system

Abstract

We provide the system. [Solution] A method for formatting multiple types of data obtained from a database, A means of analyzing current work experience based on a desired career path and identifying the most suitable job, A means of evaluating the skill sets aligned with the business objectives of each department and assigning personnel with those skills to the department, A means of identifying the experience necessary for future executive candidates and assigning them to roles where they can gain that experience, A system that includes means for integrating the analysis results from each perspective and outputting an optimal layout plan.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Appropriate personnel placement in modern enterprises is a complex issue involving various factors such as the career growth of employees, the maximization of the business value of the organization, and the cultivation of next-generation cadres. Therefore, there is a problem that there is a lack of a personnel matching mechanism that comprehensively and efficiently considers these factors. In addition, with conventional methods, it is difficult to appropriately utilize self-declaration data and various evaluation files, resulting in insufficient matching.

Means for Solving the Problems

[0005] This invention provides a means for formatting multiple types of data obtained from a database and analyzing the current professional history of employees based on their career goals. This enables the identification of optimal job roles. It also includes means for evaluating skill sets aligned with the business objectives of each department and assigning personnel with those skills to those departments. Furthermore, it includes means for identifying the experience necessary for future executive candidates and assigning them to jobs that will allow them to gain that experience. Through these means, the invention provides a system that can integrate the analysis results from each perspective and output optimal placement plans.

[0006] A "database" is a structured collection of data used to efficiently store, manage, retrieve, and update information.

[0007] "Formatting" refers to the process of converting data into a format that is easy to analyze and use.

[0008] A "career path" refers to the long-term professional career route or plan that an employee aims for.

[0009] "Work history" refers to a record of the jobs and roles one has held to date, and the experience gained from them.

[0010] "Business value" refers to the total economic or social value for a company or organization.

[0011] A "skill set" is a combination of skills and knowledge required for a specific job or task.

[0012] "Personnel assignment" refers to placing individuals who are suitable for a specific project or job.

[0013] "Next-generation executive candidates" refer to individuals who are expected to assume important positions within the organization in the future.

[0014] "Duties" refer to the specific tasks and roles assigned within an organization.

[0015] "Analysis results" refer to the conclusions and findings obtained from the analysis based on data.

Brief Explanation of Drawings

[0016] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which multiple emotions are mapped. [Figure 10] It shows an emotion map to which multiple emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Mode for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be one arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be one type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0020] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0021] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention provides an embodiment of a system for efficiently optimizing talent matching within a company, and has multiple functions centered around an artificial intelligence agent. This enables support for employees' career paths, maximization of business value for each department, and development of next-generation executives.

[0038] The server first retrieves relevant data from the company's database, such as self-reported data, evaluation files, and executive biographies. Then, it processes this data to standardize it and prepares it for various analyses.

[0039] Next, the server analyzes the employee's professional history based on their career path and identifies roles that support appropriate career growth. For example, if an employee in the sales department wants to move into product management in the future, the server will recommend participation in projects that align with their goals.

[0040] In parallel, the server evaluates each department's business objectives and displays employees with the necessary skills as options. If a department head wants to strengthen their new product development team, the server will suggest the most suitable technical experts for the project and make assignments to enhance organizational capabilities.

[0041] Furthermore, with the development of next-generation executives in mind, the server extracts necessary skill sets from executive resume data and places candidates in positions where they can gain the required experience. For example, if a candidate for executive status in the technology department needs experience in company-wide projects, the server will place them in such international projects.

[0042] This system presents optimal staffing plans on a dashboard for HR personnel and department managers. Based on the results, users can make final adjustments, resulting in efficient staffing assignments that are tailored to the entire company.

[0043] Through these processes, servers help companies maximize the use of their internal resources and support data-driven decision-making to improve their competitiveness.

[0044] The following describes the processing flow.

[0045] Step 1:

[0046] The server accesses the company's database to retrieve HR-related data. This includes employee self-report data, various performance evaluation files, and executive career histories. The server collects this data and prepares it for processing.

[0047] Step 2:

[0048] The server processes the acquired data and verifies its consistency. This involves filling in missing data points and standardizing the format. This prepares the data for easier use in subsequent analysis.

[0049] Step 3:

[0050] The server begins its analysis from the employee's perspective. First, it analyzes the employee's self-reported data and career aspirations, matching them with their current work experience. The server then identifies and lists the job roles that offer the most suitable growth opportunities for each employee.

[0051] Step 4:

[0052] The server conducts an analysis from a departmental perspective. It evaluates each department's business objectives and identifies their current skill sets. The server identifies the resources needed to achieve the business objectives and creates a proposal for optimal personnel allocation.

[0053] Step 5:

[0054] The server identifies potential next-generation executives from a human resources perspective. Based on executive resume data, it extracts the experience required by future executives and suggests suitable roles or projects. This ensures that executive candidates are placed in positions where they can acquire the necessary experience.

[0055] Step 6:

[0056] The server integrates analysis results from various perspectives to generate a comprehensive personnel placement plan. Based on these results, it presents the optimal placement plan to the user, such as HR personnel or department managers.

[0057] Step 7:

[0058] Users review the personnel assignment proposals presented by the server and make revisions or adjustments as needed. This leads to the final personnel assignment being determined and implemented.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] In corporate talent allocation, a challenge exists where the abilities of employees do not align with the needs of departments, making efficient talent utilization difficult. In particular, there is a need to address the insufficient understanding and placement of appropriate career backgrounds in career paths and the development of future leaders, which hinders the improvement of corporate competitiveness.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for converting information obtained from a data source into a standard format, means for analyzing the current professional experience based on desired economic activities and identifying the optimal role, and means for evaluating the set of skills that match the business objectives of each department and assigning individuals with those skills to the department. This enables the optimal allocation of personnel within the company.

[0064] "Data sources" refer to various types of information collected from both inside and outside the company, including employee self-reported information, evaluation documents, and past career data.

[0065] A "standard format" refers to a data structure that has been formatted to enable unified analysis of data in different formats.

[0066] "Economic activity" refers to activities related to human resource utilization within an organization, such as employees' work history and career path goals.

[0067] "Experienced professional background" refers to the history of work experience and skill sets that an employee has accumulated to date.

[0068] "Role" refers to the specific duties or positions that employees are expected to perform within a company.

[0069] "Business objectives" refer to the business achievement targets set by a company or its department, and the necessary skills and personnel requirements are determined based on these objectives.

[0070] A "skill set" refers to the combination of knowledge and skills required to perform a specific job.

[0071] "Person" refers to an employee or a person who is to be assigned to a position within a corporate organization.

[0072] A "leadership candidate" refers to an employee who is scheduled to be trained to assume a leadership position in the next generation.

[0073] This invention is a system aimed at optimizing personnel allocation within a company and promoting information-based decision-making. The system mainly consists of a server, terminals, and users.

[0074] The server first collects information from data sources both inside and outside the organization. This includes employee self-reported information, performance evaluations, and past professional experience data. The server retrieves the collected information using database technology and formats it into a standard format using the Python Pandas library.

[0075] The server then analyzes the employee's professional background and desired economic activities. Using data analysis libraries such as Scikit-learn and TENSORFLOW®, it predicts the optimal role based on past work experience and skill sets. For example, if an employee in sales aims to become a product manager, the server will recommend projects suitable for that goal.

[0076] The terminal processes the data aggregated by the server and displays the analysis results in a dashboard format. This dashboard is created using Power BI or Tableau and provides visual information for personnel allocation decisions.

[0077] HR personnel and department managers, who are the users, use the provided dashboard to finalize employee placement and role assignments. A concrete example using prompts is, "This employee is aiming to become a product manager from the sales department. What is the best project for them?" In response, the system can suggest the most suitable project.

[0078] By utilizing this system, companies can efficiently match the skills of their employees with the needs of their departments, and make the most of their inherent resources.

[0079] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0080] Step 1:

[0081] The server collects information from data sources. As input, it retrieves employee self-reported information, performance evaluations, and past professional experience data from the company's database. It uses SQL queries to retrieve the information and then converts it to a standard format using the Python Pandas library for output. This output data forms the basis for subsequent analysis.

[0082] Step 2:

[0083] The server analyzes the formatted data to evaluate employees' professional backgrounds and desired economic activities. It receives the formatted data from the previous step as input and performs analysis using Scikit-learn and TensorFlow. Specific data calculations include applying predictive models based on past work experience and skill sets. The output provides analysis results to identify optimal roles. For example, it might suggest a suitable path for a sales employee to become a product manager.

[0084] Step 3:

[0085] The server creates employee placement plans based on the analysis results. It uses the results of career analysis and departmental work objectives as input. Based on this, it identifies individuals with the appropriate skill sets and proposes their assignment to departments. Specifically, it generates and outputs a list of recommended personnel.

[0086] Step 4:

[0087] The terminal displays the calculated placement plan on a dashboard. It receives the personnel placement plan sent from the server as input and visualizes it using Power BI or Tableau. A user interface is established, and the output is a visually organized dashboard of the placement plan. This serves as the basis for users to view this information and make their final decisions.

[0088] Step 5:

[0089] The user uses the presented dashboard to make the final personnel placement decision. As input, they refer to the visualized placement proposals displayed on the terminal and use prompts to inquire about further information. For example, they might use a prompt such as, "This employee is aiming to become a product manager from the sales department. What project would be best suited for them?" to confirm specific placements and project assignments. The output is the final placement decision.

[0090] (Application Example 1)

[0091] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0092] Existing talent matching systems have limitations in optimizing career paths within companies and evaluating skill sets by department, and are particularly inadequate for guiding citizens to volunteer and participate in projects in smart cities. Therefore, there is a need to achieve efficient allocation of talent throughout the city and propose appropriate activity participation based on citizens' skills and interests.

[0093] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0094] In this invention, the server includes means for formatting multiple types of data obtained from a database, means for analyzing the current occupational history based on a desired career path and identifying the optimal role, means for evaluating the technical requirements that match the business objectives of each department and assigning personnel with the necessary skills to the department, means for generating optimal proposals for city-wide activities based on input of citizens' skills and interests, and means for outputting optimal assignment plans and activity participation plans. This makes it possible to propose optimal personnel allocation and effective citizen participation not only for companies but for the entire smart city.

[0095] A "database" is a foundation for systematically organizing and managing information, and is a system that can effectively store and retrieve diverse information.

[0096] "Formatting" refers to the process of standardizing acquired data and converting it into an analyzable format.

[0097] "Career path" is a concept that indicates the direction of an individual's career and activity plan.

[0098] "Work history" refers to a record of the jobs and occupations an individual has held up to date.

[0099] "Role" is a concept that includes the responsibilities and functions that individuals and organizations are expected to fulfill.

[0100] "Business objectives" refer to specific goals that an organization or department should achieve within a particular timeframe.

[0101] "Technical requirements" refer to the set of specialized skills and knowledge needed to successfully complete a particular task or project.

[0102] The term "citizen" refers to people who reside in a specific area and participate in that society.

[0103] "Generating proposals" refers to the process of creating optimal options and action plans based on analysis results.

[0104] A "placement plan" refers to a plan for assigning individuals and resources to the most optimal locations and roles.

[0105] A "proposed activity participation" refers to a specific proposal for a project or event in which citizens should be involved.

[0106] This invention provides a system that optimizes talent matching and citizen participation in businesses and smart cities. The implementation of the invention involves a system consisting of a database, an AI algorithm, and a user interface.

[0107] The server first retrieves employee data from a database and citizen information from within the smart city, and then standardizes this information. Next, based on the desired career path, it uses an AI algorithm to analyze the current work history and propose the most suitable role. The AI ​​algorithm is often built using machine learning frameworks such as TensorFlow or PyTorch.

[0108] Furthermore, the server evaluates technical requirements in line with the operational objectives of each department. Here, the AI ​​algorithm uses data on citizens' skills and interests to suggest appropriate projects and events. This information is provided to the user through a user interface and output as individual placement and activity participation suggestions.

