system

Abstract

We provide the system. [Solution] Means for collecting past business data, seasonal data, and promotional information, A method for predicting the required number of employees based on the above data, A means of optimizing work schedules based on prediction results, A means of detecting abnormal attendance data, A means of detecting violations by comparing attendance information with the latest legal information, A means of predicting employee overwork and notifying managers, A system that includes means for receiving employee health status data and analyzing work patterns.

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 character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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 enterprises, it is important to effectively utilize the labor force while adapting to fluctuations in business. However, currently, it is difficult to predict the appropriate number of employees and efficiently schedule shifts. As a result, there may be waste in labor costs or overwork of employees. In addition, it is required to manage work attendance in compliance with regulations, but this requires a lot of labor and time to do manually. Furthermore, if shift management considering the health status of employees is not sufficiently carried out, there is a risk of impairing the health of employees. Therefore, there is a need for new technologies that solve these problems and improve the labor environment while enhancing the operational efficiency of enterprises. 【Means for Solving the Problems】 【0005】 This invention provides a system that collects historical business data, seasonal data, and promotional information, predicts the required number of employees based on this data, and optimizes work schedules. This system can detect abnormal attendance data and compare the attendance information with the latest legal information to detect violations. It also includes a means to predict employee overwork and notify managers, thereby preventing overwork. Furthermore, by receiving employee health status data and analyzing work patterns based on this data, it enables health-conscious shift management. As a result, companies can optimally utilize their workforce, reduce costs, and manage labor in compliance with laws and regulations. 【0006】 "Past business data" refers to a collection of information related to a company's past business operations, including indicators such as sales, customer numbers, and transaction history. 【0007】 "Seasonal data" refers to information about patterns of economic activity and consumer behavior that typically fluctuate over time throughout the year. 【0008】 "Promotional information" refers to information about campaigns and promotional activities conducted with the aim of promoting the sale of products or services. 【0009】 "Employees" refer to individuals who are hired by a company to perform duties and whose schedules are related to their job duties and roles. 【0010】 "Required number of personnel" refers to the number of people needed to properly perform their duties for a specific task or period. 【0011】 "Prediction" is the act of estimating future events and the resources needed based on past data and patterns. 【0012】 A "work schedule" is a time allocation chart that outlines what tasks an employee should perform during specific time periods. 【0013】 "Abnormal attendance data" refers to information about attendance that deviates from the normal work pattern, including, for example, tardiness, early departures, and unauthorized attendance. 【0014】 "Legal information" refers to information regarding labor-related laws and regulations in each country or region, as well as guidelines for their implementation. 【0015】 "Overwork" refers to a situation in which an employee works for such long hours or excessively that it puts them at risk of harming their own health. 【0016】 "Health status data" refers to information regarding the physical and mental health status of employees, including health checkup results and self-reported symptoms. [Brief explanation of the drawing] 【0017】 [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]Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined. 【Mode for Carrying Out the Invention】 【0018】 Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings. 【0019】 First, the terms used in the following description will be explained. 【0020】 In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of 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. 【0021】 In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0022】 In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes. 【0023】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0024】 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." 【0025】 [First Embodiment] 【0026】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0027】 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. 【0028】 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). 【0029】 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. 【0030】 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. 【0031】 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. 【0032】 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. 【0033】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0034】 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. 【0035】 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. 【0036】 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. 【0037】 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". 【0038】 This invention provides a system for streamlining attendance management within companies and optimizing personnel allocation. This system consists of a server, terminals, and users. The roles and specific processes of each component are described below. 【0039】 Server roles and processing 【0040】 Data collection and analysis 【0041】 The server collects historical business data, seasonal data, and promotional information from the database. After cleaning this data and extracting the necessary information, it is fed into a machine learning model to predict the number of employees needed. For example, if a user provides information about a summer campaign, the server analyzes past summer data to estimate the number of personnel required. 【0042】 Shift optimization 【0043】 After predicting the required number of employees, the server generates an optimal work schedule. This is done using linear programming and heuristic algorithms. The generated schedule is then optimized to accommodate employee preferences and legal requirements. 【0044】 Anomaly detection and legal / regulatory checks 【0045】 The server monitors daily attendance data and detects any abnormal data or patterns. It also verifies that employee shifts comply with labor laws by cross-referencing them with dynamically updated legal information. For example, if an employee is expected to work beyond the legally mandated working hours, the server will notify the manager in advance. 【0046】 Analysis of health status data 【0047】 The system receives employee health data and analyzes work patterns based on this data. This allows the server to suggest shift adjustments that take employee health into consideration. For example, if a particular employee reports a health problem in the morning, their schedule will be adjusted. 【0048】 Terminal roles and processing 【0049】 Interaction and Input Reception 【0050】 The terminal receives daily attendance data and health status information from employees as input. This is done via a web interface or mobile app. For example, if a user uses the terminal to request leave, that information is immediately sent to the server and reflected in the shift schedule. 【0051】 Feedback and notifications 【0052】 The terminals provide employees with shift information and notifications generated by the server. This allows employees to quickly understand their schedules and necessary actions. 【0053】 User roles and processes 【0054】 Data provision and verification 【0055】 Users provide their work status and healthcare information via their devices. They can also check their attendance and shift information. This allows users to properly manage their work status and request adjustments as needed. 【0056】 For example, if a user checks their schedule for next month and discovers that they have excessive work scheduled on a particular day, they can request a correction. 【0057】 This system allows companies to optimize staffing levels in response to constantly changing business demands, ensuring employee health and compliance with labor laws. As a result, workers can enjoy a healthier work environment, and companies can achieve cost-effective operations. 【0058】 The following describes the processing flow. 【0059】 Step 1: 【0060】 The server collects historical business data, seasonal data, and promotional information from external databases and internal information systems. This allows it to verify the basic information necessary for analysis. 【0061】 Step 2: 【0062】 The server cleans the collected data, correcting inaccurate data and missing values. It converts the data to an appropriate format and prepares it for analysis. 【0063】 Step 3: 【0064】 The server uses machine learning algorithms to predict the number of employees needed. This prediction is made by using time series analysis models to understand demand peaks and trends. 【0065】 Step 4: 【0066】 The server optimizes work schedules based on the predicted number of people needed. This streamlines personnel allocation using linear programming techniques. 【0067】 Step 5: 【0068】 The terminal receives daily attendance data and health status information from employees. This includes inputting working hours and self-reporting about their health condition. 【0069】 Step 6: 【0070】 The server monitors attendance data in real time and detects abnormal patterns. For example, it checks for arrival and departure times outside of normal shift ranges. 【0071】 Step 7: 【0072】 The server checks the collected attendance data against labor laws to verify that there are no legal violations. Any detected problems are immediately notified to the administrator. 【0073】 Step 8: 【0074】 The server analyzes employee health data and adjusts work patterns based on this analysis. It then proposes possible adjustments to managers and provides shifts that take employee health into consideration. 【0075】 Step 9: 【0076】 Users can check their schedules through their devices and request revisions as needed. This allows users to create a work environment that suits their needs. 【0077】 Step 10: 【0078】 The server reports the overall analysis results to the administrator and presents comprehensive improvement plans based on long-term attendance data and health status. This contributes to improving the working environment throughout the company. 【0079】 (Example 1) 【0080】 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." 【0081】 Corporate attendance management systems face various challenges in efficiently managing employee work conditions and health status. Specifically, they require appropriate staffing, detection of attendance anomalies, compliance with legal regulations, and shift scheduling that takes employee health into consideration. However, conventional systems have struggled to effectively achieve these goals. Furthermore, there is a need to meet these challenges while reducing costs. 【0082】 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. 【0083】 In this invention, the server includes means for collecting historical industry data, periodic data, and sales promotion information; analytical device means for predicting the required number of workers based on the above data; and calculator means for optimizing work plans based on the prediction results. This makes it possible to manage the work status of workers in real time and optimize staffing. It also provides flexible shift plans that take into account the health status of workers and can reduce the operating costs of the company. 【0084】 "Historical industry data" refers to information about operations accumulated over time in a specific industry, and serves as a foundation for analyzing long-term trends and patterns. 【0085】 "Seasonal data" refers to information collected in relation to a specific season or period, and is used to analyze the impact of seasonal variations on business operations. 【0086】 "Sales promotion information" refers to data on activities and campaigns planned with the aim of expanding the market for a product or service, and is useful for demand forecasting and formulating marketing strategies. 【0087】 An "analytical device for predicting the number of workers needed" is a means used to estimate the future labor force required based on collected data. 【0088】 A "calculator for optimizing work schedules" is a means of efficiently setting individual workers' working hours and holidays based on predicted workforce. 【0089】 A "monitoring device for detecting abnormal working time data" is a means of analyzing workers' working time data in real time and identifying abnormal patterns that deviate from the normal range. 【0090】 "Up-to-date regulatory information" refers to information that complies with current laws and industry rules, and indicates the standards that must be legally adhered to in the work environment and business operations. 【0091】 A "reporting device for predicting excessive workload and notifying managers" is a means of analyzing working hours and conditions to identify the possibility of excessive workload in advance and warn managers. 【0092】 An "analyzer for receiving workers' health status data and analyzing work patterns" is a means of optimizing workers' work schedules based on health information. 【0093】 A "communication device for providing information to a generative AI model and generating instructions based on that information" is a means of inputting necessary data into an artificial intelligence model and then issuing specific instructions or performing processing based on the generated results. 【0094】 A "reception device for receiving online inquiries and leave requests" is a digital interface for receiving and processing inquiries and leave requests from workers. 【0095】 A "support system for optimizing labor allocation and pursuing cost reduction in business operations" is a means of providing strategic support for efficiently allocating personnel and reducing operational costs. 【0096】 This invention provides a system for companies to efficiently improve employee attendance management and staffing. The system consists of a server, terminals, and users, and each component works in coordination to achieve effective attendance management. 【0097】 The server collects historical industry data, periodic data, and sales promotion information from the company's database and performs data cleaning. This is done using the Pandas library implemented in Python, removing duplicate data and missing values ​​to ensure consistent data. Next, the server inputs the collected data into a generative AI model. This generative AI model is built using machine learning libraries such as Scikit-learn and predicts the required number of employees. The prompt used is "Predict the number of personnel required for the next peak season and optimize the shift schedule." 【0098】 The terminal receives real-time data from employees via a web interface or mobile app. Specifically, it receives daily attendance data and health status information as input. For example, if an employee reports feeling unwell on a given day using the terminal, that information is immediately sent to the server and reflected in their shift schedule. The terminal also provides employees with shift information and important notifications generated from the server, allowing them to check and adjust their schedules. 【0099】 Users can regularly provide their work status and healthcare information via their devices. For example, they can check their work schedule for the next month and use a digital calendar to request adjustments to days when excessive work is anticipated. This allows companies to optimize staffing in response to dynamically changing work demands, reducing costs while ensuring compliance with labor laws and employee health. 【0100】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0101】 Step 1: 【0102】 The server collects historical industry data, periodic data, and sales promotion information from the database. Receiving this data as input, the server uses the Pandas library to clean the data, removing missing and outlier values ​​to output a balanced dataset. This step involves important data processing, including standardizing data formatting and handling outliers. 【0103】 Step 2: 【0104】 The server passes the cleaned data as input to the generative AI model. At this time, it provides instructions using the prompt "Predict the number of personnel needed during the next peak season and optimize the shift schedule." The generative AI model uses the Scikit-learn library to analyze the data and outputs a prediction of the required personnel. This output is then used for subsequent schedule optimization. 【0105】 Step 3: 【0106】 The server optimizes work shifts based on the prediction results of the generated AI model. Linear programming is performed using the PuLP library to calculate the optimal shift schedule, taking into account legal constraints and employee preferences. The input consists of the prediction results and constraints, and the optimized shift schedule is saved to the database as output. 【0107】 Step 4: 【0108】 The terminal receives daily attendance data and health status information from employees. This data, updated in real time, is sent to a server and used for anomaly detection and scheduling in the next step. This input process utilizes a web interface and a mobile app. 【0109】 Step 5: 【0110】 The server monitors the received daily attendance data and applies anomaly detection algorithms. The attendance data, which serves as input, includes working hours and health status information, and the server uses this data to detect anomalies and notify administrators. Anomalies include consecutive absences and working hours exceeding legal limits, and notifications are sent via email or in-app notifications. 【0111】 Step 6: 【0112】 The terminal provides employees with shift information generated by the server. Based on the shift schedule information as input, the terminal notifies employees and prompts them to confirm their schedules. Furthermore, it also provides a function to send necessary change requests from the terminal to the server. 【0113】 Step 7: 【0114】 Users can check their work status and healthcare information through their terminals and request adjustments as needed. This allows users to change their schedules to avoid excessive work. User input is reflected on the server via the terminals and used for the operation of the entire system. 【0115】 (Application Example 1) 【0116】 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." 【0117】 Optimizing the workforce across cities and allocating personnel across different industries is a complex and challenging task using traditional methods. This can lead to labor shortages or surpluses during events and peak seasons, hindering efficient operations and potentially increasing costs. 【0118】 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. 【0119】 In this invention, the server includes means for collecting historical information data, timely data, and advertising information; means for estimating the required number of employees based on the above data; and means for optimizing the workforce of the entire city and supporting personnel allocation across different industries. This enables efficient management and allocation of the workforce of the entire city. 【0120】 "Past information data" refers to all data related to past operations and transactions, and is fundamental information necessary for attendance management and optimizing personnel allocation. 【0121】 "Seasonal data" refers to data that shows fluctuations in business operations and demand during specific periods or seasons, and is an important element for making appropriate personnel allocation decisions. 【0122】 "Promotional information" refers to information about sales promotion activities conducted by companies and organizations, and is used to predict the personnel needs that arise from these activities. 【0123】 "Estimating the required number of employees" is the process of predicting the labor force needed over a certain period of time, based on collected data, using machine learning models or similar methods. 