System

Abstract

A system is provided.SOLUTION: A system comprising: generative artificial intelligence means for interactively collecting employee skills; means for storing the collected skills in a database; means for visualizing the stored skill information; generative artificial intelligence means for interactively collecting required personnel work requirements; means for storing the collected work requirements in a database; means for visualizing the stored work requirements; means for comparing employee skills and work requirements and calculating a matching score; and means for visualizing and displaying the matching score.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In modern companies, assigning the right people to the right jobs is a major challenge. Although there is a great deal of potential skill and experience within a company, these are often not properly understood, leading to inefficient allocation. Furthermore, when transferring an employee with the necessary skills, there is a lack of means to objectively evaluate their suitability. This leads to a shortage of talent and inappropriate allocation, which reduces work efficiency and employee satisfaction. Therefore, there is a need for a system that can effectively reallocate talent within a company and improve work efficiency and employee satisfaction. [Means for solving the problem]

[0005] In order to solve the above-mentioned problems, the present invention provides a system that utilizes generative artificial intelligence. This system includes generative artificial intelligence means for interactively collecting employee skills and storing the collected skills in a database. It also includes means for visualizing the stored skill information. It further includes generative artificial intelligence means for interactively collecting job requirements for required personnel and storing the collected job requirements in a database. It also includes means for visualizing the stored job requirements, and provides means for comparing employee skills with job requirements and calculating a matching score. Finally, the system includes means for visualizing and displaying the matching score. This supports appropriate employee allocation and improves corporate efficiency and employee satisfaction.

[0006] "Generative artificial intelligence" refers to programs and systems that use natural language processing techniques to generate and analyze information interactively.

[0007] "Employee" refers to an individual who belongs to a company or organization and is employed to perform a specific task or function.

[0008] "Skills" refers to the knowledge, abilities, techniques, experience, etc. required to perform a specific job or task.

[0009] "Dialogue" refers to a communication method in which information is exchanged in the form of questions and answers.

[0010] A "database" is a collection of data stored and managed electronically, organized in a way that allows it to be searched and manipulated.

[0011] "Job requirements" refer to the conditions and standards, such as skills, experience, and abilities, required to carry out a specific job or project.

[0012] "Visualization" refers to the representation of data or information in a visual format such as a graph or chart.

[0013] "Matching score" refers to a numerical evaluation value that quantifies the degree of compatibility between an employee's skills and job requirements.

[0014] "Specialized application" refers to software designed specifically for a specific purpose or function.

[0015] "Web interface" refers to the screens and functions that allow users to access and operate systems and services through a web browser.

[0016] A "leader" is an individual who assumes a leadership or management role within a particular organization or department. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0018] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0019] First, the terms used in the following description will be explained.

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0022] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

[0026] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0031] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0033] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence. This system verbalizes employee skills in detail, clarifies the job requirements of the required human resources, and compares them to achieve optimal matching. Below is a detailed explanation of how the system of the present invention is implemented.

[0039] System Overview

[0040] The system consists of the following elements:

[0041] 1. Visualization of employee skills

[0042] 2. Visualization of business requirements

[0043] 3. Matching and Scoring

[0044] Program processing

[0045] 1. Visualization of employee skills

[0046] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. These questions are generated using generative artificial intelligence. The answered data is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[0047] Examples:

[0048] If an employee answers, "I have programming experience in C++ and Python," the server recognizes this data as "Skills: C++, Python" and stores it in the database. It then generates a visualization view of the employee's skill profile and displays this information on the terminal.

[0049] 2. Visualization of business requirements

[0050] The terminal launches a dedicated application or web interface, and the leader logs in. After successful login, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[0051] Examples:

[0052] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required skills: Data analysis, Presentation" and stores it in the database. Based on this, a visualization view is generated as a business requirements profile and this information is displayed on the terminal.

[0053] 3. Matching and Scoring

[0054] The server retrieves employee skill profiles and department job requirement profiles from the database. This information is compared and a matching score is calculated based on the degree of compatibility. The server visualizes the scores and generates a dashboard. Finally, managers or HR personnel can view these results on their terminals and use them to make decisions about optimal personnel placement.

[0055] Examples:

[0056] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information and calculates the cosine similarity. Based on this, a matching score is calculated and the results are visualized. The results are displayed on a terminal where the manager can view them and make a transfer decision.

[0057] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific business requirements of each department, thereby realizing optimal personnel allocation within a company.

[0058] The processing flow will be explained below.

[0059] Visualization of employee skills

[0060] Step 1:

[0061] The device launches a dedicated application or web interface and the employee logs in.

[0062] Specific behavior:

[0063] The terminal sends an authentication request to the authentication server.

[0064] The server generates an authentication token and returns it to the terminal.

[0065] Step 2:

[0066] The user (employee) accesses the interactive form and answers the questions.

[0067] Specific behavior:

[0068] The terminal requests interactive questions from the generating artificial intelligence.

[0069] The generation artificial intelligence generates questions and sends them to the terminal.

[0070] The user answers the questions, and the terminal transmits the answer data to the server.

[0071] Step 3:

[0072] The server receives and analyzes the user's response.

[0073] Specific behavior:

[0074] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0075] Generative AI analyzes answers and extracts skill sets.

[0076] The server stores the extracted skill set in a database.

[0077] Step 4:

[0078] The server generates a skill profile and displays it on the device.

[0079] Specific behavior:

[0080] The server retrieves the skill information from the database.

[0081] The server renders the skill profile view based on the retrieved information.

[0082] Sends the skill profile to the device and displays it.

[0083] Visualization of business requirements

[0084] Step 1:

[0085] The device launches a dedicated application or web interface, and the department leader logs in.

[0086] Specific behavior:

[0087] The terminal sends an authentication request to the authentication server.

[0088] The server generates an authentication token and returns it to the terminal.

[0089] Step 2:

[0090] The user (reader) accesses the interactive form and answers the questions.

[0091] Specific behavior:

[0092] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[0093] The generation artificial intelligence generates questions and sends them to the terminal.

[0094] The user answers the questions, and the terminal transmits the answer data to the server.

[0095] Step 3:

[0096] The server receives and analyzes the user's response.

[0097] Specific behavior:

[0098] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0099] Generative AI analyzes the answers and extracts the required skill sets.

[0100] The server stores the extracted business requirements in a database.

[0101] Step 4:

[0102] The server generates a business requirement profile and displays it on the terminal.

[0103] Specific behavior:

[0104] The server retrieves business requirement information from the database.

[0105] The server renders a business requirement profile view based on the acquired information.

[0106] The business requirements profile is sent to the terminal and displayed.

[0107] Matching and Scoring

[0108] Step 1:

[0109] The server obtains the employee's skill profile and the department's job requirement profile.

[0110] Specific behavior:

[0111] The server retrieves the employee's skill profile from the database.

[0112] Similarly, the server acquires the business requirement profile of each department.

[0113] Step 2:

[0114] The server compares employee skills with job requirements and calculates a matching score.

[0115] Specific behavior:

[0116] The server vectorizes the skills and business requirements and calculates the cosine similarity based on the correlation.

[0117] Based on the calculation results, a matching score is derived.

[0118] Step 3:

[0119] The server visualizes the matching score and displays it on the device.

[0120] Specific behavior:

[0121] The server generates graphs and dashboards based on the matching scores.

[0122] The generated visual is sent to the terminal and displayed.

[0123] Specific examples

[0124] If employee A answers, "I have programming experience in C++ and Python," the server analyzes it as "Skills: C++, Python" and saves it in the database. This is then reflected in the visualization view. Similarly, if the sales department leader answers, "Data analysis and presentation skills are required," the server analyzes it as "Required skills: Data analysis, Presentation" and saves it in the database. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[0125] Example 1

[0126] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0127] In order to properly allocate human resources within a company, it is necessary to understand employee skills in detail and match them with job requirements based on that information. However, conventional systems lack the means to efficiently collect, analyze, and visualize skills and job requirements, making it difficult to achieve optimal matching. To solve this problem, a system is needed that can efficiently collect skills and job requirements and score the degree of compatibility.

[0128] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0129] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for storing the collected skills in a database, a means for visualizing the stored skill information, and a generating artificial intelligence means for interactively collecting the business requirements of required personnel. This makes it possible to efficiently collect employee skills and business requirements and score the compatibility, thereby enabling optimal personnel allocation.

[0130] "Generative artificial intelligence means" refers to means for generating dialogue-style questions using natural language processing and analyzing collected information.

[0131] "Means for storing in a database" refers to means for permanently storing the collected information on skills and job requirements.

[0132] The "visualization means" is a means for converting the stored skill information and business requirement information into a visual format such as a graph or chart, and displaying it.

[0133] A "means for calculating a matching score" is a means for comparing an employee's skills with job requirements, quantifying the degree of compatibility, and calculating a score.

[0134] The "means for displaying as a dashboard" is a means for displaying the calculated matching scores as an integrated view so that the administrator can check them.

[0135] The "means of authenticating login" refers to the means of verifying the login information of employees and administrators and authenticating that they are legitimate users.

[0136] "Means for launching a dedicated application or web interface" refers to means for launching an interface that allows a user to interactively input information.

[0137] "Means for utilizing a generative artificial intelligence model" refers to means for generating dialogue-style questions using a generative AI model and analyzing input data.

[0138] The "means for calculating similarity" is a means for using an algorithm such as cosine similarity to calculate the degree of compatibility between skill information and job requirement information.

[0139] This invention is a system for optimally allocating personnel within a company using a generative AI model. This system efficiently collects employee skills and job requirements and scores the degree of compatibility to achieve optimal personnel allocation. The following describes in detail the mode for implementing this invention.

[0140] System Overview

[0141] The system includes a generative artificial intelligence means, a database storage means, a visualization means, a matching score calculation means, a dashboard display means, a login authentication means, a dedicated application or web interface launch means, a generative artificial intelligence model utilization means, and a similarity calculation means.

[0142] Hardware and software used

[0143] Server: Stores, analyzes, and visualizes data.

[0144] Terminal: A device (PC, tablet, smartphone, etc.) that a user uses to input and view information.

[0145] Generative AI models: Natural language processing models such as ChatGPT.

[0146] Database: Used to store skills information and job requirements information.

[0147] Web interface or dedicated application: The interface through which users access the site.

[0148] Program processing

[0149] Collecting and visualizing employee skills

[0150] The terminal launches a dedicated application or web interface, and the employee logs in. The user (employee) accesses the dialogue form and answers the generated questions. The questions are generated using a generative AI model. The answered data is sent to the server and analyzed using the generative AI model. The analysis results are saved in a database, and a view is generated by a visualization means and displayed on the terminal.

[0151] Examples:

[0152] If an employee answers, "I have programming experience in C++ and Python," the server will recognize and store the data as "Skills: C++, Python," and display it on the terminal as a visualization view.

[0153] Example prompt sentence:

[0154] "Create questions that capture the employee's skill set. For example, consider the answer 'I have programming experience in C++ and Python.'"

[0155] Gathering and visualizing business requirements

[0156] The device launches a dedicated application or web interface, and the reader logs in. The user (reader) accesses the dialogue form and answers the generated questions. The answer data is sent to the server and analyzed using the generative AI model. The analysis results are stored in a database, and a view is generated by the visualization means and displayed on the device.

[0157] Examples:

[0158] If the sales department leader answers, "Data analysis and presentation skills are required," the server will recognize this as "Required skills: Data analysis, Presentation" and save it. This will be displayed on the device as a visualization view.

[0159] Example prompt sentence:

[0160] "Create questions to capture job requirements. For example, consider the answer 'Data analysis and presentation skills required.'"

[0161] Matching and Scoring

[0162] The server retrieves employee skill profiles and department job requirement profiles from the database. The retrieved information is compared, and the degree of compatibility is calculated using algorithms such as cosine similarity, generating a matching score. The generated score is visualized using a dashboard and displayed on the terminal.

[0163] Examples:

[0164] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information, calculates the cosine similarity, calculates a matching score, and visualizes it. The results are displayed on the terminal, and managers can use this information to make transfer decisions.

[0165] Example prompt sentence:

[0166] "Describe a method for comparing an employee's skill profile with a job requirements profile and calculating the degree of fit. For example, consider a scenario where you use cosine similarity to calculate a matching score."

[0167] As described above, this invention is a system that utilizes a generative AI model to efficiently and effectively realize optimal personnel allocation within a company.

[0168] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0169] Step 1:

[0170] Launching the dedicated application or web interface

[0171] The device launches a dedicated application or web interface in response to user operation, and a login screen is displayed for the user.

[0172] Input: The user interacts with the application or web interface.

[0173] Output:Login screen

[0174] Step 2:

[0175] Login authentication

[0176] A user (employee or leader) logs in by entering their username and password. The server receives this authentication information and authenticates it against the database. If the login is successful, the main screen will be displayed on the terminal.

[0177] Input: Username, Password

[0178] Output: Authentication result (success / failure), main screen

[0179] Step 3:

[0180] Entering skill information

[0181] The user (employee) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[0182] Input: User's skill information

[0183] Output: Sending skill information to the server

[0184] Step 4:

[0185] Skill information analysis

[0186] The server analyzes the received skill information using a generative AI model, and the analyzed information is stored in a database as a "skill profile."

[0187] Input: User's skill information

[0188] Output: Parsed skill profile, saved to database

[0189] Step 5:

[0190] Skills profile visualization

[0191] The server generates a view to visualize the skill profile and displays it on the terminal.

[0192] Input: Skill Profile

[0193] Output: A visualized view of the skill profile

[0194] Step 6:

[0195] Entering business requirements information

[0196] The user (reader) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[0197] Input: User's business requirements information

[0198] Output: Sending business requirements information to the server

[0199] Step 7:

[0200] Analysis of business requirements information

[0201] The server analyzes the received business requirements information using a generative AI model, and the analyzed information is stored in a database as a "business requirements profile."

[0202] Input: User's business requirements information

[0203] Output: Analyzed business requirement profile, saved in database

[0204] Step 8:

[0205] Visualization of business requirements profile

[0206] The server generates a view for visualizing the business requirement profile and displays it on the terminal.

[0207] Input: Business Requirement Profile

[0208] Output: A visualized view of the business requirements profile

[0209] Step 9:

[0210] Obtaining skill profiles and job requirements profiles

[0211] The server retrieves the employee's skill profile and the department's job requirement profile from the database.

[0212] Input: None

[0213] Output: Skill profile, job requirement profile

[0214] Step 10:

[0215] Compare profile information

[0216] The server compares the skill profile with the job requirement profile and calculates the degree of compatibility, specifically using an algorithm such as cosine similarity.

[0217] Input: Skill profile, Job requirements profile

[0218] Output: Relevance score

[0219] Step 11:

[0220] Matching Score Calculation

[0221] The server calculates a matching score based on the compatibility score.

[0222] Input: Relevance score

[0223] Output: Matching score

[0224] Step 12:

[0225] Score visualization and dashboard display

[0226] The server visualizes the matching scores and generates a dashboard. Managers or HR personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[0227] Input: Matching score

[0228] Output: Dashboard view

[0229] (Application example 1)

[0230] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0231] In factories, it is extremely difficult to properly understand the skill sets and capabilities of workers and robots and assign optimal tasks to them. In particular, to improve factory production efficiency, a system is needed to allocate tasks in real time based on the skills and capabilities of each worker and robot, and to appropriately monitor and instruct them. However, such a system does not currently exist, resulting in problems such as reduced production efficiency and wasted human resources. Technology to solve this problem is needed.

[0232] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0233] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for saving the collected skills in a database, a means for visualizing the saved skill information, a generating artificial intelligence means for interactively collecting job requirements for required personnel, a means for saving the collected job requirements in a database, a means for visualizing the saved job requirements, a means for comparing employee skills with the job requirements and calculating a matching score, a means for visualizing and displaying the matching score, a means for assigning optimal tasks to workers and robots in a factory, and a means for monitoring and instructing optimal task assignment in real time. This enables optimal matching of skills and tasks for workers and robots in a factory, thereby improving production efficiency and optimizing the use of human resources.

[0234] "Employee" means a person who works in a factory.

[0235] "Skills" are the abilities and knowledge that employees or robots have to perform specific tasks.

[0236] A "dialogue" is a format in which information is collected through an exchange of questions and answers.

[0237] "Generative artificial intelligence means" refers to technology or devices that use natural language processing or machine learning to generate appropriate questions in response to user input and collect information in an interactive format.

[0238] A "database" is an information system for managing and storing collected data.

[0239] "Visualization" refers to visually expressing collected data and displaying it in an easy-to-understand manner.

[0240] "In-demand talent" refers to people who have the skills and abilities required to perform a particular task.

[0241] "Job requirements" refer to the skills and conditions necessary to perform a specific job.

[0242] A "matching score" is a numerical indicator of the degree of compatibility between an employee's skills and job requirements.

[0243] A "factory worker" is someone who physically performs work within a factory.

[0244] A "robot" is a mechanical device that performs autonomous or semi-autonomous tasks in a factory.

[0245] A "task" is a specific job or piece of work.

[0246] "Real-time" refers to processing or reaction occurring immediately in the present time.

[0247] "Monitoring" means continuously checking the progress and work status of a task.

[0248] "Directions" are the provision of orders or guidelines for carrying out specific actions or tasks.

[0249] The system for realizing this invention collects and analyzes the skills and requirements of employees and their work, and performs optimal task allocation and real-time monitoring. Specific embodiments of this system will be described below.

[0250] First, an employee logs in to a dedicated application or web interface using a terminal. They enter their skills through an interactive form. The entered skill information is analyzed using a generative AI model (ChatGPT). The analysis results are stored in a database (PostgreSQL) as a skillset, and a visualized view is displayed on the terminal.

[0251] Next, the administrator also logs in to a dedicated application or web interface using a terminal, and again enters the necessary business requirements using an interactive form. This information is also analyzed using the generative AI model, saved in the database as a business requirements profile, and a visualized view is displayed on the terminal.

[0252] The server retrieves the skill profile and job requirement profile from the database, compares them, and calculates a matching score. The server visualizes the score and displays it on the terminal in a dashboard format. This dashboard allows managers to optimally allocate personnel and tasks.

[0253] The system also includes a means for assigning optimal tasks to workers and robots in the factory, monitoring the progress of tasks in real time, and providing appropriate instructions to workers using head-mounted displays or smartphones as needed.

[0254] For example, if an employee answers, "I have experience in machine operation and maintenance," the server recognizes this information as "Skills: Machine Operation, Maintenance" and stores it in the database. If an administrator enters, "I need to set up and maintain a new machine," this information is stored as "Required Skills: Machine Operation, Maintenance." The server compares this information, calculates a matching score, and assigns the work to the best employee.

[0255] An example prompt might look like this:

[0256] "Please tell me the skills related to employee ID 123."

[0257] "What skills are required for task ID 456?"

[0258] In this way, the present invention realizes optimal matching of the skills and tasks of workers and robots within a factory, thereby enabling improved production efficiency and optimal utilization of human resources.

[0259] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0260] Step 1:

[0261] A user (employee) logs in to a dedicated application or web interface using a terminal. After successfully logging in, the user accesses an interactive form and enters their skills. For example, the user might enter "I have experience in machine operation and maintenance." This input is sent to a generative AI model (ChatGPT), which analyzes the input string and generates a skillset. This skillset is then stored in the database as, for example, "Skills: Machine Operation, Maintenance."