[0109] For example, if a citizen wishes to participate in an eco-event, the server will refer to the citizen's past eco-activity history and relevant skills to recommend the most suitable role or project. Furthermore, users can input their personal preferences and requirements into the AI ​​using prompts, and receive participation suggestions based on the analysis results.

[0110] Example of a prompt:

[0111] "Enter your skills and interests, and let the AI ​​suggest which smart city project is best suited for you."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The server retrieves data from the database, including an individual's work history, skills, and interests. To standardize this input data, it performs data cleansing, such as unifying the data format and content and imputing missing values. This results in the well-organized data needed for subsequent AI analysis.

[0115] Step 2:

[0116] The server uses standardized data to perform analysis using AI algorithms. Specifically, it generates prompts and trains a model to suggest optimal roles and activities based on user data. In this process, the machine learning model predicts future role suitability and suitability for civic activities based on the data. The output is a list of suggested roles and projects.

[0117] Step 3:

[0118] The user enters additional information about their interests and skills through the interface. The device sends this information to the server and receives further personalized suggestions from the AI. The input data and the AI ​​model's predictions are combined to output specific roles and projects tailored to the user's preferences.

[0119] Step 4:

[0120] The server presents the final proposal results to the user. The user can review the proposals through the screen display and confirm or apply for the most suitable role or activity. In this step, the server uses an interactive UI to allow the user to easily navigate the information. The output includes the user's application decision and additional feedback.

[0121] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0122] This invention is a system that combines an emotion engine to efficiently optimize talent matching within a company. In this system, the server primarily handles data processing and emotion recognition, and optimizes talent allocation through a user-friendly interface.

[0123] The server first retrieves necessary HR-related data from the database. This includes self-reported data, performance evaluations, and executive biographies. The server then formats this data to suit various analyses. Next, based on career paths and departmental business objectives, the server analyzes employees' work history and skills and proposes optimal job roles and placements for future executive candidates.

[0124] This is where the emotion engine comes into play. The server evaluates the user's emotions in real time on the user interface. For example, if the emotion engine perceives an HR representative's reaction as anxiety, the server will either display additional explanatory materials or activate a function to suggest alternatives. Also, if the evaluation of a career path is not positive, the emotion engine analyzes this and uses it to generate assignments that can reduce employee stress.

[0125] As a concrete example, consider a scenario where a department manager is trying to select suitable personnel for a new project. The server automatically analyzes the project requirements and recommends personnel aligned with maximizing the department's business value, while simultaneously monitoring the manager's reaction using an emotion engine. If negative emotions are detected, the server will explain the reasons for the recommendation in detail and strive to improve the manager's satisfaction.

[0126] In this way, the emotion engine and the personnel allocation system work together to enable flexible responses tailored to the user's emotional state, resulting in the evolution of the company's overall human resource strategy. Through this, the server contributes to the company's sustainable growth and enhanced competitiveness.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] The server accesses the database and retrieves HR-related data. This data includes employee self-report data, performance evaluation files, and executive biographies. The server then prepares this data to be formatted into a standard format.

[0130] Step 2:

[0131] The server performs formatting and transforms the acquired data into a unified format. For example, it normalizes data in different formats and prepares it for analysis.

[0132] Step 3:

[0133] The server begins an analysis from the employee's perspective. It analyzes self-reported data and career aspirations, and lists appropriate job roles based on the employee's desired career path. The server matches the employee's current skills with their desired growth direction.

[0134] Step 4:

[0135] The server performs departmental-level analysis. The server evaluates each department's business objectives and identifies the necessary skill sets. This generates information to optimize the allocation of personnel needed to achieve those business objectives.

[0136] Step 5:

[0137] The server conducts analysis for developing next-generation executives. It identifies the experience required by executive candidates and recommends appropriate positions where they can acquire that experience. The server utilizes executive career data to create a skills upgrade map for executives.

[0138] Step 6:

[0139] The device uses an emotion engine to evaluate the user's emotions through the user interface. This evaluation monitors the user's reaction to the proposed layout and generates emotionally appropriate feedback.

[0140] Step 7:

[0141] The user reviews the optimal placement plan presented by the server and makes further adjustments based on the results from the emotion engine. They interact with the server as needed, modifying the placement plan to determine the final personnel assignment.

[0142] (Example 2)

[0143] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0144] In modern companies, it is crucial for managers and project leaders to select and place the most suitable personnel. However, accurately evaluating employees' skills, career paths, and suitability as management candidates, while also considering real-time emotional awareness and making flexible placements, is a challenging task. A manager's own emotional state can influence personnel selection, sometimes preventing them from making optimal decisions.

[0145] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0146] In this invention, the server includes means for formatting information obtained from data storage, means for analyzing occupational history based on desired professional experience to identify the most suitable job, and means for evaluating the user's emotional state in real time and providing additional information as needed. This enables efficient and objective personnel allocation within a company while allowing for flexible responses based on the user's emotions.

[0147] "Data storage" refers to an electronic medium or device used to collect and store information.

[0148] "Means of formatting information" refer to techniques and methods for structuring acquired raw data and converting it into a format that is easy to analyze and process.

[0149] "Analysis based on professional experience" is a method of identifying the most suitable job or career path for an individual by considering their professional experience and skills.

[0150] "Means of identifying optimal job roles" refers to techniques and methods for selecting the most suitable roles and duties for employees based on analyzed data.

[0151] "Methods for evaluating a user's emotional state in real time" refers to technologies that analyze information obtained from user interfaces and sensors to instantly grasp the user's emotional state.

[0152] "Means of providing additional information" refers to methods of showing users new information or alternatives as needed, based on the results of the sentiment evaluation.

[0153] This invention is a system for efficiently allocating personnel within a company, integrating data processing and sentiment recognition functions. The server retrieves HR-related information from data storage and formats it into an analyzable form. Specifically, it uses SQL queries to collect data on employee careers, evaluations, and self-reports from the database, and performs data cleansing using a Python script.

[0154] The server uses the analyzed data to apply machine learning algorithms and evaluate each employee's work history and skills. This allows it to select the employee best suited for a specific project or job. For example, it uses a library called scikit-learn to determine job suitability through clustering.

[0155] Furthermore, the server activates an emotion engine on the user interface to perform real-time emotion assessment. This engine analyzes input from the camera and microphone to evaluate the user's emotional state. For example, if a project manager shows signs of anxiety, the server immediately provides additional information and alternative solutions to support better decision-making.

[0156] As a concrete example, when a department manager selects suitable personnel for a new project, the server analyzes the project requirements and recommends the most suitable candidates. During this process, the emotion engine monitors the manager's reactions in real time and provides additional information as needed. This enables optimal personnel placement for project success.

[0157] An example of a prompt to the generating AI model would be, "Please suggest a recommended strategy for selecting the best personnel for a project within our company. Also, please include a clear explanation of the selection criteria." This allows the server to optimize the company's overall talent strategy while providing flexible responses based on the user's emotions.

[0158] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0159] Step 1:

[0160] The server accesses data storage and retrieves employee self-reported data, evaluation files, work history information, etc., using SQL queries. It receives this information as input data and performs data cleansing. During this process, incomplete data is supplemented, the format is standardized, and the formatted data is output.

[0161] Step 2:

[0162] The server uses pre-formatted data and applies machine learning algorithms to analyze employees' work history and skills. Specifically, it uses scikit-learn's clustering method to determine job suitability. This outputs which job each employee is best suited for.

[0163] Step 3:

[0164] The server activates the emotion engine via the user interface and monitors the user's reactions in real time. It evaluates the emotional state using data collected from cameras and microphones as input. Based on this analysis, it outputs additional information or alternatives as needed.

[0165] Step 4:

[0166] The terminal displays recommendation information received from the server, which the user then reviews. It lists the most suitable employees based on project and job requirements, supporting the user in making a final decision. Based on the displayed information, the user determines the optimal personnel placement.

[0167] Step 5:

[0168] When a user makes a personnel allocation decision based on server output, the terminal records the result for later reference and analysis. This process generates a data log that proves the decision was made more data-driven.

[0169] (Application Example 2)

[0170] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0171] In today's business environment, the appropriate allocation of personnel and the maintenance of their mental health are issues directly linked to the productivity of the entire organization. In particular, in workplaces such as factories, the emotional state of workers affects work efficiency and safety, so there is a need for a system that can evaluate these in real time and respond appropriately.

[0172] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0173] In this invention, the server includes means for formatting multiple types of information obtained from a database, means for analyzing current work experience based on a desired career path and identifying appropriate tasks, and means for evaluating the emotional state of workers in real time and adjusting the workload based on the emotional state. This enables efficient and safe work performance by workers through optimal personnel allocation and real-time emotional evaluation.

[0174] A "database" is a structured collection of digital data designed to manage information systematically and make it easily accessible and analyzeable.

[0175] "Career path" refers to the path of development and progress in an individual's profession, and encompasses the accumulation of career-related experiences and skills.

[0176] A "skill set" refers to a collection of abilities and skills required to perform a specific job or task.

[0177] "Next-generation management candidates" refer to individuals identified as potential leaders who will shape the future of the organization.

[0178] "Emotional state" refers to the result of an evaluation of an individual's psychological and physiological emotional responses, and represents their mental state at any given time.

[0179] "Workload" refers to the quantity and complexity of tasks that a worker must complete within a specific time frame, and indicates the degree of burden on the worker.

[0180] "Real-time" refers to a process where data is processed or analyzed immediately at the moment it is generated, meaning there is no delay.

[0181] "Personnel placement" is the process of assigning the right individuals to the right positions according to the operational needs within an organization.

[0182] The system for realizing this invention uses a database to manage information about workers within an organization and performs analysis based on the acquired information. This system uses a dedicated algorithm built in a programming language such as Python to evaluate workers' career paths, skill sets, and suitability as future management candidates.

[0183] The server organizes and stores information using a database management system (e.g., MySQL®). AI and machine learning algorithms are used for data analysis to generate suggestions for optimizing worker suitability and placement. Real-time assessment of emotional states is performed using cloud-based emotion recognition APIs such as Microsoft® Azure® Cognitive Services.

[0184] Information regarding emotional states is collected in real time through sensors and cameras. Based on this data, the server dynamically adjusts the workload to provide a work environment that prevents worker burnout.

[0185] As a specific example, the system monitors the stress levels of factory workers during the summer and automatically suggests shift changes or breaks if discomfort is detected due to high temperatures.

[0186] Example prompts for a generative AI model:

[0187] "Adjust the layout plan based on real-time worker sentiment data. The current factory temperature is 32 degrees Celsius, and several workers are expressing discomfort. Please describe your proposal in detail."

[0188] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0189] Step 1:

[0190] The server retrieves information from the database, such as the worker's career path, skill set, and emotional state. Input data includes work history, skill set, self-reported information, and past emotional data. The server formats this data and converts it into an analyzable format.

[0191] Step 2:

[0192] Based on the acquired data, the server uses an AI algorithm to propose the optimal job assignment for workers. This process evaluates how well each worker's skill set aligns with the business objectives of each department. The input consists of formatted user data and departmental objective data, and the output is a recommended job assignment.

[0193] Step 3:

[0194] The server receives data from sensors and cameras to acquire real-time emotional data. The input is real-time sensor data. The server analyzes the emotional state via an emotion recognition API and evaluates this data to infer the worker's mental state. The output is the instantaneous emotional state.

[0195] Step 4:

[0196] The server adjusts the workload as needed based on the emotional state. The input is the detected emotional state and current workload data. The server uses a generative AI model to generate prompts, suggesting appropriate workloads or recommending rest to workers. The output is the proposed adjustments or suggestions.