【0124】 "Optimizing work plans based on estimation results" is a method for creating the optimal work schedule based on estimated personnel needs. 【0125】 "Abnormal work information" refers to information or data that deviates from normal work patterns, and is important for detecting unexpected problems or errors. 【0126】 "Up-to-date legal information" refers to the latest information on labor laws and regulations, and is used to conduct lawful personnel management based on this information. 【0127】 "Predicting excessive work hours" means predicting in advance, based on collected data, the likelihood that workers will exceed their normal working hours. 【0128】 "Worker health status data" refers to information about the health of individual workers and is used to design healthy work schedules. 【0129】 "Analyzing work patterns" refers to examining workers' work patterns and styles in order to propose more appropriate shifts. 【0130】 "Optimizing the entire urban workforce" is the process of efficiently allocating all the workforce within a city to increase overall labor efficiency. 【0131】 "Supporting personnel allocation across different industries" means providing support to facilitate the exchange of labor between multiple industries and achieve efficient personnel utilization. 【0132】 The system for realizing this invention includes a server, a terminal, and a user as its main components. 【0133】 The server collects historical data, seasonal data, and promotional information and stores it in a database. This uses programming languages ​​such as Python and database management systems such as MySQL®. The collected data is analyzed using machine learning models to estimate the number of employees needed. This analysis utilizes machine learning libraries such as TENSORFLOW® and scikit-learn. Based on the estimated personnel needs, the work schedule is optimized using linear programming. 【0134】 The terminal receives work information and health status data from workers via a web interface or mobile app. This enables real-time inquiries and leave requests, and quickly transmits data to the server. The terminal also presents users with notifications and feedback from the server, informing workers of the latest work schedules and anomaly detection results. 【0135】 Through these systems, users can provide their work status and health information and check their latest work schedules. To support workforce optimization across cities, personnel allocation information across different industries is also analyzed by the server, and this information is shared among companies. 【0136】 As a concrete example, when multiple companies hold an event in Tokyo, they can use this system to optimally allocate security guards and guidance staff across specific industries, enabling efficient use of personnel. Furthermore, specific prompts for inputting data into the generating AI model could include questions such as, "Based on the list of events scheduled in Tokyo this weekend, please suggest the optimal staffing arrangements." 【0137】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0138】 Step 1: 【0139】 The server collects historical data, timely data, and promotional information from the database. This data is extracted using SQL queries, undergoes a data cleansing process to remove unnecessary information, and is formatted into the required format. As a result, a clean dataset is generated that is ready to be input into machine learning models. 【0140】 Step 2: 【0141】 The server uses the collected data to input into a machine learning model to estimate the required number of employees. The dataset provided as input is processed by a prediction model using TensorFlow, which predicts the number of personnel that will match future business needs. The output of this process is the estimated number of personnel required within a specific period. 【0142】 Step 3: 【0143】 The server optimizes work schedules based on estimation results using linear programming. Estimated staffing needs are processed considering worker preferences and legal constraints to generate efficient and compliant work schedules. The output is an optimized work schedule for all workers. 【0144】 Step 4: 【0145】 The terminal receives work information and health status data from users and sends it to the server. The entered information is collected via a web interface and immediately transferred to the server. This allows attendance and health management data to be updated in real time on the server. 【0146】 Step 5: 【0147】 The server analyzes work patterns based on received health data and readjusts work plans if necessary. Health data is analyzed, and schedules that minimize health burdens are proposed for specific workers. The output is the adjusted work plan. 【0148】 Step 6: 【0149】 Users check their work schedules, anomaly detection results, and notifications through their terminals. Based on the information received, users can make necessary adjustments and checks, allowing them to work with peace of mind. 【0150】 Step 7: 【0151】 The server assists in optimizing the workforce across cities by facilitating personnel allocation across different sectors. The server aggregates this information to promote the effective movement of labor between multiple sectors. The output is a proposed optimization of personnel allocation at the city level. 【0152】 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. 【0153】 This invention is a system for comprehensively optimizing employee work and health management, and in particular, it utilizes an emotion engine to achieve dynamic management that takes into account the user's psychological state. This system consists of a server, terminals, an emotion engine, and user interaction. 【0154】 Server roles and processing 【0155】 Data collection and analysis 【0156】 The server collects data related to business operations as well as data to reference the user's emotional state. This includes output generated by an emotion engine using natural language processing and speech analysis technologies. Specifically, the emotion engine analyzes the user's voice tone and quantifies positive / negative emotions. 【0157】 Optimizing work schedules 【0158】 Based on collected emotional data, work schedules are adjusted to take into account the emotional state of employees. For example, users experiencing certain emotions may be offered appropriate breaks to reduce stress. 【0159】 Anomaly detection and legal / regulatory checks 【0160】 The server continues to monitor attendance data for anomalies and ensure compliance with legal regulations. If emotional data indicates high stress levels, it will notify administrators with additional alerts based on legal standards. 【0161】 Terminal roles and processing 【0162】 Interaction and state recording 【0163】 The device receives input from the user and collects data for analysis by the emotion engine. This can be implemented as voice input using a microphone or facial recognition using a camera. For example, when a user speaks into the device, their voice is recorded and analyzed by the emotion engine. 【0164】 Provide feedback 【0165】 The terminal provides users with feedback on emotional information and schedule changes generated by the server. This allows users to instantly understand their emotional state and work status and make necessary adjustments. 【0166】 The role and processing of the emotional engine 【0167】 sentiment analysis 【0168】 The emotion engine analyzes the user's voice and text input to identify and quantify their emotions. If the emotions are strongly negative, it provides feedback to the server as a stress warning. This feature helps to improve the user's work experience. 【0169】 User roles and processes 【0170】 Data provision and verification 【0171】 Users provide feedback on their emotions and physical condition via their devices. Based on this data, they can see how their work schedules are adjusted. For example, if a user reports feeling stressed, the system will make suggestions that reflect that data. 【0172】 This system considers employee health and well-being, enhances labor productivity, and provides an optimal framework for companies to utilize their human resources efficiently. As a result, it can achieve both improved work environments and cost reductions for companies. 【0173】 The following describes the processing flow. 【0174】 Step 1: 【0175】 Users input information about their emotions and physical condition via voice or text through their device. The device then sends this input data to the emotion engine. 【0176】 Step 2: 【0177】 The emotion engine analyzes received audio and text data and uses natural language processing and speech recognition technologies to identify the user's emotions. This emotion information is returned to the server as numerical data. 【0178】 Step 3: 【0179】 The server evaluates the user's psychological state based on the emotional numerical data received from the emotion engine and determines the stress level as needed. 【0180】 Step 4: 【0181】 The server uses the stress level assessment results to dynamically adjust the work schedule. For example, if the stress level is high, it suggests additional break time to the user. 【0182】 Step 5: 【0183】 The server combines existing attendance data with other data to re-evaluate unusual work patterns and potential legal violations, and alerts administrators to any identified issues. 【0184】 Step 6: 【0185】 The device receives feedback from the server and notifies the user of sentiment analysis results and schedule adjustments. 【0186】 Step 7: 【0187】 The user uses their device to review the feedback and, if necessary, provide additional emotion input or request schedule adjustments. This information is then sent back to the server and emotion engine, and the process repeats. 【0188】 Through this series of processes, the system can manage labor in real time while taking into account the user's emotional state, thereby providing a comfortable working environment. 【0189】 (Example 2) 【0190】 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." 【0191】 Traditional work management systems fail to adequately consider workers' emotions and health conditions, making efficient staffing and improvements to the work environment difficult. Furthermore, limitations in complying with legal regulations and detecting abnormal work situations make it difficult to prevent workplace stress and health risks. This results in problems such as decreased labor productivity and increased costs for companies. 【0192】 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. 【0193】 In this invention, the server includes functional means for aggregating past work data, seasonal data, and sales information; functional means for receiving worker health status information and analyzing work patterns; and functional means for analyzing voice and text to identify and quantify the worker's emotional state and reflect it in the work plan. This enables real-time understanding of workers' emotions and health status, allowing for the proposal of optimal work schedules and rapid response through anomaly detection, while complying with legal regulations. 【0194】 "Past work data" refers to work history and performance information, including information about an employee's past work content and productivity. 【0195】 "Seasonal data" refers to information about environmental factors and demand fluctuations that change in relation to the seasons. 【0196】 "Sales information" refers to all data related to the provision of goods and services to consumers. 【0197】 "Health status information" refers to data concerning the physical and mental health of workers. 【0198】 A "work pattern" is a set of plans regarding working hours, break times, and task assignments. 【0199】 The "ability to analyze speech and text" refers to a technology that uses natural language processing to identify the intentions and emotions of workers. 【0200】 "Quantifying emotional states" is the process of measuring, quantifying, and representing the psychological state of workers. 【0201】 "Reflecting in work plans" means adjusting employees' work schedules based on the analyzed data. 【0202】 An "optimal work schedule" is a work plan that is effective for both employees and management, taking into consideration the efficiency and health of workers. 【0203】 Anomaly detection is the process of identifying and distinguishing behavior that deviates from known standards or patterns. 【0204】 This invention is a system that integrates labor management and emotional state analysis. This system utilizes servers, terminals, and generative AI models to analyze workers' emotional and health states in real time, enabling the management of optimal work schedules. 【0205】 The server functions as a central data processing unit, aggregating historical work data, seasonal data, sales information, and more. This data is obtained from external databases and work management software. The server also uses an emotion engine to analyze voice and text data and quantify the emotional state of workers. Specifically, natural language processing technology and voice analysis algorithms are used. The emotion engine's output classifies the user's psychological state as positive, negative, or neutral, and adjusts the work plan accordingly. Based on emotion analysis and health status data, the work plan is optimized, and if an anomaly is detected, an alert can be immediately sent to the administrator. 【0206】 The terminal is responsible for user interaction. It has the functionality to receive voice and text input from the user and transmit it to the server. Specifically, workers can use input devices such as microphones and cameras to communicate their emotional state to the terminal. The terminal transmits this information to the server in real time. The terminal also receives feedback from the server and presents the user with emotional assessment results and a new work schedule. 【0207】 Through this system, users can provide feedback on their emotions and health status, and see how this is reflected in their work schedules. For example, if a user enters "I have something on my mind today" into the terminal, the system will use that information to assess their stress level and suggest necessary rest. 【0208】 As an example of a prompt, instructions can be given to the generating AI model in the form of, "Based on user X's recent emotional data, please suggest a work schedule to reduce stress." In this way, the present invention supports the efficient use of human resources by companies while taking into consideration the mental and physical health of workers. 【0209】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0210】 Step 1: 【0211】 The device collects voice data from the user. The user uses the microphone to speak into the device, providing voice input to indicate their emotional state. The collected voice data is converted into a digital format and sent to the server. 【0212】 Step 2: 【0213】 The server passes the received audio data to the emotion engine. Here, natural language processing and speech analysis techniques are used to extract emotional features from the audio and convert them into numerical data. For example, it analyzes the tone of voice and emphasized words to output positive, negative, or neutral emotion scores. 【0214】 Step 3: 【0215】 The server integrates emotion scores obtained from the emotion engine with historical work data, seasonal data, and sales information. Based on this data, a generative AI model is used to generate an appropriate work schedule. This model takes the prompt "Based on the user's emotion data, suggest a work schedule to reduce stress" as input and generates an optimized work schedule as output. 【0216】 Step 4: 【0217】 The server sends the generated work schedule to the terminal. The terminal displays feedback on the user's screen, such as the new work schedule and recommended break times. As a result, the user can review their work plan and make adjustments as needed. 【0218】 Step 5: 【0219】 Users review their emotional state and work schedule based on feedback received through their device. If there are areas that need improvement in the emotional state or feedback they entered, they can provide updated information to the system by re-entering it via voice input. 【0220】 (Application Example 2) 【0221】 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". 【0222】 There is a need for a system that can grasp employees' emotions and health status in real time and flexibly optimize work schedules based on that information. However, existing technology makes it difficult to adequately consider employees' psychological state and respond quickly. As a result, there is a problem of a lack of effective means to reduce employee stress and improve the workplace environment. 【0223】 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. 【0224】 In this invention, the server includes means for analyzing voice data and text to identify and quantify the user's emotional state, means for suggesting breaks and adjusting work schedules based on the quantified emotional data, and means for providing feedback according to the user's psychological state. This enables the dynamic adjustment of the work environment to take into account the employee's psychological state. 【0225】 "Voice data" refers to information recorded digitally from a user's speech or voice, and is used for analyzing their emotional state. 【0226】 "Text" refers to linguistic data converted from audio data into written characters, and is information used for sentiment analysis and communication. 【0227】 "Emotional state" refers to the psychological or emotional condition a user exhibits at a specific point in time, which is then quantified and used for management purposes. 【0228】 "Quantification" is a method of quantifying emotional states and other qualitative data and expressing them in a measurable form. 【0229】 A "suggestion for a break" is an act of encouraging employees to take appropriate rest based on their emotional state and work situation, with the aim of reducing employee stress. 【0230】 "Adjusting work schedules" refers to the process of changing or modifying schedules in order to optimize employees' working hours and break times. 【0231】 "Feedback" is a means of providing information about a user's state and behavior to encourage improvement and correction. 【0232】 To implement this invention, a server equipped with an emotion engine, a terminal for user interaction, and a user who provides data are required. The server analyzes voice data using natural language processing technology and quantifies the emotional state. Specifically, it performs speech recognition using Google Cloud's Speech-to-Text API and analyzes the emotion of that voice data using AWS Comprehend or Microsoft Azure Text Analytics. 【0233】 The device collects audio and video data from the user through input devices such as microphones and cameras, and sends this data to a server to understand the user's emotional state. The application on the device runs on smartphones and tablets and provides the user with feedback on their emotional state and work schedule using a graphical user interface (GUI). 【0234】 Users input their situation and emotions using a device, and receive feedback from the server based on the analysis results from the emotion engine. If the user's emotional state exceeds a certain threshold, the server automatically suggests taking a break or adjusts the schedule. 【0235】 For example, if a caregiver says to their terminal during work, "I'm a little tired today," the server analyzes the audio and determines that the staff member is in a high-stress state. In this case, the application provides feedback such as, "We recommend you take a break for a while." 【0236】 An example of a prompt is: "Generate code that analyzes the emotional data of care staff and suggests specific actions to reduce stress." 【0237】 This system will enable the creation of a dynamic work environment that reflects employees' emotional states in real time, and is expected to contribute to both improved workplace productivity and health management. 【0238】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0239】 Step 1: 【0240】 The device enters standby mode and waits for voice input from the user. When the user speaks into the device, the microphone collects voice data. This voice data is temporarily stored in the device's memory. 【0241】 Step 2: 【0242】 The terminal sends the collected voice data to the server. At this time, the voice data is converted into data packets and transferred to the server using a secure communication protocol. 