System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting information on skills and career vision of employees; database means for storing and analyzing the information; generation and AI means for proposing optimum staffing based on the analyzed result; means for periodically performing condition check of employees and analyzing the result; and means for providing feedback based on the result of the condition check.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] As a company grows, it becomes difficult to frequently hold individual personnel interviews with all employees. As a result, it becomes difficult to assign the right people to the right positions, which can lead to a decline in employee motivation, a decline in organizational efficiency, and even a decline in employee satisfaction. Furthermore, even if stress checks are conducted regularly, the results are often not utilized appropriately, making it difficult to maintain optimal employee condition. The purpose of this invention is to address these issues, maximize the skills and aspirations of each employee, and strengthen the organization and improve employee satisfaction. [Means for solving the problem]

[0005] The present invention is a system that includes a means for collecting information on employee skills and career vision, a database means for storing and analyzing that information, and a generation AI means for proposing optimal personnel placement based on the analysis results. It also includes a means for periodically conducting employee condition checks and analyzing the results, and a means for providing feedback based on the condition check results. Furthermore, by providing a means for matching employee skill sets with desired conditions and selecting the most suitable candidates based on requests from departments, appropriate personnel placement is achieved. Additionally, by analyzing the overall condition of the organization using long-term data based on the results of the condition checks and proposing organizational improvement measures, the system aims to improve the health management of each employee and the operational efficiency of the entire organization. In this way, a system is provided that comprehensively supports the placement of the right person in the right position and employee health management.

[0006] An "employee" is an employee who belongs to a company and performs work based on the organization's instructions.

[0007] "Skills" are the specific techniques and abilities that employees possess to perform their jobs.

[0008] A "career vision" is an employee's goal regarding the job or role they would like to achieve in the future.

[0009] "Means of collecting information" refers to devices and methods for obtaining data on employees' skills and career vision.

[0010] "Database means" refers to systems and software for storing, managing, and analyzing collected information.

[0011] "Generative AI means" refers to devices or methods that use AI technology to analyze accumulated data and generate optimal personnel placement and feedback.

[0012] A "condition check" is a periodic survey conducted to assess an employee's current condition, such as their health and job satisfaction.

[0013] "Means for providing feedback" refers to devices or methods for providing advice or suggestions to employees based on the results of the condition check.

[0014] A "department request" is a request from a department that needs employees with specific skills and presents their requirements.

[0015] A "skill set" is a collective term for the multiple skills and abilities that an employee possesses.

[0016] "Candidate selection methods" refer to the devices and methods used to select the best candidates based on employee skills and desired conditions.

[0017] "Longitudinal data" refers to the accumulation of multiple condition check results and other relevant data collected over a period of time.

[0018] "Organizational improvement measures" refer to specific policies and plans to improve the efficiency of the entire organization and employee satisfaction. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a 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.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the 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.

[0033] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process 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.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] MODE FOR CARRYING OUT THE INVENTION

[0041] This invention is a system that efficiently matches the skills and aspirations of corporate employees and periodically checks their health status, thereby achieving optimal personnel placement and high employee satisfaction. This system is equipped with a means for collecting information on employees' skills and career visions and a database means for storing and analyzing that information. It also uses a generative AI means to propose optimal personnel placement, periodically checks employees' condition, and provides feedback.

[0042] Information collection method

[0043] The server schedules regular AI interviews for all employees. At the scheduled date and time, the terminal presents each employee with questions about their skills and career vision. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are presented. The user (employee) answers these questions. The terminal sends the collected answers to the server, which stores them in a database.

[0044] Data analysis and matching implementation

[0045] Once data on skills and career vision has been accumulated, the server passes it to the generation AI for analysis. For example, if a project requires an employee with specific skills, the department manager requests the necessary skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generation AI selects the most suitable candidate, and the server notifies the manager of the results.

[0046] Condition check implementation example

[0047] The server schedules condition checks for all employees at the beginning of each month. The device presents employees with questions about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are displayed. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answers to a generative AI for analysis and provides immediate feedback. For example, it provides advice such as "It seems you are feeling stressed, so consider taking more breaks." The server also analyzes long-term data to identify the health status and problems of the entire organization and propose improvement measures to managers.

[0048] Specific examples

[0049] For example, suppose Department A needs an employee with Java programming skills for a new project. The manager of Department A inputs this request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. The Generative AI confirms this and suggests an allocation. If Employee C reports a high stress level in a regular condition check, the Generative AI analyzes the cause and provides feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[0050] In this way, the system aims to optimize personnel allocation and health management within the company and improve employee satisfaction.

[0051] The processing flow will be explained below.

[0052] Specific steps of the program's processing

[0053] Processing of information collection

[0054] Step 1:

[0055] The server starts a task to schedule AI interviews for all employees at the end of the month.

[0056] Step 2:

[0057] At the scheduled time, the device will prompt each employee with questions about their skills and career aspirations, such as "What are your strengths?" and "What position do you want to be in the future?"

[0058] Step 3:

[0059] The user (employee) inputs answers to the questions presented. For example, they input answers such as "I'm good at Java programming" or "I want to be a project manager."

[0060] Step 4:

[0061] The terminal sends the collected answers to the server.

[0062] Step 5:

[0063] The server saves and accumulates the received response data in a database.

[0064] Data analysis and matching process

[0065] Step 1:

[0066] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[0067] Step 2:

[0068] The server searches the database to obtain information about employees with a particular skill set.

[0069] Step 3:

[0070] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[0071] Step 4:

[0072] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[0073] Step 5:

[0074] The server notifies the administrator of the results of the generative AI's proposal, for example, saying, "Employee A is the best match for the required skills."

[0075] Condition Checks and Feedback Handling

[0076] Step 1:

[0077] The server schedules condition checks for all employees at the beginning of each month.

[0078] Step 2:

[0079] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[0080] Step 3:

[0081] The user (employee) inputs an answer to the presented question, such as "I am satisfied" or "I feel stressed."

[0082] Step 4:

[0083] The terminal transmits the collected response data to the server.

[0084] Step 5:

[0085] The server passes the received response data to the generation AI for analysis.

[0086] Step 6:

[0087] The generative AI provides immediate feedback based on the analysis results, such as advice like "Take more breaks to reduce stress."

[0088] Step 7:

[0089] The server notifies the employee of the generated feedback.

[0090] Step 8:

[0091] The server analyzes long-term data to understand the overall health and trends of the organization. If necessary, it will suggest organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[0092] Example 1

[0093] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0094] Companies are required to efficiently manage employee skills and career prospects and achieve optimal personnel allocation. However, conventional systems require a cumbersome process for collecting and analyzing employee skills and career prospects, making it difficult to continuously monitor the health and satisfaction of individual employees. Responding quickly and accurately to requests from departments is also an issue. This can lead to a decline in employee satisfaction and productivity.

[0095] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0096] In this invention, the server includes a means for collecting information on employee skills and career prospects, a data storage means for accumulating and analyzing the information, a generation AI means for proposing optimal personnel allocation based on the analysis results, a means for periodically conducting employee health checks and analyzing the results, and a means for providing feedback based on the results of the health checks. This allows for efficient management of employee skills and career prospects, making it possible to maintain optimal personnel allocation and high employee satisfaction.

[0097] "Employee skills" refers to the specialized knowledge, abilities, and techniques that employees possess within a company to perform specific tasks.

[0098] "Career outlook" refers to the career goals and desired career path that an employee wants to achieve in the future.

[0099] "Information gathering means" refers to the methods and devices used to obtain data from employees regarding their skills and career prospects.

[0100] "Data storage means" refers to systems or devices that securely store collected information and data and make them accessible when needed.

[0101] "Generative AI means" refers to a system that uses artificial intelligence technology to analyze data and propose optimal personnel placement, etc.

[0102] "Means for conducting health checks" refers to methods and devices for periodically assessing employees' health status and collecting that data.

[0103] "Means for providing feedback" refers to systems or devices that provide advice or information to employees or managers based on the results of health checks.

[0104] "Means for presenting questions to employees" refers to a method or device for displaying questions about skills and career prospects to employees and having them answer the questions.

[0105] "Means for selecting the best candidates based on requests from business units" refers to a system or device that searches a database of employee skills and desired conditions and selects the best employee who matches the specified requirements.

[0106] "Staffing" refers to appropriately determining employees' work locations and responsibilities within a company.

[0107] This invention is a system for efficiently managing the skills and career prospects of employees within a company and achieving optimal personnel allocation. This system collects information on employees' skills and career prospects, stores it in a database, analyzes it, and proposes optimal personnel allocation based on the results. Furthermore, by conducting regular health checks on employees and providing feedback, employee satisfaction and productivity can be improved.

[0108] Hardware and software used

[0109] The system utilizes the following hardware and software:

[0110] Server: Data storage means, generation AI means, scheduling software (e.g., Chronos Scheduler), database management system (e.g., MySQL)

[0111] Terminal: Data input / display device (e.g., PC, tablet), communication protocol (e.g., HTTPS)

[0112] Generative AI models: used for data analysis and feedback generation (e.g., GPT-4)

[0113] Data collection

[0114] The server periodically schedules AI interviews for all employees. Scheduling software (e.g., Chronos Scheduler) is used here. At the set date and time, the terminal presents each employee with questions about their skills and career prospects. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are displayed. The user (employee) answers these questions, and the terminal sends the collected answers to the server. The server stores the answer data in a database.

[0115] Data analysis and optimal staffing proposals

[0116] The server passes the accumulated data to a generative AI model, where the generative AI model (e.g., GPT-4) performs the analysis. For example, if a project requires an employee with specific skills, the department manager requests the skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generative AI model selects the most suitable candidate, and the server notifies the manager of the results.

[0117] Health Check and Feedback

[0118] The server schedules health checks for all employees at the beginning of each month. The device displays questions to employees about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answer data to a generative AI model for analysis and provides immediate feedback. For example, advice such as "It seems you are feeling stressed, so consider taking more breaks" is displayed.

[0119] Specific examples

[0120] Specific examples of skill matching

[0121] If Department A needs an employee with Java programming skills for a new project, the manager of Department A inputs the request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. A generative AI model (e.g., GPT-4) confirms this and suggests placing Employee B.

[0122] Specific examples of health checks

[0123] If employee C reports high stress levels during a regular health check, the generative AI model will analyze the cause and provide feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[0124] Example prompts for generative AI models

[0125] Here are some example prompts to input to a generative AI model:

[0126] "Please select the best person for Project A based on employee skill data."

[0127] "Please analyze the cause of employee C's high stress level and propose solutions."

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The server periodically schedules AI interviews for all employees. The schedule is set using scheduling software (e.g., Chronos Scheduler). The input is the interview date and time data generated by the scheduling software, and the output is the confirmed schedule for each interview.

[0131] Step 2:

[0132] The terminal presents employees with questions about their skills and career prospects at a set date and time. The questions are displayed in a predefined format. The input is schedule data and a list of questions, and the output is the questions presented to the employee. Examples of questions include "What are your strengths?" and "What position do you hope to have in the future?"

[0133] Step 3:

[0134] The user (employee) answers the questions displayed on the terminal. The input is the answer data that the employee enters into the terminal, and the output is the completed answer data. Specifically, the employee enters the answer into the terminal using a keyboard or touch screen.

[0135] Step 4:

[0136] The terminal sends the collected response data to the server using a secure communication protocol (e.g., HTTPS). The input is the employee's response data, and the output is the data that has been sent.

[0137] Step 5:

[0138] The server stores the received response data in a database. A database management system (e.g., MySQL) is used to ensure the integrity of the stored data. The input is the response data, and the output is the data stored in the database.

[0139] Step 6:

[0140] The server passes the accumulated data to a generative AI model for analysis. The input is data extracted from the database, and the output is the results of analysis by the generative AI model. A generative AI model (e.g., GPT-4) is used to analyze the data and find trends and patterns.

[0141] Step 7:

[0142] Department managers input the skill requirements for a specific project into the server. The input is the skill requirement data entered by the manager, and the output is the requirement data stored on the server. A web interface (e.g., Admin Dashboard) is used for administrators.

[0143] Step 8:

[0144] The server searches the database based on the skill requirements received from the administrator and selects the most suitable employees. The input is the skill requirements data and employee data in the database, and the output is a list of the most suitable employees. This search and selection is performed using a generative AI model.

[0145] Step 9:

[0146] The server notifies the administrator of the results of the selection. Notifications can be sent via email or a web interface. The input is the list of best-fit employees, and the output is the notification data sent to the administrator.

[0147] Step 10:

[0148] The server schedules health checks for all employees at the beginning of each month. Again, it uses scheduling software (e.g., Chronos Scheduler). The input is the schedule data, and the output is the information about the scheduled health checks.

[0149] Step 11:

[0150] At the scheduled date and time, the terminal displays questions to employees about their health and job satisfaction. The input is the schedule data and a list of questions, and the output is the questions presented to the employee. Example questions include, "How satisfied are you with your current job?" and "Have you been feeling stressed recently?"

[0151] Step 12:

[0152] The user (employee) answers questions about their health condition displayed on the terminal. The input is the question data about their health condition, and the output is the completed answer data. Specifically, the employee answers the questions on the terminal using the keyboard or touch screen.

[0153] Step 13:

[0154] The device sends the collected answers to the server, again using a secure communication protocol. The input is the health status response data, and the output is the completed data.

[0155] Step 14:

[0156] The server passes the received health status response data to the generative AI model for analysis. The input is the health status response data, and the output is the analysis result by the generative AI model. This allows for immediate feedback.

[0157] Step 15:

[0158] The server provides feedback to employees based on the results of analysis by the generative AI model. For example, it displays advice such as, "You seem to be feeling stressed, so consider taking more breaks." The input is the analysis result data, and the output is the provided feedback.

[0159] Step 16:

[0160] The server analyzes long-term data and identifies the overall health status and problems of the organization. The input is past health status data, and the output is the long-term analysis results. Based on these results, it proposes improvement measures to managers. For example, it may suggest, "Overall stress levels remain high, so we recommend stress management training for all employees."

[0161] (Application example 1)

[0162] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0163] Conventional in-house personnel placement and health management systems do not adequately grasp employees' skills and career vision or manage their health status, making it difficult to achieve optimal personnel placement and improve employee satisfaction. Furthermore, in factories, it is necessary to select and place optimal robots according to complex manufacturing processes, and also to properly manage their operating status and maintenance status, but current systems make it difficult to do this efficiently.

[0164] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0165] In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel placement based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; means for collecting information on the skills and maintenance status of factory workers and proposing optimal placement; means for periodically collecting and analyzing robot operation status and maintenance data; and means for providing maintenance proposals based on the operation status and maintenance data. This enables optimal personnel placement and employee health management within a company, as well as efficient operation and maintenance management of factory robots.

[0166] "Employee skills" refers to the knowledge and abilities of employees of a company regarding specific tasks or technologies.

[0167] "Career vision" refers to an employee's hopes and goals regarding the occupation or role they desire in the future.

[0168] "Means for collecting information" refers to the methods and devices used to obtain the necessary data from employees.

[0169] "Database means for storage and analysis" refers to a system or method for storing collected information and analyzing it as needed.

[0170] "Generative AI methods" refer to technologies and programs that use artificial intelligence to automatically generate optimal solutions based on collected data.

[0171] A "condition check" is an evaluation or examination to check an employee's health status and job satisfaction.

[0172] A "means for providing feedback" is a method or device for providing advice or instructions to an employee based on the results of the check.

[0173] "Worker skills" refers to the skills and abilities possessed by the workers who operate the machines and equipment within the factory.

[0174] "Maintenance status" refers to the state of machinery and equipment, indicating whether it is properly maintained and managed.

[0175] "Operation status" refers to data that indicates how long a machine or device is operating or how it is being used.

[0176] "Proposal methods" refer to methods and techniques for presenting optimal options and solutions based on the results of the analysis.

[0177] "Maintenance data" refers to the past maintenance history and repair records of machines and equipment.

[0178] This invention is a system that collects and analyzes information about company employees and factory workers (robots) to optimally allocate them and manage their health.

[0179] Feature Overview

[0180] The system has the following main functions:

[0181] 1. Information collection function:

[0182] The server periodically collects information about the skills and career vision of employees or workers, and the terminal presents these questions to employees at set dates and times and collects their answers.

[0183] Furthermore, operational status and maintenance data of the robots in the factory is collected via sensors, and the data is stored in a cloud-connected database (e.g., AWS RDS) via IoT devices.

[0184] 2. Data analysis and matching function:

[0185] The server passes the collected data to a generative AI model (e.g., GPT-4) for analysis, which then recommends the optimal placement of employees or workers (robots) based on the required skill sets and health status.

[0186] When a department requests employees with specific skills or workers best suited to a project, the server searches the database and selects and notifies the best candidates based on analysis by generative AI.

[0187] 3. Condition check function:

[0188] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminals ask employees questions about their health and job satisfaction and collect their responses.

[0189] The server passes the answers to a generative AI model, which provides immediate feedback based on the analysis results. Long-term data analysis identifies the overall health and problems of the organization and provides improvement suggestions to managers.

[0190] Specific examples of program processing

[0191] Hardware and Software

[0192] Server: High-performance cloud server (e.g. AWS EC2)

[0193] Database: Cloud-based database (e.g. AWS RDS)

[0194] Generative AI models: Generative AI models such as GPT-4

[0195] Sensors and IoT devices: Sensors for obtaining information on the robot's operating status and maintenance status

[0196] Information collection and data processing

[0197] The server stores skill information, career vision, and operational status data sent from sensors and devices in a cloud database.

[0198] This data is analyzed at regular intervals to quantify and classify specific skills and conditions.

[0199] Data analysis and feedback

[0200] When a department manager requests a specific skill set, the server passes the relevant personnel or worker information from the database to the generative AI model.

[0201] The generative AI model analyzes skills and health data to select the best candidates.

[0202] The results are notified to the administrator and user via the server.

[0203] Prompt Sentence Examples

[0204] For skill requests by admins:

[0205] "Assign robots with high welding skills for three-hour shifts."

[0206] For employee career vision surveys:

[0207] "What are your best skills? What position would you like to have in the future?"

[0208] As described above, the system enables efficient allocation and condition management of personnel and factory robots within a company, thereby improving overall business efficiency and the health of employees and workers.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1: Gather information

[0211] The terminal presents employees and workers (robots) with questions about their skills and career vision at a pre-set date and time. At this time, the terminal displays questions to the employee such as "What skills are you good at?" and "What position do you want in the future?" The user (employee) answers these questions, and the terminal sends the answers to the server. At the same time, sensors on the robots in the factory collect operating status and maintenance data, and this data is also sent to the server. Input data: Employee answers and robot sensor data. Output data: Answers and sensor data sent to the server.

