and shift schedule for the future," the generative AI model will generate these suggestions. 【0621】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0622】 Step 1: 【0623】 The server uses sensors and RFID tags within the factory to collect work-related information such as workers' skills, qualifications, and work performance. This information is acquired as raw input data. The server then preprocesses the data and converts it into the required format. At this stage, the acquired data is reviewed to check for any missing or incorrect information. 【0624】 Step 2: 【0625】 The server sends pre-processed information to a data integration system, where it is centralized into an SQL or NoSQL database. This process integrates data with different formats into a consistent data format. The integrated data is output as input data for analysis. Data normalization and aggregation are performed during this integration process. 【0626】 Step 3: 【0627】 The server processes the integrated data using analytical tools and performs analysis using machine learning algorithms. Here, TensorFlow and Scikit-Learn are used to calculate individual worker performance evaluations and AI collaborative scores. The analysis results are output as generated evaluation scores and trend data. This allows for an objective evaluation of worker performance and mental health status. 【0628】 Step 4: 【0629】 Users view the analysis results from the server on a dashboard using their terminal. The dashboard visually displays the optimal personnel allocation plan generated by the proposed methods. Here, users make decisions to improve the current situation using the system's suggestions. The output information is presented in the form of specific allocation proposals and schedules, making it easy to use. 【0630】 Step 5: 【0631】 The server generates instructions to optimize the roles of workers and automated equipment based on the deployment plan. This includes real-time adjustments to deployments and suggestions for new shift schedules. These instructions are communicated to the user via a terminal and evaluated as feasible improvements. In this step, for example, a prompt might be provided such as, "Based on yesterday's work data, please suggest the optimal robot deployment and shift schedule for the future," and the generated AI model optimizes the operation. 【0632】 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. 【0633】 This invention aims to optimize a company's human resources strategy by combining an emotion engine with a system to propose personnel placement and training programs that take into account the emotional state of employees. This system consists of a server, terminals, and users (human resources personnel). 【0634】 The server collects and manages information on employees' skills, qualifications, work performance, and mental health status as integrated data. This includes employee PC logs and activity logs, centralizing information from various data sources. 【0635】 The server uses data integration to consolidate data from different formats into a standard format that can be analyzed, preparing it for transfer to the emotion engine. The emotion engine analyzes the user's voice and facial expressions in real time and quantifies their emotional state. In this process, the emotion engine uses machine learning techniques to recognize emotions and integrates the results with other data. 【0636】 The AI ​​agent uses collected data and emotional information obtained from the emotion engine to calculate employees' job suitability, potential, and AI collaboration score through analytical methods. The analysis results are displayed on the device in a user-friendly dashboard format. 【0637】 The AI ​​agent uses suggestion tools to generate proposals for personnel placement, recruitment, side job placement, and training programs based on analysis results. In particular, it utilizes emotional information obtained from the emotion engine to propose optimal measures that take into account employees' emotional states and motivations. 【0638】 For example, if emotional data is obtained indicating that an employee is experiencing high levels of stress during work, the AI ​​agent can use this data to generate and present suggestions for training or job reassignment aimed at reducing stress. 【0639】 Thus, this system, which incorporates emotion recognition, enables accurate talent utilization that takes into account the emotional state of each employee, leading to increased corporate productivity and maximized operational efficiency. 【0640】 The following describes the processing flow. 【0641】 Step 1: 【0642】 The server collects data such as employees' skills, qualifications, work performance, mental health status, and PC logs from internal systems and external databases. Real-time behavioral data is also acquired from sensors and cameras as needed. 【0643】 Step 2: 【0644】 The server integrates the collected data in various formats and converts it into a format that can be analyzed. By using data integration means to standardize the data into a standard format, it manages information from different data sources in a consistent manner. 【0645】 Step 3: 【0646】 The server uses an emotion engine to analyze the user's voice and facial expression data in real time, quantifying the user's emotional state. This allows for continuous monitoring of emotional changes and the accumulation of this data. 【0647】 Step 4: 【0648】 The AI ​​agent uses integrated data and sentiment data to analyze and calculate employees' job suitability, potential, and AI collaboration scores. This analysis utilizes machine learning algorithms to ensure highly accurate evaluations. 