【0243】 Step 3: 【0244】 The server converts the received audio data into text using Google Cloud's Speech-to-Text API. This conversion process utilizes natural language processing techniques. The input is audio data, and the output is text data in string format. 【0245】 Step 4: 【0246】 The server performs sentiment analysis on text data using AWS Comprehend or Microsoft Azure Text Analytics. The input for this stage is text data, and the output is numerical data representing specific emotional states. The analysis process classifies the data into emotional categories and displays their trends numerically. 【0247】 Step 5: 【0248】 Based on the analysis results, the server sets thresholds and determines the content of the feedback when a specific emotional state is identified. For example, if a high stress level is numerically indicated, suggestions for taking a break will be listed. 【0249】 Step 6: 【0250】 The server converts the determined feedback back into a data packet and sends it to the terminal. The data is then transferred according to the communication protocol. 【0251】 Step 7: 【0252】 The terminal receives feedback from the server and presents it to the user via a visual interface or audio. For example, it might display "We recommend you take a break" on the screen. This process converts the feedback data into an appropriate user interface format and presents it. 【0253】 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. 【0254】 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. 【0255】 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. 【0256】 [Second Embodiment] 【0257】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0258】 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. 【0259】 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). 【0260】 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. 【0261】 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. 【0262】 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). 【0263】 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. 【0264】 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. 【0265】 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. 【0266】 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. 【0267】 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. 【0268】 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". 【0269】 This invention provides a system for streamlining attendance management within companies and optimizing personnel allocation. This system consists of a server, terminals, and users. The roles and specific processes of each component are described below. 【0270】 Server roles and processing 【0271】 Data collection and analysis 【0272】 The server collects historical business data, seasonal data, and promotional information from the database. After cleaning this data and extracting the necessary information, it is fed into a machine learning model to predict the number of employees needed. For example, if a user provides information about a summer campaign, the server analyzes past summer data to estimate the number of personnel required. 【0273】 Shift optimization 【0274】 After predicting the required number of employees, the server generates an optimal work schedule. This is done using linear programming and heuristic algorithms. The generated schedule is then optimized to accommodate employee preferences and legal requirements. 【0275】 Anomaly detection and legal / regulatory checks 【0276】 The server monitors daily attendance data and detects any abnormal data or patterns. It also verifies that employee shifts comply with labor laws by cross-referencing them with dynamically updated legal information. For example, if an employee is expected to work beyond the legally mandated working hours, the server will notify the manager in advance. 【0277】 Analysis of health status data 【0278】 The system receives employee health data and analyzes work patterns based on this data. This allows the server to suggest shift adjustments that take employee health into consideration. For example, if a particular employee reports a health problem in the morning, their schedule will be adjusted. 【0279】 Terminal roles and processing 【0280】 Interaction and Input Reception 【0281】 The terminal receives daily attendance and health data from employees as input. This is achieved via a web interface or a mobile app. For example, when a user uses the terminal to apply for leave, the information is immediately sent to the server and reflected in the schedule. 【0282】 Feedback and Notifications 【0283】 The terminal provides feedback to employees on shift information and notifications generated by the server. This enables employees to quickly grasp their schedules and necessary actions. 【0284】 User Roles and Processes 【0285】 Data Provision and Confirmation 【0286】 Users provide their work status and healthcare information via the terminal. They can also check their attendance and shift information. This allows users to appropriately manage their work status and request adjustments if necessary. 【0287】 For example, if a user checks the schedule for next month and discovers that there is excessive work scheduled on a particular day, they can request a correction. 【0288】 With this system, enterprises can optimize staffing according to constantly changing business needs and ensure compliance with employees' health and labor laws. As a result, workers can enjoy a healthier working environment, and enterprises can achieve cost-effective operations. 【0289】 The following describes the process flow. 【0290】 Step 1: 【0291】 The server collects historical business data, seasonal data, and promotional information from external databases and internal information systems. This allows it to verify the basic information necessary for analysis. 【0292】 Step 2: 【0293】 The server cleans the collected data, correcting inaccurate data and missing values. It converts the data to an appropriate format and prepares it for analysis. 【0294】 Step 3: 【0295】 The server uses machine learning algorithms to predict the number of employees needed. This prediction is made by using time series analysis models to understand demand peaks and trends. 【0296】 Step 4: 【0297】 The server optimizes work schedules based on the predicted number of people needed. This streamlines personnel allocation using linear programming techniques. 【0298】 Step 5: 【0299】 The terminal receives daily attendance data and health status information from employees. This includes inputting working hours and self-reporting about their health condition. 【0300】 Step 6: 【0301】 The server monitors attendance data in real time and detects abnormal patterns. For example, it checks for arrival and departure times outside of normal shift ranges. 【0302】 Step 7: 【0303】 The server checks the collected attendance data against labor laws to verify that there are no legal violations. Any detected problems are immediately notified to the administrator. 【0304】 Step 8: 【0305】 The server analyzes the health status data of employees and adjusts the work pattern based on this. Propose possible adjustment plans to the administrator and provide shifts considering the health of employees. 【0306】 Step 9: 【0307】 The user can check the schedule through the terminal and request corrections if necessary. This allows the user to create a work environment that is easy for them to work in. 【0308】 Step 10: 【0309】 The server reports the overall analysis results to the administrator and presents comprehensive improvement plans based on long-term attendance data and health status. This contributes to the improvement of the working environment of the entire company. 【0310】 (Example 1) 【0311】 Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0312】 The attendance management system in a company has various problems in efficiently managing the working conditions and health status of employees. Specifically, appropriate staffing, detection of attendance abnormalities, compliance with regulations, and shift arrangements considering the health of employees are required, but it has been difficult for conventional systems to effectively achieve these. Furthermore, it is also required to meet these problems while reducing costs. 【0313】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0314】 In this invention, the server includes means for collecting historical industry data, periodic data, and sales promotion information; analytical device means for predicting the required number of workers based on the above data; and calculator means for optimizing work plans based on the prediction results. This makes it possible to manage the work status of workers in real time and optimize staffing. It also provides flexible shift plans that take into account the health status of workers and can reduce the operating costs of the company. 【0315】 "Historical industry data" refers to information about operations accumulated over time in a specific industry, and serves as a foundation for analyzing long-term trends and patterns. 【0316】 "Seasonal data" refers to information collected in relation to a specific season or period, and is used to analyze the impact of seasonal variations on business operations. 【0317】 "Sales promotion information" refers to data on activities and campaigns planned with the aim of expanding the market for a product or service, and is useful for demand forecasting and formulating marketing strategies. 【0318】 An "analytical device for predicting the number of workers needed" is a means used to estimate the future labor force required based on collected data. 【0319】 A "calculator for optimizing work schedules" is a means of efficiently setting individual workers' working hours and holidays based on predicted workforce. 【0320】 A "monitoring device for detecting abnormal working time data" is a means of analyzing workers' working time data in real time and identifying abnormal patterns that deviate from the normal range. 【0321】 "Up-to-date regulatory information" refers to information that complies with current laws and industry rules, and indicates the standards that must be legally adhered to in the work environment and business operations. 【0322】 A "reporting device for predicting excessive workload and notifying managers" is a means of analyzing working hours and conditions to identify the possibility of excessive workload in advance and warn managers. 【0323】 An "analyzer for receiving workers' health status data and analyzing work patterns" is a means of optimizing workers' work schedules based on health information. 【0324】 A "communication device for providing information to a generative AI model and generating instructions based on that information" is a means of inputting necessary data into an artificial intelligence model and then issuing specific instructions or performing processing based on the generated results. 【0325】 A "reception device for receiving online inquiries and leave requests" is a digital interface for receiving and processing inquiries and leave requests from workers. 【0326】 A "support system for optimizing labor allocation and pursuing cost reduction in business operations" is a means of providing strategic support for efficiently allocating personnel and reducing operational costs. 【0327】 This invention provides a system for companies to efficiently improve employee attendance management and staffing. The system consists of a server, terminals, and users, and each component works in coordination to achieve effective attendance management. 【0328】 The server collects historical industry data, periodic data, and sales promotion information from the company's database and performs data cleaning. This is done using the Pandas library implemented in Python, removing duplicate data and missing values ​​to ensure consistent data. Next, the server inputs the collected data into a generative AI model. This generative AI model is built using machine learning libraries such as Scikit-learn and predicts the required number of employees. The prompt used is "Predict the number of personnel required for the next peak season and optimize the shift schedule." 【0329】 The terminal receives real-time data from employees via a web interface or mobile app. Specifically, it receives daily attendance data and health status information as input. For example, if an employee reports feeling unwell on a given day using the terminal, that information is immediately sent to the server and reflected in their shift schedule. The terminal also provides employees with shift information and important notifications generated from the server, allowing them to check and adjust their schedules. 【0330】 Users can regularly provide their work status and healthcare information via their devices. For example, they can check their work schedule for the next month and use a digital calendar to request adjustments to days when excessive work is anticipated. This allows companies to optimize staffing in response to dynamically changing work demands, reducing costs while ensuring compliance with labor laws and employee health. 【0331】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0332】 Step 1: 【0333】 The server collects historical industry data, periodic data, and sales promotion information from the database. Receiving this data as input, the server uses the Pandas library to clean the data, removing missing and outlier values ​​to output a balanced dataset. This step involves important data processing, including standardizing data formatting and handling outliers. 【0334】 Step 2: 【0335】 The server passes the cleaned data as input to the generative AI model. At this time, it provides instructions using the prompt "Predict the number of personnel needed during the next peak season and optimize the shift schedule." The generative AI model uses the Scikit-learn library to analyze the data and outputs a prediction of the required personnel. This output is then used for subsequent schedule optimization. 【0336】 Step 3: 【0337】 The server optimizes work shifts based on the prediction results of the generated AI model. Linear programming is performed using the PuLP library to calculate the optimal shift schedule, taking into account legal constraints and employee preferences. The input consists of the prediction results and constraints, and the optimized shift schedule is saved to the database as output. 【0338】 Step 4: 【0339】 The terminal receives daily attendance data and health status information from employees. This data, updated in real time, is sent to a server and used for anomaly detection and scheduling in the next step. This input process utilizes a web interface and a mobile app. 【0340】 Step 5: 【0341】 The server monitors the received daily attendance data and applies anomaly detection algorithms. The attendance data, which serves as input, includes working hours and health status information, and the server uses this data to detect anomalies and notify administrators. Anomalies include consecutive absences and working hours exceeding legal limits, and notifications are sent via email or in-app notifications. 【0342】 Step 6: 【0343】 The terminal provides employees with shift information generated by the server. Based on the shift schedule information as input, the terminal notifies employees and prompts them to confirm their schedules. Furthermore, it also provides a function to send necessary change requests from the terminal to the server. 【0344】 Step 7: 【0345】 Users can check their work status and healthcare information through their terminals and request adjustments as needed. This allows users to change their schedules to avoid excessive work. User input is reflected on the server via the terminals and used for the operation of the entire system. 【0346】 (Application Example 1) 【0347】 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." 【0348】 Optimizing the workforce across cities and allocating personnel across different industries is a complex and challenging task using traditional methods. This can lead to labor shortages or surpluses during events and peak seasons, hindering efficient operations and potentially increasing costs. 【0349】 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. 【0350】 In this invention, the server includes means for collecting historical information data, timely data, and advertising information; means for estimating the required number of employees based on the above data; and means for optimizing the workforce of the entire city and supporting personnel allocation across different industries. This enables efficient management and allocation of the workforce of the entire city. 【0351】 "Past information data" refers to all data related to past operations and transactions, and is fundamental information necessary for attendance management and optimizing personnel allocation. 【0352】 "Seasonal data" refers to data that shows fluctuations in business operations and demand during specific periods or seasons, and is an important element for making appropriate personnel allocation decisions. 【0353】 "Promotional information" refers to information about sales promotion activities conducted by companies and organizations, and is used to predict the personnel needs that arise from these activities. 【0354】 "Estimating the required number of employees" is the process of predicting the labor force needed over a certain period of time, based on collected data, using machine learning models or similar methods. 【0355】 "Optimizing work plans based on estimation results" is a method for creating the optimal work schedule based on estimated personnel needs. 【0356】 "Abnormal work information" refers to information or data that deviates from normal work patterns, and is important for detecting unexpected problems or errors. 【0357】 "Up-to-date legal information" refers to the latest information on labor laws and regulations, and is used to conduct lawful personnel management based on this information. 【0358】 "Predicting excessive work hours" means predicting in advance, based on collected data, the likelihood that workers will exceed their normal working hours. 【0359】 "Worker health status data" refers to information about the health of individual workers and is used to design healthy work schedules. 【0360】 "Analyzing work patterns" refers to examining workers' work patterns and styles in order to propose more appropriate shifts. 【0361】 "Optimizing the entire urban workforce" is the process of efficiently allocating all the workforce within a city to increase overall labor efficiency. 【0362】 "Supporting personnel allocation across different industries" means providing support to facilitate the exchange of labor between multiple industries and achieve efficient personnel utilization. 【0363】 The system for realizing this invention includes a server, a terminal, and a user as its main components. 【0364】 The server collects historical data, seasonal data, and promotional information and stores it in a database. This uses programming languages ​​such as Python and database management systems such as MySQL. The collected data is analyzed using machine learning models to estimate the number of employees needed. Machine learning libraries such as TensorFlow and scikit-learn are used for this analysis. Based on the estimated personnel needs, a work schedule is optimized using linear programming. 【0365】 The terminal receives work information and health status data from workers via a web interface or mobile app. This enables real-time inquiries and leave requests, and quickly transmits data to the server. The terminal also presents users with notifications and feedback from the server, informing workers of the latest work schedules and anomaly detection results. 【0366】 Through these systems, users can provide their work status and health information and check their latest work schedules. To support workforce optimization across cities, personnel allocation information across different industries is also analyzed by the server, and this information is shared among companies. 【0367】 As a concrete example, when multiple companies hold an event in Tokyo, they can use this system to optimally allocate security guards and guidance staff across specific industries, enabling efficient use of personnel. Furthermore, specific prompts for inputting data into the generating AI model could include questions such as, "Based on the list of events scheduled in Tokyo this weekend, please suggest the optimal staffing arrangements." 【0368】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0369】 Step 1: 【0370】 The server collects historical data, timely data, and promotional information from the database. This data is extracted using SQL queries, undergoes a data cleansing process to remove unnecessary information, and is formatted into the required format. As a result, a clean dataset is generated that is ready to be input into machine learning models. 