[0212] Step 2: Database accumulation and processing

[0213] The server stores the employee responses and robot sensor data sent from the device in a cloud-connected database (e.g., AWS RDS). At this time, the data is classified into appropriate categories and the skill information is quantified. After data accumulation, the server prepares the data for analysis by the generative AI model. Input data: employee responses and robot sensor data. Output data: categorized and quantified database entries.

[0214] Step 3: Data analysis

[0215] The server passes skill information, career vision, and operating status data to a generative AI model (e.g., GPT-4) for analysis. This model calculates the optimal allocation of employees and robots based on specific skill sets and maintenance status, and selects recommended personnel and workers. Input data: Classified and quantified data. Output data: Optimal allocation and a list of recommended personnel.

[0216] Step 4: Requests and Recommendations

[0217] When a department manager requests personnel with specific skills or for a project, the server searches the database and performs analysis using a generative AI model. The manager is provided with a list of recommended employees or workers (robots). For example, if Java programming skills are required, employees with those skills will be selected. Input data: Request from the manager. Output data: List of recommended employees or workers.

[0218] Step 5: Condition Check

[0219] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminal displays questions about their health and job satisfaction. The user answers these questions, and the terminal sends the answers to the server. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. Input data: Employee answers. Output data: Condition data sent to the server.

[0220] Step 6: Analyze data and provide feedback

[0221] The server passes the collected condition data to the generative AI model for analysis. Based on the analysis results, it provides immediate feedback, such as "You seem to be feeling stressed, so consider increasing your break time." Long-term data analysis identifies the health status and problems of the entire organization and makes improvement suggestions to managers. Input data: condition data. Output data: individual feedback and improvement suggestions.

[0222] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0223] MODE FOR CARRYING OUT THE INVENTION

[0224] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[0225] Information collection method

[0226] The server starts by setting up regular AI interviews for all employees. The device presents each employee with questions at the scheduled date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state.

[0227] The device sends the collected responses and emotion data to a server, which stores them in a database.

[0228] Data analysis and matching implementation

[0229] The server passes skills, career vision, and emotional data to the generative AI for analysis. For example, if a department manager is looking for personnel for a specific project, they input the request into the server. The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotional engine to evaluate whether the employee's current mental state is suitable for the position.

[0230] For example, if Employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account and select the best candidate based not only on their skills, but also on their current emotional state.

[0231] Condition check and feedback implementation example

[0232] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction, including an emotion engine that assesses their actual emotional state based on facial expressions and tone of voice.

[0233] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[0234] The server performs the analysis using generative AI. For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[0235] Specific examples

[0236] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that Employee C is a little tired. As a result, the generative AI determines that another Employee D is slightly less skilled but is very mentally stable, so it suggests Employee D as the best candidate.

[0237] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[0238] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[0239] The processing flow will be explained below.

[0240] Specific steps of the program's processing

[0241] Processing of information collection

[0242] Step 1:

[0243] The server starts a task to schedule AI interviews for all employees at the end of the month.

[0244] Step 2:

[0245] At scheduled times, the devices prompt each employee with questions about their skills, career vision, and emotional state, such as "What are your best skills?", "What role do you want to play in the future?", and "How do you feel about your current job?"

[0246] Step 3:

[0247] The user (employee) inputs answers to the questions presented and displays facial expressions and tone of voice to indicate their emotional state.

[0248] Step 4:

[0249] The terminal transmits the collected answers and emotion data generated by the emotion engine to the server.

[0250] Step 5:

[0251] The server stores and accumulates the received response data and emotion data in a database.

[0252] Data analysis and matching process

[0253] Step 1:

[0254] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[0255] Step 2:

[0256] The server searches the database to obtain information about employees with a particular skill set.

[0257] Step 3:

[0258] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[0259] Step 4:

[0260] The generative AI also takes data from the emotion engine into account to make appropriate assessments, such as comparing an employee with high skills but high emotional stress levels with an employee with slightly lower skills but low emotional stress levels.

[0261] Step 5:

[0262] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[0263] Step 6:

[0264] The server then notifies the department manager of the results of the generative AI's proposal, for example, saying, "Employee A best matches the required skills and has the appropriate emotional state."

[0265] Condition Checks and Feedback Handling

[0266] Step 1:

[0267] The server schedules condition checks for all employees at the beginning of each month.

[0268] Step 2:

[0269] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[0270] Step 3:

[0271] The user (employee) inputs answers to the questions presented and also uses the emotion engine to register facial expressions and tone of voice that indicate their emotional state. For example, the user might answer "I'm satisfied" and display a relaxed facial expression and tone of voice.

[0272] Step 4:

[0273] The terminal transmits the collected response data and emotion data to the server.

[0274] Step 5:

[0275] The server passes the received response data and emotion data to the generation AI for analysis.

[0276] Step 6:

[0277] The generative AI provides immediate feedback based on the analysis results, generating specific advice such as, "It appears your stress level is high, so please consider taking regular breaks or consulting a doctor."

[0278] Step 7:

[0279] The server notifies the employee of the generated feedback.

[0280] Step 8:

[0281] The server analyzes long-term data to understand the overall health and trends of the organization, and if necessary, suggests organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[0282] In this way, the system manages employees' skills, career vision, and emotional state in an integrated manner, enabling appropriate personnel allocation and health management.

[0283] Example 2

[0284] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0285] Conventional personnel placement systems only consider employees' skills and career vision when deciding on assignments, often ignoring their emotions and physical condition. This results in increased employee stress, leading to lower work efficiency and higher employee turnover. Furthermore, because conventional systems do not adequately check employees' physical condition on a regular basis, it is difficult to prevent a decline in employee health and job satisfaction.

[0286] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0287] In this invention, the server includes means for collecting information on employee skills and career vision, database means for accumulating and analyzing data on the information and emotional state, generation AI means for proposing optimal personnel allocation based on the analysis results, means for periodically conducting employee condition checks and analyzing the results, and means for providing feedback based on the results of the condition checks. This enables optimal personnel allocation and management that takes into consideration not only employee skills and career vision, but also their emotional state and health state in an integrated manner.

[0288] An "employee" is a worker who belongs to a company or organization and is employed to perform a specific job.

[0289] "Skills" refers to the knowledge, techniques, and abilities required to perform a specific job or task.

[0290] "Career vision" refers to an employee's career goals, hopes, and path forward for the future.

[0291] "Means of collecting information" refers to methods and devices for obtaining data from employees regarding their skills and career vision.

[0292] A "database" is a system for storing and managing collected information and for searching and analyzing it as needed.

[0293] "Database means" refers to the mechanisms and methods for storing, managing, searching, and analyzing information using a database.

[0294] "Generative AI" refers to systems that use artificial intelligence techniques to analyze data and generate results or recommendations tailored to a specific purpose.

[0295] "Generative AI means" refers to a system or method that uses generative AI to analyze data and output results or suggestions.

[0296] A "condition check" is a periodic survey or evaluation to assess an employee's health, job satisfaction, stress level, etc.

[0297] "Feedback" refers to advice and suggestions provided to employees based on the results of condition checks and data analysis.

[0298] "Emotional state" refers to the emotions and psychological state an employee is feeling at a given time.

[0299] An "emotion engine" is a system or software that analyzes data such as facial expressions and tone of voice to assess emotional states.

[0300] MODE FOR CARRYING OUT THE INVENTION

[0301] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[0302] Information collection method

[0303] The server will schedule regular AI interviews with all employees, which will allow the company to understand each employee's skills, career vision, and emotional state.

[0304] The device presents each employee with questions at a set date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions.

[0305] Additionally, the device uses an Emotion AI engine to understand the user's emotional state by analyzing their facial expressions and tone of voice, and the analysis is based on data collected using a webcam and microphone.

[0306] The device sends the collected responses and emotion data to a server, typically in JSON format via the HTTP protocol, and the server stores the received data in a database (e.g., MySQL or PostgreSQL).

[0307] Data analysis and matching implementation

[0308] The server passes the skills, career vision, and emotion data to a generative AI (e.g., OpenAI's GPT-4) for analysis. For example, if a department manager is looking for talent for a specific project, they input the request into the server and give instructions to the generative AI using prompts like the following:

[0309] "Based on Employee A's recent sentiment data, please rate whether he is suitable to be the Java expert for Project B."

[0310] "If Employee E is experiencing high stress, what feedback should I provide?"

[0311] The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotion engine to evaluate whether the employee's current mental state is suitable for the position. For example, if employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account. The best candidate is selected not only based on their skills, but also on their current emotional state.

[0312] Condition check and feedback implementation example

[0313] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction. This includes an emotion engine (Emotion AI) that evaluates their actual emotional state based on facial expressions and tone of voice.

[0314] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[0315] The server analyzes this data using a generative AI (e.g., GPT-4). For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[0316] Specific examples

[0317] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that he is a little tired. As a result, the generative AI suggests Employee D as the best candidate, since he is slightly less skilled but mentally stable.

[0318] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[0319] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[0320] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0321] Step 1: Configure information collection

[0322] The server schedules regular AI interviews for all employees. This includes the ability to send notifications to all employees at a specific date and time, for example, every month. The input is a list of all employees and the interview schedule, and the output is an interview notification sent to each employee. In this step, the interview date and time and question list are set within the system.

[0323] Step 2: Question posing

[0324] The terminal presents questions to each employee at the set date and time. The input is the schedule and question list sent from the server, and the output is the employee's answers. Specifically, questions such as "What are your strengths?" and "What is your vision for your future career?" are displayed on the terminal's interface.

[0325] Step 3: Collect response and sentiment data

[0326] The user (employee) answers questions presented on the terminal. The terminal then analyzes the user's facial expressions and tone of voice and uses an emotion engine (e.g., Emotion AI) to understand the user's emotional state. The input is the user's text response and non-verbal data (facial expressions, tone of voice), and the output is analyzed emotional data. Specifically, the terminal collects data using a webcam and microphone.

[0327] Step 4: Data transmission and storage

[0328] The device sends the collected responses and emotion data to the server. The input is data from the device (JSON format), and the output is data storage on the server side. Data is sent using the HTTP protocol, and the server stores the received data in a database (e.g., MySQL, PostgreSQL).

[0329] Step 5: Acquire and analyze data

[0330] The server passes the required skill and emotion data to the generation AI (e.g., GPT-4) for analysis. The input is employee data in the database and a request from the department manager, and the output is the analysis result. Specifically, the server uses an SQL query to retrieve the required information from the database and passes it to the generation AI as a prompt. Example prompt: "Please evaluate employees who are suitable as Java experts for Project B based on their skill and emotion data."

[0331] Step 6: Select the best candidate

[0332] Generative AI selects the best candidates based on input data. The inputs are skills, career vision, emotional data, and requests, and the output is a list of the best candidates. For example, Generative AI evaluates employees' Java skills and emotional state to create a list of the best Java experts.

[0333] Step 7: Schedule a Condition Check

[0334] The server schedules regular condition checks for all employees. The input is a list of all employees and the scheduled time, and the output is a check notification sent to each employee, which includes questions about their weekly health status and job satisfaction.

[0335] Step 8: Present health status and satisfaction questions

[0336] The terminal presents employees with questions about their health and job satisfaction. The input is a list of questions from the server, and the output is the employee's answers. Examples of questions displayed include "How satisfied are you with your current job?" and "What is your stress level these days?"

[0337] Step 9: Analyze health and emotion data

[0338] The terminal analyzes the employee's responses as well as their facial expressions and voice to assess their emotional state. The input is the user's response (text) and non-verbal data, and the output is the analyzed emotional data.

[0339] Step 10: Generate feedback

[0340] The server analyzes the collected data using generative AI and generates appropriate feedback. The input is emotional data and health status data, and the output is written feedback. For example, the generated advice might be, "Try distributing your daily tasks to reduce stress."

[0341] Step 11: Provide feedback

[0342] The terminal presents the generated feedback to the user. The input is the feedback content from the server, and the output is the content presented to the user. Specifically, the feedback is displayed on the terminal interface. Based on this feedback, the user can understand specific countermeasures and improvement measures.

[0343] (Application example 2)

[0344] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0345] The present invention relates to a system for optimal personnel allocation based on the skills and career vision information of company employees, as well as their emotional state. Conventional systems do not adequately check employee condition or manage their emotional state, which can lead to the accumulation of stress and dissatisfaction among employees. Furthermore, because skill matching relies on static data, dynamic job allocation is difficult. The present invention aims to solve these problems and improve corporate efficiency while maximizing employee capabilities.

[0346] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel allocation based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; sensor means for measuring the emotional state of workers; and means for dynamically optimizing work allocation based on the emotional state of those being measured. This enables dynamic work allocation that takes into account both employee skills and emotional state, thereby improving employee satisfaction and organizational efficiency.

[0347] An "employee" is a person who belongs to a company or organization and performs work.

[0348] "Skills" are the knowledge and techniques needed to effectively perform a particular job or task.

[0349] "Career vision" refers to the professional goals and desired career path that an employee wants to achieve in the future.

[0350] "Information collection means" is a general term for methods and functions for collecting information from employees regarding their skills and career visions.

[0351] "Database means" refers to a system or method for storing collected information and conducting analysis based on it.

[0352] "Generative AI means" refers to artificial intelligence that generates optimal personnel placement and feedback based on collected and accumulated data.

[0353] "Condition checks" refer to regular assessments of employees' health and job satisfaction, and take measures if necessary.

[0354] "Means of providing feedback" refers to the methods and functions for sending improvement suggestions and encouraging messages to employees based on the results of the condition check.

[0355] "Sensor means" refers to devices or methods for measuring employees' facial expressions, tone of voice, etc. to assess their emotional state.

[0356] "Means for dynamically optimizing work allocation" refers to methods or systems for adjusting work allocation in real time based on employees' emotional states to achieve optimal allocation.

[0357] The present invention provides a system for optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company, and for periodically checking their condition and dynamically optimizing their work assignments. An embodiment of this system will be described below.

[0358] Information collection method

[0359] The server schedules regular AI interviews with employees. The device presents questions to each employee at the scheduled date and time to understand their skills, career vision, and emotional state. Employees respond to questions such as, "What are your strengths?" and "What is your future career vision?" The device also uses an emotion engine to analyze the employee's facial expressions and tone of voice to understand their emotional state.

[0360] Data accumulation and analysis

[0361] The device sends the collected responses and emotional data to a server, which stores this data in a database and uses it for analysis by the generative AI. For example, the server can learn each employee's habits and performance patterns and store the analysis results.

[0362] Condition check and feedback

[0363] The server schedules regular condition checks and asks employees questions about their health and job satisfaction via their devices. Based on these answers and emotional data, the server uses generative AI to analyze and provide feedback appropriate to the employee's condition. For example, for an employee experiencing high stress, the server might suggest, "Consider ways to distribute your work to reduce stress."

[0364] Dynamic work allocation optimization

[0365] The server uses sensors to measure the emotional state of workers in real time and dynamically optimizes work allocation based on that data. For example, if a specific project requires highly skilled employees, the server will search the database to select the most suitable candidates, taking into account data from the emotion engine and prioritizing employees with stable current emotional states.

[0366] Hardware and software used

[0367] The server is a high-performance computer system equipped with a database management system (DBMS) and software implementing AI algorithms. The terminals are smartphones or smart glasses equipped with various data collection functions and sensors. The emotion engine includes facial expression recognition software and voice analysis software.

[0368] Examples and prompts

[0369] For example, if a factory suddenly needs an engineer with Python skills, the department manager inputs the requirements into the server. The generative AI searches the database and selects the most suitable candidate. Based on emotional data, employees with low stress levels are prioritized for placement.

[0370] Example prompt sentence:

[0371] Required skills: Python

[0372] Employee emotional state: Using measurement data

[0373] Recommend the best candidates.

[0374] In this way, the system of the present invention manages employee skills, career vision, and emotional data in an integrated manner, realizing optimal personnel allocation and health management.

[0375] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0376] Step 1:

[0377] The server schedules regular AI interviews for employees.

[0378] Input: Employee list, interview schedule

[0379] Action: Set up a regular meeting at a specific date and time

[0380] Output: Interview notice

[0381] Step 2:

[0382] The device asks employees questions at set times and dates, collecting information about their skills, career vision, and emotional state.

[0383] Input: Interview schedule, question list

[0384] Action: Present a question and collect employee responses

[0385] Output: Skill information, career vision information, emotional data

[0386] Step 3:

[0387] The device analyzes employees' facial expressions and tone of voice and uses an emotion engine to understand their emotional state.

[0388] Input: Employee voice and video data

[0389] Actions: Voice analysis, facial expression recognition

[0390] Output: Emotional state data

[0391] Step 4:

[0392] The device sends the collected responses and emotion data to the server.

[0393] Input: Skill information, career vision information, emotional data

[0394] Action: Data transfer

[0395] Output: Store in database

[0396] Step 5:

[0397] The server passes the data stored in the database to a generative AI model that analyzes employees' skills, career vision, and emotional state.

[0398] Input: Employee information and emotion data in the database

[0399] Action: Data Analysis

[0400] Output: Analysis results

[0401] Step 6:

[0402] The server uses generative AI to suggest optimal staffing based on skills and career vision.

[0403] Input: Analysis results, request

[0404] Action: Matching process

[0405] Output: Optimal staffing proposal

[0406] Step 7:

[0407] The server schedules regular condition checks and presents employees with questions about their health and job satisfaction via their terminal.

[0408] Input: Condition Check Schedule

[0409] Action: Present a question and collect employee responses

[0410] Output: Condition data

[0411] Step 8:

[0412] The terminal transmits the collected condition data and emotion data to the server.

[0413] Input: condition data, emotion data

[0414] Action: Data transfer

[0415] Output: Store in database

[0416] Step 9:

[0417] The server periodically passes collected condition data to the generative AI model for analysis and provides feedback.

[0418] Input: Condition data in the database

[0419] Actions: Data analysis, feedback generation

[0420] Output: Feedback message

[0421] Step 10:

[0422] The server monitors the real-time emotional state of the workers using a sensor means and dynamically passes the data to the work allocation optimization system.

[0423] Input: Real-time emotional data

[0424] Action: Emotional data collection and analysis

[0425] Output: Work placement instructions

[0426] In this way, the system comprehensively manages employees' skills, career vision, and emotional state, enabling optimal personnel placement and feedback.