[0197] Step 5:

[0198] The user selects the actions and adjustments to be performed based on the suggestions and adjustment proposals presented by the server. The input is the server's suggestions, and the output is the actions and adjustments that will be implemented. The actual adjustments are determined by which suggestions the user selects.

[0199] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0200] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0201] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0202] [Second Embodiment]

[0203] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0204] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0205] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0206] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0207] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0208] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0209] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0210] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0211] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0212] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0213] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0214] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0215] This invention provides an embodiment of a system for efficiently optimizing talent matching within a company, and has multiple functions centered around an artificial intelligence agent. This enables support for employees' career paths, maximization of business value for each department, and development of next-generation executives.

[0216] The server first retrieves relevant data from the company's database, such as self-reported data, evaluation files, and executive biographies. Then, it processes this data to standardize it and prepares it for various analyses.

[0217] Next, the server analyzes the employee's professional history based on their career path and identifies roles that support appropriate career growth. For example, if an employee in the sales department wants to move into product management in the future, the server will recommend participation in projects that align with their goals.

[0218] In parallel, the server evaluates each department's business objectives and displays employees with the necessary skills as options. If a department head wants to strengthen their new product development team, the server will suggest the most suitable technical experts for the project and make assignments to enhance organizational capabilities.

[0219] Furthermore, with the development of next-generation executives in mind, the server extracts necessary skill sets from executive resume data and places candidates in positions where they can gain the required experience. For example, if a candidate for executive status in the technology department needs experience in company-wide projects, the server will place them in such international projects.

[0220] This system presents optimal staffing plans on a dashboard for HR personnel and department managers. Based on the results, users can make final adjustments, resulting in efficient staffing assignments that are tailored to the entire company.

[0221] Through these processes, servers help companies maximize the use of their internal resources and support data-driven decision-making to improve their competitiveness.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The server accesses the company's database to retrieve HR-related data. This includes employee self-report data, various performance evaluation files, and executive career histories. The server collects this data and prepares it for processing.

[0225] Step 2:

[0226] The server processes the acquired data and verifies its consistency. This involves filling in missing data points and standardizing the format. This prepares the data for easier use in subsequent analysis.

[0227] Step 3:

[0228] The server begins its analysis from the employee's perspective. First, it analyzes the employee's self-reported data and career aspirations, matching them with their current work experience. The server then identifies and lists the job roles that offer the most suitable growth opportunities for each employee.

[0229] Step 4:

[0230] The server conducts an analysis from a departmental perspective. It evaluates each department's business objectives and identifies their current skill sets. The server identifies the resources needed to achieve the business objectives and creates a proposal for optimal personnel allocation.

[0231] Step 5:

[0232] The server identifies potential next-generation executives from a human resources perspective. Based on executive resume data, it extracts the experience required by future executives and suggests suitable roles or projects. This ensures that executive candidates are placed in positions where they can acquire the necessary experience.

[0233] Step 6:

[0234] The server integrates analysis results from various perspectives to generate a comprehensive personnel placement plan. Based on these results, it presents the optimal placement plan to the user, such as HR personnel or department managers.

[0235] Step 7:

[0236] Users review the personnel assignment proposals presented by the server and make revisions or adjustments as needed. This leads to the final personnel assignment being determined and implemented.

[0237] (Example 1)

[0238] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0239] In corporate talent allocation, a challenge exists where the abilities of employees do not align with the needs of departments, making efficient talent utilization difficult. In particular, there is a need to address the insufficient understanding and placement of appropriate career backgrounds in career paths and the development of future leaders, which hinders the improvement of corporate competitiveness.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes means for converting information obtained from a data source into a standard format, means for analyzing the current professional experience based on desired economic activities and identifying the optimal role, and means for evaluating the set of skills that match the business objectives of each department and assigning individuals with those skills to the department. This enables the optimal allocation of personnel within the company.

[0242] "Data sources" refer to various types of information collected from both inside and outside the company, including employee self-reported information, evaluation documents, and past career data.

[0243] A "standard format" refers to a data structure that has been formatted to enable unified analysis of data in different formats.

[0244] "Economic activity" refers to activities related to human resource utilization within an organization, such as employees' work history and career path goals.

[0245] "Experienced professional background" refers to the history of work experience and skill sets that an employee has accumulated to date.

[0246] "Role" refers to the specific duties or positions that employees are expected to perform within a company.

[0247] "Business objectives" refer to the business achievement targets set by a company or its department, and the necessary skills and personnel requirements are determined based on these objectives.

[0248] A "skill set" refers to the combination of knowledge and skills required to perform a specific job.

[0249] "Person" refers to an employee or a person who is to be assigned to a position within a corporate organization.

[0250] A "leadership candidate" refers to an employee who is scheduled to be trained to assume a leadership position in the next generation.

[0251] This invention is a system aimed at optimizing personnel allocation within a company and promoting information-based decision-making. The system mainly consists of a server, terminals, and users.

[0252] The server first collects information from data sources both inside and outside the organization. This includes employee self-reported information, performance evaluations, and past professional experience data. The server retrieves the collected information using database technology and formats it into a standard format using the Python Pandas library.

[0253] The server then analyzes the employee's professional background and desired economic activities. Using data analysis libraries such as Scikit-learn and TensorFlow, it predicts the optimal role based on past work experience and skill sets. For example, if an employee in sales aims to become a product manager, the server will recommend projects suitable for that goal.

[0254] The terminal processes the data aggregated by the server and displays the analysis results in a dashboard format. This dashboard is created using Power BI or Tableau and provides visual information for personnel allocation decisions.

[0255] HR personnel and department managers, who are the users, use the provided dashboard to finalize employee placement and role assignments. A concrete example using prompts is, "This employee is aiming to become a product manager from the sales department. What is the best project for them?" In response, the system can suggest the most suitable project.

[0256] By utilizing this system, companies can efficiently match the skills of their employees with the needs of their departments, and make the most of their inherent resources.

[0257] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0258] Step 1:

[0259] The server collects information from data sources. As input, it retrieves employee self-reported information, performance evaluations, and past professional experience data from the company's database. It uses SQL queries to retrieve the information and then converts it to a standard format using the Python Pandas library for output. This output data forms the basis for subsequent analysis.

[0260] Step 2:

[0261] The server analyzes the formatted data to evaluate employees' professional backgrounds and desired economic activities. It receives the formatted data from the previous step as input and performs analysis using Scikit-learn and TensorFlow. Specific data calculations include applying predictive models based on past work experience and skill sets. The output provides analysis results to identify optimal roles. For example, it might suggest a suitable path for a sales employee to become a product manager.

[0262] Step 3:

[0263] The server creates employee placement plans based on the analysis results. It uses the results of career analysis and departmental work objectives as input. Based on this, it identifies individuals with the appropriate skill sets and proposes their assignment to departments. Specifically, it generates and outputs a list of recommended personnel.

[0264] Step 4:

[0265] The terminal displays the calculated placement plan on a dashboard. It receives the personnel placement plan sent from the server as input and visualizes it using Power BI or Tableau. A user interface is established, and the output is a visually organized dashboard of the placement plan. This serves as the basis for users to view this information and make their final decisions.

[0266] Step 5:

[0267] The user uses the presented dashboard to make the final personnel placement decision. As input, they refer to the visualized placement proposals displayed on the terminal and use prompts to inquire about further information. For example, they might use a prompt such as, "This employee is aiming to become a product manager from the sales department. What project would be best suited for them?" to confirm specific placements and project assignments. The output is the final placement decision.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] Existing talent matching systems have limitations in optimizing career paths within companies and evaluating skill sets by department, and are particularly inadequate for guiding citizens to volunteer and participate in projects in smart cities. Therefore, there is a need to achieve efficient allocation of talent throughout the city and propose appropriate activity participation based on citizens' skills and interests.

[0271] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0272] In this invention, the server includes means for formatting multiple types of data obtained from a database, means for analyzing the current occupational history based on a desired career path and identifying the optimal role, means for evaluating the technical requirements that match the business objectives of each department and assigning personnel with the necessary skills to the department, means for generating optimal proposals for city-wide activities based on input of citizens' skills and interests, and means for outputting optimal assignment plans and activity participation plans. This makes it possible to propose optimal personnel allocation and effective citizen participation not only for companies but for the entire smart city.

[0273] A "database" is a foundation for systematically organizing and managing information, and is a system that can effectively store and retrieve diverse information.

[0274] "Formatting" refers to the process of standardizing acquired data and converting it into an analyzable format.

[0275] "Career path" is a concept that indicates the direction of an individual's career and activity plan.

[0276] "Work history" refers to a record of the jobs and occupations an individual has held up to date.

[0277] "Role" is a concept that includes the responsibilities and functions that individuals and organizations are expected to fulfill.

[0278] "Business objectives" refer to the specific goals that an organization or department should achieve within a specific period.

[0279] "Technical requirements" refer to the collection of specialized skills and knowledge required to successfully complete a specific business or project.

[0280] "Citizens" refer to the people who live in a specific area and participate in its society.

[0281] "Generating proposals" refers to the process of creating optimal options and action plans based on analysis results.

[0282] "Deployment plan" refers to a plan for allocating individuals or resources to optimal positions and roles.

[0283] "Activity participation plan" refers to specific proposals for projects or events that citizens should participate in.

[0284] This invention provides a system for optimizing talent matching and citizen activity participation in enterprises and smart cities. The implementation of the invention involves a system consisting of a database, AI algorithms, and a user interface.

[0285] The server first imports employee data of enterprises and citizen information within the smart city from the database and standardizes this information. Subsequently, based on the desired career path, it analyzes the current work history using AI algorithms and proposes optimal roles. AI algorithms are often constructed using machine learning frameworks such as TensorFlow and PyTorch.

[0286] Furthermore, the server evaluates technical requirements in accordance with the business objectives of each department. Here, the AI algorithm uses data on citizens' skills and interests to propose participation in appropriate projects and events. This information is provided to the user through the user interface and output as individual deployment plans and activity participation plans.

[0287] For example, if a citizen wishes to participate in an eco-event, the server will refer to the citizen's past eco-activity history and relevant skills to recommend the most suitable role or project. Furthermore, users can input their personal preferences and requirements into the AI ​​using prompts, and receive participation suggestions based on the analysis results.

[0288] Example of a prompt:

[0289] "Enter your skills and interests, and let the AI ​​suggest which smart city project is best suited for you."

[0290] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0291] Step 1:

[0292] The server retrieves data from the database, including an individual's work history, skills, and interests. To standardize this input data, it performs data cleansing, such as unifying the data format and content and imputing missing values. This results in the well-organized data needed for subsequent AI analysis.

[0293] Step 2:

[0294] The server uses standardized data to perform analysis using AI algorithms. Specifically, it generates prompts and trains a model to suggest optimal roles and activities based on user data. In this process, the machine learning model predicts future role suitability and suitability for civic activities based on the data. The output is a list of suggested roles and projects.

[0295] Step 3:

[0296] The user enters additional information about their interests and skills through the interface. The device sends this information to the server and receives further personalized suggestions from the AI. The input data and the AI ​​model's predictions are combined to output specific roles and projects tailored to the user's preferences.

[0297] Step 4:

[0298] The server presents the final proposal results to the user. The user can review the proposals through the screen display and confirm or apply for the most suitable role or activity. In this step, the server uses an interactive UI to allow the user to easily navigate the information. The output includes the user's application decision and additional feedback.

[0299] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0300] This invention is a system that combines an emotion engine to efficiently optimize talent matching within a company. In this system, the server primarily handles data processing and emotion recognition, and optimizes talent allocation through a user-friendly interface.

[0301] The server first retrieves necessary HR-related data from the database. This includes self-reported data, performance evaluations, and executive biographies. The server then formats this data to suit various analyses. Next, based on career paths and departmental business objectives, the server analyzes employees' work history and skills and proposes optimal job roles and placements for future executive candidates.