【0371】 Step 2: 【0372】 The server uses the collected data to input into a machine learning model to estimate the required number of employees. The dataset provided as input is processed by a prediction model using TensorFlow, which predicts the number of personnel that will match future business needs. The output of this process is the estimated number of personnel required within a specific period. 【0373】 Step 3: 【0374】 The server optimizes work schedules based on estimation results using linear programming. Estimated staffing needs are processed considering worker preferences and legal constraints to generate efficient and compliant work schedules. The output is an optimized work schedule for all workers. 【0375】 Step 4: 【0376】 The terminal receives work information and health status data from users and sends it to the server. The entered information is collected via a web interface and immediately transferred to the server. This allows attendance and health management data to be updated in real time on the server. 【0377】 Step 5: 【0378】 The server analyzes work patterns based on received health data and readjusts work plans if necessary. Health data is analyzed, and schedules that minimize health burdens are proposed for specific workers. The output is the adjusted work plan. 【0379】 Step 6: 【0380】 Users check their work schedules, anomaly detection results, and notifications through their terminals. Based on the information received, users can make necessary adjustments and checks, allowing them to work with peace of mind. 【0381】 Step 7: 【0382】 The server assists in optimizing the workforce across cities by facilitating personnel allocation across different sectors. The server aggregates this information to promote the effective movement of labor between multiple sectors. The output is a proposed optimization of personnel allocation at the city level. 【0383】 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. 【0384】 This invention is a system for comprehensively optimizing employee work and health management, and in particular, it utilizes an emotion engine to achieve dynamic management that takes into account the user's psychological state. This system consists of a server, terminals, an emotion engine, and user interaction. 【0385】 Server roles and processing 【0386】 Data collection and analysis 【0387】 The server collects data related to business operations as well as data to reference the user's emotional state. This includes output generated by an emotion engine using natural language processing and speech analysis technologies. Specifically, the emotion engine analyzes the user's voice tone and quantifies positive / negative emotions. 【0388】 Optimizing work schedules 【0389】 Based on collected emotional data, work schedules are adjusted to take into account the emotional state of employees. For example, users experiencing certain emotions may be offered appropriate breaks to reduce stress. 【0390】 Anomaly detection and legal / regulatory checks 【0391】 The server continues to monitor attendance data for anomalies and ensure compliance with legal regulations. If emotional data indicates high stress levels, it will notify administrators with additional alerts based on legal standards. 【0392】 Terminal roles and processing 【0393】 Interaction and state recording 【0394】 The device receives input from the user and collects data for analysis by the emotion engine. This can be implemented as voice input using a microphone or facial recognition using a camera. For example, when a user speaks into the device, their voice is recorded and analyzed by the emotion engine. 【0395】 Provide feedback 【0396】 The terminal provides users with feedback on emotional information and schedule changes generated by the server. This allows users to instantly understand their emotional state and work status and make necessary adjustments. 【0397】 The role and processing of the emotional engine 【0398】 sentiment analysis 【0399】 The emotion engine analyzes the user's voice and text input to identify and quantify their emotions. If the emotions are strongly negative, it provides feedback to the server as a stress warning. This feature helps to improve the user's work experience. 【0400】 User roles and processes 【0401】 Data provision and verification 【0402】 Users provide feedback on their emotions and physical condition via their devices. Based on this data, they can see how their work schedules are adjusted. For example, if a user reports feeling stressed, the system will make suggestions that reflect that data. 【0403】 This system considers employee health and well-being, enhances labor productivity, and provides an optimal framework for companies to utilize their human resources efficiently. As a result, it can achieve both improved work environments and cost reductions for companies. 【0404】 The following describes the processing flow. 【0405】 Step 1: 【0406】 Users input information about their emotions and physical condition via voice or text through their device. The device then sends this input data to the emotion engine. 【0407】 Step 2: 【0408】 The emotion engine analyzes received audio and text data and uses natural language processing and speech recognition technologies to identify the user's emotions. This emotion information is returned to the server as numerical data. 【0409】 Step 3: 【0410】 The server evaluates the user's psychological state based on the emotional numerical data received from the emotion engine and determines the stress level as needed. 【0411】 Step 4: 【0412】 The server uses the stress level assessment results to dynamically adjust the work schedule. For example, if the stress level is high, it suggests additional break time to the user. 【0413】 Step 5: 【0414】 The server combines existing attendance data with other data to re-evaluate unusual work patterns and potential legal violations, and alerts administrators to any identified issues. 【0415】 Step 6: 【0416】 The device receives feedback from the server and notifies the user of sentiment analysis results and schedule adjustments. 【0417】 Step 7: 【0418】 The user uses their device to review the feedback and, if necessary, provide additional emotion input or request schedule adjustments. This information is then sent back to the server and emotion engine, and the process repeats. 【0419】 Through this series of processes, the system can manage labor in real time while taking into account the user's emotional state, thereby providing a comfortable working environment. 【0420】 (Example 2) 【0421】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0422】 Traditional work management systems fail to adequately consider workers' emotions and health conditions, making efficient staffing and improvements to the work environment difficult. Furthermore, limitations in complying with legal regulations and detecting abnormal work situations make it difficult to prevent workplace stress and health risks. This results in problems such as decreased labor productivity and increased costs for companies. 【0423】 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. 【0424】 In this invention, the server includes functional means for aggregating past work data, seasonal data, and sales information; functional means for receiving worker health status information and analyzing work patterns; and functional means for analyzing voice and text to identify and quantify the worker's emotional state and reflect it in the work plan. This enables real-time understanding of workers' emotions and health status, allowing for the proposal of optimal work schedules and rapid response through anomaly detection, while complying with legal regulations. 【0425】 "Past work data" refers to work history and performance information, including information about an employee's past work content and productivity. 【0426】 "Seasonal data" refers to information about environmental factors and demand fluctuations that change in relation to the seasons. 【0427】 "Sales information" refers to all data related to the provision of goods and services to consumers. 【0428】 "Health status information" refers to data concerning the physical and mental health of workers. 【0429】 A "work pattern" is a set of plans regarding working hours, break times, and task assignments. 【0430】 The "ability to analyze speech and text" refers to a technology that uses natural language processing to identify the intentions and emotions of workers. 【0431】 "Quantifying emotional states" is the process of measuring, quantifying, and representing the psychological state of workers. 【0432】 "Reflecting in work plans" means adjusting employees' work schedules based on the analyzed data. 【0433】 An "optimal work schedule" is a work plan that is effective for both employees and management, taking into consideration the efficiency and health of workers. 【0434】 Anomaly detection is the process of identifying and distinguishing behavior that deviates from known standards or patterns. 【0435】 This invention is a system that integrates labor management and emotional state analysis. This system utilizes servers, terminals, and generative AI models to analyze workers' emotional and health states in real time, enabling the management of optimal work schedules. 【0436】 The server functions as a central data processing unit, aggregating historical work data, seasonal data, sales information, and more. This data is obtained from external databases and work management software. The server also uses an emotion engine to analyze voice and text data and quantify the emotional state of workers. Specifically, natural language processing technology and voice analysis algorithms are used. The emotion engine's output classifies the user's psychological state as positive, negative, or neutral, and adjusts the work plan accordingly. Based on emotion analysis and health status data, the work plan is optimized, and if an anomaly is detected, an alert can be immediately sent to the administrator. 【0437】 The terminal is responsible for user interaction. It has the functionality to receive voice and text input from the user and transmit it to the server. Specifically, workers can use input devices such as microphones and cameras to communicate their emotional state to the terminal. The terminal transmits this information to the server in real time. The terminal also receives feedback from the server and presents the user with emotional assessment results and a new work schedule. 【0438】 Through this system, users can provide feedback on their emotions and health status, and see how this is reflected in their work schedules. For example, if a user enters "I have something on my mind today" into the terminal, the system will use that information to assess their stress level and suggest necessary rest. 【0439】 As an example of a prompt, instructions can be given to the generating AI model in the form of, "Based on user X's recent emotional data, please suggest a work schedule to reduce stress." In this way, the present invention supports the efficient use of human resources by companies while taking into consideration the mental and physical health of workers. 【0440】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0441】 Step 1: 【0442】 The device collects voice data from the user. The user uses the microphone to speak into the device, providing voice input to indicate their emotional state. The collected voice data is converted into a digital format and sent to the server. 【0443】 Step 2: 【0444】 The server passes the received audio data to the emotion engine. Here, natural language processing and speech analysis techniques are used to extract emotional features from the audio and convert them into numerical data. For example, it analyzes the tone of voice and emphasized words to output positive, negative, or neutral emotion scores. 【0445】 Step 3: 【0446】 The server integrates emotion scores obtained from the emotion engine with historical work data, seasonal data, and sales information. Based on this data, a generative AI model is used to generate an appropriate work schedule. This model takes the prompt "Based on the user's emotion data, suggest a work schedule to reduce stress" as input and generates an optimized work schedule as output. 【0447】 Step 4: 【0448】 The server sends the generated work schedule to the terminal. The terminal displays feedback on the user's screen, such as the new work schedule and recommended break times. As a result, the user can review their work plan and make adjustments as needed. 【0449】 Step 5: 【0450】 Users review their emotional state and work schedule based on feedback received through their device. If there are areas that need improvement in the emotional state or feedback they entered, they can provide updated information to the system by re-entering it via voice input. 【0451】 (Application Example 2) 【0452】 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." 【0453】 There is a need for a system that can grasp employees' emotions and health status in real time and flexibly optimize work schedules based on that information. However, existing technology makes it difficult to adequately consider employees' psychological state and respond quickly. As a result, there is a problem of a lack of effective means to reduce employee stress and improve the workplace environment. 【0454】 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. 【0455】 In this invention, the server includes means for analyzing voice data and text to identify and quantify the user's emotional state, means for suggesting breaks and adjusting work schedules based on the quantified emotional data, and means for providing feedback according to the user's psychological state. This enables the dynamic adjustment of the work environment to take into account the employee's psychological state. 【0456】 "Voice data" refers to information recorded digitally from a user's speech or voice, and is used for analyzing their emotional state. 【0457】 "Text" refers to linguistic data converted from audio data into written characters, and is information used for sentiment analysis and communication. 【0458】 "Emotional state" refers to the psychological or emotional condition a user exhibits at a specific point in time, which is then quantified and used for management purposes. 【0459】 "Quantification" is a method of quantifying emotional states and other qualitative data and expressing them in a measurable form. 【0460】 A "suggestion for a break" is an act of encouraging employees to take appropriate rest based on their emotional state and work situation, with the aim of reducing employee stress. 【0461】 "Adjusting work schedules" refers to the process of changing or modifying schedules in order to optimize employees' working hours and break times. 【0462】 "Feedback" is a means of providing information about a user's state and behavior to encourage improvement and correction. 【0463】 To implement this invention, a server equipped with an emotion engine, a terminal for user interaction, and a user who provides data are required. The server analyzes voice data using natural language processing technology and quantifies the emotional state. Specifically, it performs speech recognition using Google Cloud's Speech-to-Text API and analyzes the emotion of that voice data using AWS Comprehend or Microsoft's Azure Text Analytics. 【0464】 The device collects audio and video data from the user through input devices such as microphones and cameras, and sends this data to a server to understand the user's emotional state. The application on the device runs on smartphones and tablets and provides the user with feedback on their emotional state and work schedule using a graphical user interface (GUI). 【0465】 Users input their situation and emotions using a device, and receive feedback from the server based on the analysis results from the emotion engine. If the user's emotional state exceeds a certain threshold, the server automatically suggests taking a break or adjusts the schedule. 【0466】 For example, if a caregiver says to their terminal during work, "I'm a little tired today," the server analyzes the audio and determines that the staff member is in a high-stress state. In this case, the application provides feedback such as, "We recommend you take a break for a while." 【0467】 An example of a prompt is: "Generate code that analyzes the emotional data of care staff and suggests specific actions to reduce stress." 【0468】 This system will enable the creation of a dynamic work environment that reflects employees' emotional states in real time, and is expected to contribute to both improved workplace productivity and health management. 【0469】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0470】 Step 1: 【0471】 The device enters standby mode and waits for voice input from the user. When the user speaks into the device, the microphone collects voice data. This voice data is temporarily stored in the device's memory. 【0472】 Step 2: 【0473】 The terminal sends the collected voice data to the server. At this time, the voice data is converted into data packets and transferred to the server using a secure communication protocol. 【0474】 Step 3: 【0475】 The server converts the received audio data into text using Google Cloud's Speech-to-Text API. This conversion process utilizes natural language processing techniques. The input is audio data, and the output is text data in string format. 【0476】 Step 4: 【0477】 The server performs sentiment analysis on text data using AWS Comprehend or Microsoft Azure Text Analytics. The input for this stage is text data, and the output is numerical data representing specific emotional states. The analysis process classifies the data into emotional categories and displays their trends numerically. 【0478】 Step 5: 【0479】 Based on the analysis results, the server sets thresholds and determines the content of the feedback when a specific emotional state is identified. For example, if a high stress level is numerically indicated, suggestions for taking a break will be listed. 【0480】 Step 6: 【0481】 The server converts the determined feedback back into a data packet and sends it to the terminal. The data is then transferred according to the communication protocol. 【0482】 Step 7: 【0483】 The terminal receives feedback from the server and presents it to the user via a visual interface or audio. For example, it might display "We recommend you take a break" on the screen. This process converts the feedback data into an appropriate user interface format and presents it. 【0484】 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. 【0485】 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. 【0486】 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. 【0487】 [Third Embodiment] 【0488】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0489】 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. 【0490】 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). 【0491】 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. 【0492】 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. 【0493】 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). 【0494】 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. 【0495】 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. 【0496】 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. 【0497】 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. 【0498】 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. 【0499】 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". 【0500】 This invention provides a system for streamlining attendance management within companies and optimizing personnel allocation. This system consists of a server, terminals, and users. The roles and specific processes of each component are described below. 【0501】 Server roles and processing 【0502】 Data collection and analysis 【0503】 The server collects historical business data, seasonal data, and promotional information from the database. After cleaning this data and extracting the necessary information, it is fed into a machine learning model to predict the number of employees needed. For example, if a user provides information about a summer campaign, the server analyzes past summer data to estimate the number of personnel required. 【0504】 Shift optimization 【0505】 After predicting the required number of employees, the server generates an optimal work schedule. This is done using linear programming and heuristic algorithms. The generated schedule is then optimized to accommodate employee preferences and legal requirements. 