[0427] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0428] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0429] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0430] [Second embodiment]

[0431] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0432] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0433] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0434] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0435] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0436] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0437] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0438] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0439] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0440] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0441] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0442] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0443] MODE FOR CARRYING OUT THE INVENTION

[0444] This invention is a system that efficiently matches the skills and aspirations of corporate employees and periodically checks their health status, thereby achieving optimal personnel placement and high employee satisfaction. This system is equipped with a means for collecting information on employees' skills and career visions and a database means for storing and analyzing that information. It also uses a generative AI means to propose optimal personnel placement, periodically checks employees' condition, and provides feedback.

[0445] Information collection method

[0446] The server schedules regular AI interviews for all employees. At the scheduled date and time, the terminal presents each employee with questions about their skills and career vision. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are presented. The user (employee) answers these questions. The terminal sends the collected answers to the server, which stores them in a database.

[0447] Data analysis and matching implementation

[0448] Once data on skills and career vision has been accumulated, the server passes it to the generation AI for analysis. For example, if a project requires an employee with specific skills, the department manager requests the necessary skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generation AI selects the most suitable candidate, and the server notifies the manager of the results.

[0449] Condition check implementation example

[0450] The server schedules condition checks for all employees at the beginning of each month. The device presents employees with questions about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are displayed. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answers to a generative AI for analysis and provides immediate feedback. For example, it provides advice such as "It seems you are feeling stressed, so consider taking more breaks." The server also analyzes long-term data to identify the health status and problems of the entire organization and propose improvement measures to managers.

[0451] Specific examples

[0452] For example, suppose Department A needs an employee with Java programming skills for a new project. The manager of Department A inputs this request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. The Generative AI confirms this and suggests an allocation. If Employee C reports a high stress level in a regular condition check, the Generative AI analyzes the cause and provides feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[0453] In this way, the system aims to optimize personnel allocation and health management within the company and improve employee satisfaction.

[0454] The processing flow will be explained below.

[0455] Specific steps of the program's processing

[0456] Processing of information collection

[0457] Step 1:

[0458] The server starts a task to schedule AI interviews for all employees at the end of the month.

[0459] Step 2:

[0460] At the scheduled time, the device will prompt each employee with questions about their skills and career aspirations, such as "What are your strengths?" and "What position do you want to be in the future?"

[0461] Step 3:

[0462] The user (employee) inputs answers to the questions presented. For example, they input answers such as "I'm good at Java programming" or "I want to be a project manager."

[0463] Step 4:

[0464] The terminal sends the collected answers to the server.

[0465] Step 5:

[0466] The server saves and accumulates the received response data in a database.

[0467] Data analysis and matching process

[0468] Step 1:

[0469] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[0470] Step 2:

[0471] The server searches the database to obtain information about employees with a particular skill set.

[0472] Step 3:

[0473] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[0474] Step 4:

[0475] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[0476] Step 5:

[0477] The server notifies the administrator of the results of the generative AI's proposal, for example, saying, "Employee A is the best match for the required skills."

[0478] Condition Checks and Feedback Handling

[0479] Step 1:

[0480] The server schedules condition checks for all employees at the beginning of each month.

[0481] Step 2:

[0482] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[0483] Step 3:

[0484] The user (employee) inputs an answer to the presented question, such as "I am satisfied" or "I feel stressed."

[0485] Step 4:

[0486] The terminal transmits the collected response data to the server.

[0487] Step 5:

[0488] The server passes the received response data to the generation AI for analysis.

[0489] Step 6:

[0490] The generative AI provides immediate feedback based on the analysis results, such as advice like "Take more breaks to reduce stress."

[0491] Step 7:

[0492] The server notifies the employee of the generated feedback.

[0493] Step 8:

[0494] The server analyzes long-term data to understand the overall health and trends of the organization. If necessary, it will suggest organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[0495] Example 1

[0496] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0497] Companies are required to efficiently manage employee skills and career prospects and achieve optimal personnel allocation. However, conventional systems require a cumbersome process for collecting and analyzing employee skills and career prospects, making it difficult to continuously monitor the health and satisfaction of individual employees. Responding quickly and accurately to requests from departments is also an issue. This can lead to a decline in employee satisfaction and productivity.

[0498] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0499] In this invention, the server includes a means for collecting information on employee skills and career prospects, a data storage means for accumulating and analyzing the information, a generation AI means for proposing optimal personnel allocation based on the analysis results, a means for periodically conducting employee health checks and analyzing the results, and a means for providing feedback based on the results of the health checks. This allows for efficient management of employee skills and career prospects, making it possible to maintain optimal personnel allocation and high employee satisfaction.

[0500] "Employee skills" refers to the specialized knowledge, abilities, and techniques that employees possess within a company to perform specific tasks.

[0501] "Career outlook" refers to the career goals and desired career path that an employee wants to achieve in the future.

[0502] "Information gathering means" refers to the methods and devices used to obtain data from employees regarding their skills and career prospects.

[0503] "Data storage means" refers to systems or devices that securely store collected information and data and make them accessible when needed.

[0504] "Generative AI means" refers to a system that uses artificial intelligence technology to analyze data and propose optimal personnel placement, etc.

[0505] "Means for conducting health checks" refers to methods and devices for periodically assessing employees' health status and collecting that data.

[0506] "Means for providing feedback" refers to systems or devices that provide advice or information to employees or managers based on the results of health checks.

[0507] "Means for presenting questions to employees" refers to a method or device for displaying questions about skills and career prospects to employees and having them answer the questions.

[0508] "Means for selecting the best candidates based on requests from business units" refers to a system or device that searches a database of employee skills and desired conditions and selects the best employee who matches the specified requirements.

[0509] "Staffing" refers to appropriately determining employees' work locations and responsibilities within a company.

[0510] This invention is a system for efficiently managing the skills and career prospects of employees within a company and achieving optimal personnel allocation. This system collects information on employees' skills and career prospects, stores it in a database, analyzes it, and proposes optimal personnel allocation based on the results. Furthermore, by conducting regular health checks on employees and providing feedback, employee satisfaction and productivity can be improved.

[0511] Hardware and software used

[0512] The system utilizes the following hardware and software:

[0513] Server: Data storage means, generation AI means, scheduling software (e.g., Chronos Scheduler), database management system (e.g., MySQL)

[0514] Terminal: Data input / display device (e.g., PC, tablet), communication protocol (e.g., HTTPS)

[0515] Generative AI models: used for data analysis and feedback generation (e.g., GPT-4)

[0516] Data collection

[0517] The server periodically schedules AI interviews for all employees. Scheduling software (e.g., Chronos Scheduler) is used here. At the set date and time, the terminal presents each employee with questions about their skills and career prospects. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are displayed. The user (employee) answers these questions, and the terminal sends the collected answers to the server. The server stores the answer data in a database.

[0518] Data analysis and optimal staffing proposals

[0519] The server passes the accumulated data to a generative AI model, where the generative AI model (e.g., GPT-4) performs the analysis. For example, if a project requires an employee with specific skills, the department manager requests the skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generative AI model selects the most suitable candidate, and the server notifies the manager of the results.

[0520] Health Check and Feedback

[0521] The server schedules health checks for all employees at the beginning of each month. The device displays questions to employees about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answer data to a generative AI model for analysis and provides immediate feedback. For example, advice such as "It seems you are feeling stressed, so consider taking more breaks" is displayed.

[0522] Specific examples

[0523] Specific examples of skill matching

[0524] If Department A needs an employee with Java programming skills for a new project, the manager of Department A inputs the request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. A generative AI model (e.g., GPT-4) confirms this and suggests placing Employee B.

[0525] Specific examples of health checks

[0526] If employee C reports high stress levels during a regular health check, the generative AI model will analyze the cause and provide feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[0527] Example prompts for generative AI models

[0528] Here are some example prompts to input to a generative AI model:

[0529] "Please select the best person for Project A based on employee skill data."

[0530] "Please analyze the cause of employee C's high stress level and propose solutions."

[0531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] The server periodically schedules AI interviews for all employees. The schedule is set using scheduling software (e.g., Chronos Scheduler). The input is the interview date and time data generated by the scheduling software, and the output is the confirmed schedule for each interview.

[0534] Step 2:

[0535] The terminal presents employees with questions about their skills and career prospects at a set date and time. The questions are displayed in a predefined format. The input is schedule data and a list of questions, and the output is the questions presented to the employee. Examples of questions include "What are your strengths?" and "What position do you hope to have in the future?"

[0536] Step 3:

[0537] The user (employee) answers the questions displayed on the terminal. The input is the answer data that the employee enters into the terminal, and the output is the completed answer data. Specifically, the employee enters the answer into the terminal using a keyboard or touch screen.

[0538] Step 4:

[0539] The terminal sends the collected response data to the server using a secure communication protocol (e.g., HTTPS). The input is the employee's response data, and the output is the data that has been sent.

[0540] Step 5:

[0541] The server stores the received response data in a database. A database management system (e.g., MySQL) is used to ensure the integrity of the stored data. The input is the response data, and the output is the data stored in the database.

[0542] Step 6:

[0543] The server passes the accumulated data to a generative AI model for analysis. The input is data extracted from the database, and the output is the results of analysis by the generative AI model. A generative AI model (e.g., GPT-4) is used to analyze the data and find trends and patterns.

[0544] Step 7:

[0545] Department managers input the skill requirements for a specific project into the server. The input is the skill requirement data entered by the manager, and the output is the requirement data stored on the server. A web interface (e.g., Admin Dashboard) is used for administrators.

[0546] Step 8:

[0547] The server searches the database based on the skill requirements received from the administrator and selects the most suitable employees. The input is the skill requirements data and employee data in the database, and the output is a list of the most suitable employees. This search and selection is performed using a generative AI model.

[0548] Step 9:

[0549] The server notifies the administrator of the results of the selection. Notifications can be sent via email or a web interface. The input is the list of best-fit employees, and the output is the notification data sent to the administrator.

[0550] Step 10:

[0551] The server schedules health checks for all employees at the beginning of each month. Again, it uses scheduling software (e.g., Chronos Scheduler). The input is the schedule data, and the output is the information about the scheduled health checks.

[0552] Step 11:

[0553] At the scheduled date and time, the terminal displays questions to employees about their health and job satisfaction. The input is the schedule data and a list of questions, and the output is the questions presented to the employee. Example questions include, "How satisfied are you with your current job?" and "Have you been feeling stressed recently?"

[0554] Step 12:

[0555] The user (employee) answers questions about their health condition displayed on the terminal. The input is the question data about their health condition, and the output is the completed answer data. Specifically, the employee answers the questions on the terminal using the keyboard or touch screen.

[0556] Step 13:

[0557] The device sends the collected answers to the server, again using a secure communication protocol. The input is the health status response data, and the output is the completed data.

[0558] Step 14:

[0559] The server passes the received health status response data to the generative AI model for analysis. The input is the health status response data, and the output is the analysis result by the generative AI model. This allows for immediate feedback.

[0560] Step 15:

[0561] The server provides feedback to employees based on the results of analysis by the generative AI model. For example, it displays advice such as, "You seem to be feeling stressed, so consider taking more breaks." The input is the analysis result data, and the output is the provided feedback.

[0562] Step 16:

[0563] The server analyzes long-term data and identifies the overall health status and problems of the organization. The input is past health status data, and the output is the long-term analysis results. Based on these results, it proposes improvement measures to managers. For example, it may suggest, "Overall stress levels remain high, so we recommend stress management training for all employees."

[0564] (Application example 1)

[0565] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0566] Conventional in-house personnel placement and health management systems do not adequately grasp employees' skills and career vision or manage their health status, making it difficult to achieve optimal personnel placement and improve employee satisfaction. Furthermore, in factories, it is necessary to select and place optimal robots according to complex manufacturing processes, and also to properly manage their operating status and maintenance status, but current systems make it difficult to do this efficiently.

[0567] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0568] In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel placement based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; means for collecting information on the skills and maintenance status of factory workers and proposing optimal placement; means for periodically collecting and analyzing robot operation status and maintenance data; and means for providing maintenance proposals based on the operation status and maintenance data. This enables optimal personnel placement and employee health management within a company, as well as efficient operation and maintenance management of factory robots.

[0569] "Employee skills" refers to the knowledge and abilities of employees of a company regarding specific tasks or technologies.

[0570] "Career vision" refers to an employee's hopes and goals regarding the occupation or role they desire in the future.

[0571] "Means for collecting information" refers to the methods and devices used to obtain the necessary data from employees.

[0572] "Database means for storage and analysis" refers to a system or method for storing collected information and analyzing it as needed.

[0573] "Generative AI methods" refer to technologies and programs that use artificial intelligence to automatically generate optimal solutions based on collected data.

[0574] A "condition check" is an evaluation or examination to check an employee's health status and job satisfaction.

[0575] A "means for providing feedback" is a method or device for providing advice or instructions to an employee based on the results of the check.

[0576] "Worker skills" refers to the skills and abilities possessed by the workers who operate the machines and equipment within the factory.

[0577] "Maintenance status" refers to the state of machinery and equipment, indicating whether it is properly maintained and managed.

[0578] "Operation status" refers to data that indicates how long a machine or device is operating or how it is being used.

[0579] "Proposal methods" refer to methods and techniques for presenting optimal options and solutions based on the results of the analysis.

[0580] "Maintenance data" refers to the past maintenance history and repair records of machines and equipment.

[0581] This invention is a system that collects and analyzes information about company employees and factory workers (robots) to optimally allocate them and manage their health.

[0582] Feature Overview

[0583] The system has the following main functions:

[0584] 1. Information collection function:

[0585] The server periodically collects information about the skills and career vision of employees or workers, and the terminal presents these questions to employees at set dates and times and collects their answers.

[0586] Furthermore, operational status and maintenance data of the robots in the factory is collected via sensors, and the data is stored in a cloud-connected database (e.g., AWS RDS) via IoT devices.

[0587] 2. Data analysis and matching function:

[0588] The server passes the collected data to a generative AI model (e.g., GPT-4) for analysis, which then recommends the optimal placement of employees or workers (robots) based on the required skill sets and health status.

[0589] When a department requests employees with specific skills or workers best suited to a project, the server searches the database and selects and notifies the best candidates based on analysis by generative AI.

[0590] 3. Condition check function:

[0591] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminals ask employees questions about their health and job satisfaction and collect their responses.

[0592] The server passes the answers to a generative AI model, which provides immediate feedback based on the analysis results. Long-term data analysis identifies the overall health and problems of the organization and provides improvement suggestions to managers.

[0593] Specific examples of program processing

[0594] Hardware and Software

[0595] Server: High-performance cloud server (e.g. AWS EC2)

[0596] Database: Cloud-based database (e.g. AWS RDS)

[0597] Generative AI models: Generative AI models such as GPT-4

[0598] Sensors and IoT devices: Sensors for obtaining information on the robot's operating status and maintenance status

[0599] Information collection and data processing

[0600] The server stores skill information, career vision, and operational status data sent from sensors and devices in a cloud database.

[0601] This data is analyzed at regular intervals to quantify and classify specific skills and conditions.

[0602] Data analysis and feedback

[0603] When a department manager requests a specific skill set, the server passes the relevant personnel or worker information from the database to the generative AI model.

[0604] The generative AI model analyzes skills and health data to select the best candidates.

[0605] The results are notified to the administrator and user via the server.

[0606] Prompt Sentence Examples

[0607] For skill requests by admins:

[0608] "Assign robots with high welding skills for three-hour shifts."

[0609] For employee career vision surveys:

[0610] "What are your best skills? What position would you like to have in the future?"

[0611] As described above, the system enables efficient allocation and condition management of personnel and factory robots within a company, thereby improving overall business efficiency and the health of employees and workers.

[0612] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0613] Step 1: Gather information

[0614] The terminal presents employees and workers (robots) with questions about their skills and career vision at a pre-set date and time. At this time, the terminal displays questions to the employee such as "What skills are you good at?" and "What position do you want in the future?" The user (employee) answers these questions, and the terminal sends the answers to the server. At the same time, sensors on the robots in the factory collect operating status and maintenance data, and this data is also sent to the server. Input data: Employee answers and robot sensor data. Output data: Answers and sensor data sent to the server.

[0615] Step 2: Database accumulation and processing

[0616] The server stores the employee responses and robot sensor data sent from the device in a cloud-connected database (e.g., AWS RDS). At this time, the data is classified into appropriate categories and the skill information is quantified. After data accumulation, the server prepares the data for analysis by the generative AI model. Input data: employee responses and robot sensor data. Output data: categorized and quantified database entries.

[0617] Step 3: Data analysis

[0618] The server passes skill information, career vision, and operating status data to a generative AI model (e.g., GPT-4) for analysis. This model calculates the optimal allocation of employees and robots based on specific skill sets and maintenance status, and selects recommended personnel and workers. Input data: Classified and quantified data. Output data: Optimal allocation and a list of recommended personnel.

[0619] Step 4: Requests and Recommendations

[0620] When a department manager requests personnel with specific skills or for a project, the server searches the database and performs analysis using a generative AI model. The manager is provided with a list of recommended employees or workers (robots). For example, if Java programming skills are required, employees with those skills will be selected. Input data: Request from the manager. Output data: List of recommended employees or workers.

[0621] Step 5: Condition Check

[0622] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminal displays questions about their health and job satisfaction. The user answers these questions, and the terminal sends the answers to the server. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. Input data: Employee answers. Output data: Condition data sent to the server.

[0623] Step 6: Analyze data and provide feedback

[0624] The server passes the collected condition data to the generative AI model for analysis. Based on the analysis results, it provides immediate feedback, such as "You seem to be feeling stressed, so consider increasing your break time." Long-term data analysis identifies the health status and problems of the entire organization and makes improvement suggestions to managers. Input data: condition data. Output data: individual feedback and improvement suggestions.

[0625] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0626] MODE FOR CARRYING OUT THE INVENTION

[0627] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[0628] Information collection method

[0629] The server starts by setting up regular AI interviews for all employees. The device presents each employee with questions at the scheduled date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state.

[0630] The device sends the collected responses and emotion data to a server, which stores them in a database.

[0631] Data analysis and matching implementation

[0632] The server passes skills, career vision, and emotional data to the generative AI for analysis. For example, if a department manager is looking for personnel for a specific project, they input the request into the server. The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotional engine to evaluate whether the employee's current mental state is suitable for the position.

[0633] For example, if Employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account and select the best candidate based not only on their skills, but also on their current emotional state.

[0634] Condition check and feedback implementation example

[0635] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction, including an emotion engine that assesses their actual emotional state based on facial expressions and tone of voice.

[0636] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[0637] The server performs the analysis using generative AI. For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[0638] Specific examples

[0639] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that Employee C is a little tired. As a result, the generative AI determines that another Employee D is slightly less skilled but is very mentally stable, so it suggests Employee D as the best candidate.