[0302] Here, the emotion engine functions effectively. The server evaluates the user's emotions in real time on the user interface. For example, when the emotion engine perceives the reaction of a personnel officer as anxiety, the server activates a function to display additional explanatory materials or propose alternatives. Also, when the evaluation of the career path is not positive, the emotion engine analyzes this and uses it to generate assignments that can reduce the stress of employees.

[0303] As a specific example, consider a scenario where a department manager is trying to select personnel suitable for a new project. The server automatically analyzes the requirements of the project and recommends personnel in line with maximizing the business value of the department, while at the same time using the emotion engine to monitor the manager's reaction. If negative emotions are detected, the server explains the reasons for the recommendation in detail and endeavors to improve understanding.

[0304] In this way, the emotion engine and the personnel placement system are integrated, enabling flexible responses according to the user's emotional state, and as a result, the overall corporate personnel strategy evolves. Thereby, the server contributes to the sustainable growth and competitiveness enhancement of the enterprise.

[0305] The following explains the process flow.

[0306] Step 1:

[0307] The server accesses the database and retrieves personnel-related data. This data includes employees' self-declaration data, evaluation files, and executive resumes. The server prepares to format this data into a standard form.

[0308] Step 2:

[0309] The server executes formatting processing to put the retrieved data into a unified form. For example, it normalizes data in different formats and arranges it in a state suitable for analysis.

[0310] Step 3:

[0311] The server begins an analysis from the employee's perspective. It analyzes self-reported data and career aspirations, and lists appropriate job roles based on the employee's desired career path. The server matches the employee's current skills with their desired growth direction.

[0312] Step 4:

[0313] The server performs departmental-level analysis. The server evaluates each department's business objectives and identifies the necessary skill sets. This generates information to optimize the allocation of personnel needed to achieve those business objectives.

[0314] Step 5:

[0315] The server conducts analysis for developing next-generation executives. It identifies the experience required by executive candidates and recommends appropriate positions where they can acquire that experience. The server utilizes executive career data to create a skills upgrade map for executives.

[0316] Step 6:

[0317] The device uses an emotion engine to evaluate the user's emotions through the user interface. This evaluation monitors the user's reaction to the proposed layout and generates emotionally appropriate feedback.

[0318] Step 7:

[0319] The user reviews the optimal placement plan presented by the server and makes further adjustments based on the results from the emotion engine. They interact with the server as needed, modifying the placement plan to determine the final personnel assignment.

[0320] (Example 2)

[0321] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0322] In modern companies, it is crucial for managers and project leaders to select and place the most suitable personnel. However, accurately evaluating employees' skills, career paths, and suitability as management candidates, while also considering real-time emotional awareness and making flexible placements, is a challenging task. A manager's own emotional state can influence personnel selection, sometimes preventing them from making optimal decisions.

[0323] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0324] In this invention, the server includes means for formatting information obtained from data storage, means for analyzing occupational history based on desired professional experience to identify the most suitable job, and means for evaluating the user's emotional state in real time and providing additional information as needed. This enables efficient and objective personnel allocation within a company while allowing for flexible responses based on the user's emotions.

[0325] "Data storage" refers to an electronic medium or device used to collect and store information.

[0326] "Means of formatting information" refer to techniques and methods for structuring acquired raw data and converting it into a format that is easy to analyze and process.

[0327] "Analysis based on professional experience" is a method of identifying the most suitable job or career path for an individual by considering their professional experience and skills.

[0328] "Means of identifying optimal job roles" refers to techniques and methods for selecting the most suitable roles and duties for employees based on analyzed data.

[0329] "Methods for evaluating a user's emotional state in real time" refers to technologies that analyze information obtained from user interfaces and sensors to instantly grasp the user's emotional state.

[0330] "Means of providing additional information" refers to methods of showing users new information or alternatives as needed, based on the results of the sentiment evaluation.

[0331] This invention is a system for efficiently allocating personnel within a company, integrating data processing and sentiment recognition functions. The server retrieves HR-related information from data storage and formats it into an analyzable form. Specifically, it uses SQL queries to collect data on employee careers, evaluations, and self-reports from the database, and performs data cleansing using a Python script.

[0332] The server uses the analyzed data to apply machine learning algorithms and evaluate each employee's work history and skills. This allows it to select the employee best suited for a specific project or job. For example, it uses a library called scikit-learn to determine job suitability through clustering.

[0333] Furthermore, the server activates an emotion engine on the user interface to perform real-time emotion assessment. This engine analyzes input from the camera and microphone to evaluate the user's emotional state. For example, if a project manager shows signs of anxiety, the server immediately provides additional information and alternative solutions to support better decision-making.

[0334] As a concrete example, when a department manager selects suitable personnel for a new project, the server analyzes the project requirements and recommends the most suitable candidates. During this process, the emotion engine monitors the manager's reactions in real time and provides additional information as needed. This enables optimal personnel placement for project success.

[0335] An example of a prompt to the generating AI model would be, "Please suggest a recommended strategy for selecting the best personnel for a project within our company. Also, please include a clear explanation of the selection criteria." This allows the server to optimize the company's overall talent strategy while providing flexible responses based on the user's emotions.

[0336] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0337] Step 1:

[0338] The server accesses data storage and retrieves employee self-reported data, evaluation files, work history information, etc., using SQL queries. It receives this information as input data and performs data cleansing. During this process, incomplete data is supplemented, the format is standardized, and the formatted data is output.

[0339] Step 2:

[0340] The server uses pre-formatted data and applies machine learning algorithms to analyze employees' work history and skills. Specifically, it uses scikit-learn's clustering method to determine job suitability. This outputs which job each employee is best suited for.

[0341] Step 3:

[0342] The server activates the emotion engine via the user interface and monitors the user's reactions in real time. It evaluates the emotional state using data collected from cameras and microphones as input. Based on this analysis, it outputs additional information or alternatives as needed.

[0343] Step 4:

[0344] The terminal displays recommendation information received from the server, which the user then reviews. It lists the most suitable employees based on project and job requirements, supporting the user in making a final decision. Based on the displayed information, the user determines the optimal personnel placement.

[0345] Step 5:

[0346] When a user makes a personnel allocation decision based on server output, the terminal records the result for later reference and analysis. This process generates a data log that proves the decision was made more data-driven.

[0347] (Application Example 2)

[0348] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0349] In today's business environment, the appropriate allocation of personnel and the maintenance of their mental health are issues directly linked to the productivity of the entire organization. In particular, in workplaces such as factories, the emotional state of workers affects work efficiency and safety, so there is a need for a system that can evaluate these in real time and respond appropriately.

[0350] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0351] In this invention, the server includes means for formatting multiple types of information obtained from a database, means for analyzing current work experience based on a desired career path and identifying appropriate tasks, and means for evaluating the emotional state of workers in real time and adjusting the workload based on the emotional state. This enables efficient and safe work performance by workers through optimal personnel allocation and real-time emotional evaluation.

[0352] A "database" is a structured collection of digital data designed to manage information systematically and make it easily accessible and analyzeable.

[0353] "Career path" refers to the path of development and progress in an individual's profession, and encompasses the accumulation of career-related experiences and skills.

[0354] A "skill set" refers to a collection of abilities and skills required to perform a specific job or task.

[0355] "Next-generation management candidates" refer to individuals identified as potential leaders who will shape the future of the organization.

[0356] "Emotional state" refers to the result of an evaluation of an individual's psychological and physiological emotional responses, and represents their mental state at any given time.

[0357] "Workload" refers to the quantity and complexity of tasks that a worker must complete within a specific time frame, and indicates the degree of burden on the worker.

[0358] "Real-time" refers to a process where data is processed or analyzed immediately at the moment it is generated, meaning there is no delay.

[0359] "Personnel placement" is the process of assigning the right individuals to the right positions according to the operational needs within an organization.

[0360] The system for realizing this invention uses a database to manage information about workers within an organization and performs analysis based on the acquired information. This system uses a dedicated algorithm built in a programming language such as Python to evaluate workers' career paths, skill sets, and suitability as future management candidates.

[0361] The server organizes and stores information using a database management system (e.g., MySQL). AI and machine learning algorithms are used for data analysis to generate suggestions for optimizing worker suitability and placement. Real-time assessment of emotional states is performed using cloud-based emotion recognition APIs such as Microsoft Azure Cognitive Services.

[0362] Information regarding emotional states is collected in real time through sensors and cameras. Based on this data, the server dynamically adjusts the workload to provide a work environment that prevents worker burnout.

[0363] As a specific example, the system monitors the stress levels of factory workers during the summer and automatically suggests shift changes or breaks if discomfort is detected due to high temperatures.

[0364] Example prompts for a generative AI model:

[0365] "Adjust the layout plan based on real-time worker sentiment data. The current factory temperature is 32 degrees Celsius, and several workers are expressing discomfort. Please describe your proposal in detail."

[0366] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0367] Step 1:

[0368] The server retrieves information from the database, such as the worker's career path, skill set, and emotional state. Input data includes work history, skill set, self-reported information, and past emotional data. The server formats this data and converts it into an analyzable format.

[0369] Step 2:

[0370] Based on the acquired data, the server uses an AI algorithm to propose the optimal job assignment for workers. This process evaluates how well each worker's skill set aligns with the business objectives of each department. The input consists of formatted user data and departmental objective data, and the output is a recommended job assignment.

[0371] Step 3:

[0372] The server receives data from sensors and cameras to acquire real-time emotional data. The input is real-time sensor data. The server analyzes the emotional state via an emotion recognition API and evaluates this data to infer the worker's mental state. The output is the instantaneous emotional state.

[0373] Step 4:

[0374] The server adjusts the workload as needed based on the emotional state. The input is the detected emotional state and current workload data. The server uses a generative AI model to generate prompts, suggesting appropriate workloads or recommending rest to workers. The output is the proposed adjustments or suggestions.

[0375] Step 5:

[0376] The user selects the actions and adjustments to be performed based on the suggestions and adjustment proposals presented by the server. The input is the server's suggestions, and the output is the actions and adjustments that will be implemented. The actual adjustments are determined by which suggestions the user selects.

[0377] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0378] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0379] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0380] [Third Embodiment]

[0381] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0382] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0383] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0384] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0385] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0386] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0387] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0388] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0389] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0390] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0391] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0392] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0393] This invention provides an embodiment of a system for efficiently optimizing talent matching within a company, and has multiple functions centered around an artificial intelligence agent. This enables support for employees' career paths, maximization of business value for each department, and development of next-generation executives.

[0394] The server first retrieves relevant data from the company's database, such as self-reported data, evaluation files, and executive biographies. Then, it processes this data to standardize it and prepares it for various analyses.

[0395] Next, the server analyzes the employee's professional history based on their career path and identifies roles that support appropriate career growth. For example, if an employee in the sales department wants to move into product management in the future, the server will recommend participation in projects that align with their goals.

[0396] In parallel, the server evaluates each department's business objectives and displays employees with the necessary skills as options. If a department head wants to strengthen their new product development team, the server will suggest the most suitable technical experts for the project and make assignments to enhance organizational capabilities.

[0397] Furthermore, with the development of next-generation executives in mind, the server extracts necessary skill sets from executive resume data and places candidates in positions where they can gain the required experience. For example, if a candidate for executive status in the technology department needs experience in company-wide projects, the server will place them in such international projects.

[0398] This system presents optimal staffing plans on a dashboard for HR personnel and department managers. Based on the results, users can make final adjustments, resulting in efficient staffing assignments that are tailored to the entire company.

[0399] Through these processes, servers help companies maximize the use of their internal resources and support data-driven decision-making to improve their competitiveness.

[0400] The following describes the processing flow.

[0401] Step 1:

[0402] The server accesses the company's database to retrieve HR-related data. This includes employee self-report data, various performance evaluation files, and executive career histories. The server collects this data and prepares it for processing.