[0262] Step 2:

[0263] The server retrieves the stored skill information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows employees' skill sets to be visually confirmed. For example, "Skills: Machine operation, maintenance" is visualized and displayed graphically.

[0264] Step 3:

[0265] The user (administrator) uses a terminal to log in to a dedicated application or web interface. After successfully logging in, the administrator accesses an interactive form and enters the business requirements. For example, the administrator might enter, "A new machine needs to be set up and maintained." This input is also sent to the generative AI model, which analyzes it and generates the business requirements. The generated business requirements are stored in the database as, for example, "Required skills: machine operation, maintenance."

[0266] Step 4:

[0267] The server retrieves the saved business requirements information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows the business requirements to be visually confirmed. For example, "Required skills: machine operation, maintenance" is visualized and displayed graphically.

[0268] Step 5:

[0269] The server retrieves the employee's skill profile and job requirement profile from the database. Based on these profiles, it calculates the degree of match between the skills and requirements. Specifically, it calculates a matching score using an algorithm such as cosine similarity. For example, it calculates the similarity between the skill "machine operation, maintenance" and the job requirement "machine operation, maintenance" and calculates a matching rate of 90%.

[0270] Step 6:

[0271] The server visualizes the calculated matching scores and generates a dashboard. The dashboard is displayed on the terminal, allowing managers to optimally assign tasks based on the scores. This dashboard displays employees with high scores, their skill sets, and the corresponding job requirements. For example, it displays Employee A (Skills: Machine Operation, Maintenance), Task 1 (Required Skills: Machine Operation, Maintenance), Matching Score: 90%.

[0272] Step 7:

[0273] The user (manager) refers to the displayed dashboard and assigns tasks to the most suitable employee. The task assignment results are notified to the worker and robot in real time. Using a smartphone or head-mounted display, the progress of the assigned task is monitored and necessary instructions are provided in real time. For example, specific task instructions such as "Employee A will be responsible for setting up the new machine" are notified.

[0274] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0275] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence and an emotion engine. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[0276] System Overview

[0277] The system consists of the following elements:

[0278] 1. Visualization of employee skills

[0279] 2. Visualization of business requirements

[0280] 3. Matching and Scoring

[0281] 4. Emotion engine integration

[0282] Program processing

[0283] 1. Visualization of employee skills

[0284] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. In addition, the emotion engine recognizes the user's emotions in real time. The server receives the user's answers and emotion information and analyzes them using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[0285] Examples:

[0286] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[0287] 2. Visualization of business requirements

[0288] The terminal launches a dedicated application or web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The emotion engine recognizes the leader's emotions in real time. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[0289] Examples:

[0290] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required Skills: Data Analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[0291] 3. Matching and Scoring

[0292] The server retrieves the employee's skill profile and related emotional information, as well as the department's job requirements profile and related emotional information from the database. This information is compared and a matching score is calculated based on the degree of compatibility. By taking emotional information into account, more accurate matching is achieved. The server visualizes the score and generates a dashboard. Finally, managers or HR personnel can view these results on their devices and use them as information to make decisions about optimal personnel placement.

[0293] Examples:

[0294] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[0295] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific work requirements of each department, and further integrates user emotional information to achieve more accurate and optimal personnel allocation within a company.

[0296] The processing flow will be explained below.

[0297] Visualization of employee skills

[0298] Step 1:

[0299] The device launches a dedicated application or web interface and the employee logs in.

[0300] Specific behavior:

[0301] The terminal sends an authentication request to the authentication server.

[0302] The server generates an authentication token and returns it to the terminal.

[0303] Step 2:

[0304] The user (employee) answers interactive questions.

[0305] Specific behavior:

[0306] The terminal requests interactive questions from the generating artificial intelligence.

[0307] The generation artificial intelligence generates questions and sends them to the terminal.

[0308] As the user answers the questions, the emotion engine analyzes the user's emotions.

[0309] Step 3:

[0310] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[0311] Specific behavior:

[0312] The device transmits the emotion recognition data to the server.

[0313] The server stores the emotion data in a database.

[0314] Step 4:

[0315] The server receives and analyzes the user's response.

[0316] Specific behavior:

[0317] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0318] Generative AI analyzes answers and extracts skill sets.

[0319] The server stores the extracted skill sets and emotion data in a database.

[0320] Step 5:

[0321] The server generates a skill profile and displays it on the device.

[0322] Specific behavior:

[0323] The server retrieves skill information and emotion data from the database.

[0324] The server uses this information to render the skill profile view.

[0325] Sends the skill profile to the device and displays it.

[0326] Visualization of business requirements

[0327] Step 1:

[0328] The device launches a dedicated application or web interface, and the department leader logs in.

[0329] Specific behavior:

[0330] The terminal sends an authentication request to the authentication server.

[0331] The server generates an authentication token and returns it to the terminal.

[0332] Step 2:

[0333] The user (reader) answers interactive questions.

[0334] Specific behavior:

[0335] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[0336] The generation artificial intelligence generates questions and sends them to the terminal.

[0337] As the user answers the questions, the emotion engine analyzes the user's emotions.

[0338] Step 3:

[0339] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[0340] Specific behavior:

[0341] The device transmits the emotion recognition data to the server.

[0342] The server stores the emotion data in a database.

[0343] Step 4:

[0344] The server receives and analyzes the user's response.

[0345] Specific behavior:

[0346] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0347] Generative AI analyzes the answers and extracts the required skill sets.

[0348] The server stores the extracted business requirements and emotion data in a database.

[0349] Step 5:

[0350] The server generates a business requirement profile and displays it on the terminal.

[0351] Specific behavior:

[0352] The server acquires the business requirement information and emotion data from the database.

[0353] The server uses this information to render a business requirement profile view.

[0354] The business requirements profile is sent to the terminal and displayed.

[0355] Matching and Scoring

[0356] Step 1:

[0357] The server acquires the skill profile and related emotion information of the employee, and the business requirement profile and related emotion information of the department.

[0358] Specific behavior:

[0359] The server retrieves employee skill profiles and emotional data from the database.

[0360] The server also obtains the business requirement profile and emotion data of each department.

[0361] Step 2:

[0362] The server compares skills with job requirements and calculates a matching score.

[0363] Specific behavior:

[0364] The server vectorizes the skills, job requirements, and related emotion data, and calculates the cosine similarity based on the correlation.

[0365] Based on the calculation results, a matching score is derived.

[0366] Step 3:

[0367] The server visualizes the matching score and displays it on the device.

[0368] Specific behavior:

[0369] The server generates graphs and dashboards based on the matching scores.

[0370] The generated visual is sent to the terminal and displayed.

[0371] Specific examples

[0372] If employee A answers, "I have programming experience in C++ and Python," and the emotion engine determines that the user is confident, the server analyzes this as "Skills: C++, Python" and saves it in the database. At the same time, the emotion data is also saved. If the sales department leader answers, "Data analysis and presentation skills are required," and the server determines that the leader is confident, the server recognizes this as "Required skills: Data analysis, Presentation," saves it in the database, and saves the emotion data as well. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[0373] Example 2

[0374] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0375] Conventional personnel placement systems have the problem that collecting employee skills and job requirements is subjective, making it difficult to optimally place personnel within a company. Furthermore, because they are unable to take into account the user's emotional information, it is difficult to grasp the true intentions and confidence levels of employees and leaders. This reduces the accuracy of matching, hindering the efficient operation of the company.

[0376] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0377] In this invention, the server includes artificial intelligence means for interactively collecting employee skills, means for saving the collected skills in a database, means for visualizing the saved skill information, artificial intelligence means for interactively collecting job requirements for required personnel, means for saving the collected job requirements in a database, means for visualizing the saved job requirements, means for comparing employee skills with the job requirements and calculating a matching score, means for visualizing and displaying the matching score, emotion recognition means for recognizing user emotions in real time, means for saving the emotion information in a database together with the user's responses, and means for reflecting the emotion information in the calculation of the matching score. This enables more accurate matching that also takes employee emotion information into account.

[0378] "Employee" means an individual employed by a company or organization and engaged in business.

[0379] "Skills" refer to the knowledge and abilities needed to effectively perform a particular task or activity.

[0380] "Dialogue" refers to the way humans and systems communicate through questions and answers in natural language.

[0381] "Artificial intelligence means" refers to algorithms or software designed to perform specific tasks automatically.

[0382] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0383] "Visualization" is the process of visually displaying data and information using graphs, tables, diagrams, etc., to make them easier to understand.

[0384] "Business requirements" refer to the abilities and conditions necessary to carry out a specific business.

[0385] A "match score" is a numerical representation of how well an employee's skills match the job requirements.

[0386] "Emotion recognition means" refers to technology or devices for identifying a user's emotional state by analyzing their facial expressions, tone of voice, etc.

[0387] A "user" is an employee, department leader, manager, or anyone who uses the system to enter information or view results.

[0388] "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to access the system.

[0389] "Server" refers to a central computer system that stores and processes data.

[0390] This invention is a system that utilizes generative artificial intelligence and an emotion engine to optimally allocate human resources within a company. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[0391] Hardware and software used

[0392] Terminal: Refers to an electronic device such as a computer or smartphone that allows a user to access the system using a dedicated application or web browser.

[0393] Server: The central computer system that stores and processes data. It operates ChatGPT and the database.

[0394] Emotion engine: Software that analyzes a user's facial expressions and tone of voice to identify their emotional state.

[0395] Program processing overview

[0396] Visualization of employee skills

[0397] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions.

[0398] The emotion engine recognizes the user's emotions in real time. The user's answers and emotional information are sent to the server, which analyzes the answers using ChatGPT and stores the skill set in a database.

[0399] The server generates a visualization view and displays it on the terminal.

[0400] Examples:

[0401] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[0402] Example prompt sentence:

[0403] "Tell us about your programming experience and how confident you are in your skills."

[0404] Visualization of business requirements

[0405] The terminal starts a dedicated application or a web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements.

[0406] The emotion engine recognizes the leader's emotions in real time. The leader's response is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated and displayed on the device.

[0407] Examples:

[0408] If the sales department leader responds, "Data analysis and presentation skills are required," the server analyzes this as "Required skills: Data analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[0409] Example prompt sentence:

[0410] "Tell us what skills your department needs and how important those skills are."

[0411] Matching and Scoring

[0412] The server retrieves the employee's skill profile and related emotional information from the database, and the department's job requirement profile and related emotional information. It compares these pieces of information and calculates a matching score based on the degree of compatibility.

[0413] Taking emotional information into account also enables more accurate matching. The server visualizes the scores and generates a dashboard. Finally, managers or human resources personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[0414] Examples:

[0415] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[0416] Example prompt sentence:

[0417] "Compare the employee's skills with the department's required skills and score how well they match. Take into account the user's emotional responses."

[0418] In this way, the present invention reveals the hidden skills of employees and the specific work requirements of each department, and by integrating user emotional information, it is possible to more accurately achieve optimal personnel allocation within a company.

[0419] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0420] Step 1:

[0421] The device launches a dedicated application or web interface.

[0422] Specific actions: Launch an application or web browser on the device used by the user and display the system login screen.

[0423] Input: Launch a browser or application.

[0424] Output: Login screen displayed.

[0425] Step 2:

[0426] A user logs in.

[0427] Specific operation: The user enters their ID and password, goes through the authentication process, and logs in to the system. If authentication is successful, they are redirected to the main screen.

[0428] Input: ID and password.

[0429] Output: Main screen for authenticated user.

[0430] Step 3:

[0431] The user accesses the interactive form and answers the questions.

[0432] Specific operation: The user selects the "Enter Skills" or "Enter Job Requirements" option from the main screen, moves to the interactive form, and answers questions about skills and job requirements.

[0433] Input: The user's answer.

[0434] Output: Response data.

[0435] Step 4:

[0436] The emotion engine recognizes the user's emotions in real time.

[0437] Specific operation: When the user answers a question, the system analyzes the user's facial expressions and tone of voice through a camera and microphone to recognize emotional information.

[0438] Input: User's facial expression and tone of voice.

[0439] Output: Emotional information.

[0440] Step 5:

[0441] The server receives the user's response and emotion information.

[0442] Specific operation: The text data and emotion information entered by the user are sent from the device to the server.

[0443] Input: Response data and sentiment information.

[0444] Output: Received data at the server.

[0445] Step 6:

[0446] The server parses the response using ChatGPT.

[0447] Specific operation: The text data received by the server is analyzed using ChatGPT to extract skills and job content.

[0448] Input: Received data.

[0449] Output: Analysis results.

[0450] Step 7:

[0451] The server stores the analysis results in a database.

[0452] Specific operation: Record the extracted skill sets, job requirements, and emotional information in a database.

[0453] Input: Analysis results.

[0454] Output: Saved data.

[0455] Step 8:

[0456] The server generates a visualization view and displays it on the terminal.

[0457] Specific operation: Based on the stored data, a view is generated to visually display the employee's skill profile and job requirement profile, and sent to the terminal.

[0458] Input: Saved data.

[0459] Output: The visualization view displayed on the terminal.

[0460] Step 9:

[0461] The server obtains the employee's skill profile and related emotional information, and the department's job requirement profile and related emotional information from the database.

[0462] What it does: Queries the database to retrieve the required skills information and job requirements.

[0463] Input: Query.

[0464] Output: The retrieved data.

[0465] Step 10:

[0466] The server compares the employee's skill profile with the job requirement profile.

[0467] What it does: Runs an algorithm to compare skill sets with job requirements and calculate the degree of fit.

[0468] Input: The retrieved data.

[0469] Output: Goodness-of-fit data.

[0470] Step 11:

[0471] The server calculates a matching score based on the degree of suitability.

[0472] Specific operation: Based on the acquired data, the degree of compatibility between skills and job requirements is quantified to calculate a matching score. Emotional information is also taken into account to calculate an overall score.

[0473] Input: Relevance data and sentiment information.

[0474] Output: Matching score.

[0475] Step 12:

[0476] The server visualizes the scores and generates a dashboard.

[0477] Specific behavior: Visually represent matching scores in graphs, charts, etc., and generate a final dashboard.

[0478] Input: Matching score.

[0479] Output: Dashboard.

[0480] Step 13:

[0481] The device displays the dashboard.

[0482] Specific operation: The generated dashboard is displayed on the device so that HR personnel and managers can view it.

[0483] Enter: Dashboard.

[0484] Output: The displayed dashboard.

[0485] (Application example 2)

[0486] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0487] In conventional personnel placement systems, employee skills and job requirements are often treated simply as data, and human factors such as the emotions and confidence of employees and leaders are not taken into account, making it difficult to achieve optimal matching. Another problem is the lack of a system that can check this information in real time and quickly make optimal placements.

[0488] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0489] In this invention, the server includes: a generating artificial intelligence means for interactively collecting employee skills; a means for saving the collected skills in a database; a means for visualizing the saved skill information; a generating artificial intelligence means for interactively collecting job requirements for required personnel; a means for saving the collected job requirements in a database; a means for visualizing the saved job requirements; a means for comparing employee skills with job requirements and calculating a matching score; a means for visualizing and displaying the matching score; an emotion engine for recognizing the emotions of employees and leaders in real time; a means for saving the emotion information collected by the emotion engine in a database and integrating it with skill and job requirement information; a means for improving matching accuracy based on the emotion information; and an application that runs on a smartphone or tablet and suggests optimal personnel placement. This enables more accurate personnel placement based on comprehensive information including emotion information.

[0490] An "employee" is someone who belongs to a company or organization and is employed to perform a specific job or task.

[0491] "Skills" refer to the knowledge, abilities, experience, etc. required to effectively carry out a specific job or task.

[0492] "Dialogue" is a method of collecting information through questions and answers, and is a format in which data is obtained through interaction between the user and the system.

[0493] "Generative AI" refers to artificial intelligence technology used for natural language generation and data analysis, and is used in question-answering systems, etc.

[0494] An "emotion engine" is an artificial intelligence technology that analyzes and evaluates a user's emotions based on facial expressions, tone of voice, text, etc.

[0495] A "database" is a system that systematically organizes and stores information and manages it so that it can be retrieved as needed.

[0496] "Storing" refers to keeping data or information in a state where it can be used at a later time.

[0497] "Visualization" is a technique for displaying data and information in visual formats such as graphs and charts to make them easier to understand.

[0498] "Business requirements" are the skills, conditions, and needs necessary to carry out a specific job or project.

[0499] The "matching score" is a numerical representation of the degree of compatibility between an employee's skills and the job requirements, and serves as a criterion for determining appropriate placement.

[0500] A "smartphone" is a highly functional mobile phone that can connect to the Internet and use various applications.

[0501] A "tablet" is a portable computing device with a touchscreen interface.

[0502] An "application" is a software program that provides a particular function or service.

[0503] The system for implementing this invention collects, analyzes, and visualizes employee skills and job requirements, and performs a series of processes to optimize personnel allocation. The main components and their functions are as follows:

[0504] 1. Hardware and Software Used

[0505] The system of the present invention uses the following hardware and software:

[0506] Smartphones and tablets: Android and iOS devices.

[0507] Server: A central server for managing and analyzing data. It is generally operated on a cloud service (AWS, Google Cloud, Microsoft Azure, etc.).

[0508] Emotion engine: Emotion analysis software such as Affectiva SDK.

[0509] Generative AI models: such as OpenAI's ChatGPT.

[0510] Database: A database solution such as Firebase or AWS RDS.

[0511] 2. Data Collection and Storage

[0512] (1) Collecting employee skill information

[0513] Employees, who are users, enter their skill information interactively using a dedicated application on their smartphones or tablets. At this time, the emotion engine analyzes the user's emotions from their facial expressions and tone of voice. The skill and emotion information is sent to the server and stored in a database.

[0514] (2) Gathering business requirements

[0515] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. At this time, the emotion engine analyzes the leader's emotions. The business requirement information and emotion information are sent to the server and stored in a database.

[0516] 3. Data Visualization

[0517] The stored skill information and job requirements information is analyzed by the server and generated as a visualization view, which can be viewed on a smartphone or tablet, allowing employees' skills, emotional state, and job requirements to be checked at a glance.

[0518] 4. Matching and Scoring

[0519] The server retrieves employee skill information, emotional information, and job requirements information from the database and performs matching using a generative AI model. Matching scores are calculated based on a comprehensive evaluation of skill compatibility and emotional information. The final score is visualized as a dashboard and displayed to managers and HR personnel on smartphones or tablets.

[0520] 5. Proposal for optimal layout

[0521] Managers and HR personnel can receive optimal staffing recommendations based on matching scores via a smartphone or tablet application. Because these recommendations are based on comprehensive information, they can make more accurate staffing decisions than ever before.

[0522] 6. Examples of concrete examples and prompts

[0523] Examples:

[0524] Let's say there is a position in a factory department that requires "skills in electrical circuit design and mechatronics." The department leader enters that information on their smartphone, and the emotion engine analyzes the leader's high confidence. Meanwhile, employee A confidently enters that he has "skills in electrical circuit design and mechatronics." The app matches the skills with the job requirements and displays a high score.

[0525] Example prompt sentence:

[0526] "We are looking for the perfect candidate for a new project. The project requires knowledge of electrical circuit design and mechatronics. What's more important is that the candidate has confidence in these skills. Please suggest the perfect candidate."

[0527] In this way, the present invention realizes optimal personnel allocation within a company through detailed collection of employee skills and job requirements, analysis of emotional information, real-time visualization and matching scoring.