[0640] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[0641] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[0642] The processing flow will be explained below.

[0643] Specific steps of the program's processing

[0644] Processing of information collection

[0645] Step 1:

[0646] The server starts a task to schedule AI interviews for all employees at the end of the month.

[0647] Step 2:

[0648] At scheduled times, the devices prompt each employee with questions about their skills, career vision, and emotional state, such as "What are your best skills?", "What role do you want to play in the future?", and "How do you feel about your current job?"

[0649] Step 3:

[0650] The user (employee) inputs answers to the questions presented and displays facial expressions and tone of voice to indicate their emotional state.

[0651] Step 4:

[0652] The terminal transmits the collected answers and emotion data generated by the emotion engine to the server.

[0653] Step 5:

[0654] The server stores and accumulates the received response data and emotion data in a database.

[0655] Data analysis and matching process

[0656] Step 1:

[0657] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[0658] Step 2:

[0659] The server searches the database to obtain information about employees with a particular skill set.

[0660] Step 3:

[0661] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[0662] Step 4:

[0663] The generative AI also takes data from the emotion engine into account to make appropriate assessments, such as comparing an employee with high skills but high emotional stress levels with an employee with slightly lower skills but low emotional stress levels.

[0664] Step 5:

[0665] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[0666] Step 6:

[0667] The server then notifies the department manager of the results of the generative AI's proposal, for example, saying, "Employee A best matches the required skills and has the appropriate emotional state."

[0668] Condition Checks and Feedback Handling

[0669] Step 1:

[0670] The server schedules condition checks for all employees at the beginning of each month.

[0671] Step 2:

[0672] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[0673] Step 3:

[0674] The user (employee) inputs answers to the questions presented and also uses the emotion engine to register facial expressions and tone of voice that indicate their emotional state. For example, the user might answer "I'm satisfied" and display a relaxed facial expression and tone of voice.

[0675] Step 4:

[0676] The terminal transmits the collected response data and emotion data to the server.

[0677] Step 5:

[0678] The server passes the received response data and emotion data to the generation AI for analysis.

[0679] Step 6:

[0680] The generative AI provides immediate feedback based on the analysis results, generating specific advice such as, "It appears your stress level is high, so please consider taking regular breaks or consulting a doctor."

[0681] Step 7:

[0682] The server notifies the employee of the generated feedback.

[0683] Step 8:

[0684] The server analyzes long-term data to understand the overall health and trends of the organization, and if necessary, suggests organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[0685] In this way, the system manages employees' skills, career vision, and emotional state in an integrated manner, enabling appropriate personnel allocation and health management.

[0686] Example 2

[0687] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0688] Conventional personnel placement systems only consider employees' skills and career vision when deciding on assignments, often ignoring their emotions and physical condition. This results in increased employee stress, leading to lower work efficiency and higher employee turnover. Furthermore, because conventional systems do not adequately check employees' physical condition on a regular basis, it is difficult to prevent a decline in employee health and job satisfaction.

[0689] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0690] In this invention, the server includes means for collecting information on employee skills and career vision, database means for accumulating and analyzing data on the information and emotional state, generation AI means for proposing optimal personnel allocation based on the analysis results, means for periodically conducting employee condition checks and analyzing the results, and means for providing feedback based on the results of the condition checks. This enables optimal personnel allocation and management that takes into consideration not only employee skills and career vision, but also their emotional state and health state in an integrated manner.

[0691] An "employee" is a worker who belongs to a company or organization and is employed to perform a specific job.

[0692] "Skills" refers to the knowledge, techniques, and abilities required to perform a specific job or task.

[0693] "Career vision" refers to an employee's career goals, hopes, and path forward for the future.

[0694] "Means of collecting information" refers to methods and devices for obtaining data from employees regarding their skills and career vision.

[0695] A "database" is a system for storing and managing collected information and for searching and analyzing it as needed.

[0696] "Database means" refers to the mechanisms and methods for storing, managing, searching, and analyzing information using a database.

[0697] "Generative AI" refers to systems that use artificial intelligence techniques to analyze data and generate results or recommendations tailored to a specific purpose.

[0698] "Generative AI means" refers to a system or method that uses generative AI to analyze data and output results or suggestions.

[0699] A "condition check" is a periodic survey or evaluation to assess an employee's health, job satisfaction, stress level, etc.

[0700] "Feedback" refers to advice and suggestions provided to employees based on the results of condition checks and data analysis.

[0701] "Emotional state" refers to the emotions and psychological state an employee is feeling at a given time.

[0702] An "emotion engine" is a system or software that analyzes data such as facial expressions and tone of voice to assess emotional states.

[0703] MODE FOR CARRYING OUT THE INVENTION

[0704] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[0705] Information collection method

[0706] The server will schedule regular AI interviews with all employees, which will allow the company to understand each employee's skills, career vision, and emotional state.

[0707] The device presents each employee with questions at a set date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions.

[0708] Additionally, the device uses an Emotion AI engine to understand the user's emotional state by analyzing their facial expressions and tone of voice, and the analysis is based on data collected using a webcam and microphone.

[0709] The device sends the collected responses and emotion data to a server, typically in JSON format via the HTTP protocol, and the server stores the received data in a database (e.g., MySQL or PostgreSQL).

[0710] Data analysis and matching implementation

[0711] The server passes the skills, career vision, and emotion data to a generative AI (e.g., OpenAI's GPT-4) for analysis. For example, if a department manager is looking for talent for a specific project, they input the request into the server and give instructions to the generative AI using prompts like the following:

[0712] "Based on Employee A's recent sentiment data, please rate whether he is suitable to be the Java expert for Project B."

[0713] "If Employee E is experiencing high stress, what feedback should I provide?"

[0714] The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotion engine to evaluate whether the employee's current mental state is suitable for the position. For example, if employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account. The best candidate is selected not only based on their skills, but also on their current emotional state.

[0715] Condition check and feedback implementation example

[0716] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction. This includes an emotion engine (Emotion AI) that evaluates their actual emotional state based on facial expressions and tone of voice.

[0717] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[0718] The server analyzes this data using a generative AI (e.g., GPT-4). For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[0719] Specific examples

[0720] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that he is a little tired. As a result, the generative AI suggests Employee D as the best candidate, since he is slightly less skilled but mentally stable.

[0721] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[0722] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[0723] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0724] Step 1: Configure information collection

[0725] The server schedules regular AI interviews for all employees. This includes the ability to send notifications to all employees at a specific date and time, for example, every month. The input is a list of all employees and the interview schedule, and the output is an interview notification sent to each employee. In this step, the interview date and time and question list are set within the system.

[0726] Step 2: Question posing

[0727] The terminal presents questions to each employee at the set date and time. The input is the schedule and question list sent from the server, and the output is the employee's answers. Specifically, questions such as "What are your strengths?" and "What is your vision for your future career?" are displayed on the terminal's interface.

[0728] Step 3: Collect response and sentiment data

[0729] The user (employee) answers questions presented on the terminal. The terminal then analyzes the user's facial expressions and tone of voice and uses an emotion engine (e.g., Emotion AI) to understand the user's emotional state. The input is the user's text response and non-verbal data (facial expressions, tone of voice), and the output is analyzed emotional data. Specifically, the terminal collects data using a webcam and microphone.

[0730] Step 4: Data transmission and storage

[0731] The device sends the collected responses and emotion data to the server. The input is data from the device (JSON format), and the output is data storage on the server side. Data is sent using the HTTP protocol, and the server stores the received data in a database (e.g., MySQL, PostgreSQL).

[0732] Step 5: Acquire and analyze data

[0733] The server passes the required skill and emotion data to the generation AI (e.g., GPT-4) for analysis. The input is employee data in the database and a request from the department manager, and the output is the analysis result. Specifically, the server uses an SQL query to retrieve the required information from the database and passes it to the generation AI as a prompt. Example prompt: "Please evaluate employees who are suitable as Java experts for Project B based on their skill and emotion data."

[0734] Step 6: Select the best candidate

[0735] Generative AI selects the best candidates based on input data. The inputs are skills, career vision, emotional data, and requests, and the output is a list of the best candidates. For example, Generative AI evaluates employees' Java skills and emotional state to create a list of the best Java experts.

[0736] Step 7: Schedule a Condition Check

[0737] The server schedules regular condition checks for all employees. The input is a list of all employees and the scheduled time, and the output is a check notification sent to each employee, which includes questions about their weekly health status and job satisfaction.

[0738] Step 8: Present health status and satisfaction questions

[0739] The terminal presents employees with questions about their health and job satisfaction. The input is a list of questions from the server, and the output is the employee's answers. Examples of questions displayed include "How satisfied are you with your current job?" and "What is your stress level these days?"

[0740] Step 9: Analyze health and emotion data

[0741] The terminal analyzes the employee's responses as well as their facial expressions and voice to assess their emotional state. The input is the user's response (text) and non-verbal data, and the output is the analyzed emotional data.

[0742] Step 10: Generate feedback

[0743] The server analyzes the collected data using generative AI and generates appropriate feedback. The input is emotional data and health status data, and the output is written feedback. For example, the generated advice might be, "Try distributing your daily tasks to reduce stress."

[0744] Step 11: Provide feedback

[0745] The terminal presents the generated feedback to the user. The input is the feedback content from the server, and the output is the content presented to the user. Specifically, the feedback is displayed on the terminal interface. Based on this feedback, the user can understand specific countermeasures and improvement measures.

[0746] (Application example 2)

[0747] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0748] The present invention relates to a system for optimal personnel allocation based on the skills and career vision information of company employees, as well as their emotional state. Conventional systems do not adequately check employee condition or manage their emotional state, which can lead to the accumulation of stress and dissatisfaction among employees. Furthermore, because skill matching relies on static data, dynamic job allocation is difficult. The present invention aims to solve these problems and improve corporate efficiency while maximizing employee capabilities.

[0749] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel allocation based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; sensor means for measuring the emotional state of workers; and means for dynamically optimizing work allocation based on the emotional state of those being measured. This enables dynamic work allocation that takes into account both employee skills and emotional state, thereby improving employee satisfaction and organizational efficiency.

[0750] An "employee" is a person who belongs to a company or organization and performs work.

[0751] "Skills" are the knowledge and techniques needed to effectively perform a particular job or task.

[0752] "Career vision" refers to the professional goals and desired career path that an employee wants to achieve in the future.

[0753] "Information collection means" is a general term for methods and functions for collecting information from employees regarding their skills and career visions.

[0754] "Database means" refers to a system or method for storing collected information and conducting analysis based on it.

[0755] "Generative AI means" refers to artificial intelligence that generates optimal personnel placement and feedback based on collected and accumulated data.

[0756] "Condition checks" refer to regular assessments of employees' health and job satisfaction, and take measures if necessary.

[0757] "Means of providing feedback" refers to the methods and functions for sending improvement suggestions and encouraging messages to employees based on the results of the condition check.

[0758] "Sensor means" refers to devices or methods for measuring employees' facial expressions, tone of voice, etc. to assess their emotional state.

[0759] "Means for dynamically optimizing work allocation" refers to methods or systems for adjusting work allocation in real time based on employees' emotional states to achieve optimal allocation.

[0760] The present invention provides a system for optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company, and for periodically checking their condition and dynamically optimizing their work assignments. An embodiment of this system will be described below.

[0761] Information collection method

[0762] The server schedules regular AI interviews with employees. The device presents questions to each employee at the scheduled date and time to understand their skills, career vision, and emotional state. Employees respond to questions such as, "What are your strengths?" and "What is your future career vision?" The device also uses an emotion engine to analyze the employee's facial expressions and tone of voice to understand their emotional state.

[0763] Data accumulation and analysis

[0764] The device sends the collected responses and emotional data to a server, which stores this data in a database and uses it for analysis by the generative AI. For example, the server can learn each employee's habits and performance patterns and store the analysis results.

[0765] Condition check and feedback

[0766] The server schedules regular condition checks and asks employees questions about their health and job satisfaction via their devices. Based on these answers and emotional data, the server uses generative AI to analyze and provide feedback appropriate to the employee's condition. For example, for an employee experiencing high stress, the server might suggest, "Consider ways to distribute your work to reduce stress."

[0767] Dynamic work allocation optimization

[0768] The server uses sensors to measure the emotional state of workers in real time and dynamically optimizes work allocation based on that data. For example, if a specific project requires highly skilled employees, the server will search the database to select the most suitable candidates, taking into account data from the emotion engine and prioritizing employees with stable current emotional states.

[0769] Hardware and software used

[0770] The server is a high-performance computer system equipped with a database management system (DBMS) and software implementing AI algorithms. The terminals are smartphones or smart glasses equipped with various data collection functions and sensors. The emotion engine includes facial expression recognition software and voice analysis software.

[0771] Examples and prompts

[0772] For example, if a factory suddenly needs an engineer with Python skills, the department manager inputs the requirements into the server. The generative AI searches the database and selects the most suitable candidate. Based on emotional data, employees with low stress levels are prioritized for placement.

[0773] Example prompt sentence:

[0774] Required skills: Python

[0775] Employee emotional state: Using measurement data

[0776] Recommend the best candidates.

[0777] In this way, the system of the present invention manages employee skills, career vision, and emotional data in an integrated manner, realizing optimal personnel allocation and health management.

[0778] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0779] Step 1:

[0780] The server schedules regular AI interviews for employees.

[0781] Input: Employee list, interview schedule

[0782] Action: Set up a regular meeting at a specific date and time

[0783] Output: Interview notice

[0784] Step 2:

[0785] The device asks employees questions at set times and dates, collecting information about their skills, career vision, and emotional state.

[0786] Input: Interview schedule, question list

[0787] Action: Present a question and collect employee responses

[0788] Output: Skill information, career vision information, emotional data

[0789] Step 3:

[0790] The device analyzes employees' facial expressions and tone of voice and uses an emotion engine to understand their emotional state.

[0791] Input: Employee voice and video data

[0792] Actions: Voice analysis, facial expression recognition

[0793] Output: Emotional state data

[0794] Step 4:

[0795] The device sends the collected responses and emotion data to the server.

[0796] Input: Skill information, career vision information, emotional data

[0797] Action: Data transfer

[0798] Output: Store in database

[0799] Step 5:

[0800] The server passes the data stored in the database to a generative AI model that analyzes employees' skills, career vision, and emotional state.

[0801] Input: Employee information and emotion data in the database

[0802] Action: Data Analysis

[0803] Output: Analysis results

[0804] Step 6:

[0805] The server uses generative AI to suggest optimal staffing based on skills and career vision.

[0806] Input: Analysis results, request

[0807] Action: Matching process

[0808] Output: Optimal staffing proposal

[0809] Step 7:

[0810] The server schedules regular condition checks and presents employees with questions about their health and job satisfaction via their terminal.

[0811] Input: Condition Check Schedule

[0812] Action: Present a question and collect employee responses

[0813] Output: Condition data

[0814] Step 8:

[0815] The terminal transmits the collected condition data and emotion data to the server.

[0816] Input: condition data, emotion data

[0817] Action: Data transfer

[0818] Output: Store in database

[0819] Step 9:

[0820] The server periodically passes collected condition data to the generative AI model for analysis and provides feedback.

[0821] Input: Condition data in the database

[0822] Actions: Data analysis, feedback generation

[0823] Output: Feedback message

[0824] Step 10:

[0825] The server monitors the real-time emotional state of the workers using a sensor means and dynamically passes the data to the work allocation optimization system.

[0826] Input: Real-time emotional data

[0827] Action: Emotional data collection and analysis

[0828] Output: Work placement instructions

[0829] In this way, the system comprehensively manages employees' skills, career vision, and emotional state, enabling optimal personnel placement and feedback.

[0830] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0831] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0832] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0833] [Third embodiment]

[0834] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0835] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0836] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0837] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0838] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0839] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0840] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0841] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0842] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[0843] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0844] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0845] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0846] MODE FOR CARRYING OUT THE INVENTION

[0847] This invention is a system that efficiently matches the skills and aspirations of corporate employees and periodically checks their health status, thereby achieving optimal personnel placement and high employee satisfaction. This system is equipped with a means for collecting information on employees' skills and career visions and a database means for storing and analyzing that information. It also uses a generative AI means to propose optimal personnel placement, periodically checks employees' condition, and provides feedback.

[0848] Information collection method

[0849] The server schedules regular AI interviews for all employees. At the scheduled date and time, the terminal presents each employee with questions about their skills and career vision. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are presented. The user (employee) answers these questions. The terminal sends the collected answers to the server, which stores them in a database.

[0850] Data analysis and matching implementation

[0851] Once data on skills and career vision has been accumulated, the server passes it to the generation AI for analysis. For example, if a project requires an employee with specific skills, the department manager requests the necessary skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generation AI selects the most suitable candidate, and the server notifies the manager of the results.

[0852] Condition check implementation example

[0853] The server schedules condition checks for all employees at the beginning of each month. The device presents employees with questions about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are displayed. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answers to a generative AI for analysis and provides immediate feedback. For example, it provides advice such as "It seems you are feeling stressed, so consider taking more breaks." The server also analyzes long-term data to identify the health status and problems of the entire organization and propose improvement measures to managers.

[0854] Specific examples

[0855] For example, suppose Department A needs an employee with Java programming skills for a new project. The manager of Department A inputs this request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. The Generative AI confirms this and suggests an allocation. If Employee C reports a high stress level in a regular condition check, the Generative AI analyzes the cause and provides feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[0856] In this way, the system aims to optimize personnel allocation and health management within the company and improve employee satisfaction.

[0857] The processing flow will be explained below.

[0858] Specific steps of the program's processing

[0859] Processing of information collection

[0860] Step 1:

[0861] The server starts a task to schedule AI interviews for all employees at the end of the month.

[0862] Step 2:

[0863] At the scheduled time, the device will prompt each employee with questions about their skills and career aspirations, such as "What are your strengths?" and "What position do you want to be in the future?"

[0864] Step 3:

[0865] The user (employee) inputs answers to the questions presented. For example, they input answers such as "I'm good at Java programming" or "I want to be a project manager."

[0866] Step 4:

[0867] The terminal sends the collected answers to the server.

[0868] Step 5:

[0869] The server saves and accumulates the received response data in a database.

[0870] Data analysis and matching process

[0871] Step 1:

[0872] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[0873] Step 2:

[0874] The server searches the database to obtain information about employees with a particular skill set.

[0875] Step 3:

[0876] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[0877] Step 4:

[0878] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[0879] Step 5:

[0880] The server notifies the administrator of the results of the generative AI's proposal, for example, saying, "Employee A is the best match for the required skills."