【0649】 Step 5: 【0650】 Based on the analysis results, the AI ​​agent uses suggestion tools to generate proposals for personnel placement, recruitment, side job placement, and training programs. It places particular emphasis on options that improve employees' emotional well-being, taking emotional data into consideration. 【0651】 Step 6: 【0652】 Through the terminal, the user (HR representative) can view a dashboard displaying the AI ​​agent's suggestions and, if necessary, schedule follow-up meetings with employees. They can also provide situation-specific feedback to the AI ​​agent. 【0653】 Step 7: 【0654】 Users implement specific HR strategies based on the suggestions provided. This includes things like reassigning employees or directing them to participate in specific training programs. By implementing the suggestions, the overall improvement in employee performance and satisfaction is achieved. 【0655】 (Example 2) 【0656】 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". 【0657】 In today's business environment, there is a growing need for personnel placement and performance evaluation that take into account the emotional state and mental health of each employee. However, traditional methods rely solely on quantitative performance metrics, making it difficult to optimize personnel utilization by reflecting individual emotional states. This can lead to decreased employee motivation and imbalances in productivity. 【0658】 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. 【0659】 In this invention, the server includes information gathering means, information integration means, and analysis means. This makes it possible to utilize employee sentiment data for performance evaluation and to propose optimal placement, selection, and training programs. 【0660】 "Information gathering means" refers to components that have the function of acquiring employees' work-related information and emotional data. 【0661】 An "information integration means" is a component that has the function of converting collected information in different formats into a standardized format. 【0662】 An "analysis tool" is a component that has the function of quantifying and analyzing employees' work performance and mental state using integrated information and emotional data. 【0663】 The "proposal means" is a component that has the function of generating proposals to optimize employee placement, selection, and training programs based on the analysis results. 【0664】 A "display means" is a component that has the function of visually presenting analysis results and providing information in a format that can be easily understood by the user. 【0665】 A "means of communication" refers to a component that has the function of immediately conveying proposals regarding placement to the human resources department. 【0666】 The embodiments for carrying out the invention are described below. 【0667】 This invention provides a system for companies to optimize personnel placement and training programs by taking into account the individual emotional states of their employees. The system mainly consists of a server, terminals, and HR personnel who act as users. 【0668】 The server is equipped with information gathering mechanisms to collect employee work-related information and real-time emotional data. The server acquires voice, facial expressions, and work data through sensors and digital logs, and stores them in a database. The collected data is converted into a standardized format using information integration mechanisms. ETL tools may be used for this conversion to streamline data processing. 【0669】 The server further analyzes the integrated dataset using machine learning algorithms. This analysis quantifies employees' job suitability, potential, and emotional state, enabling performance evaluations. The analysis results are visually presented to the user via a terminal to support appropriate and timely decision-making. The terminal features a customizable dashboard, allowing users to easily review the analysis results. 【0670】 HR personnel, as users, can refer to the AI's suggestions for optimal personnel placement and training programs based on the displayed analytical information. These suggestions are based on employees' emotional states and include suggestions for job reassignment and training for employees experiencing high stress levels. 【0671】 For example, if an employee shows high stress levels during work, the server can analyze this data and, based on emotional and work data, suggest participation in stress reduction training. This is expected to provide appropriate support and lead to increased productivity. 【0672】 An example of a prompt might be, "Explain how the emotion engine quantifies stress using employee voice data and behavioral logs." This prompt allows the AI ​​model to generate the necessary response, providing the user with the information needed to take appropriate action. 【0673】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0674】 Step 1: 【0675】 The server collects employee work-related information and emotional data. Using data collection methods, it acquires voice, facial expressions, and work data from sensors and digital logs. Inputs include employee PC logs and activity logs, and this data is acquired as output and stored in the database. This allows for the collection of detailed data on employee work and behavior. 