[0528] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0529] Step 1:

[0530] Employees use a dedicated application on their smartphone or tablet to interactively input their skill information. The emotion engine analyzes emotions from the employee's facial expressions and tone of voice. The input is text data, and emotions are obtained as emotion analysis data. Input: Employee's skill information and emotion information. Output: Data packet containing skill information and emotion information.

[0531] Step 2:

[0532] The skill and emotion information sent from the device is sent to the server. The server analyzes the received data and formats it as required. Data processing includes natural language analysis using a generative AI model. Input: Data packet containing skill and emotion information. Output: Formatted skill and emotion information.

[0533] Step 3:

[0534] The formatted skill information and emotion information are stored in a database by the server. The stored information includes the employee's ID, skill information, emotion information, etc. Input: Formatted skill information and emotion information. Output: Employee information stored in the database.

[0535] Step 4:

[0536] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. The emotion engine analyzes emotions from the leader's facial expressions and tone of voice. Input: Business requirements and emotion information. Output: Data packet containing business requirements information and emotion information.

[0537] Step 5:

[0538] The task requirement information and emotion information sent from the device are sent to the server. The server analyzes the received data and formats it into the required format. Natural language analysis is performed using a generative AI model. Input: Data packet containing task requirement information and emotion information. Output: Formatted task requirement information and emotion information.

[0539] Step 6:

[0540] The formatted business requirement information and emotion information are stored in a database by the server. The stored information includes department ID, business requirements, emotion information, etc. Input: Formatted business requirement information and emotion information. Output: Business requirement information stored in the database.

[0541] Step 7:

[0542] The server retrieves employee skill information, emotional information, and job requirement information from the database. Based on this data, it uses a generative AI model to calculate the skill compatibility and emotional match, and calculates a matching score. Input: Skill information, emotional information, and job requirement information retrieved from the database. Output: Matching score.

[0543] Step 8:

[0544] The matching scores are visualized by the server and generated as a dashboard. This dashboard is displayed to managers and HR personnel on their smartphones or tablets. Input: Matching scores. Output: Visualized dashboard.

[0545] Step 9:

[0546] Managers and HR personnel can view the dashboard via smartphone or tablet and receive optimal staffing recommendations. The recommendations are based on skill matching accuracy and sentiment information. Input: Visualized dashboard. Output: Optimal staffing recommendations.

[0547] This series of processes enables integrated management of employee skill information, emotional information, and job requirement information, enabling highly accurate personnel allocation.

[0548] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0549] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0550] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0551] [Second embodiment]

[0552] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0553] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0554] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0555] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0556] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0557] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0558] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0559] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0560] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0561] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0562] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0563] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0564] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence. This system verbalizes employee skills in detail, clarifies the job requirements of the required human resources, and compares them to achieve optimal matching. Below is a detailed explanation of how the system of the present invention is implemented.

[0565] System Overview

[0566] The system consists of the following elements:

[0567] 1. Visualization of employee skills

[0568] 2. Visualization of business requirements

[0569] 3. Matching and Scoring

[0570] Program processing

[0571] 1. Visualization of employee skills

[0572] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. These questions are generated using generative artificial intelligence. The answered data is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[0573] Examples:

[0574] If an employee answers, "I have programming experience in C++ and Python," the server recognizes this data as "Skills: C++, Python" and stores it in the database. It then generates a visualization view of the employee's skill profile and displays this information on the terminal.

[0575] 2. Visualization of business requirements

[0576] The terminal launches a dedicated application or web interface, and the leader logs in. After successful login, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[0577] Examples:

[0578] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required skills: Data analysis, Presentation" and stores it in the database. Based on this, a visualization view is generated as a business requirements profile and this information is displayed on the terminal.

[0579] 3. Matching and Scoring

[0580] The server retrieves employee skill profiles and department job requirement profiles from the database. This information is compared and a matching score is calculated based on the degree of compatibility. The server visualizes the scores and generates a dashboard. Finally, managers or HR personnel can view these results on their terminals and use them to make decisions about optimal personnel placement.

[0581] Examples:

[0582] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information and calculates the cosine similarity. Based on this, a matching score is calculated and the results are visualized. The results are displayed on a terminal where the manager can view them and make a transfer decision.

[0583] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific business requirements of each department, thereby realizing optimal personnel allocation within a company.

[0584] The processing flow will be explained below.

[0585] Visualization of employee skills

[0586] Step 1:

[0587] The device launches a dedicated application or web interface and the employee logs in.

[0588] Specific behavior:

[0589] The terminal sends an authentication request to the authentication server.

[0590] The server generates an authentication token and returns it to the terminal.

[0591] Step 2:

[0592] The user (employee) accesses the interactive form and answers the questions.

[0593] Specific behavior:

[0594] The terminal requests interactive questions from the generating artificial intelligence.

[0595] The generation artificial intelligence generates questions and sends them to the terminal.

[0596] The user answers the questions, and the terminal transmits the answer data to the server.

[0597] Step 3:

[0598] The server receives and analyzes the user's response.

[0599] Specific behavior:

[0600] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0601] Generative AI analyzes answers and extracts skill sets.

[0602] The server stores the extracted skill set in a database.

[0603] Step 4:

[0604] The server generates a skill profile and displays it on the device.

[0605] Specific behavior:

[0606] The server retrieves the skill information from the database.

[0607] The server renders the skill profile view based on the retrieved information.

[0608] Sends the skill profile to the device and displays it.

[0609] Visualization of business requirements

[0610] Step 1:

[0611] The device launches a dedicated application or web interface, and the department leader logs in.

[0612] Specific behavior:

[0613] The terminal sends an authentication request to the authentication server.

[0614] The server generates an authentication token and returns it to the terminal.

[0615] Step 2:

[0616] The user (reader) accesses the interactive form and answers the questions.

[0617] Specific behavior:

[0618] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[0619] The generation artificial intelligence generates questions and sends them to the terminal.

[0620] The user answers the questions, and the terminal transmits the answer data to the server.

[0621] Step 3:

[0622] The server receives and analyzes the user's response.

[0623] Specific behavior:

[0624] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0625] Generative AI analyzes the answers and extracts the required skill sets.

[0626] The server stores the extracted business requirements in a database.

[0627] Step 4:

[0628] The server generates a business requirement profile and displays it on the terminal.

[0629] Specific behavior:

[0630] The server retrieves business requirement information from the database.

[0631] The server renders a business requirement profile view based on the acquired information.

[0632] The business requirements profile is sent to the terminal and displayed.

[0633] Matching and Scoring

[0634] Step 1:

[0635] The server obtains the employee's skill profile and the department's job requirement profile.

[0636] Specific behavior:

[0637] The server retrieves the employee's skill profile from the database.

[0638] Similarly, the server acquires the business requirement profile of each department.

[0639] Step 2:

[0640] The server compares employee skills with job requirements and calculates a matching score.

[0641] Specific behavior:

[0642] The server vectorizes the skills and business requirements and calculates the cosine similarity based on the correlation.

[0643] Based on the calculation results, a matching score is derived.

[0644] Step 3:

[0645] The server visualizes the matching score and displays it on the device.

[0646] Specific behavior:

[0647] The server generates graphs and dashboards based on the matching scores.

[0648] The generated visual is sent to the terminal and displayed.

[0649] Specific examples

[0650] If employee A answers, "I have programming experience in C++ and Python," the server analyzes it as "Skills: C++, Python" and saves it in the database. This is then reflected in the visualization view. Similarly, if the sales department leader answers, "Data analysis and presentation skills are required," the server analyzes it as "Required skills: Data analysis, Presentation" and saves it in the database. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[0651] Example 1

[0652] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0653] In order to properly allocate human resources within a company, it is necessary to understand employee skills in detail and match them with job requirements based on that information. However, conventional systems lack the means to efficiently collect, analyze, and visualize skills and job requirements, making it difficult to achieve optimal matching. To solve this problem, a system is needed that can efficiently collect skills and job requirements and score the degree of compatibility.

[0654] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0655] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for storing the collected skills in a database, a means for visualizing the stored skill information, and a generating artificial intelligence means for interactively collecting the business requirements of required personnel. This makes it possible to efficiently collect employee skills and business requirements and score the compatibility, thereby enabling optimal personnel allocation.

[0656] "Generative artificial intelligence means" refers to means for generating dialogue-style questions using natural language processing and analyzing collected information.

[0657] "Means for storing in a database" refers to means for permanently storing the collected information on skills and job requirements.

[0658] The "visualization means" is a means for converting the stored skill information and business requirement information into a visual format such as a graph or chart, and displaying it.

[0659] A "means for calculating a matching score" is a means for comparing an employee's skills with job requirements, quantifying the degree of compatibility, and calculating a score.

[0660] The "means for displaying as a dashboard" is a means for displaying the calculated matching scores as an integrated view so that the administrator can check them.

[0661] The "means of authenticating login" refers to the means of verifying the login information of employees and administrators and authenticating that they are legitimate users.

[0662] "Means for launching a dedicated application or web interface" refers to means for launching an interface that allows a user to interactively input information.

[0663] "Means for utilizing a generative artificial intelligence model" refers to means for generating dialogue-style questions using a generative AI model and analyzing input data.

[0664] The "means for calculating similarity" is a means for using an algorithm such as cosine similarity to calculate the degree of compatibility between skill information and job requirement information.

[0665] This invention is a system for optimally allocating personnel within a company using a generative AI model. This system efficiently collects employee skills and job requirements and scores the degree of compatibility to achieve optimal personnel allocation. The following describes in detail the mode for implementing this invention.

[0666] System Overview

[0667] The system includes a generative artificial intelligence means, a database storage means, a visualization means, a matching score calculation means, a dashboard display means, a login authentication means, a dedicated application or web interface launch means, a generative artificial intelligence model utilization means, and a similarity calculation means.

[0668] Hardware and software used

[0669] Server: Stores, analyzes, and visualizes data.

[0670] Terminal: A device (PC, tablet, smartphone, etc.) that a user uses to input and view information.

[0671] Generative AI models: Natural language processing models such as ChatGPT.

[0672] Database: Used to store skills information and job requirements information.

[0673] Web interface or dedicated application: The interface through which users access the site.

[0674] Program processing

[0675] Collecting and visualizing employee skills

[0676] The terminal launches a dedicated application or web interface, and the employee logs in. The user (employee) accesses the dialogue form and answers the generated questions. The questions are generated using a generative AI model. The answered data is sent to the server and analyzed using the generative AI model. The analysis results are saved in a database, and a view is generated by a visualization means and displayed on the terminal.

[0677] Examples:

[0678] If an employee answers, "I have programming experience in C++ and Python," the server will recognize and store the data as "Skills: C++, Python," and display it on the terminal as a visualization view.

[0679] Example prompt sentence:

[0680] "Create questions that capture the employee's skill set. For example, consider the answer 'I have programming experience in C++ and Python.'"

[0681] Gathering and visualizing business requirements

[0682] The device launches a dedicated application or web interface, and the reader logs in. The user (reader) accesses the dialogue form and answers the generated questions. The answer data is sent to the server and analyzed using the generative AI model. The analysis results are stored in a database, and a view is generated by the visualization means and displayed on the device.

[0683] Examples:

[0684] If the sales department leader answers, "Data analysis and presentation skills are required," the server will recognize this as "Required skills: Data analysis, Presentation" and save it. This will be displayed on the device as a visualization view.

[0685] Example prompt sentence:

[0686] "Create questions to capture job requirements. For example, consider the answer 'Data analysis and presentation skills required.'"

[0687] Matching and Scoring

[0688] The server retrieves employee skill profiles and department job requirement profiles from the database. The retrieved information is compared, and the degree of compatibility is calculated using algorithms such as cosine similarity, generating a matching score. The generated score is visualized using a dashboard and displayed on the terminal.

[0689] Examples:

[0690] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information, calculates the cosine similarity, calculates a matching score, and visualizes it. The results are displayed on the terminal, and managers can use this information to make transfer decisions.

[0691] Example prompt sentence:

[0692] "Describe a method for comparing an employee's skill profile with a job requirements profile and calculating the degree of fit. For example, consider a scenario where you use cosine similarity to calculate a matching score."

[0693] As described above, this invention is a system that utilizes a generative AI model to efficiently and effectively realize optimal personnel allocation within a company.

[0694] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0695] Step 1:

[0696] Launching the dedicated application or web interface

[0697] The device launches a dedicated application or web interface in response to user operation, and a login screen is displayed for the user.

[0698] Input: The user interacts with the application or web interface.

[0699] Output:Login screen

[0700] Step 2:

[0701] Login authentication

[0702] A user (employee or leader) logs in by entering their username and password. The server receives this authentication information and authenticates it against the database. If the login is successful, the main screen will be displayed on the terminal.

[0703] Input: Username, Password

[0704] Output: Authentication result (success / failure), main screen

[0705] Step 3:

[0706] Entering skill information

[0707] The user (employee) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[0708] Input: User's skill information

[0709] Output: Sending skill information to the server

[0710] Step 4:

[0711] Skill information analysis

[0712] The server analyzes the received skill information using a generative AI model, and the analyzed information is stored in a database as a "skill profile."

[0713] Input: User's skill information

[0714] Output: Parsed skill profile, saved to database

[0715] Step 5:

[0716] Skills profile visualization

[0717] The server generates a view to visualize the skill profile and displays it on the terminal.

[0718] Input: Skill Profile

[0719] Output: A visualized view of the skill profile

[0720] Step 6:

[0721] Entering business requirements information

[0722] The user (reader) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[0723] Input: User's business requirements information

[0724] Output: Sending business requirements information to the server

[0725] Step 7:

[0726] Analysis of business requirements information

[0727] The server analyzes the received business requirements information using a generative AI model, and the analyzed information is stored in a database as a "business requirements profile."

[0728] Input: User's business requirements information

[0729] Output: Analyzed business requirement profile, saved in database

[0730] Step 8:

[0731] Visualization of business requirements profile

[0732] The server generates a view for visualizing the business requirement profile and displays it on the terminal.

[0733] Input: Business Requirement Profile

[0734] Output: A visualized view of the business requirements profile

[0735] Step 9:

[0736] Obtaining skill profiles and job requirements profiles

[0737] The server retrieves the employee's skill profile and the department's job requirement profile from the database.

[0738] Input: None

[0739] Output: Skill profile, job requirement profile

[0740] Step 10:

[0741] Compare profile information

[0742] The server compares the skill profile with the job requirement profile and calculates the degree of compatibility, specifically using an algorithm such as cosine similarity.

[0743] Input: Skill profile, Job requirements profile

[0744] Output: Relevance score

[0745] Step 11:

[0746] Matching Score Calculation

[0747] The server calculates a matching score based on the compatibility score.

[0748] Input: Relevance score

[0749] Output: Matching score

[0750] Step 12:

[0751] Score visualization and dashboard display

[0752] The server visualizes the matching scores and generates a dashboard. Managers or HR personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[0753] Input: Matching score

[0754] Output: Dashboard view

[0755] (Application example 1)

[0756] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0757] In factories, it is extremely difficult to properly understand the skill sets and capabilities of workers and robots and assign optimal tasks to them. In particular, to improve factory production efficiency, a system is needed to allocate tasks in real time based on the skills and capabilities of each worker and robot, and to appropriately monitor and instruct them. However, such a system does not currently exist, resulting in problems such as reduced production efficiency and wasted human resources. Technology to solve this problem is needed.

[0758] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0759] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for saving the collected skills in a database, a means for visualizing the saved skill information, a generating artificial intelligence means for interactively collecting job requirements for required personnel, a means for saving the collected job requirements in a database, a means for visualizing the saved job requirements, a means for comparing employee skills with the job requirements and calculating a matching score, a means for visualizing and displaying the matching score, a means for assigning optimal tasks to workers and robots in a factory, and a means for monitoring and instructing optimal task assignment in real time. This enables optimal matching of skills and tasks for workers and robots in a factory, thereby improving production efficiency and optimizing the use of human resources.

[0760] "Employee" means a person who works in a factory.

[0761] "Skills" are the abilities and knowledge that employees or robots have to perform specific tasks.

[0762] A "dialogue" is a format in which information is collected through an exchange of questions and answers.

[0763] "Generative artificial intelligence means" refers to technology or devices that use natural language processing or machine learning to generate appropriate questions in response to user input and collect information in an interactive format.

[0764] A "database" is an information system for managing and storing collected data.

[0765] "Visualization" refers to visually expressing collected data and displaying it in an easy-to-understand manner.

[0766] "In-demand talent" refers to people who have the skills and abilities required to perform a particular task.

[0767] "Job requirements" refer to the skills and conditions necessary to perform a specific job.

[0768] A "matching score" is a numerical indicator of the degree of compatibility between an employee's skills and job requirements.

[0769] A "factory worker" is someone who physically performs work within a factory.

[0770] A "robot" is a mechanical device that performs autonomous or semi-autonomous tasks in a factory.

[0771] A "task" is a specific job or piece of work.

[0772] "Real-time" refers to processing or reaction occurring immediately in the present time.

[0773] "Monitoring" means continuously checking the progress and work status of a task.

[0774] "Directions" are the provision of orders or guidelines for carrying out specific actions or tasks.

[0775] The system for realizing this invention collects and analyzes the skills and requirements of employees and their work, and performs optimal task allocation and real-time monitoring. Specific embodiments of this system will be described below.

[0776] First, an employee logs in to a dedicated application or web interface using a terminal. They enter their skills through an interactive form. The entered skill information is analyzed using a generative AI model (ChatGPT). The analysis results are stored in a database (PostgreSQL) as a skillset, and a visualized view is displayed on the terminal.

[0777] Next, the administrator also logs in to a dedicated application or web interface using a terminal, and again enters the necessary business requirements using an interactive form. This information is also analyzed using the generative AI model, saved in the database as a business requirements profile, and a visualized view is displayed on the terminal.

[0778] The server retrieves the skill profile and job requirement profile from the database, compares them, and calculates a matching score. The server visualizes the score and displays it on the terminal in a dashboard format. This dashboard allows managers to optimally allocate personnel and tasks.

[0779] The system also includes a means for assigning optimal tasks to workers and robots in the factory, monitoring the progress of tasks in real time, and providing appropriate instructions to workers using head-mounted displays or smartphones as needed.

[0780] For example, if an employee answers, "I have experience in machine operation and maintenance," the server recognizes this information as "Skills: Machine Operation, Maintenance" and stores it in the database. If an administrator enters, "I need to set up and maintain a new machine," this information is stored as "Required Skills: Machine Operation, Maintenance." The server compares this information, calculates a matching score, and assigns the work to the best employee.

[0781] An example prompt might look like this:

[0782] "Please tell me the skills related to employee ID 123."

[0783] "What skills are required for task ID 456?"

[0784] In this way, the present invention realizes optimal matching of the skills and tasks of workers and robots within a factory, thereby enabling improved production efficiency and optimal utilization of human resources.

[0785] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0786] Step 1:

[0787] A user (employee) logs in to a dedicated application or web interface using a terminal. After successfully logging in, the user accesses an interactive form and enters their skills. For example, the user might enter "I have experience in machine operation and maintenance." This input is sent to a generative AI model (ChatGPT), which analyzes the input string and generates a skillset. This skillset is then stored in the database as, for example, "Skills: Machine Operation, Maintenance."

[0788] Step 2:

[0789] The server retrieves the stored skill information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows employees' skill sets to be visually confirmed. For example, "Skills: Machine operation, maintenance" is visualized and displayed graphically.