[0881] Condition Checks and Feedback Handling

[0882] Step 1:

[0883] The server schedules condition checks for all employees at the beginning of each month.

[0884] Step 2:

[0885] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[0886] Step 3:

[0887] The user (employee) inputs an answer to the presented question, such as "I am satisfied" or "I feel stressed."

[0888] Step 4:

[0889] The terminal transmits the collected response data to the server.

[0890] Step 5:

[0891] The server passes the received response data to the generation AI for analysis.

[0892] Step 6:

[0893] The generative AI provides immediate feedback based on the analysis results, such as advice like "Take more breaks to reduce stress."

[0894] Step 7:

[0895] The server notifies the employee of the generated feedback.

[0896] Step 8:

[0897] The server analyzes long-term data to understand the overall health and trends of the organization. If necessary, it will suggest organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[0898] Example 1

[0899] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0900] Companies are required to efficiently manage employee skills and career prospects and achieve optimal personnel allocation. However, conventional systems require a cumbersome process for collecting and analyzing employee skills and career prospects, making it difficult to continuously monitor the health and satisfaction of individual employees. Responding quickly and accurately to requests from departments is also an issue. This can lead to a decline in employee satisfaction and productivity.

[0901] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0902] In this invention, the server includes a means for collecting information on employee skills and career prospects, a data storage means for accumulating and analyzing the information, a generation AI means for proposing optimal personnel allocation based on the analysis results, a means for periodically conducting employee health checks and analyzing the results, and a means for providing feedback based on the results of the health checks. This allows for efficient management of employee skills and career prospects, making it possible to maintain optimal personnel allocation and high employee satisfaction.

[0903] "Employee skills" refers to the specialized knowledge, abilities, and techniques that employees possess within a company to perform specific tasks.

[0904] "Career outlook" refers to the career goals and desired career path that an employee wants to achieve in the future.

[0905] "Information gathering means" refers to the methods and devices used to obtain data from employees regarding their skills and career prospects.

[0906] "Data storage means" refers to systems or devices that securely store collected information and data and make them accessible when needed.

[0907] "Generative AI means" refers to a system that uses artificial intelligence technology to analyze data and propose optimal personnel placement, etc.

[0908] "Means for conducting health checks" refers to methods and devices for periodically assessing employees' health status and collecting that data.

[0909] "Means for providing feedback" refers to systems or devices that provide advice or information to employees or managers based on the results of health checks.

[0910] "Means for presenting questions to employees" refers to a method or device for displaying questions about skills and career prospects to employees and having them answer the questions.

[0911] "Means for selecting the best candidates based on requests from business units" refers to a system or device that searches a database of employee skills and desired conditions and selects the best employee who matches the specified requirements.

[0912] "Staffing" refers to appropriately determining employees' work locations and responsibilities within a company.

[0913] This invention is a system for efficiently managing the skills and career prospects of employees within a company and achieving optimal personnel allocation. This system collects information on employees' skills and career prospects, stores it in a database, analyzes it, and proposes optimal personnel allocation based on the results. Furthermore, by conducting regular health checks on employees and providing feedback, employee satisfaction and productivity can be improved.

[0914] Hardware and software used

[0915] The system utilizes the following hardware and software:

[0916] Server: Data storage means, generation AI means, scheduling software (e.g., Chronos Scheduler), database management system (e.g., MySQL)

[0917] Terminal: Data input / display device (e.g., PC, tablet), communication protocol (e.g., HTTPS)

[0918] Generative AI models: used for data analysis and feedback generation (e.g., GPT-4)

[0919] Data collection

[0920] The server periodically schedules AI interviews for all employees. Scheduling software (e.g., Chronos Scheduler) is used here. At the set date and time, the terminal presents each employee with questions about their skills and career prospects. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are displayed. The user (employee) answers these questions, and the terminal sends the collected answers to the server. The server stores the answer data in a database.

[0921] Data analysis and optimal staffing proposals

[0922] The server passes the accumulated data to a generative AI model, where the generative AI model (e.g., GPT-4) performs the analysis. For example, if a project requires an employee with specific skills, the department manager requests the skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generative AI model selects the most suitable candidate, and the server notifies the manager of the results.

[0923] Health Check and Feedback

[0924] The server schedules health checks for all employees at the beginning of each month. The device displays questions to employees about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answer data to a generative AI model for analysis and provides immediate feedback. For example, advice such as "It seems you are feeling stressed, so consider taking more breaks" is displayed.

[0925] Specific examples

[0926] Specific examples of skill matching

[0927] If Department A needs an employee with Java programming skills for a new project, the manager of Department A inputs the request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. A generative AI model (e.g., GPT-4) confirms this and suggests placing Employee B.

[0928] Specific examples of health checks

[0929] If employee C reports high stress levels during a regular health check, the generative AI model will analyze the cause and provide feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[0930] Example prompts for generative AI models

[0931] Here are some example prompts to input to a generative AI model:

[0932] "Please select the best person for Project A based on employee skill data."

[0933] "Please analyze the cause of employee C's high stress level and propose solutions."

[0934] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0935] Step 1:

[0936] The server periodically schedules AI interviews for all employees. The schedule is set using scheduling software (e.g., Chronos Scheduler). The input is the interview date and time data generated by the scheduling software, and the output is the confirmed schedule for each interview.

[0937] Step 2:

[0938] The terminal presents employees with questions about their skills and career prospects at a set date and time. The questions are displayed in a predefined format. The input is schedule data and a list of questions, and the output is the questions presented to the employee. Examples of questions include "What are your strengths?" and "What position do you hope to have in the future?"

[0939] Step 3:

[0940] The user (employee) answers the questions displayed on the terminal. The input is the answer data that the employee enters into the terminal, and the output is the completed answer data. Specifically, the employee enters the answer into the terminal using a keyboard or touch screen.

[0941] Step 4:

[0942] The terminal sends the collected response data to the server using a secure communication protocol (e.g., HTTPS). The input is the employee's response data, and the output is the data that has been sent.

[0943] Step 5:

[0944] The server stores the received response data in a database. A database management system (e.g., MySQL) is used to ensure the integrity of the stored data. The input is the response data, and the output is the data stored in the database.

[0945] Step 6:

[0946] The server passes the accumulated data to a generative AI model for analysis. The input is data extracted from the database, and the output is the results of analysis by the generative AI model. A generative AI model (e.g., GPT-4) is used to analyze the data and find trends and patterns.

[0947] Step 7:

[0948] Department managers input the skill requirements for a specific project into the server. The input is the skill requirement data entered by the manager, and the output is the requirement data stored on the server. A web interface (e.g., Admin Dashboard) is used for administrators.

[0949] Step 8:

[0950] The server searches the database based on the skill requirements received from the administrator and selects the most suitable employees. The input is the skill requirements data and employee data in the database, and the output is a list of the most suitable employees. This search and selection is performed using a generative AI model.

[0951] Step 9:

[0952] The server notifies the administrator of the results of the selection. Notifications can be sent via email or a web interface. The input is the list of best-fit employees, and the output is the notification data sent to the administrator.

[0953] Step 10:

[0954] The server schedules health checks for all employees at the beginning of each month. Again, it uses scheduling software (e.g., Chronos Scheduler). The input is the schedule data, and the output is the information about the scheduled health checks.

[0955] Step 11:

[0956] At the scheduled date and time, the terminal displays questions to employees about their health and job satisfaction. The input is the schedule data and a list of questions, and the output is the questions presented to the employee. Example questions include, "How satisfied are you with your current job?" and "Have you been feeling stressed recently?"

[0957] Step 12:

[0958] The user (employee) answers questions about their health condition displayed on the terminal. The input is the question data about their health condition, and the output is the completed answer data. Specifically, the employee answers the questions on the terminal using the keyboard or touch screen.

[0959] Step 13:

[0960] The device sends the collected answers to the server, again using a secure communication protocol. The input is the health status response data, and the output is the completed data.

[0961] Step 14:

[0962] The server passes the received health status response data to the generative AI model for analysis. The input is the health status response data, and the output is the analysis result by the generative AI model. This allows for immediate feedback.

[0963] Step 15:

[0964] The server provides feedback to employees based on the results of analysis by the generative AI model. For example, it displays advice such as, "You seem to be feeling stressed, so consider taking more breaks." The input is the analysis result data, and the output is the provided feedback.

[0965] Step 16:

[0966] The server analyzes long-term data and identifies the overall health status and problems of the organization. The input is past health status data, and the output is the long-term analysis results. Based on these results, it proposes improvement measures to managers. For example, it may suggest, "Overall stress levels remain high, so we recommend stress management training for all employees."

[0967] (Application example 1)

[0968] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0969] Conventional in-house personnel placement and health management systems do not adequately grasp employees' skills and career vision or manage their health status, making it difficult to achieve optimal personnel placement and improve employee satisfaction. Furthermore, in factories, it is necessary to select and place optimal robots according to complex manufacturing processes, and also to properly manage their operating status and maintenance status, but current systems make it difficult to do this efficiently.

[0970] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0971] In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel placement based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; means for collecting information on the skills and maintenance status of factory workers and proposing optimal placement; means for periodically collecting and analyzing robot operation status and maintenance data; and means for providing maintenance proposals based on the operation status and maintenance data. This enables optimal personnel placement and employee health management within a company, as well as efficient operation and maintenance management of factory robots.

[0972] "Employee skills" refers to the knowledge and abilities of employees of a company regarding specific tasks or technologies.

[0973] "Career vision" refers to an employee's hopes and goals regarding the occupation or role they desire in the future.

[0974] "Means for collecting information" refers to the methods and devices used to obtain the necessary data from employees.

[0975] "Database means for storage and analysis" refers to a system or method for storing collected information and analyzing it as needed.

[0976] "Generative AI methods" refer to technologies and programs that use artificial intelligence to automatically generate optimal solutions based on collected data.

[0977] A "condition check" is an evaluation or examination to check an employee's health status and job satisfaction.

[0978] A "means for providing feedback" is a method or device for providing advice or instructions to an employee based on the results of the check.

[0979] "Worker skills" refers to the skills and abilities possessed by the workers who operate the machines and equipment within the factory.

[0980] "Maintenance status" refers to the state of machinery and equipment, indicating whether it is properly maintained and managed.

[0981] "Operation status" refers to data that indicates how long a machine or device is operating or how it is being used.

[0982] "Proposal methods" refer to methods and techniques for presenting optimal options and solutions based on the results of the analysis.

[0983] "Maintenance data" refers to the past maintenance history and repair records of machines and equipment.

[0984] This invention is a system that collects and analyzes information about company employees and factory workers (robots) to optimally allocate them and manage their health.

[0985] Feature Overview

[0986] The system has the following main functions:

[0987] 1. Information collection function:

[0988] The server periodically collects information about the skills and career vision of employees or workers, and the terminal presents these questions to employees at set dates and times and collects their answers.

[0989] Furthermore, operational status and maintenance data of the robots in the factory is collected via sensors, and the data is stored in a cloud-connected database (e.g., AWS RDS) via IoT devices.

[0990] 2. Data analysis and matching function:

[0991] The server passes the collected data to a generative AI model (e.g., GPT-4) for analysis, which then recommends the optimal placement of employees or workers (robots) based on the required skill sets and health status.

[0992] When a department requests employees with specific skills or workers best suited to a project, the server searches the database and selects and notifies the best candidates based on analysis by generative AI.

[0993] 3. Condition check function:

[0994] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminals ask employees questions about their health and job satisfaction and collect their responses.

[0995] The server passes the answers to a generative AI model, which provides immediate feedback based on the analysis results. Long-term data analysis identifies the overall health and problems of the organization and provides improvement suggestions to managers.

[0996] Specific examples of program processing

[0997] Hardware and Software

[0998] Server: High-performance cloud server (e.g. AWS EC2)

[0999] Database: Cloud-based database (e.g. AWS RDS)

[1000] Generative AI models: Generative AI models such as GPT-4

[1001] Sensors and IoT devices: Sensors for obtaining information on the robot's operating status and maintenance status

[1002] Information collection and data processing

[1003] The server stores skill information, career vision, and operational status data sent from sensors and devices in a cloud database.

[1004] This data is analyzed at regular intervals to quantify and classify specific skills and conditions.

[1005] Data analysis and feedback

[1006] When a department manager requests a specific skill set, the server passes the relevant personnel or worker information from the database to the generative AI model.

[1007] The generative AI model analyzes skills and health data to select the best candidates.

[1008] The results are notified to the administrator and user via the server.

[1009] Prompt Sentence Examples

[1010] For skill requests by admins:

[1011] "Assign robots with high welding skills for three-hour shifts."

[1012] For employee career vision surveys:

[1013] "What are your best skills? What position would you like to have in the future?"

[1014] As described above, the system enables efficient allocation and condition management of personnel and factory robots within a company, thereby improving overall business efficiency and the health of employees and workers.

[1015] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1016] Step 1: Gather information

[1017] The terminal presents employees and workers (robots) with questions about their skills and career vision at a pre-set date and time. At this time, the terminal displays questions to the employee such as "What skills are you good at?" and "What position do you want in the future?" The user (employee) answers these questions, and the terminal sends the answers to the server. At the same time, sensors on the robots in the factory collect operating status and maintenance data, and this data is also sent to the server. Input data: Employee answers and robot sensor data. Output data: Answers and sensor data sent to the server.

[1018] Step 2: Database accumulation and processing

[1019] The server stores the employee responses and robot sensor data sent from the device in a cloud-connected database (e.g., AWS RDS). At this time, the data is classified into appropriate categories and the skill information is quantified. After data accumulation, the server prepares the data for analysis by the generative AI model. Input data: employee responses and robot sensor data. Output data: categorized and quantified database entries.

[1020] Step 3: Data analysis

[1021] The server passes skill information, career vision, and operating status data to a generative AI model (e.g., GPT-4) for analysis. This model calculates the optimal allocation of employees and robots based on specific skill sets and maintenance status, and selects recommended personnel and workers. Input data: Classified and quantified data. Output data: Optimal allocation and a list of recommended personnel.

[1022] Step 4: Requests and Recommendations

[1023] When a department manager requests personnel with specific skills or for a project, the server searches the database and performs analysis using a generative AI model. The manager is provided with a list of recommended employees or workers (robots). For example, if Java programming skills are required, employees with those skills will be selected. Input data: Request from the manager. Output data: List of recommended employees or workers.

[1024] Step 5: Condition Check

[1025] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminal displays questions about their health and job satisfaction. The user answers these questions, and the terminal sends the answers to the server. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. Input data: Employee answers. Output data: Condition data sent to the server.

[1026] Step 6: Analyze data and provide feedback

[1027] The server passes the collected condition data to the generative AI model for analysis. Based on the analysis results, it provides immediate feedback, such as "You seem to be feeling stressed, so consider increasing your break time." Long-term data analysis identifies the health status and problems of the entire organization and makes improvement suggestions to managers. Input data: condition data. Output data: individual feedback and improvement suggestions.

[1028] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1029] MODE FOR CARRYING OUT THE INVENTION

[1030] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[1031] Information collection method

[1032] The server starts by setting up regular AI interviews for all employees. The device presents each employee with questions at the scheduled date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state.

[1033] The device sends the collected responses and emotion data to a server, which stores them in a database.

[1034] Data analysis and matching implementation

[1035] The server passes skills, career vision, and emotional data to the generative AI for analysis. For example, if a department manager is looking for personnel for a specific project, they input the request into the server. The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotional engine to evaluate whether the employee's current mental state is suitable for the position.

[1036] For example, if Employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account and select the best candidate based not only on their skills, but also on their current emotional state.

[1037] Condition check and feedback implementation example

[1038] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction, including an emotion engine that assesses their actual emotional state based on facial expressions and tone of voice.

[1039] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[1040] The server performs the analysis using generative AI. For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[1041] Specific examples

[1042] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that Employee C is a little tired. As a result, the generative AI determines that another Employee D is slightly less skilled but is very mentally stable, so it suggests Employee D as the best candidate.

[1043] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[1044] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[1045] The processing flow will be explained below.

[1046] Specific steps of the program's processing

[1047] Processing of information collection

[1048] Step 1:

[1049] The server starts a task to schedule AI interviews for all employees at the end of the month.

[1050] Step 2:

[1051] At scheduled times, the devices prompt each employee with questions about their skills, career vision, and emotional state, such as "What are your best skills?", "What role do you want to play in the future?", and "How do you feel about your current job?"

[1052] Step 3:

[1053] The user (employee) inputs answers to the questions presented and displays facial expressions and tone of voice to indicate their emotional state.

[1054] Step 4:

[1055] The terminal transmits the collected answers and emotion data generated by the emotion engine to the server.

[1056] Step 5:

[1057] The server stores and accumulates the received response data and emotion data in a database.

[1058] Data analysis and matching process

[1059] Step 1:

[1060] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[1061] Step 2:

[1062] The server searches the database to obtain information about employees with a particular skill set.

[1063] Step 3:

[1064] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[1065] Step 4:

[1066] The generative AI also takes data from the emotion engine into account to make appropriate assessments, such as comparing an employee with high skills but high emotional stress levels with an employee with slightly lower skills but low emotional stress levels.

[1067] Step 5:

[1068] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[1069] Step 6:

[1070] The server then notifies the department manager of the results of the generative AI's proposal, for example, saying, "Employee A best matches the required skills and has the appropriate emotional state."

[1071] Condition Checks and Feedback Handling

[1072] Step 1:

[1073] The server schedules condition checks for all employees at the beginning of each month.

[1074] Step 2:

[1075] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[1076] Step 3:

[1077] The user (employee) inputs answers to the questions presented and also uses the emotion engine to register facial expressions and tone of voice that indicate their emotional state. For example, the user might answer "I'm satisfied" and display a relaxed facial expression and tone of voice.

[1078] Step 4:

[1079] The terminal transmits the collected response data and emotion data to the server.

[1080] Step 5:

[1081] The server passes the received response data and emotion data to the generation AI for analysis.

[1082] Step 6:

[1083] The generative AI provides immediate feedback based on the analysis results, generating specific advice such as, "It appears your stress level is high, so please consider taking regular breaks or consulting a doctor."

[1084] Step 7:

[1085] The server notifies the employee of the generated feedback.

[1086] Step 8:

[1087] The server analyzes long-term data to understand the overall health and trends of the organization, and if necessary, suggests organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[1088] In this way, the system manages employees' skills, career vision, and emotional state in an integrated manner, enabling appropriate personnel allocation and health management.