[0403] Step 2:

[0404] The server processes the acquired data and verifies its consistency. This involves filling in missing data points and standardizing the format. This prepares the data for easier use in subsequent analysis.

[0405] Step 3:

[0406] The server begins its analysis from the employee's perspective. First, it analyzes the employee's self-reported data and career aspirations, matching them with their current work experience. The server then identifies and lists the job roles that offer the most suitable growth opportunities for each employee.

[0407] Step 4:

[0408] The server conducts an analysis from a departmental perspective. It evaluates each department's business objectives and identifies their current skill sets. The server identifies the resources needed to achieve the business objectives and creates a proposal for optimal personnel allocation.

[0409] Step 5:

[0410] The server identifies potential next-generation executives from a human resources perspective. Based on executive resume data, it extracts the experience required by future executives and suggests suitable roles or projects. This ensures that executive candidates are placed in positions where they can acquire the necessary experience.

[0411] Step 6:

[0412] The server integrates analysis results from various perspectives to generate a comprehensive personnel placement plan. Based on these results, it presents the optimal placement plan to the user, such as HR personnel or department managers.

[0413] Step 7:

[0414] Users review the personnel assignment proposals presented by the server and make revisions or adjustments as needed. This leads to the final personnel assignment being determined and implemented.

[0415] (Example 1)

[0416] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0417] In corporate talent allocation, a challenge exists where the abilities of employees do not align with the needs of departments, making efficient talent utilization difficult. In particular, there is a need to address the insufficient understanding and placement of appropriate career backgrounds in career paths and the development of future leaders, which hinders the improvement of corporate competitiveness.

[0418] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0419] In this invention, the server includes means for converting information obtained from a data source into a standard format, means for analyzing the current professional experience based on desired economic activities and identifying the optimal role, and means for evaluating the set of skills that match the business objectives of each department and assigning individuals with those skills to the department. This enables the optimal allocation of personnel within the company.

[0420] "Data sources" refer to various types of information collected from both inside and outside the company, including employee self-reported information, evaluation documents, and past career data.

[0421] A "standard format" refers to a data structure that has been formatted to enable unified analysis of data in different formats.

[0422] "Economic activity" refers to activities related to human resource utilization within an organization, such as employees' work history and career path goals.

[0423] "Experienced professional background" refers to the history of work experience and skill sets that an employee has accumulated to date.

[0424] "Role" refers to the specific duties or positions that employees are expected to perform within a company.

[0425] "Business objectives" refer to the business achievement targets set by a company or its department, and the necessary skills and personnel requirements are determined based on these objectives.

[0426] A "skill set" refers to the combination of knowledge and skills required to perform a specific job.

[0427] "Person" refers to an employee or a person who is to be assigned to a position within a corporate organization.

[0428] A "leadership candidate" refers to an employee who is scheduled to be trained to assume a leadership position in the next generation.

[0429] This invention is a system aimed at optimizing personnel allocation within a company and promoting information-based decision-making. The system mainly consists of a server, terminals, and users.

[0430] The server first collects information from data sources both inside and outside the organization. This includes employee self-reported information, performance evaluations, and past professional experience data. The server retrieves the collected information using database technology and formats it into a standard format using the Python Pandas library.

[0431] The server then analyzes the employee's professional background and desired economic activities. Using data analysis libraries such as Scikit-learn and TensorFlow, it predicts the optimal role based on past work experience and skill sets. For example, if an employee in sales aims to become a product manager, the server will recommend projects suitable for that goal.

[0432] The terminal processes the data aggregated by the server and displays the analysis results in a dashboard format. This dashboard is created using Power BI or Tableau and provides visual information for personnel allocation decisions.

[0433] HR personnel and department managers, who are the users, use the provided dashboard to finalize employee placement and role assignments. A concrete example using prompts is, "This employee is aiming to become a product manager from the sales department. What is the best project for them?" In response, the system can suggest the most suitable project.

[0434] By utilizing this system, companies can efficiently match the skills of their employees with the needs of their departments, and make the most of their inherent resources.

[0435] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0436] Step 1:

[0437] The server collects information from data sources. As input, it retrieves employee self-reported information, performance evaluations, and past professional experience data from the company's database. It uses SQL queries to retrieve the information and then converts it to a standard format using the Python Pandas library for output. This output data forms the basis for subsequent analysis.

[0438] Step 2:

[0439] The server analyzes the formatted data to evaluate employees' professional backgrounds and desired economic activities. It receives the formatted data from the previous step as input and performs analysis using Scikit-learn and TensorFlow. Specific data calculations include applying predictive models based on past work experience and skill sets. The output provides analysis results to identify optimal roles. For example, it might suggest a suitable path for a sales employee to become a product manager.

[0440] Step 3:

[0441] The server creates employee placement plans based on the analysis results. It uses the results of career analysis and departmental work objectives as input. Based on this, it identifies individuals with the appropriate skill sets and proposes their assignment to departments. Specifically, it generates and outputs a list of recommended personnel.

[0442] Step 4:

[0443] The terminal displays the calculated placement plan on a dashboard. It receives the personnel placement plan sent from the server as input and visualizes it using Power BI or Tableau. A user interface is established, and the output is a visually organized dashboard of the placement plan. This serves as the basis for users to view this information and make their final decisions.

[0444] Step 5:

[0445] The user uses the presented dashboard to make the final personnel placement decision. As input, they refer to the visualized placement proposals displayed on the terminal and use prompts to inquire about further information. For example, they might use a prompt such as, "This employee is aiming to become a product manager from the sales department. What project would be best suited for them?" to confirm specific placements and project assignments. The output is the final placement decision.

[0446] (Application Example 1)

[0447] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0448] Existing talent matching systems have limitations in optimizing career paths within companies and evaluating skill sets by department, and are particularly inadequate for guiding citizens to volunteer and participate in projects in smart cities. Therefore, there is a need to achieve efficient allocation of talent throughout the city and propose appropriate activity participation based on citizens' skills and interests.

[0449] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0450] In this invention, the server includes means for formatting multiple types of data obtained from a database, means for analyzing the current occupational history based on a desired career path and identifying the optimal role, means for evaluating the technical requirements that match the business objectives of each department and assigning personnel with the necessary skills to the department, means for generating optimal proposals for city-wide activities based on input of citizens' skills and interests, and means for outputting optimal assignment plans and activity participation plans. This makes it possible to propose optimal personnel allocation and effective citizen participation not only for companies but for the entire smart city.

[0451] A "database" is a foundation for systematically organizing and managing information, and is a system that can effectively store and retrieve diverse information.

[0452] "Formatting" refers to the process of standardizing acquired data and converting it into an analyzable format.

[0453] "Career path" is a concept that indicates the direction of an individual's career and activity plan.

[0454] "Work history" refers to a record of the jobs and occupations an individual has held up to date.

[0455] "Role" is a concept that includes the responsibilities and functions that individuals and organizations are expected to fulfill.

[0456] "Business objectives" refer to specific goals that an organization or department should achieve within a particular timeframe.

[0457] "Technical requirements" refer to the set of specialized skills and knowledge needed to successfully complete a particular task or project.

[0458] The term "citizen" refers to people who reside in a specific area and participate in that society.

[0459] "Generating proposals" refers to the process of creating optimal options and action plans based on analysis results.

[0460] A "placement plan" refers to a plan for assigning individuals and resources to the most optimal locations and roles.

[0461] A "proposed activity participation" refers to a specific proposal for a project or event in which citizens should be involved.

[0462] This invention provides a system that optimizes talent matching and citizen participation in businesses and smart cities. The implementation of the invention involves a system consisting of a database, an AI algorithm, and a user interface.

[0463] The server first retrieves employee data from a database and citizen information from within the smart city, and then standardizes this information. Next, based on the desired career path, it uses an AI algorithm to analyze the current work history and propose the most suitable role. The AI ​​algorithm is often built using machine learning frameworks such as TensorFlow or PyTorch.

[0464] Furthermore, the server evaluates technical requirements in line with the operational objectives of each department. Here, the AI ​​algorithm uses data on citizens' skills and interests to suggest appropriate projects and events. This information is provided to the user through a user interface and output as individual placement and activity participation suggestions.

[0465] For example, if a citizen wishes to participate in an eco-event, the server will refer to the citizen's past eco-activity history and relevant skills to recommend the most suitable role or project. Furthermore, users can input their personal preferences and requirements into the AI ​​using prompts, and receive participation suggestions based on the analysis results.

[0466] Example of a prompt:

[0467] "Enter your skills and interests, and let the AI ​​suggest which smart city project is best suited for you."

[0468] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0469] Step 1:

[0470] The server retrieves data from the database, including an individual's work history, skills, and interests. To standardize this input data, it performs data cleansing, such as unifying the data format and content and imputing missing values. This results in the well-organized data needed for subsequent AI analysis.

[0471] Step 2:

[0472] The server uses standardized data to perform analysis using AI algorithms. Specifically, it generates prompts and trains a model to suggest optimal roles and activities based on user data. In this process, the machine learning model predicts future role suitability and suitability for civic activities based on the data. The output is a list of suggested roles and projects.

[0473] Step 3:

[0474] The user enters additional information about their interests and skills through the interface. The device sends this information to the server and receives further personalized suggestions from the AI. The input data and the AI ​​model's predictions are combined to output specific roles and projects tailored to the user's preferences.

[0475] Step 4:

[0476] The server presents the final proposal results to the user. The user can review the proposals through the screen display and confirm or apply for the most suitable role or activity. In this step, the server uses an interactive UI to allow the user to easily navigate the information. The output includes the user's application decision and additional feedback.

[0477] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0478] This invention is a system that combines an emotion engine to efficiently optimize talent matching within a company. In this system, the server primarily handles data processing and emotion recognition, and optimizes talent allocation through a user-friendly interface.

[0479] The server first retrieves necessary HR-related data from the database. This includes self-reported data, performance evaluations, and executive biographies. The server then formats this data to suit various analyses. Next, based on career paths and departmental business objectives, the server analyzes employees' work history and skills and proposes optimal job roles and placements for future executive candidates.

[0480] This is where the emotion engine comes into play. The server evaluates the user's emotions in real time on the user interface. For example, if the emotion engine perceives an HR representative's reaction as anxiety, the server will either display additional explanatory materials or activate a function to suggest alternatives. Also, if the evaluation of a career path is not positive, the emotion engine analyzes this and uses it to generate assignments that can reduce employee stress.

[0481] As a concrete example, consider a scenario where a department manager is trying to select suitable personnel for a new project. The server automatically analyzes the project requirements and recommends personnel aligned with maximizing the department's business value, while simultaneously monitoring the manager's reaction using an emotion engine. If negative emotions are detected, the server will explain the reasons for the recommendation in detail and strive to improve the manager's satisfaction.

[0482] In this way, the emotion engine and the personnel allocation system work together to enable flexible responses tailored to the user's emotional state, resulting in the evolution of the company's overall human resource strategy. Through this, the server contributes to the company's sustainable growth and enhanced competitiveness.

[0483] The following describes the processing flow.

[0484] Step 1:

[0485] The server accesses the database and retrieves HR-related data. This data includes employee self-report data, performance evaluation files, and executive biographies. The server then prepares this data to be formatted into a standard format.

[0486] Step 2:

[0487] The server performs formatting and transforms the acquired data into a unified format. For example, it normalizes data in different formats and prepares it for analysis.

[0488] Step 3:

[0489] The server begins an analysis from the employee's perspective. It analyzes self-reported data and career aspirations, and lists appropriate job roles based on the employee's desired career path. The server matches the employee's current skills with their desired growth direction.

[0490] Step 4:

[0491] The server performs departmental-level analysis. The server evaluates each department's business objectives and identifies the necessary skill sets. This generates information to optimize the allocation of personnel needed to achieve those business objectives.