[0790] Step 3:

[0791] The user (administrator) uses a terminal to log in to a dedicated application or web interface. After successfully logging in, the administrator accesses an interactive form and enters the business requirements. For example, the administrator might enter, "A new machine needs to be set up and maintained." This input is also sent to the generative AI model, which analyzes it and generates the business requirements. The generated business requirements are stored in the database as, for example, "Required skills: machine operation, maintenance."

[0792] Step 4:

[0793] The server retrieves the saved business requirements information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows the business requirements to be visually confirmed. For example, "Required skills: machine operation, maintenance" is visualized and displayed graphically.

[0794] Step 5:

[0795] The server retrieves the employee's skill profile and job requirement profile from the database. Based on these profiles, it calculates the degree of match between the skills and requirements. Specifically, it calculates a matching score using an algorithm such as cosine similarity. For example, it calculates the similarity between the skill "machine operation, maintenance" and the job requirement "machine operation, maintenance" and calculates a matching rate of 90%.

[0796] Step 6:

[0797] The server visualizes the calculated matching scores and generates a dashboard. The dashboard is displayed on the terminal, allowing managers to optimally assign tasks based on the scores. This dashboard displays employees with high scores, their skill sets, and the corresponding job requirements. For example, it displays Employee A (Skills: Machine Operation, Maintenance), Task 1 (Required Skills: Machine Operation, Maintenance), Matching Score: 90%.

[0798] Step 7:

[0799] The user (manager) refers to the displayed dashboard and assigns tasks to the most suitable employee. The task assignment results are notified to the worker and robot in real time. Using a smartphone or head-mounted display, the progress of the assigned task is monitored and necessary instructions are provided in real time. For example, specific task instructions such as "Employee A will be responsible for setting up the new machine" are notified.

[0800] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0801] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence and an emotion engine. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[0802] System Overview

[0803] The system consists of the following elements:

[0804] 1. Visualization of employee skills

[0805] 2. Visualization of business requirements

[0806] 3. Matching and Scoring

[0807] 4. Emotion engine integration

[0808] Program processing

[0809] 1. Visualization of employee skills

[0810] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. In addition, the emotion engine recognizes the user's emotions in real time. The server receives the user's answers and emotion information and analyzes them using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[0811] Examples:

[0812] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[0813] 2. Visualization of business requirements

[0814] The terminal launches a dedicated application or web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The emotion engine recognizes the leader's emotions in real time. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[0815] Examples:

[0816] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required Skills: Data Analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[0817] 3. Matching and Scoring

[0818] The server retrieves the employee's skill profile and related emotional information, as well as the department's job requirements profile and related emotional information from the database. This information is compared and a matching score is calculated based on the degree of compatibility. By taking emotional information into account, more accurate matching is achieved. The server visualizes the score and generates a dashboard. Finally, managers or HR personnel can view these results on their devices and use them as information to make decisions about optimal personnel placement.

[0819] Examples:

[0820] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[0821] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific work requirements of each department, and further integrates user emotional information to achieve more accurate and optimal personnel allocation within a company.

[0822] The processing flow will be explained below.

[0823] Visualization of employee skills

[0824] Step 1:

[0825] The device launches a dedicated application or web interface and the employee logs in.

[0826] Specific behavior:

[0827] The terminal sends an authentication request to the authentication server.

[0828] The server generates an authentication token and returns it to the terminal.

[0829] Step 2:

[0830] The user (employee) answers interactive questions.

[0831] Specific behavior:

[0832] The terminal requests interactive questions from the generating artificial intelligence.

[0833] The generation artificial intelligence generates questions and sends them to the terminal.

[0834] As the user answers the questions, the emotion engine analyzes the user's emotions.

[0835] Step 3:

[0836] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[0837] Specific behavior:

[0838] The device transmits the emotion recognition data to the server.

[0839] The server stores the emotion data in a database.

[0840] Step 4:

[0841] The server receives and analyzes the user's response.

[0842] Specific behavior:

[0843] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0844] Generative AI analyzes answers and extracts skill sets.

[0845] The server stores the extracted skill sets and emotion data in a database.

[0846] Step 5:

[0847] The server generates a skill profile and displays it on the device.

[0848] Specific behavior:

[0849] The server retrieves skill information and emotion data from the database.

[0850] The server uses this information to render the skill profile view.

[0851] Sends the skill profile to the device and displays it.

[0852] Visualization of business requirements

[0853] Step 1:

[0854] The device launches a dedicated application or web interface, and the department leader logs in.

[0855] Specific behavior:

[0856] The terminal sends an authentication request to the authentication server.

[0857] The server generates an authentication token and returns it to the terminal.

[0858] Step 2:

[0859] The user (reader) answers interactive questions.

[0860] Specific behavior:

[0861] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[0862] The generation artificial intelligence generates questions and sends them to the terminal.

[0863] As the user answers the questions, the emotion engine analyzes the user's emotions.

[0864] Step 3:

[0865] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[0866] Specific behavior:

[0867] The device transmits the emotion recognition data to the server.

[0868] The server stores the emotion data in a database.

[0869] Step 4:

[0870] The server receives and analyzes the user's response.

[0871] Specific behavior:

[0872] The server generates the answer data and sends an analysis request to the artificial intelligence.

[0873] Generative AI analyzes the answers and extracts the required skill sets.

[0874] The server stores the extracted business requirements and emotion data in a database.

[0875] Step 5:

[0876] The server generates a business requirement profile and displays it on the terminal.

[0877] Specific behavior:

[0878] The server acquires the business requirement information and emotion data from the database.

[0879] The server uses this information to render a business requirement profile view.

[0880] The business requirements profile is sent to the terminal and displayed.

[0881] Matching and Scoring

[0882] Step 1:

[0883] The server acquires the skill profile and related emotion information of the employee, and the business requirement profile and related emotion information of the department.

[0884] Specific behavior:

[0885] The server retrieves employee skill profiles and emotional data from the database.

[0886] The server also obtains the business requirement profile and emotion data of each department.

[0887] Step 2:

[0888] The server compares skills with job requirements and calculates a matching score.

[0889] Specific behavior:

[0890] The server vectorizes the skills, job requirements, and related emotion data, and calculates the cosine similarity based on the correlation.

[0891] Based on the calculation results, a matching score is derived.

[0892] Step 3:

[0893] The server visualizes the matching score and displays it on the device.

[0894] Specific behavior:

[0895] The server generates graphs and dashboards based on the matching scores.

[0896] The generated visual is sent to the terminal and displayed.

[0897] Specific examples

[0898] If employee A answers, "I have programming experience in C++ and Python," and the emotion engine determines that the user is confident, the server analyzes this as "Skills: C++, Python" and saves it in the database. At the same time, the emotion data is also saved. If the sales department leader answers, "Data analysis and presentation skills are required," and the server determines that the leader is confident, the server recognizes this as "Required skills: Data analysis, Presentation," saves it in the database, and saves the emotion data as well. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[0899] Example 2

[0900] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0901] Conventional personnel placement systems have the problem that collecting employee skills and job requirements is subjective, making it difficult to optimally place personnel within a company. Furthermore, because they are unable to take into account the user's emotional information, it is difficult to grasp the true intentions and confidence levels of employees and leaders. This reduces the accuracy of matching, hindering the efficient operation of the company.

[0902] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0903] In this invention, the server includes artificial intelligence means for interactively collecting employee skills, means for saving the collected skills in a database, means for visualizing the saved skill information, artificial intelligence means for interactively collecting job requirements for required personnel, means for saving the collected job requirements in a database, means for visualizing the saved job requirements, means for comparing employee skills with the job requirements and calculating a matching score, means for visualizing and displaying the matching score, emotion recognition means for recognizing user emotions in real time, means for saving the emotion information in a database together with the user's responses, and means for reflecting the emotion information in the calculation of the matching score. This enables more accurate matching that also takes employee emotion information into account.

[0904] "Employee" means an individual employed by a company or organization and engaged in business.

[0905] "Skills" refer to the knowledge and abilities needed to effectively perform a particular task or activity.

[0906] "Dialogue" refers to the way humans and systems communicate through questions and answers in natural language.

[0907] "Artificial intelligence means" refers to algorithms or software designed to perform specific tasks automatically.

[0908] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[0909] "Visualization" is the process of visually displaying data and information using graphs, tables, diagrams, etc., to make them easier to understand.

[0910] "Business requirements" refer to the abilities and conditions necessary to carry out a specific business.

[0911] A "match score" is a numerical representation of how well an employee's skills match the job requirements.

[0912] "Emotion recognition means" refers to technology or devices for identifying a user's emotional state by analyzing their facial expressions, tone of voice, etc.

[0913] A "user" is an employee, department leader, manager, or anyone who uses the system to enter information or view results.

[0914] "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to access the system.

[0915] "Server" refers to a central computer system that stores and processes data.

[0916] This invention is a system that utilizes generative artificial intelligence and an emotion engine to optimally allocate human resources within a company. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[0917] Hardware and software used

[0918] Terminal: Refers to an electronic device such as a computer or smartphone that allows a user to access the system using a dedicated application or web browser.

[0919] Server: The central computer system that stores and processes data. It operates ChatGPT and the database.

[0920] Emotion engine: Software that analyzes a user's facial expressions and tone of voice to identify their emotional state.

[0921] Program processing overview

[0922] Visualization of employee skills

[0923] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions.

[0924] The emotion engine recognizes the user's emotions in real time. The user's answers and emotional information are sent to the server, which analyzes the answers using ChatGPT and stores the skill set in a database.

[0925] The server generates a visualization view and displays it on the terminal.

[0926] Examples:

[0927] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[0928] Example prompt sentence:

[0929] "Tell us about your programming experience and how confident you are in your skills."

[0930] Visualization of business requirements

[0931] The terminal starts a dedicated application or a web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements.

[0932] The emotion engine recognizes the leader's emotions in real time. The leader's response is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated and displayed on the device.

[0933] Examples:

[0934] If the sales department leader responds, "Data analysis and presentation skills are required," the server analyzes this as "Required skills: Data analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[0935] Example prompt sentence:

[0936] "Tell us what skills your department needs and how important those skills are."

[0937] Matching and Scoring

[0938] The server retrieves the employee's skill profile and related emotional information from the database, and the department's job requirement profile and related emotional information. It compares these pieces of information and calculates a matching score based on the degree of compatibility.

[0939] Taking emotional information into account also enables more accurate matching. The server visualizes the scores and generates a dashboard. Finally, managers or human resources personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[0940] Examples:

[0941] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[0942] Example prompt sentence:

[0943] "Compare the employee's skills with the department's required skills and score how well they match. Take into account the user's emotional responses."

[0944] In this way, the present invention reveals the hidden skills of employees and the specific work requirements of each department, and by integrating user emotional information, it is possible to more accurately achieve optimal personnel allocation within a company.

[0945] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0946] Step 1:

[0947] The device launches a dedicated application or web interface.

[0948] Specific actions: Launch an application or web browser on the device used by the user and display the system login screen.

[0949] Input: Launch a browser or application.

[0950] Output: Login screen displayed.

[0951] Step 2:

[0952] A user logs in.

[0953] Specific operation: The user enters their ID and password, goes through the authentication process, and logs in to the system. If authentication is successful, they are redirected to the main screen.

[0954] Input: ID and password.

[0955] Output: Main screen for authenticated user.

[0956] Step 3:

[0957] The user accesses the interactive form and answers the questions.

[0958] Specific operation: The user selects the "Enter Skills" or "Enter Job Requirements" option from the main screen, moves to the interactive form, and answers questions about skills and job requirements.

[0959] Input: The user's answer.

[0960] Output: Response data.

[0961] Step 4:

[0962] The emotion engine recognizes the user's emotions in real time.

[0963] Specific operation: When the user answers a question, the system analyzes the user's facial expressions and tone of voice through a camera and microphone to recognize emotional information.

[0964] Input: User's facial expression and tone of voice.

[0965] Output: Emotional information.

[0966] Step 5:

[0967] The server receives the user's response and emotion information.

[0968] Specific operation: The text data and emotion information entered by the user are sent from the device to the server.

[0969] Input: Response data and sentiment information.

[0970] Output: Received data at the server.

[0971] Step 6:

[0972] The server parses the response using ChatGPT.

[0973] Specific operation: The text data received by the server is analyzed using ChatGPT to extract skills and job content.

[0974] Input: Received data.

[0975] Output: Analysis results.

[0976] Step 7:

[0977] The server stores the analysis results in a database.

[0978] Specific operation: Record the extracted skill sets, job requirements, and emotional information in a database.

[0979] Input: Analysis results.

[0980] Output: Saved data.

[0981] Step 8:

[0982] The server generates a visualization view and displays it on the terminal.

[0983] Specific operation: Based on the stored data, a view is generated to visually display the employee's skill profile and job requirement profile, and sent to the terminal.

[0984] Input: Saved data.

[0985] Output: The visualization view displayed on the terminal.

[0986] Step 9:

[0987] The server obtains the employee's skill profile and related emotional information, and the department's job requirement profile and related emotional information from the database.

[0988] What it does: Queries the database to retrieve the required skills information and job requirements.

[0989] Input: Query.

[0990] Output: The retrieved data.

[0991] Step 10:

[0992] The server compares the employee's skill profile with the job requirement profile.

[0993] What it does: Runs an algorithm to compare skill sets with job requirements and calculate the degree of fit.

[0994] Input: The retrieved data.

[0995] Output: Goodness-of-fit data.

[0996] Step 11:

[0997] The server calculates a matching score based on the degree of suitability.

[0998] Specific operation: Based on the acquired data, the degree of compatibility between skills and job requirements is quantified to calculate a matching score. Emotional information is also taken into account to calculate an overall score.

[0999] Input: Relevance data and sentiment information.

[1000] Output: Matching score.

[1001] Step 12:

[1002] The server visualizes the scores and generates a dashboard.

[1003] Specific behavior: Visually represent matching scores in graphs, charts, etc., and generate a final dashboard.

[1004] Input: Matching score.

[1005] Output: Dashboard.

[1006] Step 13:

[1007] The device displays the dashboard.

[1008] Specific operation: The generated dashboard is displayed on the device so that HR personnel and managers can view it.

[1009] Enter: Dashboard.

[1010] Output: The displayed dashboard.

[1011] (Application example 2)

[1012] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[1013] In conventional personnel placement systems, employee skills and job requirements are often treated simply as data, and human factors such as the emotions and confidence of employees and leaders are not taken into account, making it difficult to achieve optimal matching. Another problem is the lack of a system that can check this information in real time and quickly make optimal placements.

[1014] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1015] In this invention, the server includes: a generating artificial intelligence means for interactively collecting employee skills; a means for saving the collected skills in a database; a means for visualizing the saved skill information; a generating artificial intelligence means for interactively collecting job requirements for required personnel; a means for saving the collected job requirements in a database; a means for visualizing the saved job requirements; a means for comparing employee skills with job requirements and calculating a matching score; a means for visualizing and displaying the matching score; an emotion engine for recognizing the emotions of employees and leaders in real time; a means for saving the emotion information collected by the emotion engine in a database and integrating it with skill and job requirement information; a means for improving matching accuracy based on the emotion information; and an application that runs on a smartphone or tablet and suggests optimal personnel placement. This enables more accurate personnel placement based on comprehensive information including emotion information.

[1016] An "employee" is someone who belongs to a company or organization and is employed to perform a specific job or task.

[1017] "Skills" refer to the knowledge, abilities, experience, etc. required to effectively carry out a specific job or task.

[1018] "Dialogue" is a method of collecting information through questions and answers, and is a format in which data is obtained through interaction between the user and the system.

[1019] "Generative AI" refers to artificial intelligence technology used for natural language generation and data analysis, and is used in question-answering systems, etc.

[1020] An "emotion engine" is an artificial intelligence technology that analyzes and evaluates a user's emotions based on facial expressions, tone of voice, text, etc.

[1021] A "database" is a system that systematically organizes and stores information and manages it so that it can be retrieved as needed.

[1022] "Storing" refers to keeping data or information in a state where it can be used at a later time.

[1023] "Visualization" is a technique for displaying data and information in visual formats such as graphs and charts to make them easier to understand.

[1024] "Business requirements" are the skills, conditions, and needs necessary to carry out a specific job or project.

[1025] The "matching score" is a numerical representation of the degree of compatibility between an employee's skills and the job requirements, and serves as a criterion for determining appropriate placement.

[1026] A "smartphone" is a highly functional mobile phone that can connect to the Internet and use various applications.

[1027] A "tablet" is a portable computing device with a touchscreen interface.

[1028] An "application" is a software program that provides a particular function or service.

[1029] The system for implementing this invention collects, analyzes, and visualizes employee skills and job requirements, and performs a series of processes to optimize personnel allocation. The main components and their functions are as follows:

[1030] 1. Hardware and Software Used

[1031] The system of the present invention uses the following hardware and software:

[1032] Smartphones and tablets: Android and iOS devices.

[1033] Server: A central server for managing and analyzing data. It is generally operated on a cloud service (AWS, Google Cloud, Microsoft Azure, etc.).

[1034] Emotion engine: Emotion analysis software such as Affectiva SDK.

[1035] Generative AI models: such as OpenAI's ChatGPT.

[1036] Database: A database solution such as Firebase or AWS RDS.

[1037] 2. Data Collection and Storage

[1038] (1) Collecting employee skill information

[1039] Employees, who are users, enter their skill information interactively using a dedicated application on their smartphones or tablets. At this time, the emotion engine analyzes the user's emotions from their facial expressions and tone of voice. The skill and emotion information is sent to the server and stored in a database.

[1040] (2) Gathering business requirements

[1041] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. At this time, the emotion engine analyzes the leader's emotions. The business requirement information and emotion information are sent to the server and stored in a database.

[1042] 3. Data Visualization

[1043] The stored skill information and job requirements information is analyzed by the server and generated as a visualization view, which can be viewed on a smartphone or tablet, allowing employees' skills, emotional state, and job requirements to be checked at a glance.

[1044] 4. Matching and Scoring

[1045] The server retrieves employee skill information, emotional information, and job requirements information from the database and performs matching using a generative AI model. Matching scores are calculated based on a comprehensive evaluation of skill compatibility and emotional information. The final score is visualized as a dashboard and displayed to managers and HR personnel on smartphones or tablets.

[1046] 5. Proposal for optimal layout

[1047] Managers and HR personnel can receive optimal staffing recommendations based on matching scores via a smartphone or tablet application. Because these recommendations are based on comprehensive information, they can make more accurate staffing decisions than ever before.

[1048] 6. Examples of concrete examples and prompts

[1049] Examples:

[1050] Let's say there is a position in a factory department that requires "skills in electrical circuit design and mechatronics." The department leader enters that information on their smartphone, and the emotion engine analyzes the leader's high confidence. Meanwhile, employee A confidently enters that he has "skills in electrical circuit design and mechatronics." The app matches the skills with the job requirements and displays a high score.

[1051] Example prompt sentence:

[1052] "We are looking for the perfect candidate for a new project. The project requires knowledge of electrical circuit design and mechatronics. What's more important is that the candidate has confidence in these skills. Please suggest the perfect candidate."

[1053] In this way, the present invention realizes optimal personnel allocation within a company through detailed collection of employee skills and job requirements, analysis of emotional information, real-time visualization and matching scoring.