【0676】 Step 2: 【0677】 The server performs information integration, converting collected data into a standardized format. It utilizes data integration tools to transform data in different formats into a unified schema. The input consists of collected data in various formats, and the output is standardized data integrated into a database. ETL tools are used during this process to perform data formatting. 【0678】 Step 3: 【0679】 The server performs analysis using integrated data. Using analytical tools and machine learning algorithms, it quantifies and analyzes employees' job suitability, potential, and emotional state. The input includes integrated data, and the output provides individual analysis results for each employee. This allows for a better understanding of each employee's situation. 【0680】 Step 4: 【0681】 The terminal displays the analysis results sent from the server on a dashboard. The terminal displays visually processed data for easy user understanding. Input is analysis data from the server, and output is displayed in a user-viewable format. This display includes graphs and charts to provide information for decision-making. 【0682】 Step 5: 【0683】 The server generates proposals based on the analysis results. Using these proposal tools, it creates optimized plans for personnel allocation and training programs. The input includes analyzed data, and the output is specific proposals presented to the user. The generated proposals take into account the emotional state of employees and include optimal placement and training strategies. 【0684】 Step 6: 【0685】 The terminal presents suggestions from the server to the user. Feedback mechanisms ensure that the suggestions are delivered to the user, supporting their decision-making. The input is the suggestion text from the server, and the output is an action plan that leads to the user's decision-making. This allows for rapid adjustment of organizational strategy. 【0686】 (Application Example 2) 【0687】 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". 【0688】 Traditional methods for optimizing worker placement and training programs rely solely on work-related information, failing to adequately consider workers' emotional states. This can lead to mental health issues and decreased work efficiency, ultimately limiting overall company productivity. In smart cities, in particular, there is a need for methods to understand workers' emotional states in real time and optimize the work environment accordingly. 【0689】 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. 【0690】 In this invention, the server includes data collection means for collecting work-related information of workers; data integration means for integrating the collected information and converting it into a format that allows for data analysis; analysis means for analyzing the integrated data and the emotional state of workers to calculate the work performance evaluation, mental health status, and AI collaborative score of workers; proposal means for proposing optimization of personnel placement, recruitment, side job placement, and training programs based on the analysis results; and environment optimization means for proposing optimization of the work environment based on the emotional state of workers. This makes it possible to provide appropriate placement and training programs based on the emotional state of workers, enabling optimization of the work environment and improvement of productivity in smart cities. 【0691】 "Data collection means for collecting work-related information of workers" refers to the functions of devices and software that aggregate information such as workers' skills, qualifications, work performance, and mental health status from various data sources. 【0692】 "Data integration means that integrate collected information and convert it into a format that can be analyzed" refers to processes and tools for standardizing data in different formats and preparing it in a form that can be used for analysis. 【0693】 "Analysis methods for analyzing workers' emotional states and calculating their work performance, mental health status, and AI collaboration score" refers to algorithms and technologies for understanding workers' emotions and, based on that understanding, evaluating their suitability for work, mental health status, and ability to collaborate with AI. 【0694】 "A proposal tool for optimizing personnel placement, recruitment, side job placement, and training programs" refers to a system that generates recommendations to provide workers with the most suitable jobs and training. 【0695】 "Environmental optimization means that proposes the optimization of the work environment based on the emotional state of workers" is a technology that uses emotional data acquired in real time to provide a comfortable working environment and present improvement measures to reduce stress. 【0696】 The system for realizing this invention consists of a server, a terminal, and a user. The server is composed of several main means for comprehensively analyzing the worker's work-related information and emotional state. 【0697】 First, data collection systems are activated to gather information such as workers' skills, qualifications, work performance, and mental health status from various data sources. This information includes computer logs and behavioral logs. The collected data is then standardized by data integration systems into a parseable format, regardless of the format. 【0698】 Next, the analysis tool calculates work performance evaluations, mental health status, and AI collaborative scores based on the worker's work-related information and emotional data acquired in real time. In this process, an analysis engine such as EmotionEngine is used, and emotion recognition is performed by machine learning algorithms. 