[0492] Step 5:

[0493] The server conducts analysis for developing next-generation executives. It identifies the experience required by executive candidates and recommends appropriate positions where they can acquire that experience. The server utilizes executive career data to create a skills upgrade map for executives.

[0494] Step 6:

[0495] The device uses an emotion engine to evaluate the user's emotions through the user interface. This evaluation monitors the user's reaction to the proposed layout and generates emotionally appropriate feedback.

[0496] Step 7:

[0497] The user reviews the optimal placement plan presented by the server and makes further adjustments based on the results from the emotion engine. They interact with the server as needed, modifying the placement plan to determine the final personnel assignment.

[0498] (Example 2)

[0499] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0500] In modern companies, it is crucial for managers and project leaders to select and place the most suitable personnel. However, accurately evaluating employees' skills, career paths, and suitability as management candidates, while also considering real-time emotional awareness and making flexible placements, is a challenging task. A manager's own emotional state can influence personnel selection, sometimes preventing them from making optimal decisions.

[0501] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0502] In this invention, the server includes means for formatting information obtained from data storage, means for analyzing occupational history based on desired professional experience to identify the most suitable job, and means for evaluating the user's emotional state in real time and providing additional information as needed. This enables efficient and objective personnel allocation within a company while allowing for flexible responses based on the user's emotions.

[0503] "Data storage" refers to an electronic medium or device used to collect and store information.

[0504] "Means of formatting information" refer to techniques and methods for structuring acquired raw data and converting it into a format that is easy to analyze and process.

[0505] "Analysis based on professional experience" is a method of identifying the most suitable job or career path for an individual by considering their professional experience and skills.

[0506] "Means of identifying optimal job roles" refers to techniques and methods for selecting the most suitable roles and duties for employees based on analyzed data.

[0507] "Methods for evaluating a user's emotional state in real time" refers to technologies that analyze information obtained from user interfaces and sensors to instantly grasp the user's emotional state.

[0508] "Means of providing additional information" refers to methods of showing users new information or alternatives as needed, based on the results of the sentiment evaluation.

[0509] This invention is a system for efficiently allocating personnel within a company, integrating data processing and sentiment recognition functions. The server retrieves HR-related information from data storage and formats it into an analyzable form. Specifically, it uses SQL queries to collect data on employee careers, evaluations, and self-reports from the database, and performs data cleansing using a Python script.

[0510] The server uses the analyzed data to apply machine learning algorithms and evaluate each employee's work history and skills. This allows it to select the employee best suited for a specific project or job. For example, it uses a library called scikit-learn to determine job suitability through clustering.

[0511] Furthermore, the server activates an emotion engine on the user interface to perform real-time emotion assessment. This engine analyzes input from the camera and microphone to evaluate the user's emotional state. For example, if a project manager shows signs of anxiety, the server immediately provides additional information and alternative solutions to support better decision-making.

[0512] As a concrete example, when a department manager selects suitable personnel for a new project, the server analyzes the project requirements and recommends the most suitable candidates. During this process, the emotion engine monitors the manager's reactions in real time and provides additional information as needed. This enables optimal personnel placement for project success.

[0513] An example of a prompt to the generating AI model would be, "Please suggest a recommended strategy for selecting the best personnel for a project within our company. Also, please include a clear explanation of the selection criteria." This allows the server to optimize the company's overall talent strategy while providing flexible responses based on the user's emotions.

[0514] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0515] Step 1:

[0516] The server accesses data storage and retrieves employee self-reported data, evaluation files, work history information, etc., using SQL queries. It receives this information as input data and performs data cleansing. During this process, incomplete data is supplemented, the format is standardized, and the formatted data is output.

[0517] Step 2:

[0518] The server uses pre-formatted data and applies machine learning algorithms to analyze employees' work history and skills. Specifically, it uses scikit-learn's clustering method to determine job suitability. This outputs which job each employee is best suited for.

[0519] Step 3:

[0520] The server activates the emotion engine via the user interface and monitors the user's reactions in real time. It evaluates the emotional state using data collected from cameras and microphones as input. Based on this analysis, it outputs additional information or alternatives as needed.

[0521] Step 4:

[0522] The terminal displays recommendation information received from the server, which the user then reviews. It lists the most suitable employees based on project and job requirements, supporting the user in making a final decision. Based on the displayed information, the user determines the optimal personnel placement.

[0523] Step 5:

[0524] When a user makes a personnel allocation decision based on server output, the terminal records the result for later reference and analysis. This process generates a data log that proves the decision was made more data-driven.

[0525] (Application Example 2)

[0526] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0527] In today's business environment, the appropriate allocation of personnel and the maintenance of their mental health are issues directly linked to the productivity of the entire organization. In particular, in workplaces such as factories, the emotional state of workers affects work efficiency and safety, so there is a need for a system that can evaluate these in real time and respond appropriately.

[0528] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0529] In this invention, the server includes means for formatting multiple types of information obtained from a database, means for analyzing current work experience based on a desired career path and identifying appropriate tasks, and means for evaluating the emotional state of workers in real time and adjusting the workload based on the emotional state. This enables efficient and safe work performance by workers through optimal personnel allocation and real-time emotional evaluation.

[0530] A "database" is a structured collection of digital data designed to manage information systematically and make it easily accessible and analyzeable.

[0531] "Career path" refers to the path of development and progress in an individual's profession, and encompasses the accumulation of career-related experiences and skills.

[0532] A "skill set" refers to a collection of abilities and skills required to perform a specific job or task.

[0533] "Next-generation management candidates" refer to individuals identified as potential leaders who will shape the future of the organization.

[0534] "Emotional state" refers to the result of an evaluation of an individual's psychological and physiological emotional responses, and represents their mental state at any given time.

[0535] "Workload" refers to the quantity and complexity of tasks that a worker must complete within a specific time frame, and indicates the degree of burden on the worker.

[0536] "Real-time" refers to a process where data is processed or analyzed immediately at the moment it is generated, meaning there is no delay.

[0537] "Personnel placement" is the process of assigning the right individuals to the right positions according to the operational needs within an organization.

[0538] The system for realizing this invention uses a database to manage information about workers within an organization and performs analysis based on the acquired information. This system uses a dedicated algorithm built in a programming language such as Python to evaluate workers' career paths, skill sets, and suitability as future management candidates.

[0539] The server organizes and stores information using a database management system (e.g., MySQL). AI and machine learning algorithms are used for data analysis to generate suggestions for optimizing worker suitability and placement. Real-time assessment of emotional states is performed using cloud-based emotion recognition APIs such as Microsoft Azure Cognitive Services.

[0540] Information regarding emotional states is collected in real time through sensors and cameras. Based on this data, the server dynamically adjusts the workload to provide a work environment that prevents worker burnout.

[0541] As a specific example, the system monitors the stress levels of factory workers during the summer and automatically suggests shift changes or breaks if discomfort is detected due to high temperatures.

[0542] Example prompts for a generative AI model:

[0543] "Adjust the layout plan based on real-time worker sentiment data. The current factory temperature is 32 degrees Celsius, and several workers are expressing discomfort. Please describe your proposal in detail."

[0544] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0545] Step 1:

[0546] The server retrieves information from the database, such as the worker's career path, skill set, and emotional state. Input data includes work history, skill set, self-reported information, and past emotional data. The server formats this data and converts it into an analyzable format.

[0547] Step 2:

[0548] Based on the acquired data, the server uses an AI algorithm to propose the optimal job assignment for workers. This process evaluates how well each worker's skill set aligns with the business objectives of each department. The input consists of formatted user data and departmental objective data, and the output is a recommended job assignment.

[0549] Step 3:

[0550] The server receives data from sensors and cameras to acquire real-time emotional data. The input is real-time sensor data. The server analyzes the emotional state via an emotion recognition API and evaluates this data to infer the worker's mental state. The output is the instantaneous emotional state.

[0551] Step 4:

[0552] The server adjusts the workload as needed based on the emotional state. The input is the detected emotional state and current workload data. The server uses a generative AI model to generate prompts, suggesting appropriate workloads or recommending rest to workers. The output is the proposed adjustments or suggestions.

[0553] Step 5:

[0554] The user selects the actions and adjustments to be performed based on the suggestions and adjustment proposals presented by the server. The input is the server's suggestions, and the output is the actions and adjustments that will be implemented. The actual adjustments are determined by which suggestions the user selects.

[0555] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0556] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0557] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0558] [Fourth Embodiment]

[0559] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0560] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0561] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0562] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0563] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0564] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0565] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0566] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0567] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0568] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0569] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0570] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0571] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0572] This invention provides an embodiment of a system for efficiently optimizing talent matching within a company, and has multiple functions centered around an artificial intelligence agent. This enables support for employees' career paths, maximization of business value for each department, and development of next-generation executives.

[0573] The server first retrieves relevant data from the company's database, such as self-reported data, evaluation files, and executive biographies. Then, it processes this data to standardize it and prepares it for various analyses.

[0574] Next, the server analyzes the employee's professional history based on their career path and identifies roles that support appropriate career growth. For example, if an employee in the sales department wants to move into product management in the future, the server will recommend participation in projects that align with their goals.

[0575] In parallel, the server evaluates each department's business objectives and displays employees with the necessary skills as options. If a department head wants to strengthen their new product development team, the server will suggest the most suitable technical experts for the project and make assignments to enhance organizational capabilities.

[0576] Furthermore, with the development of next-generation executives in mind, the server extracts necessary skill sets from executive resume data and places candidates in positions where they can gain the required experience. For example, if a candidate for executive status in the technology department needs experience in company-wide projects, the server will place them in such international projects.

[0577] This system presents optimal staffing plans on a dashboard for HR personnel and department managers. Based on the results, users can make final adjustments, resulting in efficient staffing assignments that are tailored to the entire company.

[0578] Through these processes, servers help companies maximize the use of their internal resources and support data-driven decision-making to improve their competitiveness.

[0579] The following describes the processing flow.

[0580] Step 1:

[0581] The server accesses the company's database to retrieve HR-related data. This includes employee self-report data, various performance evaluation files, and executive career histories. The server collects this data and prepares it for processing.

[0582] Step 2:

[0583] The server processes the acquired data and verifies its consistency. This involves filling in missing data points and standardizing the format. This prepares the data for easier use in subsequent analysis.

[0584] Step 3:

[0585] The server begins its analysis from the employee's perspective. First, it analyzes the employee's self-reported data and career aspirations, matching them with their current work experience. The server then identifies and lists the job roles that offer the most suitable growth opportunities for each employee.

[0586] Step 4:

[0587] The server conducts an analysis from a departmental perspective. It evaluates each department's business objectives and identifies their current skill sets. The server identifies the resources needed to achieve the business objectives and creates a proposal for optimal personnel allocation.

[0588] Step 5:

[0589] The server identifies potential next-generation executives from a human resources perspective. Based on executive resume data, it extracts the experience required by future executives and suggests suitable roles or projects. This ensures that executive candidates are placed in positions where they can acquire the necessary experience.

[0590] Step 6:

[0591] The server integrates analysis results from various perspectives to generate a comprehensive personnel placement plan. Based on these results, it presents the optimal placement plan to the user, such as HR personnel or department managers.

[0592] Step 7:

[0593] Users review the personnel assignment proposals presented by the server and make revisions or adjustments as needed. This leads to the final personnel assignment being determined and implemented.

[0594] (Example 1)

[0595] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0596] In corporate talent allocation, a challenge exists where the abilities of employees do not align with the needs of departments, making efficient talent utilization difficult. In particular, there is a need to address the insufficient understanding and placement of appropriate career backgrounds in career paths and the development of future leaders, which hinders the improvement of corporate competitiveness.

[0597] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0598] In this invention, the server includes means for converting information obtained from a data source into a standard format, means for analyzing the current professional experience based on desired economic activities and identifying the optimal role, and means for evaluating the set of skills that match the business objectives of each department and assigning individuals with those skills to the department. This enables the optimal allocation of personnel within the company.