[1054] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1055] Step 1:

[1056] Employees use a dedicated application on their smartphone or tablet to interactively input their skill information. The emotion engine analyzes emotions from the employee's facial expressions and tone of voice. The input is text data, and emotions are obtained as emotion analysis data. Input: Employee's skill information and emotion information. Output: Data packet containing skill information and emotion information.

[1057] Step 2:

[1058] The skill and emotion information sent from the device is sent to the server. The server analyzes the received data and formats it as required. Data processing includes natural language analysis using a generative AI model. Input: Data packet containing skill and emotion information. Output: Formatted skill and emotion information.

[1059] Step 3:

[1060] The formatted skill information and emotion information are stored in a database by the server. The stored information includes the employee's ID, skill information, emotion information, etc. Input: Formatted skill information and emotion information. Output: Employee information stored in the database.

[1061] Step 4:

[1062] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. The emotion engine analyzes emotions from the leader's facial expressions and tone of voice. Input: Business requirements and emotion information. Output: Data packet containing business requirements information and emotion information.

[1063] Step 5:

[1064] The task requirement information and emotion information sent from the device are sent to the server. The server analyzes the received data and formats it into the required format. Natural language analysis is performed using a generative AI model. Input: Data packet containing task requirement information and emotion information. Output: Formatted task requirement information and emotion information.

[1065] Step 6:

[1066] The formatted business requirement information and emotion information are stored in a database by the server. The stored information includes department ID, business requirements, emotion information, etc. Input: Formatted business requirement information and emotion information. Output: Business requirement information stored in the database.

[1067] Step 7:

[1068] The server retrieves employee skill information, emotional information, and job requirement information from the database. Based on this data, it uses a generative AI model to calculate the skill compatibility and emotional match, and calculates a matching score. Input: Skill information, emotional information, and job requirement information retrieved from the database. Output: Matching score.

[1069] Step 8:

[1070] The matching scores are visualized by the server and generated as a dashboard. This dashboard is displayed to managers and HR personnel on their smartphones or tablets. Input: Matching scores. Output: Visualized dashboard.

[1071] Step 9:

[1072] Managers and HR personnel can view the dashboard via smartphone or tablet and receive optimal staffing recommendations. The recommendations are based on skill matching accuracy and sentiment information. Input: Visualized dashboard. Output: Optimal staffing recommendations.

[1073] This series of processes enables integrated management of employee skill information, emotional information, and job requirement information, enabling highly accurate personnel allocation.

[1074] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1076] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1077] [Third embodiment]

[1078] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1079] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1080] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1081] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[1082] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1083] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1084] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1085] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1086] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1087] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1088] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1089] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1090] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence. This system verbalizes employee skills in detail, clarifies the job requirements of the required human resources, and compares them to achieve optimal matching. Below is a detailed explanation of how the system of the present invention is implemented.

[1091] System Overview

[1092] The system consists of the following elements:

[1093] 1. Visualization of employee skills

[1094] 2. Visualization of business requirements

[1095] 3. Matching and Scoring

[1096] Program processing

[1097] 1. Visualization of employee skills

[1098] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. These questions are generated using generative artificial intelligence. The answered data is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[1099] Examples:

[1100] If an employee answers, "I have programming experience in C++ and Python," the server recognizes this data as "Skills: C++, Python" and stores it in the database. It then generates a visualization view of the employee's skill profile and displays this information on the terminal.

[1101] 2. Visualization of business requirements

[1102] The terminal launches a dedicated application or web interface, and the leader logs in. After successful login, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[1103] Examples:

[1104] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required skills: Data analysis, Presentation" and stores it in the database. Based on this, a visualization view is generated as a business requirements profile and this information is displayed on the terminal.

[1105] 3. Matching and Scoring

[1106] The server retrieves employee skill profiles and department job requirement profiles from the database. This information is compared and a matching score is calculated based on the degree of compatibility. The server visualizes the scores and generates a dashboard. Finally, managers or HR personnel can view these results on their terminals and use them to make decisions about optimal personnel placement.

[1107] Examples:

[1108] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information and calculates the cosine similarity. Based on this, a matching score is calculated and the results are visualized. The results are displayed on a terminal where the manager can view them and make a transfer decision.

[1109] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific business requirements of each department, thereby realizing optimal personnel allocation within a company.

[1110] The processing flow will be explained below.

[1111] Visualization of employee skills

[1112] Step 1:

[1113] The device launches a dedicated application or web interface and the employee logs in.

[1114] Specific behavior:

[1115] The terminal sends an authentication request to the authentication server.

[1116] The server generates an authentication token and returns it to the terminal.

[1117] Step 2:

[1118] The user (employee) accesses the interactive form and answers the questions.

[1119] Specific behavior:

[1120] The terminal requests interactive questions from the generating artificial intelligence.

[1121] The generation artificial intelligence generates questions and sends them to the terminal.

[1122] The user answers the questions, and the terminal transmits the answer data to the server.

[1123] Step 3:

[1124] The server receives and analyzes the user's response.

[1125] Specific behavior:

[1126] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1127] Generative AI analyzes answers and extracts skill sets.

[1128] The server stores the extracted skill set in a database.

[1129] Step 4:

[1130] The server generates a skill profile and displays it on the device.

[1131] Specific behavior:

[1132] The server retrieves the skill information from the database.

[1133] The server renders the skill profile view based on the retrieved information.

[1134] Sends the skill profile to the device and displays it.

[1135] Visualization of business requirements

[1136] Step 1:

[1137] The device launches a dedicated application or web interface, and the department leader logs in.

[1138] Specific behavior:

[1139] The terminal sends an authentication request to the authentication server.

[1140] The server generates an authentication token and returns it to the terminal.

[1141] Step 2:

[1142] The user (reader) accesses the interactive form and answers the questions.

[1143] Specific behavior:

[1144] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[1145] The generation artificial intelligence generates questions and sends them to the terminal.

[1146] The user answers the questions, and the terminal transmits the answer data to the server.

[1147] Step 3:

[1148] The server receives and analyzes the user's response.

[1149] Specific behavior:

[1150] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1151] Generative AI analyzes the answers and extracts the required skill sets.

[1152] The server stores the extracted business requirements in a database.

[1153] Step 4:

[1154] The server generates a business requirement profile and displays it on the terminal.

[1155] Specific behavior:

[1156] The server retrieves business requirement information from the database.

[1157] The server renders a business requirement profile view based on the acquired information.

[1158] The business requirements profile is sent to the terminal and displayed.

[1159] Matching and Scoring

[1160] Step 1:

[1161] The server obtains the employee's skill profile and the department's job requirement profile.

[1162] Specific behavior:

[1163] The server retrieves the employee's skill profile from the database.

[1164] Similarly, the server acquires the business requirement profile of each department.

[1165] Step 2:

[1166] The server compares employee skills with job requirements and calculates a matching score.

[1167] Specific behavior:

[1168] The server vectorizes the skills and business requirements and calculates the cosine similarity based on the correlation.

[1169] Based on the calculation results, a matching score is derived.

[1170] Step 3:

[1171] The server visualizes the matching score and displays it on the device.

[1172] Specific behavior:

[1173] The server generates graphs and dashboards based on the matching scores.

[1174] The generated visual is sent to the terminal and displayed.

[1175] Specific examples

[1176] If employee A answers, "I have programming experience in C++ and Python," the server analyzes it as "Skills: C++, Python" and saves it in the database. This is then reflected in the visualization view. Similarly, if the sales department leader answers, "Data analysis and presentation skills are required," the server analyzes it as "Required skills: Data analysis, Presentation" and saves it in the database. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[1177] Example 1

[1178] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1179] In order to properly allocate human resources within a company, it is necessary to understand employee skills in detail and match them with job requirements based on that information. However, conventional systems lack the means to efficiently collect, analyze, and visualize skills and job requirements, making it difficult to achieve optimal matching. To solve this problem, a system is needed that can efficiently collect skills and job requirements and score the degree of compatibility.

[1180] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1181] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for storing the collected skills in a database, a means for visualizing the stored skill information, and a generating artificial intelligence means for interactively collecting the business requirements of required personnel. This makes it possible to efficiently collect employee skills and business requirements and score the compatibility, thereby enabling optimal personnel allocation.

[1182] "Generative artificial intelligence means" refers to means for generating dialogue-style questions using natural language processing and analyzing collected information.

[1183] "Means for storing in a database" refers to means for permanently storing the collected information on skills and job requirements.

[1184] The "visualization means" is a means for converting the stored skill information and business requirement information into a visual format such as a graph or chart, and displaying it.

[1185] A "means for calculating a matching score" is a means for comparing an employee's skills with job requirements, quantifying the degree of compatibility, and calculating a score.

[1186] The "means for displaying as a dashboard" is a means for displaying the calculated matching scores as an integrated view so that the administrator can check them.

[1187] The "means of authenticating login" refers to the means of verifying the login information of employees and administrators and authenticating that they are legitimate users.

[1188] "Means for launching a dedicated application or web interface" refers to means for launching an interface that allows a user to interactively input information.

[1189] "Means for utilizing a generative artificial intelligence model" refers to means for generating dialogue-style questions using a generative AI model and analyzing input data.

[1190] The "means for calculating similarity" is a means for using an algorithm such as cosine similarity to calculate the degree of compatibility between skill information and job requirement information.

[1191] This invention is a system for optimally allocating personnel within a company using a generative AI model. This system efficiently collects employee skills and job requirements and scores the degree of compatibility to achieve optimal personnel allocation. The following describes in detail the mode for implementing this invention.

[1192] System Overview

[1193] The system includes a generative artificial intelligence means, a database storage means, a visualization means, a matching score calculation means, a dashboard display means, a login authentication means, a dedicated application or web interface launch means, a generative artificial intelligence model utilization means, and a similarity calculation means.

[1194] Hardware and software used

[1195] Server: Stores, analyzes, and visualizes data.

[1196] Terminal: A device (PC, tablet, smartphone, etc.) that a user uses to input and view information.

[1197] Generative AI models: Natural language processing models such as ChatGPT.

[1198] Database: Used to store skills information and job requirements information.

[1199] Web interface or dedicated application: The interface through which users access the site.

[1200] Program processing

[1201] Collecting and visualizing employee skills

[1202] The terminal launches a dedicated application or web interface, and the employee logs in. The user (employee) accesses the dialogue form and answers the generated questions. The questions are generated using a generative AI model. The answered data is sent to the server and analyzed using the generative AI model. The analysis results are saved in a database, and a view is generated by a visualization means and displayed on the terminal.

[1203] Examples:

[1204] If an employee answers, "I have programming experience in C++ and Python," the server will recognize and store the data as "Skills: C++, Python," and display it on the terminal as a visualization view.

[1205] Example prompt sentence:

[1206] "Create questions that capture the employee's skill set. For example, consider the answer 'I have programming experience in C++ and Python.'"

[1207] Gathering and visualizing business requirements

[1208] The device launches a dedicated application or web interface, and the reader logs in. The user (reader) accesses the dialogue form and answers the generated questions. The answer data is sent to the server and analyzed using the generative AI model. The analysis results are stored in a database, and a view is generated by the visualization means and displayed on the device.

[1209] Examples:

[1210] If the sales department leader answers, "Data analysis and presentation skills are required," the server will recognize this as "Required skills: Data analysis, Presentation" and save it. This will be displayed on the device as a visualization view.

[1211] Example prompt sentence:

[1212] "Create questions to capture job requirements. For example, consider the answer 'Data analysis and presentation skills required.'"

[1213] Matching and Scoring

[1214] The server retrieves employee skill profiles and department job requirement profiles from the database. The retrieved information is compared, and the degree of compatibility is calculated using algorithms such as cosine similarity, generating a matching score. The generated score is visualized using a dashboard and displayed on the terminal.

[1215] Examples:

[1216] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information, calculates the cosine similarity, calculates a matching score, and visualizes it. The results are displayed on the terminal, and managers can use this information to make transfer decisions.

[1217] Example prompt sentence:

[1218] "Describe a method for comparing an employee's skill profile with a job requirements profile and calculating the degree of fit. For example, consider a scenario where you use cosine similarity to calculate a matching score."

[1219] As described above, this invention is a system that utilizes a generative AI model to efficiently and effectively realize optimal personnel allocation within a company.

[1220] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1221] Step 1:

[1222] Launching the dedicated application or web interface

[1223] The device launches a dedicated application or web interface in response to user operation, and a login screen is displayed for the user.

[1224] Input: The user interacts with the application or web interface.

[1225] Output:Login screen

[1226] Step 2:

[1227] Login authentication

[1228] A user (employee or leader) logs in by entering their username and password. The server receives this authentication information and authenticates it against the database. If the login is successful, the main screen will be displayed on the terminal.

[1229] Input: Username, Password

[1230] Output: Authentication result (success / failure), main screen

[1231] Step 3:

[1232] Entering skill information

[1233] The user (employee) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[1234] Input: User's skill information

[1235] Output: Sending skill information to the server

[1236] Step 4:

[1237] Skill information analysis

[1238] The server analyzes the received skill information using a generative AI model, and the analyzed information is stored in a database as a "skill profile."

[1239] Input: User's skill information

[1240] Output: Parsed skill profile, saved to database

[1241] Step 5:

[1242] Skills profile visualization

[1243] The server generates a view to visualize the skill profile and displays it on the terminal.

[1244] Input: Skill Profile

[1245] Output: A visualized view of the skill profile

[1246] Step 6:

[1247] Entering business requirements information

[1248] The user (reader) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[1249] Input: User's business requirements information

[1250] Output: Sending business requirements information to the server

[1251] Step 7:

[1252] Analysis of business requirements information

[1253] The server analyzes the received business requirements information using a generative AI model, and the analyzed information is stored in a database as a "business requirements profile."

[1254] Input: User's business requirements information

[1255] Output: Analyzed business requirement profile, saved in database

[1256] Step 8:

[1257] Visualization of business requirements profile

[1258] The server generates a view for visualizing the business requirement profile and displays it on the terminal.

[1259] Input: Business Requirement Profile

[1260] Output: A visualized view of the business requirements profile

[1261] Step 9:

[1262] Obtaining skill profiles and job requirements profiles

[1263] The server retrieves the employee's skill profile and the department's job requirement profile from the database.

[1264] Input: None

[1265] Output: Skill profile, job requirement profile

[1266] Step 10:

[1267] Compare profile information

[1268] The server compares the skill profile with the job requirement profile and calculates the degree of compatibility, specifically using an algorithm such as cosine similarity.

[1269] Input: Skill profile, Job requirements profile

[1270] Output: Relevance score

[1271] Step 11:

[1272] Matching Score Calculation

[1273] The server calculates a matching score based on the compatibility score.

[1274] Input: Relevance score

[1275] Output: Matching score

[1276] Step 12:

[1277] Score visualization and dashboard display

[1278] The server visualizes the matching scores and generates a dashboard. Managers or HR personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[1279] Input: Matching score

[1280] Output: Dashboard view

[1281] (Application example 1)

[1282] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1283] In factories, it is extremely difficult to properly understand the skill sets and capabilities of workers and robots and assign optimal tasks to them. In particular, to improve factory production efficiency, a system is needed to allocate tasks in real time based on the skills and capabilities of each worker and robot, and to appropriately monitor and instruct them. However, such a system does not currently exist, resulting in problems such as reduced production efficiency and wasted human resources. Technology to solve this problem is needed.

[1284] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1285] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for saving the collected skills in a database, a means for visualizing the saved skill information, a generating artificial intelligence means for interactively collecting job requirements for required personnel, a means for saving the collected job requirements in a database, a means for visualizing the saved job requirements, a means for comparing employee skills with the job requirements and calculating a matching score, a means for visualizing and displaying the matching score, a means for assigning optimal tasks to workers and robots in a factory, and a means for monitoring and instructing optimal task assignment in real time. This enables optimal matching of skills and tasks for workers and robots in a factory, thereby improving production efficiency and optimizing the use of human resources.

[1286] "Employee" means a person who works in a factory.

[1287] "Skills" are the abilities and knowledge that employees or robots have to perform specific tasks.

[1288] A "dialogue" is a format in which information is collected through an exchange of questions and answers.

[1289] "Generative artificial intelligence means" refers to technology or devices that use natural language processing or machine learning to generate appropriate questions in response to user input and collect information in an interactive format.

[1290] A "database" is an information system for managing and storing collected data.

[1291] "Visualization" refers to visually expressing collected data and displaying it in an easy-to-understand manner.

[1292] "In-demand talent" refers to people who have the skills and abilities required to perform a particular task.

[1293] "Job requirements" refer to the skills and conditions necessary to perform a specific job.

[1294] A "matching score" is a numerical indicator of the degree of compatibility between an employee's skills and job requirements.

[1295] A "factory worker" is someone who physically performs work within a factory.

[1296] A "robot" is a mechanical device that performs autonomous or semi-autonomous tasks in a factory.

[1297] A "task" is a specific job or piece of work.

[1298] "Real-time" refers to processing or reaction occurring immediately in the present time.

[1299] "Monitoring" means continuously checking the progress and work status of a task.

[1300] "Directions" are the provision of orders or guidelines for carrying out specific actions or tasks.

[1301] The system for realizing this invention collects and analyzes the skills and requirements of employees and their work, and performs optimal task allocation and real-time monitoring. Specific embodiments of this system will be described below.

[1302] First, an employee logs in to a dedicated application or web interface using a terminal. They enter their skills through an interactive form. The entered skill information is analyzed using a generative AI model (ChatGPT). The analysis results are stored in a database (PostgreSQL) as a skillset, and a visualized view is displayed on the terminal.

[1303] Next, the administrator also logs in to a dedicated application or web interface using a terminal, and again enters the necessary business requirements using an interactive form. This information is also analyzed using the generative AI model, saved in the database as a business requirements profile, and a visualized view is displayed on the terminal.

[1304] The server retrieves the skill profile and job requirement profile from the database, compares them, and calculates a matching score. The server visualizes the score and displays it on the terminal in a dashboard format. This dashboard allows managers to optimally allocate personnel and tasks.

[1305] The system also includes a means for assigning optimal tasks to workers and robots in the factory, monitoring the progress of tasks in real time, and providing appropriate instructions to workers using head-mounted displays or smartphones as needed.

[1306] For example, if an employee answers, "I have experience in machine operation and maintenance," the server recognizes this information as "Skills: Machine Operation, Maintenance" and stores it in the database. If an administrator enters, "I need to set up and maintain a new machine," this information is stored as "Required Skills: Machine Operation, Maintenance." The server compares this information, calculates a matching score, and assigns the work to the best employee.

[1307] An example prompt might look like this:

[1308] "Please tell me the skills related to employee ID 123."

[1309] "What skills are required for task ID 456?"

[1310] In this way, the present invention realizes optimal matching of the skills and tasks of workers and robots within a factory, thereby enabling improved production efficiency and optimal utilization of human resources.

[1311] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1312] Step 1:

[1313] A user (employee) logs in to a dedicated application or web interface using a terminal. After successfully logging in, the user accesses an interactive form and enters their skills. For example, the user might enter "I have experience in machine operation and maintenance." This input is sent to a generative AI model (ChatGPT), which analyzes the input string and generates a skillset. This skillset is then stored in the database as, for example, "Skills: Machine Operation, Maintenance."

[1314] Step 2:

[1315] The server retrieves the stored skill information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows employees' skill sets to be visually confirmed. For example, "Skills: Machine operation, maintenance" is visualized and displayed graphically.