【0699】 Subsequently, the proposed method generates recommendations for optimal personnel placement, recruitment, and training programs based on the analysis results. Furthermore, the environment optimization method proposes measures to improve the work environment based on the emotional state of the workers. 【0700】 The terminal displays proposed placements and training programs in a dashboard format, providing real-time feedback to the user (e.g., HR personnel). This allows the user to optimize the work environment while taking into account the emotional state of the workers. 【0701】 For example, if emotional analysis reveals a high level of stress, the server will suggest an appropriate stress reduction program to the worker. Using a generative AI model, prompts such as "Please check your current emotional state and suggest measures to reduce stress" are provided. 【0702】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0703】 Step 1: 【0704】 The server collects work-related information about workers from various data sources. Inputs include workers' PC logs, activity logs, skills, and qualifications. This information is collected and organized using data collection methods. The output is the collected raw data. 【0705】 Step 2: 【0706】 The server converts the collected information into a format that can be analyzed by the data integration mechanism. The raw data obtained in step 1 is used as input. Specifically, data in different formats is standardized and unified. This process generates integrated data as output. 【0707】 Step 3: 【0708】 The server analyzes integrated data and real-time data on workers' emotional states using analytical tools. Inputs include integrated data and emotional data obtained from voice and facial expressions. Using an analysis engine such as EmotionEngine, performance evaluations, mental health status, and AI collaborative scores are calculated. The output is the analysis results. 【0709】 Step 4: 【0710】 The server generates suggestions based on the analysis results. It uses the analysis results obtained in step 3 as input. Using the suggested methods, it generates optimal personnel placement, recruitment, and training programs for workers. The output is the recommended programs and placement plans. 【0711】 Step 5: 【0712】 The server proposes environmental optimizations based on the emotional state of the workers. The input is based on the emotional data included in the analysis results of step 3. Using environmental optimization methods, it proposes stress reduction and a more comfortable working environment. The output is a proposal for an optimized work environment. 【0713】 Step 6: 【0714】 The terminal displays suggestions from the server to the user (HR personnel) in a dashboard format. The input is the output from steps 4 and 5. The user can decide on actual actions based on the presented placement plans and training programs. The output is a visually displayed program proposal. 【0715】 Step 7: 【0716】 The user adjusts the actual work environment of workers based on prompts provided by a generated AI model. The input is the instruction in the prompt. Specific actions include adjusting HR strategies and implementing training programs. The output is the optimized work environment. 【0717】 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. 【0718】 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. 【0719】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0720】 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. 【0721】 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. 【0722】 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. 【0723】 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. 【0724】 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. 【0725】 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." 【0726】 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. 【0727】 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. 【0728】 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. 【0729】 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. 【0730】 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. 【0731】 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. 【0732】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0733】 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. 【0734】 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. 【0735】 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. 【0736】 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. 【0737】 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 as being incorporated by reference. 【0738】 The following is further disclosed regarding the embodiments described above. 【0739】 (Claim 1) 【0740】 A data collection method for collecting employee work-related information, 【0741】 A data integration means that integrates the collected information and converts it into a format that can be analyzed, 【0742】 An analytical means that analyzes integrated data to calculate employee performance evaluations, mental health status, and AI collaborative scores, 【0743】 Based on the analysis results, a proposal method is provided to suggest the optimization of personnel allocation, recruitment, side job placement, and training programs. 【0744】 A system that includes this. 【0745】 (Claim 2) 【0746】 The system according to claim 1, further comprising a feedback mechanism for providing personnel placement suggestions to human resources personnel in real time. 