[0599] "Data sources" refer to various types of information collected from both inside and outside the company, including employee self-reported information, evaluation documents, and past career data.

[0600] A "standard format" refers to a data structure that has been formatted to enable unified analysis of data in different formats.

[0601] "Economic activity" refers to activities related to human resource utilization within an organization, such as employees' work history and career path goals.

[0602] "Experienced professional background" refers to the history of work experience and skill sets that an employee has accumulated to date.

[0603] "Role" refers to the specific duties or positions that employees are expected to perform within a company.

[0604] "Business objectives" refer to the business achievement targets set by a company or its department, and the necessary skills and personnel requirements are determined based on these objectives.

[0605] A "skill set" refers to the combination of knowledge and skills required to perform a specific job.

[0606] "Person" refers to an employee or a person who is to be assigned to a position within a corporate organization.

[0607] A "leadership candidate" refers to an employee who is scheduled to be trained to assume a leadership position in the next generation.

[0608] This invention is a system aimed at optimizing personnel allocation within a company and promoting information-based decision-making. The system mainly consists of a server, terminals, and users.

[0609] The server first collects information from data sources both inside and outside the organization. This includes employee self-reported information, performance evaluations, and past professional experience data. The server retrieves the collected information using database technology and formats it into a standard format using the Python Pandas library.

[0610] The server then analyzes the employee's professional background and desired economic activities. Using data analysis libraries such as Scikit-learn and TensorFlow, it predicts the optimal role based on past work experience and skill sets. For example, if an employee in sales aims to become a product manager, the server will recommend projects suitable for that goal.

[0611] The terminal processes the data aggregated by the server and displays the analysis results in a dashboard format. This dashboard is created using Power BI or Tableau and provides visual information for personnel allocation decisions.

[0612] HR personnel and department managers, who are the users, use the provided dashboard to finalize employee placement and role assignments. A concrete example using prompts is, "This employee is aiming to become a product manager from the sales department. What is the best project for them?" In response, the system can suggest the most suitable project.

[0613] By utilizing this system, companies can efficiently match the skills of their employees with the needs of their departments, and make the most of their inherent resources.

[0614] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0615] Step 1:

[0616] The server collects information from data sources. As input, it retrieves employee self-reported information, performance evaluations, and past professional experience data from the company's database. It uses SQL queries to retrieve the information and then converts it to a standard format using the Python Pandas library for output. This output data forms the basis for subsequent analysis.

[0617] Step 2:

[0618] The server analyzes the formatted data to evaluate employees' professional backgrounds and desired economic activities. It receives the formatted data from the previous step as input and performs analysis using Scikit-learn and TensorFlow. Specific data calculations include applying predictive models based on past work experience and skill sets. The output provides analysis results to identify optimal roles. For example, it might suggest a suitable path for a sales employee to become a product manager.

[0619] Step 3:

[0620] The server creates employee placement plans based on the analysis results. It uses the results of career analysis and departmental work objectives as input. Based on this, it identifies individuals with the appropriate skill sets and proposes their assignment to departments. Specifically, it generates and outputs a list of recommended personnel.

[0621] Step 4:

[0622] The terminal displays the calculated placement plan on a dashboard. It receives the personnel placement plan sent from the server as input and visualizes it using Power BI or Tableau. A user interface is established, and the output is a visually organized dashboard of the placement plan. This serves as the basis for users to view this information and make their final decisions.

[0623] Step 5:

[0624] The user uses the presented dashboard to make the final personnel placement decision. As input, they refer to the visualized placement proposals displayed on the terminal and use prompts to inquire about further information. For example, they might use a prompt such as, "This employee is aiming to become a product manager from the sales department. What project would be best suited for them?" to confirm specific placements and project assignments. The output is the final placement decision.

[0625] (Application Example 1)

[0626] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0627] Existing talent matching systems have limitations in optimizing career paths within companies and evaluating skill sets by department, and are particularly inadequate for guiding citizens to volunteer and participate in projects in smart cities. Therefore, there is a need to achieve efficient allocation of talent throughout the city and propose appropriate activity participation based on citizens' skills and interests.

[0628] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0629] In this invention, the server includes means for formatting multiple types of data obtained from a database, means for analyzing the current occupational history based on a desired career path and identifying the optimal role, means for evaluating the technical requirements that match the business objectives of each department and assigning personnel with the necessary skills to the department, means for generating optimal proposals for city-wide activities based on input of citizens' skills and interests, and means for outputting optimal assignment plans and activity participation plans. This makes it possible to propose optimal personnel allocation and effective citizen participation not only for companies but for the entire smart city.

[0630] A "database" is a foundation for systematically organizing and managing information, and is a system that can effectively store and retrieve diverse information.

[0631] "Formatting" refers to the process of standardizing acquired data and converting it into an analyzable format.

[0632] "Career path" is a concept that indicates the direction of an individual's career and activity plan.

[0633] "Work history" refers to a record of the jobs and occupations an individual has held up to date.

[0634] "Role" is a concept that includes the responsibilities and functions that individuals and organizations are expected to fulfill.

[0635] "Business objectives" refer to specific goals that an organization or department should achieve within a particular timeframe.

[0636] "Technical requirements" refer to the set of specialized skills and knowledge needed to successfully complete a particular task or project.

[0637] The term "citizen" refers to people who reside in a specific area and participate in that society.

[0638] "Generating proposals" refers to the process of creating optimal options and action plans based on analysis results.

[0639] A "placement plan" refers to a plan for assigning individuals and resources to the most optimal locations and roles.

[0640] A "proposed activity participation" refers to a specific proposal for a project or event in which citizens should be involved.

[0641] This invention provides a system that optimizes talent matching and citizen participation in businesses and smart cities. The implementation of the invention involves a system consisting of a database, an AI algorithm, and a user interface.

[0642] The server first retrieves employee data from a database and citizen information from within the smart city, and then standardizes this information. Next, based on the desired career path, it uses an AI algorithm to analyze the current work history and propose the most suitable role. The AI ​​algorithm is often built using machine learning frameworks such as TensorFlow or PyTorch.

[0643] Furthermore, the server evaluates technical requirements in line with the operational objectives of each department. Here, the AI ​​algorithm uses data on citizens' skills and interests to suggest appropriate projects and events. This information is provided to the user through a user interface and output as individual placement and activity participation suggestions.

[0644] For example, if a citizen wishes to participate in an eco-event, the server will refer to the citizen's past eco-activity history and relevant skills to recommend the most suitable role or project. Furthermore, users can input their personal preferences and requirements into the AI ​​using prompts, and receive participation suggestions based on the analysis results.

[0645] Example of a prompt:

[0646] "Enter your skills and interests, and let the AI ​​suggest which smart city project is best suited for you."

[0647] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0648] Step 1:

[0649] The server retrieves data from the database, including an individual's work history, skills, and interests. To standardize this input data, it performs data cleansing, such as unifying the data format and content and imputing missing values. This results in the well-organized data needed for subsequent AI analysis.

[0650] Step 2:

[0651] The server uses standardized data to perform analysis using AI algorithms. Specifically, it generates prompts and trains a model to suggest optimal roles and activities based on user data. In this process, the machine learning model predicts future role suitability and suitability for civic activities based on the data. The output is a list of suggested roles and projects.

[0652] Step 3:

[0653] The user enters additional information about their interests and skills through the interface. The device sends this information to the server and receives further personalized suggestions from the AI. The input data and the AI ​​model's predictions are combined to output specific roles and projects tailored to the user's preferences.

[0654] Step 4:

[0655] The server presents the final proposal results to the user. The user can review the proposals through the screen display and confirm or apply for the most suitable role or activity. In this step, the server uses an interactive UI to allow the user to easily navigate the information. The output includes the user's application decision and additional feedback.

[0656] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0657] This invention is a system that combines an emotion engine to efficiently optimize talent matching within a company. In this system, the server primarily handles data processing and emotion recognition, and optimizes talent allocation through a user-friendly interface.

[0658] The server first retrieves necessary HR-related data from the database. This includes self-reported data, performance evaluations, and executive biographies. The server then formats this data to suit various analyses. Next, based on career paths and departmental business objectives, the server analyzes employees' work history and skills and proposes optimal job roles and placements for future executive candidates.

[0659] This is where the emotion engine comes into play. The server evaluates the user's emotions in real time on the user interface. For example, if the emotion engine perceives an HR representative's reaction as anxiety, the server will either display additional explanatory materials or activate a function to suggest alternatives. Also, if the evaluation of a career path is not positive, the emotion engine analyzes this and uses it to generate assignments that can reduce employee stress.

[0660] As a concrete example, consider a scenario where a department manager is trying to select suitable personnel for a new project. The server automatically analyzes the project requirements and recommends personnel aligned with maximizing the department's business value, while simultaneously monitoring the manager's reaction using an emotion engine. If negative emotions are detected, the server will explain the reasons for the recommendation in detail and strive to improve the manager's satisfaction.

[0661] In this way, the emotion engine and the personnel allocation system work together to enable flexible responses tailored to the user's emotional state, resulting in the evolution of the company's overall human resource strategy. Through this, the server contributes to the company's sustainable growth and enhanced competitiveness.

[0662] The following describes the processing flow.

[0663] Step 1:

[0664] The server accesses the database and retrieves HR-related data. This data includes employee self-report data, performance evaluation files, and executive biographies. The server then prepares this data to be formatted into a standard format.

[0665] Step 2:

[0666] The server performs formatting and transforms the acquired data into a unified format. For example, it normalizes data in different formats and prepares it for analysis.

[0667] Step 3:

[0668] The server begins an analysis from the employee's perspective. It analyzes self-reported data and career aspirations, and lists appropriate job roles based on the employee's desired career path. The server matches the employee's current skills with their desired growth direction.

[0669] Step 4:

[0670] The server performs departmental-level analysis. The server evaluates each department's business objectives and identifies the necessary skill sets. This generates information to optimize the allocation of personnel needed to achieve those business objectives.

[0671] Step 5:

[0672] The server conducts analysis for developing next-generation executives. It identifies the experience required by executive candidates and recommends appropriate positions where they can acquire that experience. The server utilizes executive career data to create a skills upgrade map for executives.

[0673] Step 6:

[0674] The device uses an emotion engine to evaluate the user's emotions through the user interface. This evaluation monitors the user's reaction to the proposed layout and generates emotionally appropriate feedback.

[0675] Step 7:

[0676] The user reviews the optimal placement plan presented by the server and makes further adjustments based on the results from the emotion engine. They interact with the server as needed, modifying the placement plan to determine the final personnel assignment.

[0677] (Example 2)

[0678] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] In modern companies, it is crucial for managers and project leaders to select and place the most suitable personnel. However, accurately evaluating employees' skills, career paths, and suitability as management candidates, while also considering real-time emotional awareness and making flexible placements, is a challenging task. A manager's own emotional state can influence personnel selection, sometimes preventing them from making optimal decisions.

[0680] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0681] In this invention, the server includes means for formatting information obtained from data storage, means for analyzing occupational history based on desired professional experience to identify the most suitable job, and means for evaluating the user's emotional state in real time and providing additional information as needed. This enables efficient and objective personnel allocation within a company while allowing for flexible responses based on the user's emotions.

[0682] "Data storage" refers to an electronic medium or device used to collect and store information.

[0683] "Means of formatting information" refer to techniques and methods for structuring acquired raw data and converting it into a format that is easy to analyze and process.

[0684] "Analysis based on professional experience" is a method of identifying the most suitable job or career path for an individual by considering their professional experience and skills.

[0685] "Means of identifying optimal job roles" refers to techniques and methods for selecting the most suitable roles and duties for employees based on analyzed data.

[0686] "Methods for evaluating a user's emotional state in real time" refers to technologies that analyze information obtained from user interfaces and sensors to instantly grasp the user's emotional state.