【0506】 Anomaly detection and legal / regulatory checks 【0507】 The server monitors daily attendance data and detects any abnormal data or patterns. It also verifies that employee shifts comply with labor laws by cross-referencing them with dynamically updated legal information. For example, if an employee is expected to work beyond the legally mandated working hours, the server will notify the manager in advance. 【0508】 Analysis of health status data 【0509】 The system receives employee health data and analyzes work patterns based on this data. This allows the server to suggest shift adjustments that take employee health into consideration. For example, if a particular employee reports a health problem in the morning, their schedule will be adjusted. 【0510】 Terminal roles and processing 【0511】 Interaction and Input Reception 【0512】 The terminal receives daily attendance data and health status information from employees as input. This is done via a web interface or mobile app. For example, if a user uses the terminal to request leave, that information is immediately sent to the server and reflected in the shift schedule. 【0513】 Feedback and notifications 【0514】 The terminals provide employees with shift information and notifications generated by the server. This allows employees to quickly understand their schedules and necessary actions. 【0515】 User roles and processes 【0516】 Data provision and verification 【0517】 Users provide their work status and healthcare information via their devices. They can also check their attendance and shift information. This allows users to properly manage their work status and request adjustments as needed. 【0518】 For example, if a user checks their schedule for next month and discovers that they have excessive work scheduled on a particular day, they can request a correction. 【0519】 This system allows companies to optimize staffing levels in response to constantly changing business demands, ensuring employee health and compliance with labor laws. As a result, workers can enjoy a healthier work environment, and companies can achieve cost-effective operations. 【0520】 The following describes the processing flow. 【0521】 Step 1: 【0522】 The server collects historical business data, seasonal data, and promotional information from external databases and internal information systems. This allows it to verify the basic information necessary for analysis. 【0523】 Step 2: 【0524】 The server cleans the collected data, correcting inaccurate data and missing values. It converts the data to an appropriate format and prepares it for analysis. 【0525】 Step 3: 【0526】 The server uses machine learning algorithms to predict the number of employees needed. This prediction is made by using time series analysis models to understand demand peaks and trends. 【0527】 Step 4: 【0528】 The server optimizes work schedules based on the predicted number of people needed. This streamlines personnel allocation using linear programming techniques. 【0529】 Step 5: 【0530】 The terminal receives daily attendance data and health status information from employees. This includes inputting working hours and self-reporting about their health condition. 【0531】 Step 6: 【0532】 The server monitors attendance data in real time and detects abnormal patterns. For example, it checks for arrival and departure times outside of normal shift ranges. 【0533】 Step 7: 【0534】 The server checks the collected attendance data against labor laws to verify that there are no legal violations. Any detected problems are immediately notified to the administrator. 【0535】 Step 8: 【0536】 The server analyzes employee health data and adjusts work patterns based on this analysis. It then proposes possible adjustments to managers and provides shifts that take employee health into consideration. 【0537】 Step 9: 【0538】 Users can check their schedules through their devices and request revisions as needed. This allows users to create a work environment that suits their needs. 【0539】 Step 10: 【0540】 The server reports the overall analysis results to the administrator and presents comprehensive improvement plans based on long-term attendance data and health status. This contributes to improving the working environment throughout the company. 【0541】 (Example 1) 【0542】 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." 【0543】 Corporate attendance management systems face various challenges in efficiently managing employee work conditions and health status. Specifically, they require appropriate staffing, detection of attendance anomalies, compliance with legal regulations, and shift scheduling that takes employee health into consideration. However, conventional systems have struggled to effectively achieve these goals. Furthermore, there is a need to meet these challenges while reducing costs. 【0544】 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. 【0545】 In this invention, the server includes means for collecting historical industry data, periodic data, and sales promotion information; analytical device means for predicting the required number of workers based on the above data; and calculator means for optimizing work plans based on the prediction results. This makes it possible to manage the work status of workers in real time and optimize staffing. It also provides flexible shift plans that take into account the health status of workers and can reduce the operating costs of the company. 【0546】 "Historical industry data" refers to information about operations accumulated over time in a specific industry, and serves as a foundation for analyzing long-term trends and patterns. 【0547】 "Seasonal data" refers to information collected in relation to a specific season or period, and is used to analyze the impact of seasonal variations on business operations. 【0548】 "Sales promotion information" refers to data on activities and campaigns planned with the aim of expanding the market for a product or service, and is useful for demand forecasting and formulating marketing strategies. 【0549】 An "analytical device for predicting the number of workers needed" is a means used to estimate the future labor force required based on collected data. 【0550】 A "calculator for optimizing work schedules" is a means of efficiently setting individual workers' working hours and holidays based on predicted workforce. 【0551】 A "monitoring device for detecting abnormal working time data" is a means of analyzing workers' working time data in real time and identifying abnormal patterns that deviate from the normal range. 【0552】 "Up-to-date regulatory information" refers to information that complies with current laws and industry rules, and indicates the standards that must be legally adhered to in the work environment and business operations. 【0553】 A "reporting device for predicting excessive workload and notifying managers" is a means of analyzing working hours and conditions to identify the possibility of excessive workload in advance and warn managers. 【0554】 An "analyzer for receiving workers' health status data and analyzing work patterns" is a means of optimizing workers' work schedules based on health information. 【0555】 A "communication device for providing information to a generative AI model and generating instructions based on that information" is a means of inputting necessary data into an artificial intelligence model and then issuing specific instructions or performing processing based on the generated results. 【0556】 A "reception device for receiving online inquiries and leave requests" is a digital interface for receiving and processing inquiries and leave requests from workers. 【0557】 A "support system for optimizing labor allocation and pursuing cost reduction in business operations" is a means of providing strategic support for efficiently allocating personnel and reducing operational costs. 【0558】 This invention provides a system for companies to efficiently improve employee attendance management and staffing. The system consists of a server, terminals, and users, and each component works in coordination to achieve effective attendance management. 【0559】 The server collects historical industry data, periodic data, and sales promotion information from the company's database and performs data cleaning. This is done using the Pandas library implemented in Python, removing duplicate data and missing values ​​to ensure consistent data. Next, the server inputs the collected data into a generative AI model. This generative AI model is built using machine learning libraries such as Scikit-learn and predicts the required number of employees. The prompt used is "Predict the number of personnel required for the next peak season and optimize the shift schedule." 【0560】 The terminal receives real-time data from employees via a web interface or mobile app. Specifically, it receives daily attendance data and health status information as input. For example, if an employee reports feeling unwell on a given day using the terminal, that information is immediately sent to the server and reflected in their shift schedule. The terminal also provides employees with shift information and important notifications generated from the server, allowing them to check and adjust their schedules. 【0561】 Users can regularly provide their work status and healthcare information via their devices. For example, they can check their work schedule for the next month and use a digital calendar to request adjustments to days when excessive work is anticipated. This allows companies to optimize staffing in response to dynamically changing work demands, reducing costs while ensuring compliance with labor laws and employee health. 【0562】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0563】 Step 1: 【0564】 The server collects historical industry data, periodic data, and sales promotion information from the database. Receiving this data as input, the server uses the Pandas library to clean the data, removing missing and outlier values ​​to output a balanced dataset. This step involves important data processing, including standardizing data formatting and handling outliers. 【0565】 Step 2: 【0566】 The server passes the cleaned data as input to the generative AI model. At this time, it provides instructions using the prompt "Predict the number of personnel needed during the next peak season and optimize the shift schedule." The generative AI model uses the Scikit-learn library to analyze the data and outputs a prediction of the required personnel. This output is then used for subsequent schedule optimization. 【0567】 Step 3: 【0568】 The server optimizes work shifts based on the prediction results of the generated AI model. Linear programming is performed using the PuLP library to calculate the optimal shift schedule, taking into account legal constraints and employee preferences. The input consists of the prediction results and constraints, and the optimized shift schedule is saved to the database as output. 【0569】 Step 4: 【0570】 The terminal receives daily attendance data and health status information from employees. This data, updated in real time, is sent to a server and used for anomaly detection and scheduling in the next step. This input process utilizes a web interface and a mobile app. 【0571】 Step 5: 【0572】 The server monitors the received daily attendance data and applies anomaly detection algorithms. The attendance data, which serves as input, includes working hours and health status information, and the server uses this data to detect anomalies and notify administrators. Anomalies include consecutive absences and working hours exceeding legal limits, and notifications are sent via email or in-app notifications. 【0573】 Step 6: 【0574】 The terminal provides employees with shift information generated by the server. Based on the shift schedule information as input, the terminal notifies employees and prompts them to confirm their schedules. Furthermore, it also provides a function to send necessary change requests from the terminal to the server. 【0575】 Step 7: 【0576】 Users can check their work status and healthcare information through their terminals and request adjustments as needed. This allows users to change their schedules to avoid excessive work. User input is reflected on the server via the terminals and used for the operation of the entire system. 【0577】 (Application Example 1) 【0578】 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." 【0579】 Optimizing the workforce across cities and allocating personnel across different industries is a complex and challenging task using traditional methods. This can lead to labor shortages or surpluses during events and peak seasons, hindering efficient operations and potentially increasing costs. 【0580】 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. 【0581】 In this invention, the server includes means for collecting historical information data, timely data, and advertising information; means for estimating the required number of employees based on the above data; and means for optimizing the workforce of the entire city and supporting personnel allocation across different industries. This enables efficient management and allocation of the workforce of the entire city. 【0582】 "Past information data" refers to all data related to past operations and transactions, and is fundamental information necessary for attendance management and optimizing personnel allocation. 【0583】 "Seasonal data" refers to data that shows fluctuations in business operations and demand during specific periods or seasons, and is an important element for making appropriate personnel allocation decisions. 【0584】 "Promotional information" refers to information about sales promotion activities conducted by companies and organizations, and is used to predict the personnel needs that arise from these activities. 【0585】 "Estimating the required number of employees" is the process of predicting the labor force needed over a certain period of time, based on collected data, using machine learning models or similar methods. 【0586】 "Optimizing work plans based on estimation results" is a method for creating the optimal work schedule based on estimated personnel needs. 【0587】 "Abnormal work information" refers to information or data that deviates from normal work patterns, and is important for detecting unexpected problems or errors. 【0588】 "Up-to-date legal information" refers to the latest information on labor laws and regulations, and is used to conduct lawful personnel management based on this information. 【0589】 "Predicting excessive work hours" means predicting in advance, based on collected data, the likelihood that workers will exceed their normal working hours. 【0590】 "Worker health status data" refers to information about the health of individual workers and is used to design healthy work schedules. 【0591】 "Analyzing work patterns" refers to examining workers' work patterns and styles in order to propose more appropriate shifts. 【0592】 "Optimizing the entire urban workforce" is the process of efficiently allocating all the workforce within a city to increase overall labor efficiency. 【0593】 "Supporting personnel allocation across different industries" means providing support to facilitate the exchange of labor between multiple industries and achieve efficient personnel utilization. 【0594】 The system for realizing this invention includes a server, a terminal, and a user as its main components. 【0595】 The server collects historical data, seasonal data, and promotional information and stores it in a database. This uses programming languages ​​such as Python and database management systems such as MySQL. The collected data is analyzed using machine learning models to estimate the number of employees needed. Machine learning libraries such as TensorFlow and scikit-learn are used for this analysis. Based on the estimated personnel needs, a work schedule is optimized using linear programming. 【0596】 The terminal receives work information and health status data from workers via a web interface or mobile app. This enables real-time inquiries and leave requests, and quickly transmits data to the server. The terminal also presents users with notifications and feedback from the server, informing workers of the latest work schedules and anomaly detection results. 【0597】 Through these systems, users can provide their work status and health information and check their latest work schedules. To support workforce optimization across cities, personnel allocation information across different industries is also analyzed by the server, and this information is shared among companies. 【0598】 As a concrete example, when multiple companies hold an event in Tokyo, they can use this system to optimally allocate security guards and guidance staff across specific industries, enabling efficient use of personnel. Furthermore, specific prompts for inputting data into the generating AI model could include questions such as, "Based on the list of events scheduled in Tokyo this weekend, please suggest the optimal staffing arrangements." 【0599】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0600】 Step 1: 【0601】 The server collects historical data, timely data, and promotional information from the database. This data is extracted using SQL queries, undergoes a data cleansing process to remove unnecessary information, and is formatted into the required format. As a result, a clean dataset is generated that is ready to be input into machine learning models. 【0602】 Step 2: 【0603】 The server uses the collected data to input into a machine learning model to estimate the required number of employees. The dataset provided as input is processed by a prediction model using TensorFlow, which predicts the number of personnel that will match future business needs. The output of this process is the estimated number of personnel required within a specific period. 【0604】 Step 3: 【0605】 The server optimizes work schedules based on estimation results using linear programming. Estimated staffing needs are processed considering worker preferences and legal constraints to generate efficient and compliant work schedules. The output is an optimized work schedule for all workers. 【0606】 Step 4: 【0607】 The terminal receives work information and health status data from users and sends it to the server. The entered information is collected via a web interface and immediately transferred to the server. This allows attendance and health management data to be updated in real time on the server. 【0608】 Step 5: 【0609】 The server analyzes work patterns based on received health data and readjusts work plans if necessary. Health data is analyzed, and schedules that minimize health burdens are proposed for specific workers. The output is the adjusted work plan. 【0610】 Step 6: 【0611】 Users check their work schedules, anomaly detection results, and notifications through their terminals. Based on the information received, users can make necessary adjustments and checks, allowing them to work with peace of mind. 【0612】 Step 7: 【0613】 The server assists in optimizing the workforce across cities by facilitating personnel allocation across different sectors. The server aggregates this information to promote the effective movement of labor between multiple sectors. The output is a proposed optimization of personnel allocation at the city level. 【0614】 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. 【0615】 This invention is a system for comprehensively optimizing employee work and health management, and in particular, it utilizes an emotion engine to achieve dynamic management that takes into account the user's psychological state. This system consists of a server, terminals, an emotion engine, and user interaction. 【0616】 Server roles and processing 【0617】 Data collection and analysis 【0618】 The server collects data related to business operations as well as data to reference the user's emotional state. This includes output generated by an emotion engine using natural language processing and speech analysis technologies. Specifically, the emotion engine analyzes the user's voice tone and quantifies positive / negative emotions. 【0619】 Optimizing work schedules 【0620】 Based on collected emotional data, work schedules are adjusted to take into account the emotional state of employees. For example, users experiencing certain emotions may be offered appropriate breaks to reduce stress. 【0621】 Anomaly detection and legal / regulatory checks 【0622】 The server continues to monitor attendance data for anomalies and ensure compliance with legal regulations. If emotional data indicates high stress levels, it will notify administrators with additional alerts based on legal standards. 