[1089] Example 2

[1090] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1091] Conventional personnel placement systems only consider employees' skills and career vision when deciding on assignments, often ignoring their emotions and physical condition. This results in increased employee stress, leading to lower work efficiency and higher employee turnover. Furthermore, because conventional systems do not adequately check employees' physical condition on a regular basis, it is difficult to prevent a decline in employee health and job satisfaction.

[1092] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1093] In this invention, the server includes means for collecting information on employee skills and career vision, database means for accumulating and analyzing data on the information and emotional state, generation AI means for proposing optimal personnel allocation based on the analysis results, means for periodically conducting employee condition checks and analyzing the results, and means for providing feedback based on the results of the condition checks. This enables optimal personnel allocation and management that takes into consideration not only employee skills and career vision, but also their emotional state and health state in an integrated manner.

[1094] An "employee" is a worker who belongs to a company or organization and is employed to perform a specific job.

[1095] "Skills" refers to the knowledge, techniques, and abilities required to perform a specific job or task.

[1096] "Career vision" refers to an employee's career goals, hopes, and path forward for the future.

[1097] "Means of collecting information" refers to methods and devices for obtaining data from employees regarding their skills and career vision.

[1098] A "database" is a system for storing and managing collected information and for searching and analyzing it as needed.

[1099] "Database means" refers to the mechanisms and methods for storing, managing, searching, and analyzing information using a database.

[1100] "Generative AI" refers to systems that use artificial intelligence techniques to analyze data and generate results or recommendations tailored to a specific purpose.

[1101] "Generative AI means" refers to a system or method that uses generative AI to analyze data and output results or suggestions.

[1102] A "condition check" is a periodic survey or evaluation to assess an employee's health, job satisfaction, stress level, etc.

[1103] "Feedback" refers to advice and suggestions provided to employees based on the results of condition checks and data analysis.

[1104] "Emotional state" refers to the emotions and psychological state an employee is feeling at a given time.

[1105] An "emotion engine" is a system or software that analyzes data such as facial expressions and tone of voice to assess emotional states.

[1106] MODE FOR CARRYING OUT THE INVENTION

[1107] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[1108] Information collection method

[1109] The server will schedule regular AI interviews with all employees, which will allow the company to understand each employee's skills, career vision, and emotional state.

[1110] The device presents each employee with questions at a set date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions.

[1111] Additionally, the device uses an Emotion AI engine to understand the user's emotional state by analyzing their facial expressions and tone of voice, and the analysis is based on data collected using a webcam and microphone.

[1112] The device sends the collected responses and emotion data to a server, typically in JSON format via the HTTP protocol, and the server stores the received data in a database (e.g., MySQL or PostgreSQL).

[1113] Data analysis and matching implementation

[1114] The server passes the skills, career vision, and emotion data to a generative AI (e.g., OpenAI's GPT-4) for analysis. For example, if a department manager is looking for talent for a specific project, they input the request into the server and give instructions to the generative AI using prompts like the following:

[1115] "Based on Employee A's recent sentiment data, please rate whether he is suitable to be the Java expert for Project B."

[1116] "If Employee E is experiencing high stress, what feedback should I provide?"

[1117] The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotion engine to evaluate whether the employee's current mental state is suitable for the position. For example, if employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account. The best candidate is selected not only based on their skills, but also on their current emotional state.

[1118] Condition check and feedback implementation example

[1119] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction. This includes an emotion engine (Emotion AI) that evaluates their actual emotional state based on facial expressions and tone of voice.

[1120] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[1121] The server analyzes this data using a generative AI (e.g., GPT-4). For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[1122] Specific examples

[1123] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that he is a little tired. As a result, the generative AI suggests Employee D as the best candidate, since he is slightly less skilled but mentally stable.

[1124] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[1125] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[1126] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1127] Step 1: Configure information collection

[1128] The server schedules regular AI interviews for all employees. This includes the ability to send notifications to all employees at a specific date and time, for example, every month. The input is a list of all employees and the interview schedule, and the output is an interview notification sent to each employee. In this step, the interview date and time and question list are set within the system.

[1129] Step 2: Question posing

[1130] The terminal presents questions to each employee at the set date and time. The input is the schedule and question list sent from the server, and the output is the employee's answers. Specifically, questions such as "What are your strengths?" and "What is your vision for your future career?" are displayed on the terminal's interface.

[1131] Step 3: Collect response and sentiment data

[1132] The user (employee) answers questions presented on the terminal. The terminal then analyzes the user's facial expressions and tone of voice and uses an emotion engine (e.g., Emotion AI) to understand the user's emotional state. The input is the user's text response and non-verbal data (facial expressions, tone of voice), and the output is analyzed emotional data. Specifically, the terminal collects data using a webcam and microphone.

[1133] Step 4: Data transmission and storage

[1134] The device sends the collected responses and emotion data to the server. The input is data from the device (JSON format), and the output is data storage on the server side. Data is sent using the HTTP protocol, and the server stores the received data in a database (e.g., MySQL, PostgreSQL).

[1135] Step 5: Acquire and analyze data

[1136] The server passes the required skill and emotion data to the generation AI (e.g., GPT-4) for analysis. The input is employee data in the database and a request from the department manager, and the output is the analysis result. Specifically, the server uses an SQL query to retrieve the required information from the database and passes it to the generation AI as a prompt. Example prompt: "Please evaluate employees who are suitable as Java experts for Project B based on their skill and emotion data."

[1137] Step 6: Select the best candidate

[1138] Generative AI selects the best candidates based on input data. The inputs are skills, career vision, emotional data, and requests, and the output is a list of the best candidates. For example, Generative AI evaluates employees' Java skills and emotional state to create a list of the best Java experts.

[1139] Step 7: Schedule a Condition Check

[1140] The server schedules regular condition checks for all employees. The input is a list of all employees and the scheduled time, and the output is a check notification sent to each employee, which includes questions about their weekly health status and job satisfaction.

[1141] Step 8: Present health status and satisfaction questions

[1142] The terminal presents employees with questions about their health and job satisfaction. The input is a list of questions from the server, and the output is the employee's answers. Examples of questions displayed include "How satisfied are you with your current job?" and "What is your stress level these days?"

[1143] Step 9: Analyze health and emotion data

[1144] The terminal analyzes the employee's responses as well as their facial expressions and voice to assess their emotional state. The input is the user's response (text) and non-verbal data, and the output is the analyzed emotional data.

[1145] Step 10: Generate feedback

[1146] The server analyzes the collected data using generative AI and generates appropriate feedback. The input is emotional data and health status data, and the output is written feedback. For example, the generated advice might be, "Try distributing your daily tasks to reduce stress."

[1147] Step 11: Provide feedback

[1148] The terminal presents the generated feedback to the user. The input is the feedback content from the server, and the output is the content presented to the user. Specifically, the feedback is displayed on the terminal interface. Based on this feedback, the user can understand specific countermeasures and improvement measures.

[1149] (Application example 2)

[1150] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1151] The present invention relates to a system for optimal personnel allocation based on the skills and career vision information of company employees, as well as their emotional state. Conventional systems do not adequately check employee condition or manage their emotional state, which can lead to the accumulation of stress and dissatisfaction among employees. Furthermore, because skill matching relies on static data, dynamic job allocation is difficult. The present invention aims to solve these problems and improve corporate efficiency while maximizing employee capabilities.

[1152] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel allocation based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; sensor means for measuring the emotional state of workers; and means for dynamically optimizing work allocation based on the emotional state of those being measured. This enables dynamic work allocation that takes into account both employee skills and emotional state, thereby improving employee satisfaction and organizational efficiency.

[1153] An "employee" is a person who belongs to a company or organization and performs work.

[1154] "Skills" are the knowledge and techniques needed to effectively perform a particular job or task.

[1155] "Career vision" refers to the professional goals and desired career path that an employee wants to achieve in the future.

[1156] "Information collection means" is a general term for methods and functions for collecting information from employees regarding their skills and career visions.

[1157] "Database means" refers to a system or method for storing collected information and conducting analysis based on it.

[1158] "Generative AI means" refers to artificial intelligence that generates optimal personnel placement and feedback based on collected and accumulated data.

[1159] "Condition checks" refer to regular assessments of employees' health and job satisfaction, and take measures if necessary.

[1160] "Means of providing feedback" refers to the methods and functions for sending improvement suggestions and encouraging messages to employees based on the results of the condition check.

[1161] "Sensor means" refers to devices or methods for measuring employees' facial expressions, tone of voice, etc. to assess their emotional state.

[1162] "Means for dynamically optimizing work allocation" refers to methods or systems for adjusting work allocation in real time based on employees' emotional states to achieve optimal allocation.

[1163] The present invention provides a system for optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company, and for periodically checking their condition and dynamically optimizing their work assignments. An embodiment of this system will be described below.

[1164] Information collection method

[1165] The server schedules regular AI interviews with employees. The device presents questions to each employee at the scheduled date and time to understand their skills, career vision, and emotional state. Employees respond to questions such as, "What are your strengths?" and "What is your future career vision?" The device also uses an emotion engine to analyze the employee's facial expressions and tone of voice to understand their emotional state.

[1166] Data accumulation and analysis

[1167] The device sends the collected responses and emotional data to a server, which stores this data in a database and uses it for analysis by the generative AI. For example, the server can learn each employee's habits and performance patterns and store the analysis results.

[1168] Condition check and feedback

[1169] The server schedules regular condition checks and asks employees questions about their health and job satisfaction via their devices. Based on these answers and emotional data, the server uses generative AI to analyze and provide feedback appropriate to the employee's condition. For example, for an employee experiencing high stress, the server might suggest, "Consider ways to distribute your work to reduce stress."

[1170] Dynamic work allocation optimization

[1171] The server uses sensors to measure the emotional state of workers in real time and dynamically optimizes work allocation based on that data. For example, if a specific project requires highly skilled employees, the server will search the database to select the most suitable candidates, taking into account data from the emotion engine and prioritizing employees with stable current emotional states.

[1172] Hardware and software used

[1173] The server is a high-performance computer system equipped with a database management system (DBMS) and software implementing AI algorithms. The terminals are smartphones or smart glasses equipped with various data collection functions and sensors. The emotion engine includes facial expression recognition software and voice analysis software.

[1174] Examples and prompts

[1175] For example, if a factory suddenly needs an engineer with Python skills, the department manager inputs the requirements into the server. The generative AI searches the database and selects the most suitable candidate. Based on emotional data, employees with low stress levels are prioritized for placement.

[1176] Example prompt sentence:

[1177] Required skills: Python

[1178] Employee emotional state: Using measurement data

[1179] Recommend the best candidates.

[1180] In this way, the system of the present invention manages employee skills, career vision, and emotional data in an integrated manner, realizing optimal personnel allocation and health management.

[1181] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1182] Step 1:

[1183] The server schedules regular AI interviews for employees.

[1184] Input: Employee list, interview schedule

[1185] Action: Set up a regular meeting at a specific date and time

[1186] Output: Interview notice

[1187] Step 2:

[1188] The device asks employees questions at set times and dates, collecting information about their skills, career vision, and emotional state.

[1189] Input: Interview schedule, question list

[1190] Action: Present a question and collect employee responses

[1191] Output: Skill information, career vision information, emotional data

[1192] Step 3:

[1193] The device analyzes employees' facial expressions and tone of voice and uses an emotion engine to understand their emotional state.

[1194] Input: Employee voice and video data

[1195] Actions: Voice analysis, facial expression recognition

[1196] Output: Emotional state data

[1197] Step 4:

[1198] The device sends the collected responses and emotion data to the server.

[1199] Input: Skill information, career vision information, emotional data

[1200] Action: Data transfer

[1201] Output: Store in database

[1202] Step 5:

[1203] The server passes the data stored in the database to a generative AI model that analyzes employees' skills, career vision, and emotional state.

[1204] Input: Employee information and emotion data in the database

[1205] Action: Data Analysis

[1206] Output: Analysis results

[1207] Step 6:

[1208] The server uses generative AI to suggest optimal staffing based on skills and career vision.

[1209] Input: Analysis results, request

[1210] Action: Matching process

[1211] Output: Optimal staffing proposal

[1212] Step 7:

[1213] The server schedules regular condition checks and presents employees with questions about their health and job satisfaction via their terminal.

[1214] Input: Condition Check Schedule

[1215] Action: Present a question and collect employee responses

[1216] Output: Condition data

[1217] Step 8:

[1218] The terminal transmits the collected condition data and emotion data to the server.

[1219] Input: condition data, emotion data

[1220] Action: Data transfer

[1221] Output: Store in database

[1222] Step 9:

[1223] The server periodically passes collected condition data to the generative AI model for analysis and provides feedback.

[1224] Input: Condition data in the database

[1225] Actions: Data analysis, feedback generation

[1226] Output: Feedback message

[1227] Step 10:

[1228] The server monitors the real-time emotional state of the workers using a sensor means and dynamically passes the data to the work allocation optimization system.

[1229] Input: Real-time emotional data

[1230] Action: Emotional data collection and analysis

[1231] Output: Work placement instructions

[1232] In this way, the system comprehensively manages employees' skills, career vision, and emotional state, enabling optimal personnel placement and feedback.

[1233] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1234] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1235] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1236] [Fourth embodiment]

[1237] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1238] 7, a 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.

[1239] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1240] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1241] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1242] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1243] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1244] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1245] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1246] The specific processing program 56 is an example of a "program" according to the technology of the present 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.

[1247] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1248] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1249] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1250] MODE FOR CARRYING OUT THE INVENTION

[1251] This invention is a system that efficiently matches the skills and aspirations of corporate employees and periodically checks their health status, thereby achieving optimal personnel placement and high employee satisfaction. This system is equipped with a means for collecting information on employees' skills and career visions and a database means for storing and analyzing that information. It also uses a generative AI means to propose optimal personnel placement, periodically checks employees' condition, and provides feedback.

[1252] Information collection method

[1253] The server schedules regular AI interviews for all employees. At the scheduled date and time, the terminal presents each employee with questions about their skills and career vision. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are presented. The user (employee) answers these questions. The terminal sends the collected answers to the server, which stores them in a database.

[1254] Data analysis and matching implementation

[1255] Once data on skills and career vision has been accumulated, the server passes it to the generation AI for analysis. For example, if a project requires an employee with specific skills, the department manager requests the necessary skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generation AI selects the most suitable candidate, and the server notifies the manager of the results.

[1256] Condition check implementation example

[1257] The server schedules condition checks for all employees at the beginning of each month. The device presents employees with questions about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are displayed. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answers to a generative AI for analysis and provides immediate feedback. For example, it provides advice such as "It seems you are feeling stressed, so consider taking more breaks." The server also analyzes long-term data to identify the health status and problems of the entire organization and propose improvement measures to managers.

[1258] Specific examples

[1259] For example, suppose Department A needs an employee with Java programming skills for a new project. The manager of Department A inputs this request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. The Generative AI confirms this and suggests an allocation. If Employee C reports a high stress level in a regular condition check, the Generative AI analyzes the cause and provides feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[1260] In this way, the system aims to optimize personnel allocation and health management within the company and improve employee satisfaction.

[1261] The processing flow will be explained below.

[1262] Specific steps of the program's processing

[1263] Processing of information collection

[1264] Step 1:

[1265] The server starts a task to schedule AI interviews for all employees at the end of the month.

[1266] Step 2:

[1267] At the scheduled time, the device will prompt each employee with questions about their skills and career aspirations, such as "What are your strengths?" and "What position do you want to be in the future?"

[1268] Step 3:

[1269] The user (employee) inputs answers to the questions presented. For example, they input answers such as "I'm good at Java programming" or "I want to be a project manager."

[1270] Step 4:

[1271] The terminal sends the collected answers to the server.

[1272] Step 5:

[1273] The server saves and accumulates the received response data in a database.

[1274] Data analysis and matching process

[1275] Step 1:

[1276] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[1277] Step 2:

[1278] The server searches the database to obtain information about employees with a particular skill set.

[1279] Step 3:

[1280] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[1281] Step 4:

[1282] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[1283] Step 5:

[1284] The server notifies the administrator of the results of the generative AI's proposal, for example, saying, "Employee A is the best match for the required skills."

[1285] Condition Checks and Feedback Handling

[1286] Step 1:

[1287] The server schedules condition checks for all employees at the beginning of each month.

[1288] Step 2:

[1289] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[1290] Step 3:

[1291] The user (employee) inputs an answer to the presented question, such as "I am satisfied" or "I feel stressed."

[1292] Step 4:

[1293] The terminal transmits the collected response data to the server.

[1294] Step 5:

[1295] The server passes the received response data to the generation AI for analysis.

[1296] Step 6:

[1297] The generative AI provides immediate feedback based on the analysis results, such as advice like "Take more breaks to reduce stress."

[1298] Step 7:

[1299] The server notifies the employee of the generated feedback.

[1300] Step 8:

[1301] The server analyzes long-term data to understand the overall health and trends of the organization. If necessary, it will suggest organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[1302] Example 1

[1303] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1304] Companies are required to efficiently manage employee skills and career prospects and achieve optimal personnel allocation. However, conventional systems require a cumbersome process for collecting and analyzing employee skills and career prospects, making it difficult to continuously monitor the health and satisfaction of individual employees. Responding quickly and accurately to requests from departments is also an issue. This can lead to a decline in employee satisfaction and productivity.

[1305] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1306] In this invention, the server includes a means for collecting information on employee skills and career prospects, a data storage means for accumulating and analyzing the information, a generation AI means for proposing optimal personnel allocation based on the analysis results, a means for periodically conducting employee health checks and analyzing the results, and a means for providing feedback based on the results of the health checks. This allows for efficient management of employee skills and career prospects, making it possible to maintain optimal personnel allocation and high employee satisfaction.

[1307] "Employee skills" refers to the specialized knowledge, abilities, and techniques that employees possess within a company to perform specific tasks.

[1308] "Career outlook" refers to the career goals and desired career path that an employee wants to achieve in the future.

[1309] "Information gathering means" refers to the methods and devices used to obtain data from employees regarding their skills and career prospects.

[1310] "Data storage means" refers to systems or devices that securely store collected information and data and make them accessible when needed.

[1311] "Generative AI means" refers to a system that uses artificial intelligence technology to analyze data and propose optimal personnel placement, etc.

[1312] "Means for conducting health checks" refers to methods and devices for periodically assessing employees' health status and collecting that data.

[1313] "Means for providing feedback" refers to systems or devices that provide advice or information to employees or managers based on the results of health checks.

[1314] "Means for presenting questions to employees" refers to a method or device for displaying questions about skills and career prospects to employees and having them answer the questions.