[1316] Step 3:

[1317] The user (administrator) uses a terminal to log in to a dedicated application or web interface. After successfully logging in, the administrator accesses an interactive form and enters the business requirements. For example, the administrator might enter, "A new machine needs to be set up and maintained." This input is also sent to the generative AI model, which analyzes it and generates the business requirements. The generated business requirements are stored in the database as, for example, "Required skills: machine operation, maintenance."

[1318] Step 4:

[1319] The server retrieves the saved business requirements information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows the business requirements to be visually confirmed. For example, "Required skills: machine operation, maintenance" is visualized and displayed graphically.

[1320] Step 5:

[1321] The server retrieves the employee's skill profile and job requirement profile from the database. Based on these profiles, it calculates the degree of match between the skills and requirements. Specifically, it calculates a matching score using an algorithm such as cosine similarity. For example, it calculates the similarity between the skill "machine operation, maintenance" and the job requirement "machine operation, maintenance" and calculates a matching rate of 90%.

[1322] Step 6:

[1323] The server visualizes the calculated matching scores and generates a dashboard. The dashboard is displayed on the terminal, allowing managers to optimally assign tasks based on the scores. This dashboard displays employees with high scores, their skill sets, and the corresponding job requirements. For example, it displays Employee A (Skills: Machine Operation, Maintenance), Task 1 (Required Skills: Machine Operation, Maintenance), Matching Score: 90%.

[1324] Step 7:

[1325] The user (manager) refers to the displayed dashboard and assigns tasks to the most suitable employee. The task assignment results are notified to the worker and robot in real time. Using a smartphone or head-mounted display, the progress of the assigned task is monitored and necessary instructions are provided in real time. For example, specific task instructions such as "Employee A will be responsible for setting up the new machine" are notified.

[1326] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1327] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence and an emotion engine. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[1328] System Overview

[1329] The system consists of the following elements:

[1330] 1. Visualization of employee skills

[1331] 2. Visualization of business requirements

[1332] 3. Matching and Scoring

[1333] 4. Emotion engine integration

[1334] Program processing

[1335] 1. Visualization of employee skills

[1336] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. In addition, the emotion engine recognizes the user's emotions in real time. The server receives the user's answers and emotion information and analyzes them using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[1337] Examples:

[1338] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[1339] 2. Visualization of business requirements

[1340] The terminal launches a dedicated application or web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The emotion engine recognizes the leader's emotions in real time. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[1341] Examples:

[1342] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required Skills: Data Analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[1343] 3. Matching and Scoring

[1344] The server retrieves the employee's skill profile and related emotional information, as well as the department's job requirements profile and related emotional information from the database. This information is compared and a matching score is calculated based on the degree of compatibility. By taking emotional information into account, more accurate matching is achieved. The server visualizes the score and generates a dashboard. Finally, managers or HR personnel can view these results on their devices and use them as information to make decisions about optimal personnel placement.

[1345] Examples:

[1346] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[1347] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific work requirements of each department, and further integrates user emotional information to achieve more accurate and optimal personnel allocation within a company.

[1348] The processing flow will be explained below.

[1349] Visualization of employee skills

[1350] Step 1:

[1351] The device launches a dedicated application or web interface and the employee logs in.

[1352] Specific behavior:

[1353] The terminal sends an authentication request to the authentication server.

[1354] The server generates an authentication token and returns it to the terminal.

[1355] Step 2:

[1356] The user (employee) answers interactive questions.

[1357] Specific behavior:

[1358] The terminal requests interactive questions from the generating artificial intelligence.

[1359] The generation artificial intelligence generates questions and sends them to the terminal.

[1360] As the user answers the questions, the emotion engine analyzes the user's emotions.

[1361] Step 3:

[1362] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[1363] Specific behavior:

[1364] The device transmits the emotion recognition data to the server.

[1365] The server stores the emotion data in a database.

[1366] Step 4:

[1367] The server receives and analyzes the user's response.

[1368] Specific behavior:

[1369] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1370] Generative AI analyzes answers and extracts skill sets.

[1371] The server stores the extracted skill sets and emotion data in a database.

[1372] Step 5:

[1373] The server generates a skill profile and displays it on the device.

[1374] Specific behavior:

[1375] The server retrieves skill information and emotion data from the database.

[1376] The server uses this information to render the skill profile view.

[1377] Sends the skill profile to the device and displays it.

[1378] Visualization of business requirements

[1379] Step 1:

[1380] The device launches a dedicated application or web interface, and the department leader logs in.

[1381] Specific behavior:

[1382] The terminal sends an authentication request to the authentication server.

[1383] The server generates an authentication token and returns it to the terminal.

[1384] Step 2:

[1385] The user (reader) answers interactive questions.

[1386] Specific behavior:

[1387] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[1388] The generation artificial intelligence generates questions and sends them to the terminal.

[1389] As the user answers the questions, the emotion engine analyzes the user's emotions.

[1390] Step 3:

[1391] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[1392] Specific behavior:

[1393] The device transmits the emotion recognition data to the server.

[1394] The server stores the emotion data in a database.

[1395] Step 4:

[1396] The server receives and analyzes the user's response.

[1397] Specific behavior:

[1398] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1399] Generative AI analyzes the answers and extracts the required skill sets.

[1400] The server stores the extracted business requirements and emotion data in a database.

[1401] Step 5:

[1402] The server generates a business requirement profile and displays it on the terminal.

[1403] Specific behavior:

[1404] The server acquires the business requirement information and emotion data from the database.

[1405] The server uses this information to render a business requirement profile view.

[1406] The business requirements profile is sent to the terminal and displayed.

[1407] Matching and Scoring

[1408] Step 1:

[1409] The server acquires the skill profile and related emotion information of the employee, and the business requirement profile and related emotion information of the department.

[1410] Specific behavior:

[1411] The server retrieves employee skill profiles and emotional data from the database.

[1412] The server also obtains the business requirement profile and emotion data of each department.

[1413] Step 2:

[1414] The server compares skills with job requirements and calculates a matching score.

[1415] Specific behavior:

[1416] The server vectorizes the skills, job requirements, and related emotion data, and calculates the cosine similarity based on the correlation.

[1417] Based on the calculation results, a matching score is derived.

[1418] Step 3:

[1419] The server visualizes the matching score and displays it on the device.

[1420] Specific behavior:

[1421] The server generates graphs and dashboards based on the matching scores.

[1422] The generated visual is sent to the terminal and displayed.

[1423] Specific examples

[1424] If employee A answers, "I have programming experience in C++ and Python," and the emotion engine determines that the user is confident, the server analyzes this as "Skills: C++, Python" and saves it in the database. At the same time, the emotion data is also saved. If the sales department leader answers, "Data analysis and presentation skills are required," and the server determines that the leader is confident, the server recognizes this as "Required skills: Data analysis, Presentation," saves it in the database, and saves the emotion data as well. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[1425] Example 2

[1426] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1427] Conventional personnel placement systems have the problem that collecting employee skills and job requirements is subjective, making it difficult to optimally place personnel within a company. Furthermore, because they are unable to take into account the user's emotional information, it is difficult to grasp the true intentions and confidence levels of employees and leaders. This reduces the accuracy of matching, hindering the efficient operation of the company.

[1428] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1429] In this invention, the server includes artificial intelligence means for interactively collecting employee skills, means for saving the collected skills in a database, means for visualizing the saved skill information, artificial intelligence means for interactively collecting job requirements for required personnel, means for saving the collected job requirements in a database, means for visualizing the saved job requirements, means for comparing employee skills with the job requirements and calculating a matching score, means for visualizing and displaying the matching score, emotion recognition means for recognizing user emotions in real time, means for saving the emotion information in a database together with the user's responses, and means for reflecting the emotion information in the calculation of the matching score. This enables more accurate matching that also takes employee emotion information into account.

[1430] "Employee" means an individual employed by a company or organization and engaged in business.

[1431] "Skills" refer to the knowledge and abilities needed to effectively perform a particular task or activity.

[1432] "Dialogue" refers to the way humans and systems communicate through questions and answers in natural language.

[1433] "Artificial intelligence means" refers to algorithms or software designed to perform specific tasks automatically.

[1434] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[1435] "Visualization" is the process of visually displaying data and information using graphs, tables, diagrams, etc., to make them easier to understand.

[1436] "Business requirements" refer to the abilities and conditions necessary to carry out a specific business.

[1437] A "match score" is a numerical representation of how well an employee's skills match the job requirements.

[1438] "Emotion recognition means" refers to technology or devices for identifying a user's emotional state by analyzing their facial expressions, tone of voice, etc.

[1439] A "user" is an employee, department leader, manager, or anyone who uses the system to enter information or view results.

[1440] "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to access the system.

[1441] "Server" refers to a central computer system that stores and processes data.

[1442] This invention is a system that utilizes generative artificial intelligence and an emotion engine to optimally allocate human resources within a company. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[1443] Hardware and software used

[1444] Terminal: Refers to an electronic device such as a computer or smartphone that allows a user to access the system using a dedicated application or web browser.

[1445] Server: The central computer system that stores and processes data. It operates ChatGPT and the database.

[1446] Emotion engine: Software that analyzes a user's facial expressions and tone of voice to identify their emotional state.

[1447] Program processing overview

[1448] Visualization of employee skills

[1449] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions.

[1450] The emotion engine recognizes the user's emotions in real time. The user's answers and emotional information are sent to the server, which analyzes the answers using ChatGPT and stores the skill set in a database.

[1451] The server generates a visualization view and displays it on the terminal.

[1452] Examples:

[1453] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[1454] Example prompt sentence:

[1455] "Tell us about your programming experience and how confident you are in your skills."

[1456] Visualization of business requirements

[1457] The terminal starts a dedicated application or a web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements.

[1458] The emotion engine recognizes the leader's emotions in real time. The leader's response is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated and displayed on the device.

[1459] Examples:

[1460] If the sales department leader responds, "Data analysis and presentation skills are required," the server analyzes this as "Required skills: Data analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[1461] Example prompt sentence:

[1462] "Tell us what skills your department needs and how important those skills are."

[1463] Matching and Scoring

[1464] The server retrieves the employee's skill profile and related emotional information from the database, and the department's job requirement profile and related emotional information. It compares these pieces of information and calculates a matching score based on the degree of compatibility.

[1465] Taking emotional information into account also enables more accurate matching. The server visualizes the scores and generates a dashboard. Finally, managers or human resources personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[1466] Examples:

[1467] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[1468] Example prompt sentence:

[1469] "Compare the employee's skills with the department's required skills and score how well they match. Take into account the user's emotional responses."

[1470] In this way, the present invention reveals the hidden skills of employees and the specific work requirements of each department, and by integrating user emotional information, it is possible to more accurately achieve optimal personnel allocation within a company.

[1471] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1472] Step 1:

[1473] The device launches a dedicated application or web interface.

[1474] Specific actions: Launch an application or web browser on the device used by the user and display the system login screen.

[1475] Input: Launch a browser or application.

[1476] Output: Login screen displayed.

[1477] Step 2:

[1478] A user logs in.

[1479] Specific operation: The user enters their ID and password, goes through the authentication process, and logs in to the system. If authentication is successful, they are redirected to the main screen.

[1480] Input: ID and password.

[1481] Output: Main screen for authenticated user.

[1482] Step 3:

[1483] The user accesses the interactive form and answers the questions.

[1484] Specific operation: The user selects the "Enter Skills" or "Enter Job Requirements" option from the main screen, moves to the interactive form, and answers questions about skills and job requirements.

[1485] Input: The user's answer.

[1486] Output: Response data.

[1487] Step 4:

[1488] The emotion engine recognizes the user's emotions in real time.

[1489] Specific operation: When the user answers a question, the system analyzes the user's facial expressions and tone of voice through a camera and microphone to recognize emotional information.

[1490] Input: User's facial expression and tone of voice.

[1491] Output: Emotional information.

[1492] Step 5:

[1493] The server receives the user's response and emotion information.

[1494] Specific operation: The text data and emotion information entered by the user are sent from the device to the server.

[1495] Input: Response data and sentiment information.

[1496] Output: Received data at the server.

[1497] Step 6:

[1498] The server parses the response using ChatGPT.

[1499] Specific operation: The text data received by the server is analyzed using ChatGPT to extract skills and job content.

[1500] Input: Received data.

[1501] Output: Analysis results.

[1502] Step 7:

[1503] The server stores the analysis results in a database.

[1504] Specific operation: Record the extracted skill sets, job requirements, and emotional information in a database.

[1505] Input: Analysis results.

[1506] Output: Saved data.

[1507] Step 8:

[1508] The server generates a visualization view and displays it on the terminal.

[1509] Specific operation: Based on the stored data, a view is generated to visually display the employee's skill profile and job requirement profile, and sent to the terminal.

[1510] Input: Saved data.

[1511] Output: The visualization view displayed on the terminal.

[1512] Step 9:

[1513] The server obtains the employee's skill profile and related emotional information, and the department's job requirement profile and related emotional information from the database.

[1514] What it does: Queries the database to retrieve the required skills information and job requirements.

[1515] Input: Query.

[1516] Output: The retrieved data.

[1517] Step 10:

[1518] The server compares the employee's skill profile with the job requirement profile.

[1519] What it does: Runs an algorithm to compare skill sets with job requirements and calculate the degree of fit.

[1520] Input: The retrieved data.

[1521] Output: Goodness-of-fit data.

[1522] Step 11:

[1523] The server calculates a matching score based on the degree of suitability.

[1524] Specific operation: Based on the acquired data, the degree of compatibility between skills and job requirements is quantified to calculate a matching score. Emotional information is also taken into account to calculate an overall score.

[1525] Input: Relevance data and sentiment information.

[1526] Output: Matching score.

[1527] Step 12:

[1528] The server visualizes the scores and generates a dashboard.

[1529] Specific behavior: Visually represent matching scores in graphs, charts, etc., and generate a final dashboard.

[1530] Input: Matching score.

[1531] Output: Dashboard.

[1532] Step 13:

[1533] The device displays the dashboard.

[1534] Specific operation: The generated dashboard is displayed on the device so that HR personnel and managers can view it.

[1535] Enter: Dashboard.

[1536] Output: The displayed dashboard.

[1537] (Application example 2)

[1538] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1539] In conventional personnel placement systems, employee skills and job requirements are often treated simply as data, and human factors such as the emotions and confidence of employees and leaders are not taken into account, making it difficult to achieve optimal matching. Another problem is the lack of a system that can check this information in real time and quickly make optimal placements.

[1540] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1541] In this invention, the server includes: a generating artificial intelligence means for interactively collecting employee skills; a means for saving the collected skills in a database; a means for visualizing the saved skill information; a generating artificial intelligence means for interactively collecting job requirements for required personnel; a means for saving the collected job requirements in a database; a means for visualizing the saved job requirements; a means for comparing employee skills with job requirements and calculating a matching score; a means for visualizing and displaying the matching score; an emotion engine for recognizing the emotions of employees and leaders in real time; a means for saving the emotion information collected by the emotion engine in a database and integrating it with skill and job requirement information; a means for improving matching accuracy based on the emotion information; and an application that runs on a smartphone or tablet and suggests optimal personnel placement. This enables more accurate personnel placement based on comprehensive information including emotion information.

[1542] An "employee" is someone who belongs to a company or organization and is employed to perform a specific job or task.

[1543] "Skills" refer to the knowledge, abilities, experience, etc. required to effectively carry out a specific job or task.

[1544] "Dialogue" is a method of collecting information through questions and answers, and is a format in which data is obtained through interaction between the user and the system.

[1545] "Generative AI" refers to artificial intelligence technology used for natural language generation and data analysis, and is used in question-answering systems, etc.

[1546] An "emotion engine" is an artificial intelligence technology that analyzes and evaluates a user's emotions based on facial expressions, tone of voice, text, etc.

[1547] A "database" is a system that systematically organizes and stores information and manages it so that it can be retrieved as needed.

[1548] "Storing" refers to keeping data or information in a state where it can be used at a later time.

[1549] "Visualization" is a technique for displaying data and information in visual formats such as graphs and charts to make them easier to understand.

[1550] "Business requirements" are the skills, conditions, and needs necessary to carry out a specific job or project.

[1551] The "matching score" is a numerical representation of the degree of compatibility between an employee's skills and the job requirements, and serves as a criterion for determining appropriate placement.

[1552] A "smartphone" is a highly functional mobile phone that can connect to the Internet and use various applications.

[1553] A "tablet" is a portable computing device with a touchscreen interface.

[1554] An "application" is a software program that provides a particular function or service.

[1555] The system for implementing this invention collects, analyzes, and visualizes employee skills and job requirements, and performs a series of processes to optimize personnel allocation. The main components and their functions are as follows:

[1556] 1. Hardware and Software Used

[1557] The system of the present invention uses the following hardware and software:

[1558] Smartphones and tablets: Android and iOS devices.

[1559] Server: A central server for managing and analyzing data. It is generally operated on a cloud service (AWS, Google Cloud, Microsoft Azure, etc.).

[1560] Emotion engine: Emotion analysis software such as Affectiva SDK.

[1561] Generative AI models: such as OpenAI's ChatGPT.

[1562] Database: A database solution such as Firebase or AWS RDS.

[1563] 2. Data Collection and Storage

[1564] (1) Collecting employee skill information

[1565] Employees, who are users, enter their skill information interactively using a dedicated application on their smartphones or tablets. At this time, the emotion engine analyzes the user's emotions from their facial expressions and tone of voice. The skill and emotion information is sent to the server and stored in a database.

[1566] (2) Gathering business requirements

[1567] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. At this time, the emotion engine analyzes the leader's emotions. The business requirement information and emotion information are sent to the server and stored in a database.

[1568] 3. Data Visualization

[1569] The stored skill information and job requirements information is analyzed by the server and generated as a visualization view, which can be viewed on a smartphone or tablet, allowing employees' skills, emotional state, and job requirements to be checked at a glance.

[1570] 4. Matching and Scoring

[1571] The server retrieves employee skill information, emotional information, and job requirements information from the database and performs matching using a generative AI model. Matching scores are calculated based on a comprehensive evaluation of skill compatibility and emotional information. The final score is visualized as a dashboard and displayed to managers and HR personnel on smartphones or tablets.

[1572] 5. Proposal for optimal layout

[1573] Managers and HR personnel can receive optimal staffing recommendations based on matching scores via a smartphone or tablet application. Because these recommendations are based on comprehensive information, they can make more accurate staffing decisions than ever before.

[1574] 6. Examples of concrete examples and prompts

[1575] Examples:

[1576] Let's say there is a position in a factory department that requires "skills in electrical circuit design and mechatronics." The department leader enters that information on their smartphone, and the emotion engine analyzes the leader's high confidence. Meanwhile, employee A confidently enters that he has "skills in electrical circuit design and mechatronics." The app matches the skills with the job requirements and displays a high score.

[1577] Example prompt sentence:

[1578] "We are looking for the perfect candidate for a new project. The project requires knowledge of electrical circuit design and mechatronics. What's more important is that the candidate has confidence in these skills. Please suggest the perfect candidate."

[1579] In this way, the present invention realizes optimal personnel allocation within a company through detailed collection of employee skills and job requirements, analysis of emotional information, real-time visualization and matching scoring.

[1580] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1581] Step 1:

[1582] Employees use a dedicated application on their smartphone or tablet to interactively input their skill information. The emotion engine analyzes emotions from the employee's facial expressions and tone of voice. The input is text data, and emotions are obtained as emotion analysis data. Input: Employee's skill information and emotion information. Output: Data packet containing skill information and emotion information.