【0623】 Terminal roles and processing 【0624】 Interaction and state recording 【0625】 The device receives input from the user and collects data for analysis by the emotion engine. This can be implemented as voice input using a microphone or facial recognition using a camera. For example, when a user speaks into the device, their voice is recorded and analyzed by the emotion engine. 【0626】 Provide feedback 【0627】 The terminal provides users with feedback on emotional information and schedule changes generated by the server. This allows users to instantly understand their emotional state and work status and make necessary adjustments. 【0628】 The role and processing of the emotional engine 【0629】 sentiment analysis 【0630】 The emotion engine analyzes the user's voice and text input to identify and quantify their emotions. If the emotions are strongly negative, it provides feedback to the server as a stress warning. This feature helps to improve the user's work experience. 【0631】 User roles and processes 【0632】 Data provision and verification 【0633】 Users provide feedback on their emotions and physical condition via their devices. Based on this data, they can see how their work schedules are adjusted. For example, if a user reports feeling stressed, the system will make suggestions that reflect that data. 【0634】 This system considers employee health and well-being, enhances labor productivity, and provides an optimal framework for companies to utilize their human resources efficiently. As a result, it can achieve both improved work environments and cost reductions for companies. 【0635】 The following describes the processing flow. 【0636】 Step 1: 【0637】 Users input information about their emotions and physical condition via voice or text through their device. The device then sends this input data to the emotion engine. 【0638】 Step 2: 【0639】 The emotion engine analyzes received audio and text data and uses natural language processing and speech recognition technologies to identify the user's emotions. This emotion information is returned to the server as numerical data. 【0640】 Step 3: 【0641】 The server evaluates the user's psychological state based on the emotional numerical data received from the emotion engine and determines the stress level as needed. 【0642】 Step 4: 【0643】 The server uses the stress level assessment results to dynamically adjust the work schedule. For example, if the stress level is high, it suggests additional break time to the user. 【0644】 Step 5: 【0645】 The server combines existing attendance data with other data to re-evaluate unusual work patterns and potential legal violations, and alerts administrators to any identified issues. 【0646】 Step 6: 【0647】 The device receives feedback from the server and notifies the user of sentiment analysis results and schedule adjustments. 【0648】 Step 7: 【0649】 The user uses their device to review the feedback and, if necessary, provide additional emotion input or request schedule adjustments. This information is then sent back to the server and emotion engine, and the process repeats. 【0650】 Through this series of processes, the system can manage labor in real time while taking into account the user's emotional state, thereby providing a comfortable working environment. 【0651】 (Example 2) 【0652】 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." 【0653】 Traditional work management systems fail to adequately consider workers' emotions and health conditions, making efficient staffing and improvements to the work environment difficult. Furthermore, limitations in complying with legal regulations and detecting abnormal work situations make it difficult to prevent workplace stress and health risks. This results in problems such as decreased labor productivity and increased costs for companies. 【0654】 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. 【0655】 In this invention, the server includes functional means for aggregating past work data, seasonal data, and sales information; functional means for receiving worker health status information and analyzing work patterns; and functional means for analyzing voice and text to identify and quantify the worker's emotional state and reflect it in the work plan. This enables real-time understanding of workers' emotions and health status, allowing for the proposal of optimal work schedules and rapid response through anomaly detection, while complying with legal regulations. 【0656】 "Past work data" refers to work history and performance information, including information about an employee's past work content and productivity. 【0657】 "Seasonal data" refers to information about environmental factors and demand fluctuations that change in relation to the seasons. 【0658】 "Sales information" refers to all data related to the provision of goods and services to consumers. 【0659】 "Health status information" refers to data concerning the physical and mental health of workers. 【0660】 A "work pattern" is a set of plans regarding working hours, break times, and task assignments. 【0661】 The "ability to analyze speech and text" refers to a technology that uses natural language processing to identify the intentions and emotions of workers. 【0662】 "Quantifying emotional states" is the process of measuring, quantifying, and representing the psychological state of workers. 【0663】 "Reflecting in work plans" means adjusting employees' work schedules based on the analyzed data. 【0664】 An "optimal work schedule" is a work plan that is effective for both employees and management, taking into consideration the efficiency and health of workers. 【0665】 Anomaly detection is the process of identifying and distinguishing behavior that deviates from known standards or patterns. 【0666】 This invention is a system that integrates labor management and emotional state analysis. This system utilizes servers, terminals, and generative AI models to analyze workers' emotional and health states in real time, enabling the management of optimal work schedules. 【0667】 The server functions as a central data processing unit, aggregating historical work data, seasonal data, sales information, and more. This data is obtained from external databases and work management software. The server also uses an emotion engine to analyze voice and text data and quantify the emotional state of workers. Specifically, natural language processing technology and voice analysis algorithms are used. The emotion engine's output classifies the user's psychological state as positive, negative, or neutral, and adjusts the work plan accordingly. Based on emotion analysis and health status data, the work plan is optimized, and if an anomaly is detected, an alert can be immediately sent to the administrator. 【0668】 The terminal is responsible for user interaction. It has the functionality to receive voice and text input from the user and transmit it to the server. Specifically, workers can use input devices such as microphones and cameras to communicate their emotional state to the terminal. The terminal transmits this information to the server in real time. The terminal also receives feedback from the server and presents the user with emotional assessment results and a new work schedule. 【0669】 Through this system, users can provide feedback on their emotions and health status, and see how this is reflected in their work schedules. For example, if a user enters "I have something on my mind today" into the terminal, the system will use that information to assess their stress level and suggest necessary rest. 【0670】 As an example of a prompt, instructions can be given to the generating AI model in the form of, "Based on user X's recent emotional data, please suggest a work schedule to reduce stress." In this way, the present invention supports the efficient use of human resources by companies while taking into consideration the mental and physical health of workers. 【0671】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0672】 Step 1: 【0673】 The device collects voice data from the user. The user uses the microphone to speak into the device, providing voice input to indicate their emotional state. The collected voice data is converted into a digital format and sent to the server. 【0674】 Step 2: 【0675】 The server passes the received audio data to the emotion engine. Here, natural language processing and speech analysis techniques are used to extract emotional features from the audio and convert them into numerical data. For example, it analyzes the tone of voice and emphasized words to output positive, negative, or neutral emotion scores. 【0676】 Step 3: 【0677】 The server integrates emotion scores obtained from the emotion engine with historical work data, seasonal data, and sales information. Based on this data, a generative AI model is used to generate an appropriate work schedule. This model takes the prompt "Based on the user's emotion data, suggest a work schedule to reduce stress" as input and generates an optimized work schedule as output. 【0678】 Step 4: 【0679】 The server sends the generated work schedule to the terminal. The terminal displays feedback on the user's screen, such as the new work schedule and recommended break times. As a result, the user can review their work plan and make adjustments as needed. 【0680】 Step 5: 【0681】 Users review their emotional state and work schedule based on feedback received through their device. If there are areas that need improvement in the emotional state or feedback they entered, they can provide updated information to the system by re-entering it via voice input. 【0682】 (Application Example 2) 【0683】 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." 【0684】 There is a need for a system that can grasp employees' emotions and health status in real time and flexibly optimize work schedules based on that information. However, existing technology makes it difficult to adequately consider employees' psychological state and respond quickly. As a result, there is a problem of a lack of effective means to reduce employee stress and improve the workplace environment. 【0685】 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. 【0686】 In this invention, the server includes means for analyzing voice data and text to identify and quantify the user's emotional state, means for suggesting breaks and adjusting work schedules based on the quantified emotional data, and means for providing feedback according to the user's psychological state. This enables the dynamic adjustment of the work environment to take into account the employee's psychological state. 【0687】 "Voice data" refers to information recorded digitally from a user's speech or voice, and is used for analyzing their emotional state. 【0688】 "Text" refers to linguistic data converted from audio data into written characters, and is information used for sentiment analysis and communication. 【0689】 "Emotional state" refers to the psychological or emotional condition a user exhibits at a specific point in time, which is then quantified and used for management purposes. 【0690】 "Quantification" is a method of quantifying emotional states and other qualitative data and expressing them in a measurable form. 【0691】 A "suggestion for a break" is an act of encouraging employees to take appropriate rest based on their emotional state and work situation, with the aim of reducing employee stress. 【0692】 "Adjusting work schedules" refers to the process of changing or modifying schedules in order to optimize employees' working hours and break times. 【0693】 "Feedback" is a means of providing information about a user's state and behavior to encourage improvement and correction. 【0694】 To implement this invention, a server equipped with an emotion engine, a terminal for user interaction, and a user who provides data are required. The server analyzes voice data using natural language processing technology and quantifies the emotional state. Specifically, it performs speech recognition using Google Cloud's Speech-to-Text API and analyzes the emotion of that voice data using AWS Comprehend or Microsoft's Azure Text Analytics. 【0695】 The device collects audio and video data from the user through input devices such as microphones and cameras, and sends this data to a server to understand the user's emotional state. The application on the device runs on smartphones and tablets and provides the user with feedback on their emotional state and work schedule using a graphical user interface (GUI). 【0696】 Users input their situation and emotions using a device, and receive feedback from the server based on the analysis results from the emotion engine. If the user's emotional state exceeds a certain threshold, the server automatically suggests taking a break or adjusts the schedule. 【0697】 For example, if a caregiver says to their terminal during work, "I'm a little tired today," the server analyzes the audio and determines that the staff member is in a high-stress state. In this case, the application provides feedback such as, "We recommend you take a break for a while." 【0698】 An example of a prompt is: "Generate code that analyzes the emotional data of care staff and suggests specific actions to reduce stress." 【0699】 This system will enable the creation of a dynamic work environment that reflects employees' emotional states in real time, and is expected to contribute to both improved workplace productivity and health management. 【0700】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0701】 Step 1: 【0702】 The device enters standby mode and waits for voice input from the user. When the user speaks into the device, the microphone collects voice data. This voice data is temporarily stored in the device's memory. 【0703】 Step 2: 【0704】 The terminal sends the collected voice data to the server. At this time, the voice data is converted into data packets and transferred to the server using a secure communication protocol. 【0705】 Step 3: 【0706】 The server converts the received audio data into text using Google Cloud's Speech-to-Text API. This conversion process utilizes natural language processing techniques. The input is audio data, and the output is text data in string format. 【0707】 Step 4: 【0708】 The server performs sentiment analysis on text data using AWS Comprehend or Microsoft Azure Text Analytics. The input for this stage is text data, and the output is numerical data representing specific emotional states. The analysis process classifies the data into emotional categories and displays their trends numerically. 【0709】 Step 5: 【0710】 Based on the analysis results, the server sets thresholds and determines the content of the feedback when a specific emotional state is identified. For example, if a high stress level is numerically indicated, suggestions for taking a break will be listed. 【0711】 Step 6: 【0712】 The server converts the determined feedback back into a data packet and sends it to the terminal. The data is then transferred according to the communication protocol. 【0713】 Step 7: 【0714】 The terminal receives feedback from the server and presents it to the user via a visual interface or audio. For example, it might display "We recommend you take a break" on the screen. This process converts the feedback data into an appropriate user interface format and presents it. 【0715】 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. 【0716】 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. 【0717】 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. 【0718】 [Fourth Embodiment] 【0719】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0720】 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. 【0721】 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). 【0722】 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. 【0723】 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. 【0724】 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). 【0725】 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. 【0726】 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. 【0727】 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. 【0728】 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. 【0729】 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. 【0730】 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. 【0731】 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". 【0732】 This invention provides a system for streamlining attendance management within companies and optimizing personnel allocation. This system consists of a server, terminals, and users. The roles and specific processes of each component are described below. 【0733】 Server roles and processing 【0734】 Data collection and analysis 【0735】 The server collects historical business data, seasonal data, and promotional information from the database. After cleaning this data and extracting the necessary information, it is fed into a machine learning model to predict the number of employees needed. For example, if a user provides information about a summer campaign, the server analyzes past summer data to estimate the number of personnel required. 【0736】 Shift optimization 【0737】 After predicting the required number of employees, the server generates an optimal work schedule. This is done using linear programming and heuristic algorithms. The generated schedule is then optimized to accommodate employee preferences and legal requirements. 【0738】 Anomaly detection and legal / regulatory checks 【0739】 The server monitors daily attendance data and detects any abnormal data or patterns. It also verifies that employee shifts comply with labor laws by cross-referencing them with dynamically updated legal information. For example, if an employee is expected to work beyond the legally mandated working hours, the server will notify the manager in advance. 【0740】 Analysis of health status data 【0741】 The system receives employee health data and analyzes work patterns based on this data. This allows the server to suggest shift adjustments that take employee health into consideration. For example, if a particular employee reports a health problem in the morning, their schedule will be adjusted. 【0742】 Terminal roles and processing 【0743】 Interaction and Input Reception 【0744】 The terminal receives daily attendance data and health status information from employees as input. This is done via a web interface or mobile app. For example, if a user uses the terminal to request leave, that information is immediately sent to the server and reflected in the shift schedule. 【0745】 Feedback and notifications 【0746】 The terminals provide employees with shift information and notifications generated by the server. This allows employees to quickly understand their schedules and necessary actions. 【0747】 User roles and processes 【0748】 Data provision and verification 【0749】 Users provide their work status and healthcare information via their devices. They can also check their attendance and shift information. This allows users to properly manage their work status and request adjustments as needed. 【0750】 For example, if a user checks their schedule for next month and discovers that they have excessive work scheduled on a particular day, they can request a correction. 【0751】 This system allows companies to optimize staffing levels in response to constantly changing business demands, ensuring employee health and compliance with labor laws. As a result, workers can enjoy a healthier work environment, and companies can achieve cost-effective operations. 【0752】 The following describes the processing flow. 【0753】 Step 1: 【0754】 The server collects historical business data, seasonal data, and promotional information from external databases and internal information systems. This allows it to verify the basic information necessary for analysis. 【0755】 Step 2: 【0756】 The server cleans the collected data, correcting inaccurate data and missing values. It converts the data to an appropriate format and prepares it for analysis. 【0757】 Step 3: 【0758】 The server uses machine learning algorithms to predict the number of employees needed. This prediction is made by using time series analysis models to understand demand peaks and trends. 【0759】 Step 4: 【0760】 The server optimizes work schedules based on the predicted number of people needed. This streamlines personnel allocation using linear programming techniques. 【0761】 Step 5: 【0762】 The terminal receives daily attendance data and health status information from employees. This includes inputting working hours and self-reporting about their health condition. 