[1315] "Means for selecting the best candidates based on requests from business units" refers to a system or device that searches a database of employee skills and desired conditions and selects the best employee who matches the specified requirements.

[1316] "Staffing" refers to appropriately determining employees' work locations and responsibilities within a company.

[1317] This invention is a system for efficiently managing the skills and career prospects of employees within a company and achieving optimal personnel allocation. This system collects information on employees' skills and career prospects, stores it in a database, analyzes it, and proposes optimal personnel allocation based on the results. Furthermore, by conducting regular health checks on employees and providing feedback, employee satisfaction and productivity can be improved.

[1318] Hardware and software used

[1319] The system utilizes the following hardware and software:

[1320] Server: Data storage means, generation AI means, scheduling software (e.g., Chronos Scheduler), database management system (e.g., MySQL)

[1321] Terminal: Data input / display device (e.g., PC, tablet), communication protocol (e.g., HTTPS)

[1322] Generative AI models: used for data analysis and feedback generation (e.g., GPT-4)

[1323] Data collection

[1324] The server periodically schedules AI interviews for all employees. Scheduling software (e.g., Chronos Scheduler) is used here. At the set date and time, the terminal presents each employee with questions about their skills and career prospects. For example, questions such as "What are your strengths?" and "What position do you hope to have in the future?" are displayed. The user (employee) answers these questions, and the terminal sends the collected answers to the server. The server stores the answer data in a database.

[1325] Data analysis and optimal staffing proposals

[1326] The server passes the accumulated data to a generative AI model, where the generative AI model (e.g., GPT-4) performs the analysis. For example, if a project requires an employee with specific skills, the department manager requests the skill requirements from the server. The server searches the database and matches the requested skills with employee information. The generative AI model selects the most suitable candidate, and the server notifies the manager of the results.

[1327] Health Check and Feedback

[1328] The server schedules health checks for all employees at the beginning of each month. The device displays questions to employees about their health and job satisfaction. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. The user (employee) answers these questions, and the device sends the answers to the server. The server passes the answer data to a generative AI model for analysis and provides immediate feedback. For example, advice such as "It seems you are feeling stressed, so consider taking more breaks" is displayed.

[1329] Specific examples

[1330] Specific examples of skill matching

[1331] If Department A needs an employee with Java programming skills for a new project, the manager of Department A inputs the request into the server. The server searches the database and determines that Employee B, who has Java skills, is the best fit. A generative AI model (e.g., GPT-4) confirms this and suggests placing Employee B.

[1332] Specific examples of health checks

[1333] If employee C reports high stress levels during a regular health check, the generative AI model will analyze the cause and provide feedback via the server saying, "Consider distributing your daily tasks to reduce stress."

[1334] Example prompts for generative AI models

[1335] Here are some example prompts to input to a generative AI model:

[1336] "Please select the best person for Project A based on employee skill data."

[1337] "Please analyze the cause of employee C's high stress level and propose solutions."

[1338] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1339] Step 1:

[1340] The server periodically schedules AI interviews for all employees. The schedule is set using scheduling software (e.g., Chronos Scheduler). The input is the interview date and time data generated by the scheduling software, and the output is the confirmed schedule for each interview.

[1341] Step 2:

[1342] The terminal presents employees with questions about their skills and career prospects at a set date and time. The questions are displayed in a predefined format. The input is schedule data and a list of questions, and the output is the questions presented to the employee. Examples of questions include "What are your strengths?" and "What position do you hope to have in the future?"

[1343] Step 3:

[1344] The user (employee) answers the questions displayed on the terminal. The input is the answer data that the employee enters into the terminal, and the output is the completed answer data. Specifically, the employee enters the answer into the terminal using a keyboard or touch screen.

[1345] Step 4:

[1346] The terminal sends the collected response data to the server using a secure communication protocol (e.g., HTTPS). The input is the employee's response data, and the output is the data that has been sent.

[1347] Step 5:

[1348] The server stores the received response data in a database. A database management system (e.g., MySQL) is used to ensure the integrity of the stored data. The input is the response data, and the output is the data stored in the database.

[1349] Step 6:

[1350] The server passes the accumulated data to a generative AI model for analysis. The input is data extracted from the database, and the output is the results of analysis by the generative AI model. A generative AI model (e.g., GPT-4) is used to analyze the data and find trends and patterns.

[1351] Step 7:

[1352] Department managers input the skill requirements for a specific project into the server. The input is the skill requirement data entered by the manager, and the output is the requirement data stored on the server. A web interface (e.g., Admin Dashboard) is used for administrators.

[1353] Step 8:

[1354] The server searches the database based on the skill requirements received from the administrator and selects the most suitable employees. The input is the skill requirements data and employee data in the database, and the output is a list of the most suitable employees. This search and selection is performed using a generative AI model.

[1355] Step 9:

[1356] The server notifies the administrator of the results of the selection. Notifications can be sent via email or a web interface. The input is the list of best-fit employees, and the output is the notification data sent to the administrator.

[1357] Step 10:

[1358] The server schedules health checks for all employees at the beginning of each month. Again, it uses scheduling software (e.g., Chronos Scheduler). The input is the schedule data, and the output is the information about the scheduled health checks.

[1359] Step 11:

[1360] At the scheduled date and time, the terminal displays questions to employees about their health and job satisfaction. The input is the schedule data and a list of questions, and the output is the questions presented to the employee. Example questions include, "How satisfied are you with your current job?" and "Have you been feeling stressed recently?"

[1361] Step 12:

[1362] The user (employee) answers questions about their health condition displayed on the terminal. The input is the question data about their health condition, and the output is the completed answer data. Specifically, the employee answers the questions on the terminal using the keyboard or touch screen.

[1363] Step 13:

[1364] The device sends the collected answers to the server, again using a secure communication protocol. The input is the health status response data, and the output is the completed data.

[1365] Step 14:

[1366] The server passes the received health status response data to the generative AI model for analysis. The input is the health status response data, and the output is the analysis result by the generative AI model. This allows for immediate feedback.

[1367] Step 15:

[1368] The server provides feedback to employees based on the results of analysis by the generative AI model. For example, it displays advice such as, "You seem to be feeling stressed, so consider taking more breaks." The input is the analysis result data, and the output is the provided feedback.

[1369] Step 16:

[1370] The server analyzes long-term data and identifies the overall health status and problems of the organization. The input is past health status data, and the output is the long-term analysis results. Based on these results, it proposes improvement measures to managers. For example, it may suggest, "Overall stress levels remain high, so we recommend stress management training for all employees."

[1371] (Application example 1)

[1372] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1373] Conventional in-house personnel placement and health management systems do not adequately grasp employees' skills and career vision or manage their health status, making it difficult to achieve optimal personnel placement and improve employee satisfaction. Furthermore, in factories, it is necessary to select and place optimal robots according to complex manufacturing processes, and also to properly manage their operating status and maintenance status, but current systems make it difficult to do this efficiently.

[1374] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1375] In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel placement based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; means for collecting information on the skills and maintenance status of factory workers and proposing optimal placement; means for periodically collecting and analyzing robot operation status and maintenance data; and means for providing maintenance proposals based on the operation status and maintenance data. This enables optimal personnel placement and employee health management within a company, as well as efficient operation and maintenance management of factory robots.

[1376] "Employee skills" refers to the knowledge and abilities of employees of a company regarding specific tasks or technologies.

[1377] "Career vision" refers to an employee's hopes and goals regarding the occupation or role they desire in the future.

[1378] "Means for collecting information" refers to the methods and devices used to obtain the necessary data from employees.

[1379] "Database means for storage and analysis" refers to a system or method for storing collected information and analyzing it as needed.

[1380] "Generative AI methods" refer to technologies and programs that use artificial intelligence to automatically generate optimal solutions based on collected data.

[1381] A "condition check" is an evaluation or examination to check an employee's health status and job satisfaction.

[1382] A "means for providing feedback" is a method or device for providing advice or instructions to an employee based on the results of the check.

[1383] "Worker skills" refers to the skills and abilities possessed by the workers who operate the machines and equipment within the factory.

[1384] "Maintenance status" refers to the state of machinery and equipment, indicating whether it is properly maintained and managed.

[1385] "Operation status" refers to data that indicates how long a machine or device is operating or how it is being used.

[1386] "Proposal methods" refer to methods and techniques for presenting optimal options and solutions based on the results of the analysis.

[1387] "Maintenance data" refers to the past maintenance history and repair records of machines and equipment.

[1388] This invention is a system that collects and analyzes information about company employees and factory workers (robots) to optimally allocate them and manage their health.

[1389] Feature Overview

[1390] The system has the following main functions:

[1391] 1. Information collection function:

[1392] The server periodically collects information about the skills and career vision of employees or workers, and the terminal presents these questions to employees at set dates and times and collects their answers.

[1393] Furthermore, operational status and maintenance data of the robots in the factory is collected via sensors, and the data is stored in a cloud-connected database (e.g., AWS RDS) via IoT devices.

[1394] 2. Data analysis and matching function:

[1395] The server passes the collected data to a generative AI model (e.g., GPT-4) for analysis, which then recommends the optimal placement of employees or workers (robots) based on the required skill sets and health status.

[1396] When a department requests employees with specific skills or workers best suited to a project, the server searches the database and selects and notifies the best candidates based on analysis by generative AI.

[1397] 3. Condition check function:

[1398] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminals ask employees questions about their health and job satisfaction and collect their responses.

[1399] The server passes the answers to a generative AI model, which provides immediate feedback based on the analysis results. Long-term data analysis identifies the overall health and problems of the organization and provides improvement suggestions to managers.

[1400] Specific examples of program processing

[1401] Hardware and Software

[1402] Server: High-performance cloud server (e.g. AWS EC2)

[1403] Database: Cloud-based database (e.g. AWS RDS)

[1404] Generative AI models: Generative AI models such as GPT-4

[1405] Sensors and IoT devices: Sensors for obtaining information on the robot's operating status and maintenance status

[1406] Information collection and data processing

[1407] The server stores skill information, career vision, and operational status data sent from sensors and devices in a cloud database.

[1408] This data is analyzed at regular intervals to quantify and classify specific skills and conditions.

[1409] Data analysis and feedback

[1410] When a department manager requests a specific skill set, the server passes the relevant personnel or worker information from the database to the generative AI model.

[1411] The generative AI model analyzes skills and health data to select the best candidates.

[1412] The results are notified to the administrator and user via the server.

[1413] Prompt Sentence Examples

[1414] For skill requests by admins:

[1415] "Assign robots with high welding skills for three-hour shifts."

[1416] For employee career vision surveys:

[1417] "What are your best skills? What position would you like to have in the future?"

[1418] As described above, the system enables efficient allocation and condition management of personnel and factory robots within a company, thereby improving overall business efficiency and the health of employees and workers.

[1419] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1420] Step 1: Gather information

[1421] The terminal presents employees and workers (robots) with questions about their skills and career vision at a pre-set date and time. At this time, the terminal displays questions to the employee such as "What skills are you good at?" and "What position do you want in the future?" The user (employee) answers these questions, and the terminal sends the answers to the server. At the same time, sensors on the robots in the factory collect operating status and maintenance data, and this data is also sent to the server. Input data: Employee answers and robot sensor data. Output data: Answers and sensor data sent to the server.

[1422] Step 2: Database accumulation and processing

[1423] The server stores the employee responses and robot sensor data sent from the device in a cloud-connected database (e.g., AWS RDS). At this time, the data is classified into appropriate categories and the skill information is quantified. After data accumulation, the server prepares the data for analysis by the generative AI model. Input data: employee responses and robot sensor data. Output data: categorized and quantified database entries.

[1424] Step 3: Data analysis

[1425] The server passes skill information, career vision, and operating status data to a generative AI model (e.g., GPT-4) for analysis. This model calculates the optimal allocation of employees and robots based on specific skill sets and maintenance status, and selects recommended personnel and workers. Input data: Classified and quantified data. Output data: Optimal allocation and a list of recommended personnel.

[1426] Step 4: Requests and Recommendations

[1427] When a department manager requests personnel with specific skills or for a project, the server searches the database and performs analysis using a generative AI model. The manager is provided with a list of recommended employees or workers (robots). For example, if Java programming skills are required, employees with those skills will be selected. Input data: Request from the manager. Output data: List of recommended employees or workers.

[1428] Step 5: Condition Check

[1429] The server schedules condition checks for all employees or workers at the beginning of each month, and the terminal displays questions about their health and job satisfaction. The user answers these questions, and the terminal sends the answers to the server. For example, questions such as "How satisfied are you with your current job?" and "Have you been feeling stressed recently?" are presented. Input data: Employee answers. Output data: Condition data sent to the server.

[1430] Step 6: Analyze data and provide feedback

[1431] The server passes the collected condition data to the generative AI model for analysis. Based on the analysis results, it provides immediate feedback, such as "You seem to be feeling stressed, so consider increasing your break time." Long-term data analysis identifies the health status and problems of the entire organization and makes improvement suggestions to managers. Input data: condition data. Output data: individual feedback and improvement suggestions.

[1432] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1433] MODE FOR CARRYING OUT THE INVENTION

[1434] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[1435] Information collection method

[1436] The server starts by setting up regular AI interviews for all employees. The device presents each employee with questions at the scheduled date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions. The device then uses an emotion engine to analyze the user's facial expressions and tone of voice to understand their emotional state.

[1437] The device sends the collected responses and emotion data to a server, which stores them in a database.

[1438] Data analysis and matching implementation

[1439] The server passes skills, career vision, and emotional data to the generative AI for analysis. For example, if a department manager is looking for personnel for a specific project, they input the request into the server. The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotional engine to evaluate whether the employee's current mental state is suitable for the position.

[1440] For example, if Employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account and select the best candidate based not only on their skills, but also on their current emotional state.

[1441] Condition check and feedback implementation example

[1442] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction, including an emotion engine that assesses their actual emotional state based on facial expressions and tone of voice.

[1443] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[1444] The server performs the analysis using generative AI. For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[1445] Specific examples

[1446] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that Employee C is a little tired. As a result, the generative AI determines that another Employee D is slightly less skilled but is very mentally stable, so it suggests Employee D as the best candidate.

[1447] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[1448] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[1449] The processing flow will be explained below.

[1450] Specific steps of the program's processing

[1451] Processing of information collection

[1452] Step 1:

[1453] The server starts a task to schedule AI interviews for all employees at the end of the month.

[1454] Step 2:

[1455] At scheduled times, the devices prompt each employee with questions about their skills, career vision, and emotional state, such as "What are your best skills?", "What role do you want to play in the future?", and "How do you feel about your current job?"

[1456] Step 3:

[1457] The user (employee) inputs answers to the questions presented and displays facial expressions and tone of voice to indicate their emotional state.

[1458] Step 4:

[1459] The terminal transmits the collected answers and emotion data generated by the emotion engine to the server.

[1460] Step 5:

[1461] The server stores and accumulates the received response data and emotion data in a database.

[1462] Data analysis and matching process

[1463] Step 1:

[1464] A department manager requests the skill set needed for a particular project from the server, for example, "We need an employee with Java programming skills."

[1465] Step 2:

[1466] The server searches the database to obtain information about employees with a particular skill set.

[1467] Step 3:

[1468] The generative AI analyzes the acquired employee data and runs an algorithm to select the best candidates.

[1469] Step 4:

[1470] The generative AI also takes data from the emotion engine into account to make appropriate assessments, such as comparing an employee with high skills but high emotional stress levels with an employee with slightly lower skills but low emotional stress levels.

[1471] Step 5:

[1472] The generation AI returns a list of optimal candidates to the server, for example, presenting a result such as "Employee A is the optimal candidate."

[1473] Step 6:

[1474] The server then notifies the department manager of the results of the generative AI's proposal, for example, saying, "Employee A best matches the required skills and has the appropriate emotional state."

[1475] Condition Checks and Feedback Handling

[1476] Step 1:

[1477] The server schedules condition checks for all employees at the beginning of each month.

[1478] Step 2:

[1479] The device asks employees questions about their health and job satisfaction, such as "How satisfied are you with your current job?" and "Have you been feeling stressed at work lately?"

[1480] Step 3:

[1481] The user (employee) inputs answers to the questions presented and also uses the emotion engine to register facial expressions and tone of voice that indicate their emotional state. For example, the user might answer "I'm satisfied" and display a relaxed facial expression and tone of voice.

[1482] Step 4:

[1483] The terminal transmits the collected response data and emotion data to the server.

[1484] Step 5:

[1485] The server passes the received response data and emotion data to the generation AI for analysis.

[1486] Step 6:

[1487] The generative AI provides immediate feedback based on the analysis results, generating specific advice such as, "It appears your stress level is high, so please consider taking regular breaks or consulting a doctor."

[1488] Step 7:

[1489] The server notifies the employee of the generated feedback.

[1490] Step 8:

[1491] The server analyzes long-term data to understand the overall health and trends of the organization, and if necessary, suggests organizational improvement measures to managers. For example, it might say, "Stress levels are rising across the entire department. Please consider revising your work flow."

[1492] In this way, the system manages employees' skills, career vision, and emotional state in an integrated manner, enabling appropriate personnel allocation and health management.

[1493] Example 2

[1494] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1495] Conventional personnel placement systems only consider employees' skills and career vision when deciding on assignments, often ignoring their emotions and physical condition. This results in increased employee stress, leading to lower work efficiency and higher employee turnover. Furthermore, because conventional systems do not adequately check employees' physical condition on a regular basis, it is difficult to prevent a decline in employee health and job satisfaction.

[1496] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1497] In this invention, the server includes means for collecting information on employee skills and career vision, database means for accumulating and analyzing data on the information and emotional state, generation AI means for proposing optimal personnel allocation based on the analysis results, means for periodically conducting employee condition checks and analyzing the results, and means for providing feedback based on the results of the condition checks. This enables optimal personnel allocation and management that takes into consideration not only employee skills and career vision, but also their emotional state and health state in an integrated manner.

[1498] An "employee" is a worker who belongs to a company or organization and is employed to perform a specific job.

[1499] "Skills" refers to the knowledge, techniques, and abilities required to perform a specific job or task.

[1500] "Career vision" refers to an employee's career goals, hopes, and path forward for the future.

[1501] "Means of collecting information" refers to methods and devices for obtaining data from employees regarding their skills and career vision.

[1502] A "database" is a system for storing and managing collected information and for searching and analyzing it as needed.

[1503] "Database means" refers to the mechanisms and methods for storing, managing, searching, and analyzing information using a database.

[1504] "Generative AI" refers to systems that use artificial intelligence techniques to analyze data and generate results or recommendations tailored to a specific purpose.