[1583] Step 2:

[1584] The skill and emotion information sent from the device is sent to the server. The server analyzes the received data and formats it as required. Data processing includes natural language analysis using a generative AI model. Input: Data packet containing skill and emotion information. Output: Formatted skill and emotion information.

[1585] Step 3:

[1586] The formatted skill information and emotion information are stored in a database by the server. The stored information includes the employee's ID, skill information, emotion information, etc. Input: Formatted skill information and emotion information. Output: Employee information stored in the database.

[1587] Step 4:

[1588] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. The emotion engine analyzes emotions from the leader's facial expressions and tone of voice. Input: Business requirements and emotion information. Output: Data packet containing business requirements information and emotion information.

[1589] Step 5:

[1590] The task requirement information and emotion information sent from the device are sent to the server. The server analyzes the received data and formats it into the required format. Natural language analysis is performed using a generative AI model. Input: Data packet containing task requirement information and emotion information. Output: Formatted task requirement information and emotion information.

[1591] Step 6:

[1592] The formatted business requirement information and emotion information are stored in a database by the server. The stored information includes department ID, business requirements, emotion information, etc. Input: Formatted business requirement information and emotion information. Output: Business requirement information stored in the database.

[1593] Step 7:

[1594] The server retrieves employee skill information, emotional information, and job requirement information from the database. Based on this data, it uses a generative AI model to calculate the skill compatibility and emotional match, and calculates a matching score. Input: Skill information, emotional information, and job requirement information retrieved from the database. Output: Matching score.

[1595] Step 8:

[1596] The matching scores are visualized by the server and generated as a dashboard. This dashboard is displayed to managers and HR personnel on their smartphones or tablets. Input: Matching scores. Output: Visualized dashboard.

[1597] Step 9:

[1598] Managers and HR personnel can view the dashboard via smartphone or tablet and receive optimal staffing recommendations. The recommendations are based on skill matching accuracy and sentiment information. Input: Visualized dashboard. Output: Optimal staffing recommendations.

[1599] This series of processes enables integrated management of employee skill information, emotional information, and job requirement information, enabling highly accurate personnel allocation.

[1600] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1601] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1602] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1603] [Fourth embodiment]

[1604] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1605] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1606] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1607] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1608] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1609] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1610] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1611] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1612] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1613] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1614] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1615] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1616] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1617] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence. This system verbalizes employee skills in detail, clarifies the job requirements of the required human resources, and compares them to achieve optimal matching. Below is a detailed explanation of how the system of the present invention is implemented.

[1618] System Overview

[1619] The system consists of the following elements:

[1620] 1. Visualization of employee skills

[1621] 2. Visualization of business requirements

[1622] 3. Matching and Scoring

[1623] Program processing

[1624] 1. Visualization of employee skills

[1625] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. These questions are generated using generative artificial intelligence. The answered data is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[1626] Examples:

[1627] If an employee answers, "I have programming experience in C++ and Python," the server recognizes this data as "Skills: C++, Python" and stores it in the database. It then generates a visualization view of the employee's skill profile and displays this information on the terminal.

[1628] 2. Visualization of business requirements

[1629] The terminal launches a dedicated application or web interface, and the leader logs in. After successful login, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[1630] Examples:

[1631] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required skills: Data analysis, Presentation" and stores it in the database. Based on this, a visualization view is generated as a business requirements profile and this information is displayed on the terminal.

[1632] 3. Matching and Scoring

[1633] The server retrieves employee skill profiles and department job requirement profiles from the database. This information is compared and a matching score is calculated based on the degree of compatibility. The server visualizes the scores and generates a dashboard. Finally, managers or HR personnel can view these results on their terminals and use them to make decisions about optimal personnel placement.

[1634] Examples:

[1635] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information and calculates the cosine similarity. Based on this, a matching score is calculated and the results are visualized. The results are displayed on a terminal where the manager can view them and make a transfer decision.

[1636] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific business requirements of each department, thereby realizing optimal personnel allocation within a company.

[1637] The processing flow will be explained below.

[1638] Visualization of employee skills

[1639] Step 1:

[1640] The device launches a dedicated application or web interface and the employee logs in.

[1641] Specific behavior:

[1642] The terminal sends an authentication request to the authentication server.

[1643] The server generates an authentication token and returns it to the terminal.

[1644] Step 2:

[1645] The user (employee) accesses the interactive form and answers the questions.

[1646] Specific behavior:

[1647] The terminal requests interactive questions from the generating artificial intelligence.

[1648] The generation artificial intelligence generates questions and sends them to the terminal.

[1649] The user answers the questions, and the terminal transmits the answer data to the server.

[1650] Step 3:

[1651] The server receives and analyzes the user's response.

[1652] Specific behavior:

[1653] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1654] Generative AI analyzes answers and extracts skill sets.

[1655] The server stores the extracted skill set in a database.

[1656] Step 4:

[1657] The server generates a skill profile and displays it on the device.

[1658] Specific behavior:

[1659] The server retrieves the skill information from the database.

[1660] The server renders the skill profile view based on the retrieved information.

[1661] Sends the skill profile to the device and displays it.

[1662] Visualization of business requirements

[1663] Step 1:

[1664] The device launches a dedicated application or web interface, and the department leader logs in.

[1665] Specific behavior:

[1666] The terminal sends an authentication request to the authentication server.

[1667] The server generates an authentication token and returns it to the terminal.

[1668] Step 2:

[1669] The user (reader) accesses the interactive form and answers the questions.

[1670] Specific behavior:

[1671] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[1672] The generation artificial intelligence generates questions and sends them to the terminal.

[1673] The user answers the questions, and the terminal transmits the answer data to the server.

[1674] Step 3:

[1675] The server receives and analyzes the user's response.

[1676] Specific behavior:

[1677] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1678] Generative AI analyzes the answers and extracts the required skill sets.

[1679] The server stores the extracted business requirements in a database.

[1680] Step 4:

[1681] The server generates a business requirement profile and displays it on the terminal.

[1682] Specific behavior:

[1683] The server retrieves business requirement information from the database.

[1684] The server renders a business requirement profile view based on the acquired information.

[1685] The business requirements profile is sent to the terminal and displayed.

[1686] Matching and Scoring

[1687] Step 1:

[1688] The server obtains the employee's skill profile and the department's job requirement profile.

[1689] Specific behavior:

[1690] The server retrieves the employee's skill profile from the database.

[1691] Similarly, the server acquires the business requirement profile of each department.

[1692] Step 2:

[1693] The server compares employee skills with job requirements and calculates a matching score.

[1694] Specific behavior:

[1695] The server vectorizes the skills and business requirements and calculates the cosine similarity based on the correlation.

[1696] Based on the calculation results, a matching score is derived.

[1697] Step 3:

[1698] The server visualizes the matching score and displays it on the device.

[1699] Specific behavior:

[1700] The server generates graphs and dashboards based on the matching scores.

[1701] The generated visual is sent to the terminal and displayed.

[1702] Specific examples

[1703] If employee A answers, "I have programming experience in C++ and Python," the server analyzes it as "Skills: C++, Python" and saves it in the database. This is then reflected in the visualization view. Similarly, if the sales department leader answers, "Data analysis and presentation skills are required," the server analyzes it as "Required skills: Data analysis, Presentation" and saves it in the database. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[1704] Example 1

[1705] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1706] In order to properly allocate human resources within a company, it is necessary to understand employee skills in detail and match them with job requirements based on that information. However, conventional systems lack the means to efficiently collect, analyze, and visualize skills and job requirements, making it difficult to achieve optimal matching. To solve this problem, a system is needed that can efficiently collect skills and job requirements and score the degree of compatibility.

[1707] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1708] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for storing the collected skills in a database, a means for visualizing the stored skill information, and a generating artificial intelligence means for interactively collecting the business requirements of required personnel. This makes it possible to efficiently collect employee skills and business requirements and score the compatibility, thereby enabling optimal personnel allocation.

[1709] "Generative artificial intelligence means" refers to means for generating dialogue-style questions using natural language processing and analyzing collected information.

[1710] "Means for storing in a database" refers to means for permanently storing the collected information on skills and job requirements.

[1711] The "visualization means" is a means for converting the stored skill information and business requirement information into a visual format such as a graph or chart, and displaying it.

[1712] A "means for calculating a matching score" is a means for comparing an employee's skills with job requirements, quantifying the degree of compatibility, and calculating a score.

[1713] The "means for displaying as a dashboard" is a means for displaying the calculated matching scores as an integrated view so that the administrator can check them.

[1714] The "means of authenticating login" refers to the means of verifying the login information of employees and administrators and authenticating that they are legitimate users.

[1715] "Means for launching a dedicated application or web interface" refers to means for launching an interface that allows a user to interactively input information.

[1716] "Means for utilizing a generative artificial intelligence model" refers to means for generating dialogue-style questions using a generative AI model and analyzing input data.

[1717] The "means for calculating similarity" is a means for using an algorithm such as cosine similarity to calculate the degree of compatibility between skill information and job requirement information.

[1718] This invention is a system for optimally allocating personnel within a company using a generative AI model. This system efficiently collects employee skills and job requirements and scores the degree of compatibility to achieve optimal personnel allocation. The following describes in detail the mode for implementing this invention.

[1719] System Overview

[1720] The system includes a generative artificial intelligence means, a database storage means, a visualization means, a matching score calculation means, a dashboard display means, a login authentication means, a dedicated application or web interface launch means, a generative artificial intelligence model utilization means, and a similarity calculation means.

[1721] Hardware and software used

[1722] Server: Stores, analyzes, and visualizes data.

[1723] Terminal: A device (PC, tablet, smartphone, etc.) that a user uses to input and view information.

[1724] Generative AI models: Natural language processing models such as ChatGPT.

[1725] Database: Used to store skills information and job requirements information.

[1726] Web interface or dedicated application: The interface through which users access the site.

[1727] Program processing

[1728] Collecting and visualizing employee skills

[1729] The terminal launches a dedicated application or web interface, and the employee logs in. The user (employee) accesses the dialogue form and answers the generated questions. The questions are generated using a generative AI model. The answered data is sent to the server and analyzed using the generative AI model. The analysis results are saved in a database, and a view is generated by a visualization means and displayed on the terminal.

[1730] Examples:

[1731] If an employee answers, "I have programming experience in C++ and Python," the server will recognize and store the data as "Skills: C++, Python," and display it on the terminal as a visualization view.

[1732] Example prompt sentence:

[1733] "Create questions that capture the employee's skill set. For example, consider the answer 'I have programming experience in C++ and Python.'"

[1734] Gathering and visualizing business requirements

[1735] The device launches a dedicated application or web interface, and the reader logs in. The user (reader) accesses the dialogue form and answers the generated questions. The answer data is sent to the server and analyzed using the generative AI model. The analysis results are stored in a database, and a view is generated by the visualization means and displayed on the device.

[1736] Examples:

[1737] If the sales department leader answers, "Data analysis and presentation skills are required," the server will recognize this as "Required skills: Data analysis, Presentation" and save it. This will be displayed on the device as a visualization view.

[1738] Example prompt sentence:

[1739] "Create questions to capture job requirements. For example, consider the answer 'Data analysis and presentation skills required.'"

[1740] Matching and Scoring

[1741] The server retrieves employee skill profiles and department job requirement profiles from the database. The retrieved information is compared, and the degree of compatibility is calculated using algorithms such as cosine similarity, generating a matching score. The generated score is visualized using a dashboard and displayed on the terminal.

[1742] Examples:

[1743] If employee A's skill profile includes "C++ and Python programming" and a department's job requirements profile requires "data analysis and presentation," the server compares this information, calculates the cosine similarity, calculates a matching score, and visualizes it. The results are displayed on the terminal, and managers can use this information to make transfer decisions.

[1744] Example prompt sentence:

[1745] "Describe a method for comparing an employee's skill profile with a job requirements profile and calculating the degree of fit. For example, consider a scenario where you use cosine similarity to calculate a matching score."

[1746] As described above, this invention is a system that utilizes a generative AI model to efficiently and effectively realize optimal personnel allocation within a company.

[1747] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1748] Step 1:

[1749] Launching the dedicated application or web interface

[1750] The device launches a dedicated application or web interface in response to user operation, and a login screen is displayed for the user.

[1751] Input: The user interacts with the application or web interface.

[1752] Output:Login screen

[1753] Step 2:

[1754] Login authentication

[1755] A user (employee or leader) logs in by entering their username and password. The server receives this authentication information and authenticates it against the database. If the login is successful, the main screen will be displayed on the terminal.

[1756] Input: Username, Password

[1757] Output: Authentication result (success / failure), main screen

[1758] Step 3:

[1759] Entering skill information

[1760] The user (employee) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[1761] Input: User's skill information

[1762] Output: Sending skill information to the server

[1763] Step 4:

[1764] Skill information analysis

[1765] The server analyzes the received skill information using a generative AI model, and the analyzed information is stored in a database as a "skill profile."

[1766] Input: User's skill information

[1767] Output: Parsed skill profile, saved to database

[1768] Step 5:

[1769] Skills profile visualization

[1770] The server generates a view to visualize the skill profile and displays it on the terminal.

[1771] Input: Skill Profile

[1772] Output: A visualized view of the skill profile

[1773] Step 6:

[1774] Entering business requirements information

[1775] The user (reader) accesses the interactive form and answers the generated questions. The terminal receives the user's answers and sends them to the server.

[1776] Input: User's business requirements information

[1777] Output: Sending business requirements information to the server

[1778] Step 7:

[1779] Analysis of business requirements information

[1780] The server analyzes the received business requirements information using a generative AI model, and the analyzed information is stored in a database as a "business requirements profile."

[1781] Input: User's business requirements information

[1782] Output: Analyzed business requirement profile, saved in database

[1783] Step 8:

[1784] Visualization of business requirements profile

[1785] The server generates a view for visualizing the business requirement profile and displays it on the terminal.

[1786] Input: Business Requirement Profile

[1787] Output: A visualized view of the business requirements profile

[1788] Step 9:

[1789] Obtaining skill profiles and job requirements profiles

[1790] The server retrieves the employee's skill profile and the department's job requirement profile from the database.

[1791] Input: None

[1792] Output: Skill profile, job requirement profile

[1793] Step 10:

[1794] Compare profile information

[1795] The server compares the skill profile with the job requirement profile and calculates the degree of compatibility, specifically using an algorithm such as cosine similarity.

[1796] Input: Skill profile, Job requirements profile

[1797] Output: Relevance score

[1798] Step 11:

[1799] Matching Score Calculation

[1800] The server calculates a matching score based on the compatibility score.

[1801] Input: Relevance score

[1802] Output: Matching score

[1803] Step 12:

[1804] Score visualization and dashboard display

[1805] The server visualizes the matching scores and generates a dashboard. Managers or HR personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[1806] Input: Matching score

[1807] Output: Dashboard view

[1808] (Application example 1)

[1809] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1810] In factories, it is extremely difficult to properly understand the skill sets and capabilities of workers and robots and assign optimal tasks to them. In particular, to improve factory production efficiency, a system is needed to allocate tasks in real time based on the skills and capabilities of each worker and robot, and to appropriately monitor and instruct them. However, such a system does not currently exist, resulting in problems such as reduced production efficiency and wasted human resources. Technology to solve this problem is needed.

[1811] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1812] In this invention, the server includes a generating artificial intelligence means for interactively collecting employee skills, a means for saving the collected skills in a database, a means for visualizing the saved skill information, a generating artificial intelligence means for interactively collecting job requirements for required personnel, a means for saving the collected job requirements in a database, a means for visualizing the saved job requirements, a means for comparing employee skills with the job requirements and calculating a matching score, a means for visualizing and displaying the matching score, a means for assigning optimal tasks to workers and robots in a factory, and a means for monitoring and instructing optimal task assignment in real time. This enables optimal matching of skills and tasks for workers and robots in a factory, thereby improving production efficiency and optimizing the use of human resources.

[1813] "Employee" means a person who works in a factory.

[1814] "Skills" are the abilities and knowledge that employees or robots have to perform specific tasks.

[1815] A "dialogue" is a format in which information is collected through an exchange of questions and answers.

[1816] "Generative artificial intelligence means" refers to technology or devices that use natural language processing or machine learning to generate appropriate questions in response to user input and collect information in an interactive format.

[1817] A "database" is an information system for managing and storing collected data.

[1818] "Visualization" refers to visually expressing collected data and displaying it in an easy-to-understand manner.

[1819] "In-demand talent" refers to people who have the skills and abilities required to perform a particular task.

[1820] "Job requirements" refer to the skills and conditions necessary to perform a specific job.

[1821] A "matching score" is a numerical indicator of the degree of compatibility between an employee's skills and job requirements.

[1822] A "factory worker" is someone who physically performs work within a factory.

[1823] A "robot" is a mechanical device that performs autonomous or semi-autonomous tasks in a factory.

[1824] A "task" is a specific job or piece of work.

[1825] "Real-time" refers to processing or reaction occurring immediately in the present time.

[1826] "Monitoring" means continuously checking the progress and work status of a task.

[1827] "Directions" are the provision of orders or guidelines for carrying out specific actions or tasks.

[1828] The system for realizing this invention collects and analyzes the skills and requirements of employees and their work, and performs optimal task allocation and real-time monitoring. Specific embodiments of this system will be described below.

[1829] First, an employee logs in to a dedicated application or web interface using a terminal. They enter their skills through an interactive form. The entered skill information is analyzed using a generative AI model (ChatGPT). The analysis results are stored in a database (PostgreSQL) as a skillset, and a visualized view is displayed on the terminal.

[1830] Next, the administrator also logs in to a dedicated application or web interface using a terminal, and again enters the necessary business requirements using an interactive form. This information is also analyzed using the generative AI model, saved in the database as a business requirements profile, and a visualized view is displayed on the terminal.

[1831] The server retrieves the skill profile and job requirement profile from the database, compares them, and calculates a matching score. The server visualizes the score and displays it on the terminal in a dashboard format. This dashboard allows managers to optimally allocate personnel and tasks.

[1832] The system also includes a means for assigning optimal tasks to workers and robots in the factory, monitoring the progress of tasks in real time, and providing appropriate instructions to workers using head-mounted displays or smartphones as needed.

[1833] For example, if an employee answers, "I have experience in machine operation and maintenance," the server recognizes this information as "Skills: Machine Operation, Maintenance" and stores it in the database. If an administrator enters, "I need to set up and maintain a new machine," this information is stored as "Required Skills: Machine Operation, Maintenance." The server compares this information, calculates a matching score, and assigns the work to the best employee.

[1834] An example prompt might look like this:

[1835] "Please tell me the skills related to employee ID 123."

[1836] "What skills are required for task ID 456?"

[1837] In this way, the present invention realizes optimal matching of the skills and tasks of workers and robots within a factory, thereby enabling improved production efficiency and optimal utilization of human resources.

[1838] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1839] Step 1:

[1840] A user (employee) logs in to a dedicated application or web interface using a terminal. After successfully logging in, the user accesses an interactive form and enters their skills. For example, the user might enter "I have experience in machine operation and maintenance." This input is sent to a generative AI model (ChatGPT), which analyzes the input string and generates a skillset. This skillset is then stored in the database as, for example, "Skills: Machine Operation, Maintenance."

[1841] Step 2:

[1842] The server retrieves the stored skill information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows employees' skill sets to be visually confirmed. For example, "Skills: Machine operation, maintenance" is visualized and displayed graphically.