【0763】 Step 6: 【0764】 The server monitors attendance data in real time and detects abnormal patterns. For example, it checks for arrival and departure times outside of normal shift ranges. 【0765】 Step 7: 【0766】 The server checks the collected attendance data against labor laws to verify that there are no legal violations. Any detected problems are immediately notified to the administrator. 【0767】 Step 8: 【0768】 The server analyzes employee health data and adjusts work patterns based on this analysis. It then proposes possible adjustments to managers and provides shifts that take employee health into consideration. 【0769】 Step 9: 【0770】 Users can check their schedules through their devices and request revisions as needed. This allows users to create a work environment that suits their needs. 【0771】 Step 10: 【0772】 The server reports the overall analysis results to the administrator and presents comprehensive improvement plans based on long-term attendance data and health status. This contributes to improving the working environment throughout the company. 【0773】 (Example 1) 【0774】 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". 【0775】 Corporate attendance management systems face various challenges in efficiently managing employee work conditions and health status. Specifically, they require appropriate staffing, detection of attendance anomalies, compliance with legal regulations, and shift scheduling that takes employee health into consideration. However, conventional systems have struggled to effectively achieve these goals. Furthermore, there is a need to meet these challenges while reducing costs. 【0776】 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. 【0777】 In this invention, the server includes means for collecting historical industry data, periodic data, and sales promotion information; analytical device means for predicting the required number of workers based on the above data; and calculator means for optimizing work plans based on the prediction results. This makes it possible to manage the work status of workers in real time and optimize staffing. It also provides flexible shift plans that take into account the health status of workers and can reduce the operating costs of the company. 【0778】 "Historical industry data" refers to information about operations accumulated over time in a specific industry, and serves as a foundation for analyzing long-term trends and patterns. 【0779】 "Seasonal data" refers to information collected in relation to a specific season or period, and is used to analyze the impact of seasonal variations on business operations. 【0780】 "Sales promotion information" refers to data on activities and campaigns planned with the aim of expanding the market for a product or service, and is useful for demand forecasting and formulating marketing strategies. 【0781】 An "analytical device for predicting the number of workers needed" is a means used to estimate the future labor force required based on collected data. 【0782】 A "calculator for optimizing work schedules" is a means of efficiently setting individual workers' working hours and holidays based on predicted workforce. 【0783】 A "monitoring device for detecting abnormal working time data" is a means of analyzing workers' working time data in real time and identifying abnormal patterns that deviate from the normal range. 【0784】 "Up-to-date regulatory information" refers to information that complies with current laws and industry rules, and indicates the standards that must be legally adhered to in the work environment and business operations. 【0785】 A "reporting device for predicting excessive workload and notifying managers" is a means of analyzing working hours and conditions to identify the possibility of excessive workload in advance and warn managers. 【0786】 An "analyzer for receiving workers' health status data and analyzing work patterns" is a means of optimizing workers' work schedules based on health information. 【0787】 A "communication device for providing information to a generative AI model and generating instructions based on that information" is a means of inputting necessary data into an artificial intelligence model and then issuing specific instructions or performing processing based on the generated results. 【0788】 A "reception device for receiving online inquiries and leave requests" is a digital interface for receiving and processing inquiries and leave requests from workers. 【0789】 A "support system for optimizing labor allocation and pursuing cost reduction in business operations" is a means of providing strategic support for efficiently allocating personnel and reducing operational costs. 【0790】 This invention provides a system for companies to efficiently improve employee attendance management and staffing. The system consists of a server, terminals, and users, and each component works in coordination to achieve effective attendance management. 【0791】 The server collects historical industry data, periodic data, and sales promotion information from the company's database and performs data cleaning. This is done using the Pandas library implemented in Python, removing duplicate data and missing values ​​to ensure consistent data. Next, the server inputs the collected data into a generative AI model. This generative AI model is built using machine learning libraries such as Scikit-learn and predicts the required number of employees. The prompt used is "Predict the number of personnel required for the next peak season and optimize the shift schedule." 【0792】 The terminal receives real-time data from employees via a web interface or mobile app. Specifically, it receives daily attendance data and health status information as input. For example, if an employee reports feeling unwell on a given day using the terminal, that information is immediately sent to the server and reflected in their shift schedule. The terminal also provides employees with shift information and important notifications generated from the server, allowing them to check and adjust their schedules. 【0793】 Users can regularly provide their work status and healthcare information via their devices. For example, they can check their work schedule for the next month and use a digital calendar to request adjustments to days when excessive work is anticipated. This allows companies to optimize staffing in response to dynamically changing work demands, reducing costs while ensuring compliance with labor laws and employee health. 【0794】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0795】 Step 1: 【0796】 The server collects historical industry data, periodic data, and sales promotion information from the database. Receiving this data as input, the server uses the Pandas library to clean the data, removing missing and outlier values ​​to output a balanced dataset. This step involves important data processing, including standardizing data formatting and handling outliers. 【0797】 Step 2: 【0798】 The server passes the cleaned data as input to the generative AI model. At this time, it provides instructions using the prompt "Predict the number of personnel needed during the next peak season and optimize the shift schedule." The generative AI model uses the Scikit-learn library to analyze the data and outputs a prediction of the required personnel. This output is then used for subsequent schedule optimization. 【0799】 Step 3: 【0800】 The server optimizes work shifts based on the prediction results of the generated AI model. Linear programming is performed using the PuLP library to calculate the optimal shift schedule, taking into account legal constraints and employee preferences. The input consists of the prediction results and constraints, and the optimized shift schedule is saved to the database as output. 【0801】 Step 4: 【0802】 The terminal receives daily attendance data and health status information from employees. This data, updated in real time, is sent to a server and used for anomaly detection and scheduling in the next step. This input process utilizes a web interface and a mobile app. 【0803】 Step 5: 【0804】 The server monitors the received daily attendance data and applies anomaly detection algorithms. The attendance data, which serves as input, includes working hours and health status information, and the server uses this data to detect anomalies and notify administrators. Anomalies include consecutive absences and working hours exceeding legal limits, and notifications are sent via email or in-app notifications. 【0805】 Step 6: 【0806】 The terminal provides employees with shift information generated by the server. Based on the shift schedule information as input, the terminal notifies employees and prompts them to confirm their schedules. Furthermore, it also provides a function to send necessary change requests from the terminal to the server. 【0807】 Step 7: 【0808】 Users can check their work status and healthcare information through their terminals and request adjustments as needed. This allows users to change their schedules to avoid excessive work. User input is reflected on the server via the terminals and used for the operation of the entire system. 【0809】 (Application Example 1) 【0810】 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". 【0811】 Optimizing the workforce across cities and allocating personnel across different industries is a complex and challenging task using traditional methods. This can lead to labor shortages or surpluses during events and peak seasons, hindering efficient operations and potentially increasing costs. 【0812】 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. 【0813】 In this invention, the server includes means for collecting historical information data, timely data, and advertising information; means for estimating the required number of employees based on the above data; and means for optimizing the workforce of the entire city and supporting personnel allocation across different industries. This enables efficient management and allocation of the workforce of the entire city. 【0814】 "Past information data" refers to all data related to past operations and transactions, and is fundamental information necessary for attendance management and optimizing personnel allocation. 【0815】 "Seasonal data" refers to data that shows fluctuations in business operations and demand during specific periods or seasons, and is an important element for making appropriate personnel allocation decisions. 【0816】 "Promotional information" refers to information about sales promotion activities conducted by companies and organizations, and is used to predict the personnel needs that arise from these activities. 【0817】 "Estimating the required number of employees" is the process of predicting the labor force needed over a certain period of time, based on collected data, using machine learning models or similar methods. 【0818】 "Optimizing work plans based on estimation results" is a method for creating the optimal work schedule based on estimated personnel needs. 【0819】 "Abnormal work information" refers to information or data that deviates from normal work patterns, and is important for detecting unexpected problems or errors. 【0820】 "Up-to-date legal information" refers to the latest information on labor laws and regulations, and is used to conduct lawful personnel management based on this information. 【0821】 "Predicting excessive work hours" means predicting in advance, based on collected data, the likelihood that workers will exceed their normal working hours. 【0822】 "Worker health status data" refers to information about the health of individual workers and is used to design healthy work schedules. 【0823】 "Analyzing work patterns" refers to examining workers' work patterns and styles in order to propose more appropriate shifts. 【0824】 "Optimizing the entire urban workforce" is the process of efficiently allocating all the workforce within a city to increase overall labor efficiency. 【0825】 "Supporting personnel allocation across different industries" means providing support to facilitate the exchange of labor between multiple industries and achieve efficient personnel utilization. 【0826】 The system for realizing this invention includes a server, a terminal, and a user as its main components. 【0827】 The server collects historical data, seasonal data, and promotional information and stores it in a database. This uses programming languages ​​such as Python and database management systems such as MySQL. The collected data is analyzed using machine learning models to estimate the number of employees needed. Machine learning libraries such as TensorFlow and scikit-learn are used for this analysis. Based on the estimated personnel needs, a work schedule is optimized using linear programming. 【0828】 The terminal receives work information and health status data from workers via a web interface or mobile app. This enables real-time inquiries and leave requests, and quickly transmits data to the server. The terminal also presents users with notifications and feedback from the server, informing workers of the latest work schedules and anomaly detection results. 【0829】 Through these systems, users can provide their work status and health information and check their latest work schedules. To support workforce optimization across cities, personnel allocation information across different industries is also analyzed by the server, and this information is shared among companies. 【0830】 As a concrete example, when multiple companies hold an event in Tokyo, they can use this system to optimally allocate security guards and guidance staff across specific industries, enabling efficient use of personnel. Furthermore, specific prompts for inputting data into the generating AI model could include questions such as, "Based on the list of events scheduled in Tokyo this weekend, please suggest the optimal staffing arrangements." 【0831】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0832】 Step 1: 【0833】 The server collects historical data, timely data, and promotional information from the database. This data is extracted using SQL queries, undergoes a data cleansing process to remove unnecessary information, and is formatted into the required format. As a result, a clean dataset is generated that is ready to be input into machine learning models. 【0834】 Step 2: 【0835】 The server uses the collected data to input into a machine learning model to estimate the required number of employees. The dataset provided as input is processed by a prediction model using TensorFlow, which predicts the number of personnel that will match future business needs. The output of this process is the estimated number of personnel required within a specific period. 【0836】 Step 3: 【0837】 The server optimizes work schedules based on estimation results using linear programming. Estimated staffing needs are processed considering worker preferences and legal constraints to generate efficient and compliant work schedules. The output is an optimized work schedule for all workers. 【0838】 Step 4: 【0839】 The terminal receives work information and health status data from users and sends it to the server. The entered information is collected via a web interface and immediately transferred to the server. This allows attendance and health management data to be updated in real time on the server. 【0840】 Step 5: 【0841】 The server analyzes work patterns based on received health data and readjusts work plans if necessary. Health data is analyzed, and schedules that minimize health burdens are proposed for specific workers. The output is the adjusted work plan. 【0842】 Step 6: 【0843】 Users check their work schedules, anomaly detection results, and notifications through their terminals. Based on the information received, users can make necessary adjustments and checks, allowing them to work with peace of mind. 【0844】 Step 7: 【0845】 The server assists in optimizing the workforce across cities by facilitating personnel allocation across different sectors. The server aggregates this information to promote the effective movement of labor between multiple sectors. The output is a proposed optimization of personnel allocation at the city level. 【0846】 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. 【0847】 This invention is a system for comprehensively optimizing employee work and health management, and in particular, it utilizes an emotion engine to achieve dynamic management that takes into account the user's psychological state. This system consists of a server, terminals, an emotion engine, and user interaction. 【0848】 Server roles and processing 【0849】 Data collection and analysis 【0850】 The server collects data related to business operations as well as data to reference the user's emotional state. This includes output generated by an emotion engine using natural language processing and speech analysis technologies. Specifically, the emotion engine analyzes the user's voice tone and quantifies positive / negative emotions. 【0851】 Optimizing work schedules 【0852】 Based on collected emotional data, work schedules are adjusted to take into account the emotional state of employees. For example, users experiencing certain emotions may be offered appropriate breaks to reduce stress. 【0853】 Anomaly detection and legal / regulatory checks 【0854】 The server continues to monitor attendance data for anomalies and ensure compliance with legal regulations. If emotional data indicates high stress levels, it will notify administrators with additional alerts based on legal standards. 【0855】 Terminal roles and processing 【0856】 Interaction and state recording 【0857】 The device receives input from the user and collects data for analysis by the emotion engine. This can be implemented as voice input using a microphone or facial recognition using a camera. For example, when a user speaks into the device, their voice is recorded and analyzed by the emotion engine. 【0858】 Provide feedback 【0859】 The terminal provides users with feedback on emotional information and schedule changes generated by the server. This allows users to instantly understand their emotional state and work status and make necessary adjustments. 【0860】 The role and processing of the emotional engine 【0861】 sentiment analysis 【0862】 The emotion engine analyzes the user's voice and text input to identify and quantify their emotions. If the emotions are strongly negative, it provides feedback to the server as a stress warning. This feature helps to improve the user's work experience. 【0863】 User roles and processes 【0864】 Data provision and verification 【0865】 Users provide feedback on their emotions and physical condition via their devices. Based on this data, they can see how their work schedules are adjusted. For example, if a user reports feeling stressed, the system will make suggestions that reflect that data. 【0866】 This system considers employee health and well-being, enhances labor productivity, and provides an optimal framework for companies to utilize their human resources efficiently. As a result, it can achieve both improved work environments and cost reductions for companies. 【0867】 The following describes the processing flow. 【0868】 Step 1: 【0869】 Users input information about their emotions and physical condition via voice or text through their device. The device then sends this input data to the emotion engine. 【0870】 Step 2: 【0871】 The emotion engine analyzes received audio and text data and uses natural language processing and speech recognition technologies to identify the user's emotions. This emotion information is returned to the server as numerical data. 【0872】 Step 3: 【0873】 The server evaluates the user's psychological state based on the emotional numerical data received from the emotion engine and determines the stress level as needed. 【0874】 Step 4: 【0875】 The server uses the stress level assessment results to dynamically adjust the work schedule. For example, if the stress level is high, it suggests additional break time to the user. 【0876】 Step 5: 【0877】 The server combines existing attendance data with other data to re-evaluate unusual work patterns and potential legal violations, and alerts administrators to any identified issues. 【0878】 Step 6: 【0879】 The device receives feedback from the server and notifies the user of sentiment analysis results and schedule adjustments. 【0880】 Step 7: 【0881】 The user uses their device to review the feedback and, if necessary, provide additional emotion input or request schedule adjustments. This information is then sent back to the server and emotion engine, and the process repeats. 