[1505] "Generative AI means" refers to a system or method that uses generative AI to analyze data and output results or suggestions.

[1506] A "condition check" is a periodic survey or evaluation to assess an employee's health, job satisfaction, stress level, etc.

[1507] "Feedback" refers to advice and suggestions provided to employees based on the results of condition checks and data analysis.

[1508] "Emotional state" refers to the emotions and psychological state an employee is feeling at a given time.

[1509] An "emotion engine" is a system or software that analyzes data such as facial expressions and tone of voice to assess emotional states.

[1510] MODE FOR CARRYING OUT THE INVENTION

[1511] The present invention is a system that improves employee satisfaction and organizational efficiency by optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company and conducting regular condition checks. This system includes a means for collecting information on employees' skills and career vision, a database means for storing and analyzing the information, a generation AI means, and an emotion engine.

[1512] Information collection method

[1513] The server will schedule regular AI interviews with all employees, which will allow the company to understand each employee's skills, career vision, and emotional state.

[1514] The device presents each employee with questions at a set date and time. These questions are intended to understand their skills, career vision, and emotional state. For example, questions such as "What are your strengths?", "What is your future career vision?", and "How do you feel about your work?" are presented. The user (employee) answers these questions.

[1515] Additionally, the device uses an Emotion AI engine to understand the user's emotional state by analyzing their facial expressions and tone of voice, and the analysis is based on data collected using a webcam and microphone.

[1516] The device sends the collected responses and emotion data to a server, typically in JSON format via the HTTP protocol, and the server stores the received data in a database (e.g., MySQL or PostgreSQL).

[1517] Data analysis and matching implementation

[1518] The server passes the skills, career vision, and emotion data to a generative AI (e.g., OpenAI's GPT-4) for analysis. For example, if a department manager is looking for talent for a specific project, they input the request into the server and give instructions to the generative AI using prompts like the following:

[1519] "Based on Employee A's recent sentiment data, please rate whether he is suitable to be the Java expert for Project B."

[1520] "If Employee E is experiencing high stress, what feedback should I provide?"

[1521] The server searches the database to find employees who match the required skills. The generative AI also uses data from the emotion engine to evaluate whether the employee's current mental state is suitable for the position. For example, if employee A has excellent Java programming skills, but recent emotional data indicates high stress, the generative AI will take this information into account. The best candidate is selected not only based on their skills, but also on their current emotional state.

[1522] Condition check and feedback implementation example

[1523] The server schedules regular condition checks for all employees, and the devices ask employees questions about their health and job satisfaction. This includes an emotion engine (Emotion AI) that evaluates their actual emotional state based on facial expressions and tone of voice.

[1524] The user (employee) answers questions, and the device also collects emotional data. For example, responses to questions such as "How satisfied are you with your current job?" and "What is your recent stress level?" are sent to the server along with the results of facial expression and voice analysis.

[1525] The server analyzes this data using a generative AI (e.g., GPT-4). For example, if high levels of stress are detected, the generative AI will generate feedback such as, "Try distributing your daily tasks to reduce stress." Feedback based on emotional data is more appropriate and has the effect of improving employee satisfaction.

[1526] Specific examples

[1527] For example, if a Java expert is urgently needed for Project B, the department manager inputs the requirements into the server. The generative AI determines that Employee C is good at Java, but that recent emotional data indicates that he is a little tired. As a result, the generative AI suggests Employee D as the best candidate, since he is slightly less skilled but mentally stable.

[1528] In addition, if Employee E answers "Yes" to the question "Are you feeling stressed at work recently?" during a regular condition check and the facial expression analysis results indicate dissatisfaction, the generative AI will suggest "ways to deal with recent stress." As a result, Employee E's satisfaction will improve, and the efficiency of the entire organization will increase.

[1529] In this way, the system of the present invention manages employees' skills, career vision, and emotional data in an integrated manner, realizing appropriate personnel allocation and health management.

[1530] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1531] Step 1: Configure information collection

[1532] The server schedules regular AI interviews for all employees. This includes the ability to send notifications to all employees at a specific date and time, for example, every month. The input is a list of all employees and the interview schedule, and the output is an interview notification sent to each employee. In this step, the interview date and time and question list are set within the system.

[1533] Step 2: Question posing

[1534] The terminal presents questions to each employee at the set date and time. The input is the schedule and question list sent from the server, and the output is the employee's answers. Specifically, questions such as "What are your strengths?" and "What is your vision for your future career?" are displayed on the terminal's interface.

[1535] Step 3: Collect response and sentiment data

[1536] The user (employee) answers questions presented on the terminal. The terminal then analyzes the user's facial expressions and tone of voice and uses an emotion engine (e.g., Emotion AI) to understand the user's emotional state. The input is the user's text response and non-verbal data (facial expressions, tone of voice), and the output is analyzed emotional data. Specifically, the terminal collects data using a webcam and microphone.

[1537] Step 4: Data transmission and storage

[1538] The device sends the collected responses and emotion data to the server. The input is data from the device (JSON format), and the output is data storage on the server side. Data is sent using the HTTP protocol, and the server stores the received data in a database (e.g., MySQL, PostgreSQL).

[1539] Step 5: Acquire and analyze data

[1540] The server passes the required skill and emotion data to the generation AI (e.g., GPT-4) for analysis. The input is employee data in the database and a request from the department manager, and the output is the analysis result. Specifically, the server uses an SQL query to retrieve the required information from the database and passes it to the generation AI as a prompt. Example prompt: "Please evaluate employees who are suitable as Java experts for Project B based on their skill and emotion data."

[1541] Step 6: Select the best candidate

[1542] Generative AI selects the best candidates based on input data. The inputs are skills, career vision, emotional data, and requests, and the output is a list of the best candidates. For example, Generative AI evaluates employees' Java skills and emotional state to create a list of the best Java experts.

[1543] Step 7: Schedule a Condition Check

[1544] The server schedules regular condition checks for all employees. The input is a list of all employees and the scheduled time, and the output is a check notification sent to each employee, which includes questions about their weekly health status and job satisfaction.

[1545] Step 8: Present health status and satisfaction questions

[1546] The terminal presents employees with questions about their health and job satisfaction. The input is a list of questions from the server, and the output is the employee's answers. Examples of questions displayed include "How satisfied are you with your current job?" and "What is your stress level these days?"

[1547] Step 9: Analyze health and emotion data

[1548] The terminal analyzes the employee's responses as well as their facial expressions and voice to assess their emotional state. The input is the user's response (text) and non-verbal data, and the output is the analyzed emotional data.

[1549] Step 10: Generate feedback

[1550] The server analyzes the collected data using generative AI and generates appropriate feedback. The input is emotional data and health status data, and the output is written feedback. For example, the generated advice might be, "Try distributing your daily tasks to reduce stress."

[1551] Step 11: Provide feedback

[1552] The terminal presents the generated feedback to the user. The input is the feedback content from the server, and the output is the content presented to the user. Specifically, the feedback is displayed on the terminal interface. Based on this feedback, the user can understand specific countermeasures and improvement measures.

[1553] (Application example 2)

[1554] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1555] The present invention relates to a system for optimal personnel allocation based on the skills and career vision information of company employees, as well as their emotional state. Conventional systems do not adequately check employee condition or manage their emotional state, which can lead to the accumulation of stress and dissatisfaction among employees. Furthermore, because skill matching relies on static data, dynamic job allocation is difficult. The present invention aims to solve these problems and improve corporate efficiency while maximizing employee capabilities.

[1556] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for collecting information on employee skills and career visions; database means for storing and analyzing the information; generation AI means for proposing optimal personnel allocation based on the analysis results; means for periodically conducting employee condition checks and analyzing the results; means for providing feedback based on the results of the condition checks; sensor means for measuring the emotional state of workers; and means for dynamically optimizing work allocation based on the emotional state of those being measured. This enables dynamic work allocation that takes into account both employee skills and emotional state, thereby improving employee satisfaction and organizational efficiency.

[1557] An "employee" is a person who belongs to a company or organization and performs work.

[1558] "Skills" are the knowledge and techniques needed to effectively perform a particular job or task.

[1559] "Career vision" refers to the professional goals and desired career path that an employee wants to achieve in the future.

[1560] "Information collection means" is a general term for methods and functions for collecting information from employees regarding their skills and career visions.

[1561] "Database means" refers to a system or method for storing collected information and conducting analysis based on it.

[1562] "Generative AI means" refers to artificial intelligence that generates optimal personnel placement and feedback based on collected and accumulated data.

[1563] "Condition checks" refer to regular assessments of employees' health and job satisfaction, and take measures if necessary.

[1564] "Means of providing feedback" refers to the methods and functions for sending improvement suggestions and encouraging messages to employees based on the results of the condition check.

[1565] "Sensor means" refers to devices or methods for measuring employees' facial expressions, tone of voice, etc. to assess their emotional state.

[1566] "Means for dynamically optimizing work allocation" refers to methods or systems for adjusting work allocation in real time based on employees' emotional states to achieve optimal allocation.

[1567] The present invention provides a system for optimally allocating personnel based on the skills, career vision, and emotional state of employees in a company, and for periodically checking their condition and dynamically optimizing their work assignments. An embodiment of this system will be described below.

[1568] Information collection method

[1569] The server schedules regular AI interviews with employees. The device presents questions to each employee at the scheduled date and time to understand their skills, career vision, and emotional state. Employees respond to questions such as, "What are your strengths?" and "What is your future career vision?" The device also uses an emotion engine to analyze the employee's facial expressions and tone of voice to understand their emotional state.

[1570] Data accumulation and analysis

[1571] The device sends the collected responses and emotional data to a server, which stores this data in a database and uses it for analysis by the generative AI. For example, the server can learn each employee's habits and performance patterns and store the analysis results.

[1572] Condition check and feedback

[1573] The server schedules regular condition checks and asks employees questions about their health and job satisfaction via their devices. Based on these answers and emotional data, the server uses generative AI to analyze and provide feedback appropriate to the employee's condition. For example, for an employee experiencing high stress, the server might suggest, "Consider ways to distribute your work to reduce stress."

[1574] Dynamic work allocation optimization

[1575] The server uses sensors to measure the emotional state of workers in real time and dynamically optimizes work allocation based on that data. For example, if a specific project requires highly skilled employees, the server will search the database to select the most suitable candidates, taking into account data from the emotion engine and prioritizing employees with stable current emotional states.

[1576] Hardware and software used

[1577] The server is a high-performance computer system equipped with a database management system (DBMS) and software implementing AI algorithms. The terminals are smartphones or smart glasses equipped with various data collection functions and sensors. The emotion engine includes facial expression recognition software and voice analysis software.

[1578] Examples and prompts

[1579] For example, if a factory suddenly needs an engineer with Python skills, the department manager inputs the requirements into the server. The generative AI searches the database and selects the most suitable candidate. Based on emotional data, employees with low stress levels are prioritized for placement.

[1580] Example prompt sentence:

[1581] Required skills: Python

[1582] Employee emotional state: Using measurement data

[1583] Recommend the best candidates.

[1584] In this way, the system of the present invention manages employee skills, career vision, and emotional data in an integrated manner, realizing optimal personnel allocation and health management.

[1585] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1586] Step 1:

[1587] The server schedules regular AI interviews for employees.

[1588] Input: Employee list, interview schedule

[1589] Action: Set up a regular meeting at a specific date and time

[1590] Output: Interview notice

[1591] Step 2:

[1592] The device asks employees questions at set times and dates, collecting information about their skills, career vision, and emotional state.

[1593] Input: Interview schedule, question list

[1594] Action: Present a question and collect employee responses

[1595] Output: Skill information, career vision information, emotional data

[1596] Step 3:

[1597] The device analyzes employees' facial expressions and tone of voice and uses an emotion engine to understand their emotional state.

[1598] Input: Employee voice and video data

[1599] Actions: Voice analysis, facial expression recognition

[1600] Output: Emotional state data

[1601] Step 4:

[1602] The device sends the collected responses and emotion data to the server.

[1603] Input: Skill information, career vision information, emotional data

[1604] Action: Data transfer

[1605] Output: Store in database

[1606] Step 5:

[1607] The server passes the data stored in the database to a generative AI model that analyzes employees' skills, career vision, and emotional state.

[1608] Input: Employee information and emotion data in the database

[1609] Action: Data Analysis

[1610] Output: Analysis results

[1611] Step 6:

[1612] The server uses generative AI to suggest optimal staffing based on skills and career vision.

[1613] Input: Analysis results, request

[1614] Action: Matching process

[1615] Output: Optimal staffing proposal

[1616] Step 7:

[1617] The server schedules regular condition checks and presents employees with questions about their health and job satisfaction via their terminal.

[1618] Input: Condition Check Schedule

[1619] Action: Present a question and collect employee responses

[1620] Output: Condition data

[1621] Step 8:

[1622] The terminal transmits the collected condition data and emotion data to the server.

[1623] Input: condition data, emotion data

[1624] Action: Data transfer

[1625] Output: Store in database

[1626] Step 9:

[1627] The server periodically passes collected condition data to the generative AI model for analysis and provides feedback.

[1628] Input: Condition data in the database

[1629] Actions: Data analysis, feedback generation

[1630] Output: Feedback message

[1631] Step 10:

[1632] The server monitors the real-time emotional state of the workers using a sensor means and dynamically passes the data to the work allocation optimization system.

[1633] Input: Real-time emotional data

[1634] Action: Emotional data collection and analysis

[1635] Output: Work placement instructions

[1636] In this way, the system comprehensively manages employees' skills, career vision, and emotional state, enabling optimal personnel placement and feedback.

[1637] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1638] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1639] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1640] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1641] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1642] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1643] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1644] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1645] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1646] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1647] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1648] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1649] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1650] 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.

[1651] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1652] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1653] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific processing may be a single processor.

[1654] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1655] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1656] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1657] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1658] The following is further disclosed regarding the above embodiment.

[1659] (Claim 1)

[1660] A means of collecting information about employees' skills and career vision;

[1661] a database means for storing and analyzing the information;

[1662] A generating AI means for proposing optimal personnel placement based on the analysis results;

[1663] Regularly check the condition of employees and analyze the results.

[1664] means for providing feedback based on the results of said condition check;

[1665] A system including:

[1666] (Claim 2)

[1667] 10. The system of claim 1, further comprising means for presenting the employee with questions regarding skills and career vision.

[1668] (Claim 3)

[1669] 10. The system of claim 1, further comprising means for matching employee skill sets with desired conditions and selecting the most suitable candidate based on a request from a department.

[1670] (Claim 4)

[1671] 2. The system according to claim 1, further comprising means for analyzing the overall condition of the organization using long-term data based on the results of the condition check, and proposing improvement measures for the organization.

[1672] (Claim 5)

[1673] 10. The system of claim 1, further comprising means for periodically presenting the condition check questions to the employee.

[1674] "Example 1"

[1675] (Claim 1)

[1676] means of collecting information about employees' skills and career prospects;

[1677] data storage means for storing and analyzing said information;

[1678] A generating AI means for proposing optimal personnel allocation based on the analysis results;

[1679] A means of conducting regular employee health checks and analyzing the results;

[1680] means for providing feedback based on the results of said health check;

[1681] A system including:

[1682] (Claim 2)

[1683] 10. The system of claim 1, further comprising means for presenting the employee with questions regarding skills and career prospects.

[1684] (Claim 3)

[1685] 10. The system of claim 1, further comprising means for matching employee skill sets with desired conditions and selecting the most suitable candidate based on a request from a business unit.

[1686] "Application Example 1"

[1687] (Claim 1)

[1688] A means of collecting information about employees' skills and career vision;

[1689] a database means for storing and analyzing the information;

[1690] A generating AI means for proposing optimal personnel placement based on the analysis results;

[1691] Regularly check the condition of employees and analyze the results.

[1692] means for providing feedback based on the results of said condition check;

[1693] A means of collecting information on the skills and maintenance status of factory workers and proposing optimal allocation;

[1694] A means to periodically collect and analyze robot operation status and maintenance data;

[1695] means for providing maintenance suggestions based on the operational status and maintenance data;

[1696] A system including:

[1697] (Claim 2)

[1698] 10. The system of claim 1, further comprising means for presenting the employee with questions regarding skills and career vision.

[1699] (Claim 3)

[1700] 10. The system of claim 1, further comprising means for matching employee skill sets with desired conditions and selecting the most suitable candidate based on a request from a department.

[1701] "Example 2: Combining Emotion Engines"

[1702] (Claim 1)

[1703] A means of collecting information about employees' skills and career vision;

[1704] database means for storing and analyzing data relating to said information and emotional states;

[1705] A generating AI means for proposing optimal personnel placement based on the analysis results;

[1706] Regularly check the condition of employees and analyze the results.

[1707] means for providing feedback based on the results of said condition check;

[1708] A system including:

[1709] (Claim 2)

[1710] 10. The system of claim 1, further comprising means for presenting the employee with questions regarding skills and career vision.

[1711] (Claim 3)

[1712] 10. The system of claim 1, further comprising means for matching employee skill sets with desired conditions and selecting the most suitable candidate based on a request from a department.

[1713] "Application example 2 when combining emotion engines"

[1714] (Claim 1)

[1715] A means of collecting information about employees' skills and career vision;

[1716] a database means for storing and analyzing the information;

[1717] A generating AI means for proposing optimal personnel placement based on the analysis results;

[1718] Regularly check the condition of employees and analyze the results.

[1719] means for providing feedback based on the results of said condition check;

[1720] sensor means for measuring the emotional state of the worker;

[1721] means for dynamically optimizing work assignments based on the emotional state of the person being measured;

[1722] A system including:

[1723] (Claim 2)

[1724] 10. The system of claim 1, further comprising means for presenting the employee with questions regarding skills and career vision.

[1725] (Claim 3)

[1726] 10. The system of claim 1, further comprising means for matching employee skill sets with desired conditions and selecting the most suitable candidate based on a request from a department. [Explanation of symbols]

[1727] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of collecting information about employees' skills and career vision; a database means for storing and analyzing the information; A generating AI means for proposing optimal personnel placement based on the analysis results; Regularly check the condition of employees and analyze the results. means for providing feedback based on the results of said condition check; A system including:

2. The system of claim 1 further comprising means for presenting the employee with questions regarding skills and career vision.

3. The system according to claim 1, further comprising means for matching employee skill sets with desired conditions and selecting the most suitable candidate based on a request from a department.

4. The system according to claim 1 , further comprising means for analyzing the overall condition of the organization using long-term data based on the results of the condition check, and proposing measures to improve the organization.

5. The system of claim 1 further comprising means for periodically presenting the condition check questions to an employee.

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