【0747】 (Claim 3) 【0748】 The system according to claim 1, wherein the analysis means uses a machine learning algorithm to evaluate the potential abilities and job suitability of employees. 【0749】 "Example 1" 【0750】 (Claim 1) 【0751】 Information gathering methods for collecting business-related data about employees, 【0752】 A data processing means that integrates the collected data and converts it into a format that can be analyzed, 【0753】 An analytical means for evaluating employees' job suitability, potential, and teamwork skills using integrated data, 【0754】 Based on the analysis results, a proposal method for optimizing and proposing personnel placement plans, recruitment strategies, side job proposals, and skill development programs, 【0755】 A system that includes this. 【0756】 (Claim 2) 【0757】 The system according to claim 1, further comprising a display means for providing an optimized personnel allocation plan to a user through an interface. 【0758】 (Claim 3) 【0759】 The system according to claim 1, wherein the analysis means applies a learning program to evaluate the potential abilities and suitability for the job of an employee. 【0760】 "Application Example 1" 【0761】 (Claim 1) 【0762】 A data collection method for collecting employee work-related information, 【0763】 A data integration means that integrates the collected information and converts it into a format that can be analyzed, 【0764】 An analytical means for analyzing integrated data to calculate employee performance evaluations, mental health status, and AI collaborative scores, 【0765】 Based on the analysis results, a proposal method is provided to suggest the optimization of personnel allocation, recruitment, side job placement, and training programs. 【0766】 A means for planning the layout to optimize the division of roles between human workers and automated equipment within a manufacturing facility, 【0767】 A system that includes this. 【0768】 (Claim 2) 【0769】 The system according to claim 1, further comprising a notification means for providing managers with personnel allocation suggestions in real time. 【0770】 (Claim 3) 【0771】 The system according to claim 1, wherein the analysis means uses a machine learning algorithm to evaluate the worker's potential and suitability for the job. 【0772】 "Example 2 of combining an emotion engine" 【0773】 (Claim 1) 【0774】 Information gathering methods for obtaining business-related information, 【0775】 Information integration means for integrating collected information and converting it into a standardized format, 【0776】 An analytical means that analyzes integrated information and emotional data acquired in real time to calculate work performance evaluations and mental state, 【0777】 Based on the analysis results, a proposal means for planning the optimization of placement, selection, and educational programs, 【0778】 A display means for presenting analysis results in a visual format, 【0779】 A human resource optimization system that includes this. 【0780】 (Claim 2) 【0781】 The personnel optimization system according to claim 1, further comprising a means for immediately communicating placement proposals to the person in charge. 【0782】 (Claim 3) 【0783】 The human resource optimization system according to claim 1, wherein the analysis means uses a machine learning model to evaluate potential abilities and suitability for the job. 【0784】 "Application example 2 when combining with an emotional engine" 【0785】 (Claim 1) 【0786】 A data collection method for collecting work-related information of workers, 【0787】 A data integration means that integrates the collected information and converts it into a format that can be analyzed, 【0788】 An analytical means that analyzes integrated data and the emotional state of workers to calculate workers' performance evaluations, mental health status, and AI collaborative scores, 【0789】 Based on the analysis results, a proposal method is provided to suggest the optimization of personnel allocation, recruitment, side job placement, and training programs. 【0790】 An environmental optimization method that proposes the optimization of the work environment based on the emotional state of workers, 【0791】 A system that includes this. 【0792】 (Claim 2) 【0793】 The system according to claim 1, further comprising means for analyzing the emotional state of workers in real time and providing it to human resources personnel as feedback. 【0794】 (Claim 3) 【0795】 The system according to claim 1, wherein the analysis means uses a machine learning algorithm to evaluate the worker's potential and suitability for the job and generates recommendation data based on their emotional state. [Explanation of symbols] 【0796】 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] A data collection method for collecting employee work-related information, A data integration means that integrates the collected information and converts it into a format that can be analyzed, An analytical means that analyzes integrated data to calculate employee performance evaluations, mental health status, and AI collaborative scores, Based on the analysis results, a proposal method is provided to suggest the optimization of personnel allocation, recruitment, side job placement, and training programs. A system that includes this. [Claim 2] The system according to claim 1, further comprising a feedback means for providing personnel placement suggestions to human resources personnel in real time. [Claim 3] The system according to claim 1, wherein the analysis means uses a machine learning algorithm to evaluate the potential abilities and job suitability of employees.

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