[0687] "Means of providing additional information" refers to methods of showing users new information or alternatives as needed, based on the results of the sentiment evaluation.

[0688] This invention is a system for efficiently allocating personnel within a company, integrating data processing and sentiment recognition functions. The server retrieves HR-related information from data storage and formats it into an analyzable form. Specifically, it uses SQL queries to collect data on employee careers, evaluations, and self-reports from the database, and performs data cleansing using a Python script.

[0689] The server uses the analyzed data to apply machine learning algorithms and evaluate each employee's work history and skills. This allows it to select the employee best suited for a specific project or job. For example, it uses a library called scikit-learn to determine job suitability through clustering.

[0690] Furthermore, the server activates an emotion engine on the user interface to perform real-time emotion assessment. This engine analyzes input from the camera and microphone to evaluate the user's emotional state. For example, if a project manager shows signs of anxiety, the server immediately provides additional information and alternative solutions to support better decision-making.

[0691] As a concrete example, when a department manager selects suitable personnel for a new project, the server analyzes the project requirements and recommends the most suitable candidates. During this process, the emotion engine monitors the manager's reactions in real time and provides additional information as needed. This enables optimal personnel placement for project success.

[0692] An example of a prompt to the generating AI model would be, "Please suggest a recommended strategy for selecting the best personnel for a project within our company. Also, please include a clear explanation of the selection criteria." This allows the server to optimize the company's overall talent strategy while providing flexible responses based on the user's emotions.

[0693] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0694] Step 1:

[0695] The server accesses data storage and retrieves employee self-reported data, evaluation files, work history information, etc., using SQL queries. It receives this information as input data and performs data cleansing. During this process, incomplete data is supplemented, the format is standardized, and the formatted data is output.

[0696] Step 2:

[0697] The server uses pre-formatted data and applies machine learning algorithms to analyze employees' work history and skills. Specifically, it uses scikit-learn's clustering method to determine job suitability. This outputs which job each employee is best suited for.

[0698] Step 3:

[0699] The server activates the emotion engine via the user interface and monitors the user's reactions in real time. It evaluates the emotional state using data collected from cameras and microphones as input. Based on this analysis, it outputs additional information or alternatives as needed.

[0700] Step 4:

[0701] The terminal displays recommendation information received from the server, which the user then reviews. It lists the most suitable employees based on project and job requirements, supporting the user in making a final decision. Based on the displayed information, the user determines the optimal personnel placement.

[0702] Step 5:

[0703] When a user makes a personnel allocation decision based on server output, the terminal records the result for later reference and analysis. This process generates a data log that proves the decision was made more data-driven.

[0704] (Application Example 2)

[0705] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0706] In today's business environment, the appropriate allocation of personnel and the maintenance of their mental health are issues directly linked to the productivity of the entire organization. In particular, in workplaces such as factories, the emotional state of workers affects work efficiency and safety, so there is a need for a system that can evaluate these in real time and respond appropriately.

[0707] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0708] In this invention, the server includes means for formatting multiple types of information obtained from a database, means for analyzing current work experience based on a desired career path and identifying appropriate tasks, and means for evaluating the emotional state of workers in real time and adjusting the workload based on the emotional state. This enables efficient and safe work performance by workers through optimal personnel allocation and real-time emotional evaluation.

[0709] A "database" is a structured collection of digital data designed to manage information systematically and make it easily accessible and analyzeable.

[0710] "Career path" refers to the path of development and progress in an individual's profession, and encompasses the accumulation of career-related experiences and skills.

[0711] A "skill set" refers to a collection of abilities and skills required to perform a specific job or task.

[0712] "Next-generation management candidates" refer to individuals identified as potential leaders who will shape the future of the organization.

[0713] "Emotional state" refers to the result of an evaluation of an individual's psychological and physiological emotional responses, and represents their mental state at any given time.

[0714] "Workload" refers to the quantity and complexity of tasks that a worker must complete within a specific time frame, and indicates the degree of burden on the worker.

[0715] "Real-time" refers to a process where data is processed or analyzed immediately at the moment it is generated, meaning there is no delay.

[0716] "Personnel placement" is the process of assigning the right individuals to the right positions according to the operational needs within an organization.

[0717] The system for realizing this invention uses a database to manage information about workers within an organization and performs analysis based on the acquired information. This system uses a dedicated algorithm built in a programming language such as Python to evaluate workers' career paths, skill sets, and suitability as future management candidates.

[0718] The server organizes and stores information using a database management system (e.g., MySQL). AI and machine learning algorithms are used for data analysis to generate suggestions for optimizing worker suitability and placement. Real-time assessment of emotional states is performed using cloud-based emotion recognition APIs such as Microsoft Azure Cognitive Services.

[0719] Information regarding emotional states is collected in real time through sensors and cameras. Based on this data, the server dynamically adjusts the workload to provide a work environment that prevents worker burnout.

[0720] As a specific example, the system monitors the stress levels of factory workers during the summer and automatically suggests shift changes or breaks if discomfort is detected due to high temperatures.

[0721] Example prompts for a generative AI model:

[0722] "Adjust the layout plan based on real-time worker sentiment data. The current factory temperature is 32 degrees Celsius, and several workers are expressing discomfort. Please describe your proposal in detail."

[0723] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0724] Step 1:

[0725] The server retrieves information from the database, such as the worker's career path, skill set, and emotional state. Input data includes work history, skill set, self-reported information, and past emotional data. The server formats this data and converts it into an analyzable format.

[0726] Step 2:

[0727] Based on the acquired data, the server uses an AI algorithm to propose the optimal job assignment for workers. This process evaluates how well each worker's skill set aligns with the business objectives of each department. The input consists of formatted user data and departmental objective data, and the output is a recommended job assignment.

[0728] Step 3:

[0729] The server receives data from sensors and cameras to acquire real-time emotional data. The input is real-time sensor data. The server analyzes the emotional state via an emotion recognition API and evaluates this data to infer the worker's mental state. The output is the instantaneous emotional state.

[0730] Step 4:

[0731] The server adjusts the workload as needed based on the emotional state. The input is the detected emotional state and current workload data. The server uses a generative AI model to generate prompts, suggesting appropriate workloads or recommending rest to workers. The output is the proposed adjustments or suggestions.

[0732] Step 5:

[0733] The user selects the actions and adjustments to be performed based on the suggestions and adjustment proposals presented by the server. The input is the server's suggestions, and the output is the actions and adjustments that will be implemented. The actual adjustments are determined by which suggestions the user selects.

[0734] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0735] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0736] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0737] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0738] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0739] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0740] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0741] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0742] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0743] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0744] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0745] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0746] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0747] 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.

[0748] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0749] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0750] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0751] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0752] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0753] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0754] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0755] The following is further disclosed regarding the embodiments described above.

[0756] (Claim 1)

[0757] A method for formatting multiple types of data obtained from a database,

[0758] A means of analyzing current work experience based on a desired career path and identifying the most suitable job,

[0759] A means of evaluating the skill sets aligned with the business objectives of each department and assigning personnel with those skills to the department,

[0760] A means of identifying the experience necessary for future executive candidates and assigning them to roles where they can gain that experience,

[0761] A system that includes means for integrating the analysis results from each perspective and outputting an optimal layout plan.

[0762] (Claim 2)

[0763] The system according to claim 1, which proposes job roles to support career growth based on employee self-reported data and evaluation files.

[0764] (Claim 3)

[0765] The system according to claim 1, which compares the current skill level of each department with business plan data and assigns personnel to compensate for any skill deficiencies.

[0766] "Example 1"

[0767] (Claim 1)

[0768] A means of converting information obtained from a data source into a standard format,

[0769] A means of analyzing the current professional career based on desired economic activities and identifying the optimal role,

[0770] A means of evaluating the set of skills that are appropriate for the business objectives of each department and assigning individuals with those skills to the department,

[0771] A means of identifying the experiences needed by next-generation leadership candidates and placing them in roles that allow them to gain those experiences,

[0772] A means of aggregating evaluation results from all perspectives and presenting the optimal allocation plan,

[0773] A means of making final adjustments based on the proposed allocation,

[0774] A system that includes this.

[0775] (Claim 2)

[0776] The system according to claim 1, which proposes a role in supporting economic growth based on self-reported information and evaluation documents of individual employees.

[0777] (Claim 3)

[0778] The system according to claim 1, which compares the current capacity status of each department with business plan information and allocates personnel to compensate for any capacity shortages.

[0779] "Application Example 1"

[0780] (Claim 1)

[0781] A method for formatting multiple types of data obtained from a database,

[0782] A means of analyzing the current occupational history based on the desired career path and identifying the optimal role,

[0783] A means of evaluating the technical requirements aligned with the operational objectives of each department and assigning personnel with the relevant technical skills to those departments.

[0784] A means of identifying the experience necessary for future leadership candidates and placing them in roles that allow them to gain that experience,

[0785] A means to integrate the analysis results from each perspective and generate optimal proposals for city-wide activities based on the skills and interests of citizens,

[0786] A system including means for outputting optimal deployment plans and activity participation plans.

[0787] (Claim 2)

[0788] The system according to claim 1, which proposes a role for supporting growth based on individual self-reported data and evaluation files.

[0789] (Claim 3)

[0790] The system according to claim 1, which compares the current technical status and work plan data of each department and allocates personnel to compensate for technical deficiencies.

[0791] "Example 2 of combining an emotion engine"

[0792] (Claim 1)

[0793] A means of formatting multiple types of information obtained from data storage,

[0794] A means of analyzing the current work history based on the desired professional background and identifying the most suitable job,

[0795] A means of evaluating the skill sets aligned with the operational objectives of each department and assigning personnel with those skills to the department,

[0796] A means of identifying the experience necessary for next-generation management candidates and assigning them to roles where they can gain that experience,

[0797] A means to integrate the analysis results from each perspective and output the optimal layout plan,

[0798] A means of evaluating the user's emotional state in real time and providing additional information as needed,

[0799] A means of presenting alternative solutions when emotions such as anxiety or concern are detected,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, which proposes jobs to support career growth based on an individual's self-reported information and evaluation file.

[0803] (Claim 3)

[0804] The system according to claim 1, which compares the current skill status and work plan data of each department and allocates personnel to compensate for skill shortages.

[0805] "Application example 2 when combining with an emotional engine"

[0806] (Claim 1)

[0807] A method for formatting multiple types of information obtained from a database,

[0808] A means of analyzing current work experience based on a desired career path and identifying appropriate jobs,

[0809] A means of evaluating the set of technologies aligned with the business objectives of each department and assigning personnel with those technologies to the department,

[0810] A means of identifying the experience necessary for next-generation management candidates and assigning them to roles where they can gain that experience,

[0811] A means for evaluating the emotional state of workers in real time and adjusting their workload based on that emotional state,

[0812] A system that includes means for integrating the analysis results from each perspective and outputting an optimal layout plan.

[0813] (Claim 2)

[0814] The system according to claim 1, which proposes job duties to support professional growth based on employee self-reported information and evaluation files.

[0815] (Claim 3)

[0816] The system according to claim 1, which compares the current technological status and business plan information of each department and allocates personnel to compensate for technological deficiencies. [Explanation of symbols]

[0817] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for formatting multiple types of data obtained from a database, A means of analyzing current work experience based on a desired career path and identifying the most suitable job, A means of evaluating the skill sets aligned with the business objectives of each department and assigning personnel with those skills to the department, A means of identifying the experience necessary for future executive candidates and assigning them to roles where they can gain that experience, A system that includes means for integrating the analysis results from each perspective and outputting an optimal layout plan.

2. The system according to claim 1, which proposes job duties to support career growth based on employee self-reported data and evaluation files.

3. The system according to claim 1, which compares the current skill level of each department with business plan data and assigns personnel to compensate for any skill deficiencies.

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