【0882】 Through this series of processes, the system can manage labor in real time while taking into account the user's emotional state, thereby providing a comfortable working environment. 【0883】 (Example 2) 【0884】 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". 【0885】 Traditional work management systems fail to adequately consider workers' emotions and health conditions, making efficient staffing and improvements to the work environment difficult. Furthermore, limitations in complying with legal regulations and detecting abnormal work situations make it difficult to prevent workplace stress and health risks. This results in problems such as decreased labor productivity and increased costs for companies. 【0886】 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. 【0887】 In this invention, the server includes functional means for aggregating past work data, seasonal data, and sales information; functional means for receiving worker health status information and analyzing work patterns; and functional means for analyzing voice and text to identify and quantify the worker's emotional state and reflect it in the work plan. This enables real-time understanding of workers' emotions and health status, allowing for the proposal of optimal work schedules and rapid response through anomaly detection, while complying with legal regulations. 【0888】 "Past work data" refers to work history and performance information, including information about an employee's past work content and productivity. 【0889】 "Seasonal data" refers to information about environmental factors and demand fluctuations that change in relation to the seasons. 【0890】 "Sales information" refers to all data related to the provision of goods and services to consumers. 【0891】 "Health status information" refers to data concerning the physical and mental health of workers. 【0892】 A "work pattern" is a set of plans regarding working hours, break times, and task assignments. 【0893】 The "ability to analyze speech and text" refers to a technology that uses natural language processing to identify the intentions and emotions of workers. 【0894】 "Quantifying emotional states" is the process of measuring, quantifying, and representing the psychological state of workers. 【0895】 "Reflecting in work plans" means adjusting employees' work schedules based on the analyzed data. 【0896】 An "optimal work schedule" is a work plan that is effective for both employees and management, taking into consideration the efficiency and health of workers. 【0897】 Anomaly detection is the process of identifying and distinguishing behavior that deviates from known standards or patterns. 【0898】 This invention is a system that integrates labor management and emotional state analysis. This system utilizes servers, terminals, and generative AI models to analyze workers' emotional and health states in real time, enabling the management of optimal work schedules. 【0899】 The server functions as a central data processing unit, aggregating historical work data, seasonal data, sales information, and more. This data is obtained from external databases and work management software. The server also uses an emotion engine to analyze voice and text data and quantify the emotional state of workers. Specifically, natural language processing technology and voice analysis algorithms are used. The emotion engine's output classifies the user's psychological state as positive, negative, or neutral, and adjusts the work plan accordingly. Based on emotion analysis and health status data, the work plan is optimized, and if an anomaly is detected, an alert can be immediately sent to the administrator. 【0900】 The terminal is responsible for user interaction. It has the functionality to receive voice and text input from the user and transmit it to the server. Specifically, workers can use input devices such as microphones and cameras to communicate their emotional state to the terminal. The terminal transmits this information to the server in real time. The terminal also receives feedback from the server and presents the user with emotional assessment results and a new work schedule. 【0901】 Through this system, users can provide feedback on their emotions and health status, and see how this is reflected in their work schedules. For example, if a user enters "I have something on my mind today" into the terminal, the system will use that information to assess their stress level and suggest necessary rest. 【0902】 As an example of a prompt, instructions can be given to the generating AI model in the form of, "Based on user X's recent emotional data, please suggest a work schedule to reduce stress." In this way, the present invention supports the efficient use of human resources by companies while taking into consideration the mental and physical health of workers. 【0903】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0904】 Step 1: 【0905】 The device collects voice data from the user. The user uses the microphone to speak into the device, providing voice input to indicate their emotional state. The collected voice data is converted into a digital format and sent to the server. 【0906】 Step 2: 【0907】 The server passes the received audio data to the emotion engine. Here, natural language processing and speech analysis techniques are used to extract emotional features from the audio and convert them into numerical data. For example, it analyzes the tone of voice and emphasized words to output positive, negative, or neutral emotion scores. 【0908】 Step 3: 【0909】 The server integrates emotion scores obtained from the emotion engine with historical work data, seasonal data, and sales information. Based on this data, a generative AI model is used to generate an appropriate work schedule. This model takes the prompt "Based on the user's emotion data, suggest a work schedule to reduce stress" as input and generates an optimized work schedule as output. 【0910】 Step 4: 【0911】 The server sends the generated work schedule to the terminal. The terminal displays feedback on the user's screen, such as the new work schedule and recommended break times. As a result, the user can review their work plan and make adjustments as needed. 【0912】 Step 5: 【0913】 Users review their emotional state and work schedule based on feedback received through their device. If there are areas that need improvement in the emotional state or feedback they entered, they can provide updated information to the system by re-entering it via voice input. 【0914】 (Application Example 2) 【0915】 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". 【0916】 There is a need for a system that can grasp employees' emotions and health status in real time and flexibly optimize work schedules based on that information. However, existing technology makes it difficult to adequately consider employees' psychological state and respond quickly. As a result, there is a problem of a lack of effective means to reduce employee stress and improve the workplace environment. 【0917】 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. 【0918】 In this invention, the server includes means for analyzing voice data and text to identify and quantify the user's emotional state, means for suggesting breaks and adjusting work schedules based on the quantified emotional data, and means for providing feedback according to the user's psychological state. This enables the dynamic adjustment of the work environment to take into account the employee's psychological state. 【0919】 "Voice data" refers to information recorded digitally from a user's speech or voice, and is used for analyzing their emotional state. 【0920】 "Text" refers to linguistic data converted from audio data into written characters, and is information used for sentiment analysis and communication. 【0921】 "Emotional state" refers to the psychological or emotional condition a user exhibits at a specific point in time, which is then quantified and used for management purposes. 【0922】 "Quantification" is a method of quantifying emotional states and other qualitative data and expressing them in a measurable form. 【0923】 A "suggestion for a break" is an act of encouraging employees to take appropriate rest based on their emotional state and work situation, with the aim of reducing employee stress. 【0924】 "Adjusting work schedules" refers to the process of changing or modifying schedules in order to optimize employees' working hours and break times. 【0925】 "Feedback" is a means of providing information about a user's state and behavior to encourage improvement and correction. 【0926】 To implement this invention, a server equipped with an emotion engine, a terminal for user interaction, and a user who provides data are required. The server analyzes voice data using natural language processing technology and quantifies the emotional state. Specifically, it performs speech recognition using Google Cloud's Speech-to-Text API and analyzes the emotion of that voice data using AWS Comprehend or Microsoft's Azure Text Analytics. 【0927】 The device collects audio and video data from the user through input devices such as microphones and cameras, and sends this data to a server to understand the user's emotional state. The application on the device runs on smartphones and tablets and provides the user with feedback on their emotional state and work schedule using a graphical user interface (GUI). 【0928】 Users input their situation and emotions using a device, and receive feedback from the server based on the analysis results from the emotion engine. If the user's emotional state exceeds a certain threshold, the server automatically suggests taking a break or adjusts the schedule. 【0929】 For example, if a caregiver says to their terminal during work, "I'm a little tired today," the server analyzes the audio and determines that the staff member is in a high-stress state. In this case, the application provides feedback such as, "We recommend you take a break for a while." 【0930】 An example of a prompt is: "Generate code that analyzes the emotional data of care staff and suggests specific actions to reduce stress." 【0931】 This system will enable the creation of a dynamic work environment that reflects employees' emotional states in real time, and is expected to contribute to both improved workplace productivity and health management. 【0932】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0933】 Step 1: 【0934】 The device enters standby mode and waits for voice input from the user. When the user speaks into the device, the microphone collects voice data. This voice data is temporarily stored in the device's memory. 【0935】 Step 2: 【0936】 The terminal sends the collected voice data to the server. At this time, the voice data is converted into data packets and transferred to the server using a secure communication protocol. 【0937】 Step 3: 【0938】 The server converts the received audio data into text using Google Cloud's Speech-to-Text API. This conversion process utilizes natural language processing techniques. The input is audio data, and the output is text data in string format. 【0939】 Step 4: 【0940】 The server performs sentiment analysis on text data using AWS Comprehend or Microsoft Azure Text Analytics. The input for this stage is text data, and the output is numerical data representing specific emotional states. The analysis process classifies the data into emotional categories and displays their trends numerically. 【0941】 Step 5: 【0942】 Based on the analysis results, the server sets thresholds and determines the content of the feedback when a specific emotional state is identified. For example, if a high stress level is numerically indicated, suggestions for taking a break will be listed. 【0943】 Step 6: 【0944】 The server converts the determined feedback back into a data packet and sends it to the terminal. The data is then transferred according to the communication protocol. 【0945】 Step 7: 【0946】 The terminal receives feedback from the server and presents it to the user via a visual interface or audio. For example, it might display "We recommend you take a break" on the screen. This process converts the feedback data into an appropriate user interface format and presents it. 【0947】 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. 【0948】 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. 【0949】 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. 【0950】 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. 【0951】 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. 【0952】 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. 【0953】 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. 【0954】 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. 【0955】 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." 【0956】 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. 【0957】 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. 【0958】 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. 【0959】 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. 【0960】 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. 【0961】 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. 【0962】 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. 【0963】 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. 【0964】 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. 【0965】 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. 【0966】 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. 【0967】 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. 【0968】 The following is further disclosed regarding the embodiments described above. 【0969】 (Claim 1) 【0970】 Means for collecting past business data, seasonal data, and promotional information, 【0971】 A method for predicting the required number of employees based on the above data, 【0972】 A means of optimizing work schedules based on prediction results, 【0973】 A means of detecting abnormal attendance data, 【0974】 A means of detecting violations by comparing attendance information with the latest legal information, 【0975】 A means of predicting employee overwork and notifying managers, 【0976】 A system that includes means for receiving employee health status data and analyzing work patterns. 【0977】 (Claim 2) 【0978】 The system according to claim 1, characterized by having means for receiving inquiries and leave requests in real time, and reflecting the results received by the above means in the schedule. 【0979】 (Claim 3) 【0980】 The system according to claim 1, characterized in that it has means to support the optimization of personnel allocation and reduce costs for companies. 【0981】 "Example 1" 【0982】 (Claim 1) 【0983】 Means for collecting historical industry data, periodic data, and sales promotion information, 【0984】 An analytical device for predicting the required number of workers based on the above data, 【0985】 A calculator means for optimizing the work schedule based on prediction results, 【0986】 A monitoring device means for detecting abnormal working hours data, 【0987】 A calibration means that compares working hours information with the latest regulatory information and detects violations, 【0988】 A reporting device means for predicting excessive workloads among workers and notifying managers, 【0989】 An analytical device for receiving workers' health status data and analyzing work patterns, 【0990】 A communication device for providing information to a generative AI model and generating instructions based on that information, 【0991】 A system that includes this. 【0992】 (Claim 2) 【0993】 A reception device for receiving online inquiries and leave requests, according to claim 1. 【0994】 (Claim 3) 【0995】 A support device for optimizing labor allocation and pursuing cost reduction in business operations, as described in claim 1. 【0996】 "Application Example 1" 【0997】 (Claim 1) 【0998】 Means for collecting historical information data, time-related data, and promotional information, 【0999】 Based on the above data, a method for estimating the required number of employees, 【1000】 A means of optimizing the work schedule based on the prediction results, 【1001】 A means for detecting abnormal work information, 【1002】 A means of detecting violations by cross-referencing work information with the latest legal information, 【1003】 A means of predicting excessive workloads among workers and notifying managers, 【1004】 A means of receiving workers' health status data and analyzing their work patterns, 【1005】 A system that optimizes the workforce across cities and includes means to support personnel allocation across different industries. 【1006】 (Claim 2) 【1007】 The system according to claim 1, characterized by having means for receiving inquiries and leave requests in real time, and reflecting the results received by the above means in the plan. 【1008】 (Claim 3) 【1009】 The system according to claim 1, characterized in that it provides support for optimizing personnel allocation and has means to reduce costs for companies and regions. 【1010】 "Example 2 of combining an emotion engine" 【1011】 (Claim 1) 【1012】 A functional means for aggregating past work data, seasonal data, and sales information, 【1013】 A functional means for predicting the required number of workers based on the above data, 【1014】 A functional means for optimizing work plans based on prediction results, 【1015】 A functional means for identifying abnormal working hours data, 【1016】 A functional means for comparing working hours information with the latest legal information and detecting violations, 【1017】 A functional means for predicting excessive work hours by workers and notifying managers, 【1018】 A functional means for receiving information on the health status of workers and analyzing their work patterns, 【1019】 A functional means for analyzing voice and text to identify and quantify the emotional state of workers and reflect it in work plans, 【1020】 A system that provides sentiment analysis results as feedback to workers and adapts to work conditions in real time, 【1021】 A system that includes this. 【1022】 (Claim 2) 【1023】 The system according to claim 1, which has the function of receiving immediate inquiries and leave requests and reflecting them in the work plan. 【1024】 (Claim 3) 【1025】 The system according to claim 1, which provides support for optimizing personnel allocation and aims to reduce organizational costs. 【1026】 "Application example 2 when combining with an emotional engine" 【1027】 (Claim 1) 【1028】 A means of analyzing audio data and text to identify and quantify the user's emotional state, 【1029】 A means of suggesting breaks and adjusting work schedules based on quantified emotional data, 【1030】 A means of providing feedback according to the user's psychological state, 【1031】 Means for collecting past business data, seasonal data, and promotional information, 【1032】 A method for predicting the required number of employees based on the above data, 【1033】 A means of optimizing work schedules based on prediction results, 【1034】 A means of detecting abnormal attendance data, 【1035】 A means of detecting violations by comparing attendance information with the latest legal information, 【1036】 A means of predicting employee overwork and notifying managers, 【1037】 A system that includes means for receiving employee health status data and analyzing work patterns. 【1038】 (Claim 2) 【1039】 The system according to claim 1, characterized by having means for receiving inquiries and leave requests in real time, and reflecting the results received by the above means in the schedule. 【1040】 (Claim 3) 【1041】 The system according to claim 1, characterized in that it has means to support the optimization of personnel allocation and reduce costs for companies. [Explanation of Symbols] 【1042】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Means for collecting past business data, seasonal data, and promotional information, A method for predicting the required number of employees based on the above data, A means of optimizing work schedules based on prediction results, A means of detecting abnormal attendance data, A means of detecting violations by comparing attendance information with the latest legal information, A means of predicting employee overwork and notifying managers, A system that includes means for receiving employee health status data and analyzing work patterns. [Claim 2] The system according to claim 1, characterized by having means for receiving inquiries and leave requests in real time, and reflecting the results received by the above means in the schedule. [Claim 3] The system according to claim 1, characterized in that it has means to support the optimization of personnel allocation and reduce costs for companies.

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