[1843] Step 3:

[1844] The user (administrator) uses a terminal to log in to a dedicated application or web interface. After successfully logging in, the administrator accesses an interactive form and enters the business requirements. For example, the administrator might enter, "A new machine needs to be set up and maintained." This input is also sent to the generative AI model, which analyzes it and generates the business requirements. The generated business requirements are stored in the database as, for example, "Required skills: machine operation, maintenance."

[1845] Step 4:

[1846] The server retrieves the saved business requirements information and generates a visualization view. The generated visualization view is displayed on the terminal. This visualization view allows the business requirements to be visually confirmed. For example, "Required skills: machine operation, maintenance" is visualized and displayed graphically.

[1847] Step 5:

[1848] The server retrieves the employee's skill profile and job requirement profile from the database. Based on these profiles, it calculates the degree of match between the skills and requirements. Specifically, it calculates a matching score using an algorithm such as cosine similarity. For example, it calculates the similarity between the skill "machine operation, maintenance" and the job requirement "machine operation, maintenance" and calculates a matching rate of 90%.

[1849] Step 6:

[1850] The server visualizes the calculated matching scores and generates a dashboard. The dashboard is displayed on the terminal, allowing managers to optimally assign tasks based on the scores. This dashboard displays employees with high scores, their skill sets, and the corresponding job requirements. For example, it displays Employee A (Skills: Machine Operation, Maintenance), Task 1 (Required Skills: Machine Operation, Maintenance), Matching Score: 90%.

[1851] Step 7:

[1852] The user (manager) refers to the displayed dashboard and assigns tasks to the most suitable employee. The task assignment results are notified to the worker and robot in real time. Using a smartphone or head-mounted display, the progress of the assigned task is monitored and necessary instructions are provided in real time. For example, specific task instructions such as "Employee A will be responsible for setting up the new machine" are notified.

[1853] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1854] This invention relates to a system for optimally allocating human resources within a company using generative artificial intelligence and an emotion engine. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[1855] System Overview

[1856] The system consists of the following elements:

[1857] 1. Visualization of employee skills

[1858] 2. Visualization of business requirements

[1859] 3. Matching and Scoring

[1860] 4. Emotion engine integration

[1861] Program processing

[1862] 1. Visualization of employee skills

[1863] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions. In addition, the emotion engine recognizes the user's emotions in real time. The server receives the user's answers and emotion information and analyzes them using ChatGPT. The analysis results are stored in a database as the employee's skill set, and a visualized view is generated by the server and displayed on the terminal.

[1864] Examples:

[1865] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[1866] 2. Visualization of business requirements

[1867] The terminal launches a dedicated application or web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements. These questions are also generated by generative artificial intelligence. The emotion engine recognizes the leader's emotions in real time. The leader's answers are sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated by the server and displayed on the terminal.

[1868] Examples:

[1869] If the sales department leader responds, "Data analysis and presentation skills are required," the server recognizes this as "Required Skills: Data Analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[1870] 3. Matching and Scoring

[1871] The server retrieves the employee's skill profile and related emotional information, as well as the department's job requirements profile and related emotional information from the database. This information is compared and a matching score is calculated based on the degree of compatibility. By taking emotional information into account, more accurate matching is achieved. The server visualizes the score and generates a dashboard. Finally, managers or HR personnel can view these results on their devices and use them as information to make decisions about optimal personnel placement.

[1872] Examples:

[1873] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[1874] In this way, the present invention is a system that clarifies the hidden skills of employees and the specific work requirements of each department, and further integrates user emotional information to achieve more accurate and optimal personnel allocation within a company.

[1875] The processing flow will be explained below.

[1876] Visualization of employee skills

[1877] Step 1:

[1878] The device launches a dedicated application or web interface and the employee logs in.

[1879] Specific behavior:

[1880] The terminal sends an authentication request to the authentication server.

[1881] The server generates an authentication token and returns it to the terminal.

[1882] Step 2:

[1883] The user (employee) answers interactive questions.

[1884] Specific behavior:

[1885] The terminal requests interactive questions from the generating artificial intelligence.

[1886] The generation artificial intelligence generates questions and sends them to the terminal.

[1887] As the user answers the questions, the emotion engine analyzes the user's emotions.

[1888] Step 3:

[1889] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[1890] Specific behavior:

[1891] The device transmits the emotion recognition data to the server.

[1892] The server stores the emotion data in a database.

[1893] Step 4:

[1894] The server receives and analyzes the user's response.

[1895] Specific behavior:

[1896] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1897] Generative AI analyzes answers and extracts skill sets.

[1898] The server stores the extracted skill sets and emotion data in a database.

[1899] Step 5:

[1900] The server generates a skill profile and displays it on the device.

[1901] Specific behavior:

[1902] The server retrieves skill information and emotion data from the database.

[1903] The server uses this information to render the skill profile view.

[1904] Sends the skill profile to the device and displays it.

[1905] Visualization of business requirements

[1906] Step 1:

[1907] The device launches a dedicated application or web interface, and the department leader logs in.

[1908] Specific behavior:

[1909] The terminal sends an authentication request to the authentication server.

[1910] The server generates an authentication token and returns it to the terminal.

[1911] Step 2:

[1912] The user (reader) answers interactive questions.

[1913] Specific behavior:

[1914] The terminal requests business requirement hearing questions from the generating artificial intelligence.

[1915] The generation artificial intelligence generates questions and sends them to the terminal.

[1916] As the user answers the questions, the emotion engine analyzes the user's emotions.

[1917] Step 3:

[1918] The emotion engine recognizes the user's emotion, and the server receives the emotion data.

[1919] Specific behavior:

[1920] The device transmits the emotion recognition data to the server.

[1921] The server stores the emotion data in a database.

[1922] Step 4:

[1923] The server receives and analyzes the user's response.

[1924] Specific behavior:

[1925] The server generates the answer data and sends an analysis request to the artificial intelligence.

[1926] Generative AI analyzes the answers and extracts the required skill sets.

[1927] The server stores the extracted business requirements and emotion data in a database.

[1928] Step 5:

[1929] The server generates a business requirement profile and displays it on the terminal.

[1930] Specific behavior:

[1931] The server acquires the business requirement information and emotion data from the database.

[1932] The server uses this information to render a business requirement profile view.

[1933] The business requirements profile is sent to the terminal and displayed.

[1934] Matching and Scoring

[1935] Step 1:

[1936] The server acquires the skill profile and related emotion information of the employee, and the business requirement profile and related emotion information of the department.

[1937] Specific behavior:

[1938] The server retrieves employee skill profiles and emotional data from the database.

[1939] The server also obtains the business requirement profile and emotion data of each department.

[1940] Step 2:

[1941] The server compares skills with job requirements and calculates a matching score.

[1942] Specific behavior:

[1943] The server vectorizes the skills, job requirements, and related emotion data, and calculates the cosine similarity based on the correlation.

[1944] Based on the calculation results, a matching score is derived.

[1945] Step 3:

[1946] The server visualizes the matching score and displays it on the device.

[1947] Specific behavior:

[1948] The server generates graphs and dashboards based on the matching scores.

[1949] The generated visual is sent to the terminal and displayed.

[1950] Specific examples

[1951] If employee A answers, "I have programming experience in C++ and Python," and the emotion engine determines that the user is confident, the server analyzes this as "Skills: C++, Python" and saves it in the database. At the same time, the emotion data is also saved. If the sales department leader answers, "Data analysis and presentation skills are required," and the server determines that the leader is confident, the server recognizes this as "Required skills: Data analysis, Presentation," saves it in the database, and saves the emotion data as well. This information is compared, the cosine similarity is calculated, and a matching score is displayed.

[1952] Example 2

[1953] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1954] Conventional personnel placement systems have the problem that collecting employee skills and job requirements is subjective, making it difficult to optimally place personnel within a company. Furthermore, because they are unable to take into account the user's emotional information, it is difficult to grasp the true intentions and confidence levels of employees and leaders. This reduces the accuracy of matching, hindering the efficient operation of the company.

[1955] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1956] In this invention, the server includes artificial intelligence means for interactively collecting employee skills, means for saving the collected skills in a database, means for visualizing the saved skill information, artificial intelligence means for interactively collecting job requirements for required personnel, means for saving the collected job requirements in a database, means for visualizing the saved job requirements, means for comparing employee skills with the job requirements and calculating a matching score, means for visualizing and displaying the matching score, emotion recognition means for recognizing user emotions in real time, means for saving the emotion information in a database together with the user's responses, and means for reflecting the emotion information in the calculation of the matching score. This enables more accurate matching that also takes employee emotion information into account.

[1957] "Employee" means an individual employed by a company or organization and engaged in business.

[1958] "Skills" refer to the knowledge and abilities needed to effectively perform a particular task or activity.

[1959] "Dialogue" refers to the way humans and systems communicate through questions and answers in natural language.

[1960] "Artificial intelligence means" refers to algorithms or software designed to perform specific tasks automatically.

[1961] A "database" is a system for efficiently storing, searching, and managing large amounts of data.

[1962] "Visualization" is the process of visually displaying data and information using graphs, tables, diagrams, etc., to make them easier to understand.

[1963] "Business requirements" refer to the abilities and conditions necessary to carry out a specific business.

[1964] A "match score" is a numerical representation of how well an employee's skills match the job requirements.

[1965] "Emotion recognition means" refers to technology or devices for identifying a user's emotional state by analyzing their facial expressions, tone of voice, etc.

[1966] A "user" is an employee, department leader, manager, or anyone who uses the system to enter information or view results.

[1967] "Terminal" refers to an electronic device such as a computer or smartphone that allows a user to access the system.

[1968] "Server" refers to a central computer system that stores and processes data.

[1969] This invention is a system that utilizes generative artificial intelligence and an emotion engine to optimally allocate human resources within a company. This system verbalizes employee skills in detail, clarifies the job requirements of the required personnel, and compares them to achieve optimal matching. Furthermore, by incorporating an emotion engine that recognizes the user's emotions, it achieves more accurate and effective interviews.

[1970] Hardware and software used

[1971] Terminal: Refers to an electronic device such as a computer or smartphone that allows a user to access the system using a dedicated application or web browser.

[1972] Server: The central computer system that stores and processes data. It operates ChatGPT and the database.

[1973] Emotion engine: Software that analyzes a user's facial expressions and tone of voice to identify their emotional state.

[1974] Program processing overview

[1975] Visualization of employee skills

[1976] The terminal launches a dedicated application or web interface, and the employee logs in. After successfully logging in, the user (employee) accesses an interactive form and answers questions.

[1977] The emotion engine recognizes the user's emotions in real time. The user's answers and emotional information are sent to the server, which analyzes the answers using ChatGPT and stores the skill set in a database.

[1978] The server generates a visualization view and displays it on the terminal.

[1979] Examples:

[1980] If an employee answers "I have programming experience in C++ and Python," the server parses this as "Skills: C++, Python" and stores it in the database. At the same time, the emotion engine determines whether the user is confident and stores this information in the database. A visualization view is then generated as the employee's skill profile, and this information is displayed on the device.

[1981] Example prompt sentence:

[1982] "Tell us about your programming experience and how confident you are in your skills."

[1983] Visualization of business requirements

[1984] The terminal starts a dedicated application or a web interface, and the department leader logs in. After successfully logging in, the user (leader) accesses an interactive form and answers questions about business requirements.

[1985] The emotion engine recognizes the leader's emotions in real time. The leader's response is sent to the server and analyzed using ChatGPT. The analysis results are stored in a database as business requirements, and a visualized view is generated and displayed on the device.

[1986] Examples:

[1987] If the sales department leader responds, "Data analysis and presentation skills are required," the server analyzes this as "Required skills: Data analysis, Presentation" and stores it in the database. At the same time, the emotion engine determines the leader's confidence level and stores this information in the database. Based on this, a visualized view is generated as a business requirements profile, and this information is displayed on the terminal.

[1988] Example prompt sentence:

[1989] "Tell us what skills your department needs and how important those skills are."

[1990] Matching and Scoring

[1991] The server retrieves the employee's skill profile and related emotional information from the database, and the department's job requirement profile and related emotional information. It compares these pieces of information and calculates a matching score based on the degree of compatibility.

[1992] Taking emotional information into account also enables more accurate matching. The server visualizes the scores and generates a dashboard. Finally, managers or human resources personnel can view these results on their devices and use them to make decisions about optimal personnel placement.

[1993] Examples:

[1994] If employee A's skill profile includes "C++ and Python programming" and the emotional information is determined to be "confident," the optimal matching score is calculated by comparing it with the department's job requirement profile and emotional information. For example, if the requirements for the sales department are "data analysis" and "presentation" and the leader is determined to be confident, the compatibility level, including the emotional information, is calculated and displayed as a score. Managers can use this information to make appropriate personnel placement decisions.

[1995] Example prompt sentence:

[1996] "Compare the employee's skills with the department's required skills and score how well they match. Take into account the user's emotional responses."

[1997] In this way, the present invention reveals the hidden skills of employees and the specific work requirements of each department, and by integrating user emotional information, it is possible to more accurately achieve optimal personnel allocation within a company.

[1998] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1999] Step 1:

[2000] The device launches a dedicated application or web interface.

[2001] Specific actions: Launch an application or web browser on the device used by the user and display the system login screen.

[2002] Input: Launch a browser or application.

[2003] Output: Login screen displayed.

[2004] Step 2:

[2005] A user logs in.

[2006] Specific operation: The user enters their ID and password, goes through the authentication process, and logs in to the system. If authentication is successful, they are redirected to the main screen.

[2007] Input: ID and password.

[2008] Output: Main screen for authenticated user.

[2009] Step 3:

[2010] The user accesses the interactive form and answers the questions.

[2011] Specific operation: The user selects the "Enter Skills" or "Enter Job Requirements" option from the main screen, moves to the interactive form, and answers questions about skills and job requirements.

[2012] Input: The user's answer.

[2013] Output: Response data.

[2014] Step 4:

[2015] The emotion engine recognizes the user's emotions in real time.

[2016] Specific operation: When the user answers a question, the system analyzes the user's facial expressions and tone of voice through a camera and microphone to recognize emotional information.

[2017] Input: User's facial expression and tone of voice.

[2018] Output: Emotional information.

[2019] Step 5:

[2020] The server receives the user's response and emotion information.

[2021] Specific operation: The text data and emotion information entered by the user are sent from the device to the server.

[2022] Input: Response data and sentiment information.

[2023] Output: Received data at the server.

[2024] Step 6:

[2025] The server parses the response using ChatGPT.

[2026] Specific operation: The text data received by the server is analyzed using ChatGPT to extract skills and job content.

[2027] Input: Received data.

[2028] Output: Analysis results.

[2029] Step 7:

[2030] The server stores the analysis results in a database.

[2031] Specific operation: Record the extracted skill sets, job requirements, and emotional information in a database.

[2032] Input: Analysis results.

[2033] Output: Saved data.

[2034] Step 8:

[2035] The server generates a visualization view and displays it on the terminal.

[2036] Specific operation: Based on the stored data, a view is generated to visually display the employee's skill profile and job requirement profile, and sent to the terminal.

[2037] Input: Saved data.

[2038] Output: The visualization view displayed on the terminal.

[2039] Step 9:

[2040] The server obtains the employee's skill profile and related emotional information, and the department's job requirement profile and related emotional information from the database.

[2041] What it does: Queries the database to retrieve the required skills information and job requirements.

[2042] Input: Query.

[2043] Output: The retrieved data.

[2044] Step 10:

[2045] The server compares the employee's skill profile with the job requirement profile.

[2046] What it does: Runs an algorithm to compare skill sets with job requirements and calculate the degree of fit.

[2047] Input: The retrieved data.

[2048] Output: Goodness-of-fit data.

[2049] Step 11:

[2050] The server calculates a matching score based on the degree of suitability.

[2051] Specific operation: Based on the acquired data, the degree of compatibility between skills and job requirements is quantified to calculate a matching score. Emotional information is also taken into account to calculate an overall score.

[2052] Input: Relevance data and sentiment information.

[2053] Output: Matching score.

[2054] Step 12:

[2055] The server visualizes the scores and generates a dashboard.

[2056] Specific behavior: Visually represent matching scores in graphs, charts, etc., and generate a final dashboard.

[2057] Input: Matching score.

[2058] Output: Dashboard.

[2059] Step 13:

[2060] The device displays the dashboard.

[2061] Specific operation: The generated dashboard is displayed on the device so that HR personnel and managers can view it.

[2062] Enter: Dashboard.

[2063] Output: The displayed dashboard.

[2064] (Application example 2)

[2065] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[2066] In conventional personnel placement systems, employee skills and job requirements are often treated simply as data, and human factors such as the emotions and confidence of employees and leaders are not taken into account, making it difficult to achieve optimal matching. Another problem is the lack of a system that can check this information in real time and quickly make optimal placements.

[2067] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2068] In this invention, the server includes: a generating artificial intelligence means for interactively collecting employee skills; a means for saving the collected skills in a database; a means for visualizing the saved skill information; a generating artificial intelligence means for interactively collecting job requirements for required personnel; a means for saving the collected job requirements in a database; a means for visualizing the saved job requirements; a means for comparing employee skills with job requirements and calculating a matching score; a means for visualizing and displaying the matching score; an emotion engine for recognizing the emotions of employees and leaders in real time; a means for saving the emotion information collected by the emotion engine in a database and integrating it with skill and job requirement information; a means for improving matching accuracy based on the emotion information; and an application that runs on a smartphone or tablet and suggests optimal personnel placement. This enables more accurate personnel placement based on comprehensive information including emotion information.

[2069] An "employee" is someone who belongs to a company or organization and is employed to perform a specific job or task.

[2070] "Skills" refer to the knowledge, abilities, experience, etc. required to effectively carry out a specific job or task.

[2071] "Dialogue" is a method of collecting information through questions and answers, and is a format in which data is obtained through interaction between the user and the system.

[2072] "Generative AI" refers to artificial intelligence technology used for natural language generation and data analysis, and is used in question-answering systems, etc.

[2073] An "emotion engine" is an artificial intelligence technology that analyzes and evaluates a user's emotions based on facial expressions, tone of voice, text, etc.

[2074] A "database" is a system that systematically organizes and stores information and manages it so that it can be retrieved as needed.

[2075] "Storing" refers to keeping data or information in a state where it can be used at a later time.

[2076] "Visualization" is a technique for displaying data and information in visual formats such as graphs and charts to make them easier to understand.

[2077] "Business requirements" are the skills, conditions, and needs necessary to carry out a specific job or project.

[2078] The "matching score" is a numerical representation of the degree of compatibility between an employee's skills and the job requirements, and serves as a criterion for determining appropriate placement.

[2079] A "smartphone" is a highly functional mobile phone that can connect to the Internet and use various applications.

[2080] A "tablet" is a portable computing device with a touchscreen interface.

[2081] An "application" is a software program that provides a particular function or service.

[2082] The system for implementing this invention collects, analyzes, and visualizes employee skills and job requirements, and performs a series of processes to optimize personnel allocation. The main components and their functions are as follows:

[2083] 1. Hardware and Software Used

[2084] The system of the present invention uses the following hardware and software:

[2085] Smartphones and tablets: Android and iOS devices.

[2086] Server: A central server for managing and analyzing data. It is generally operated on a cloud service (AWS, Google Cloud, Microsoft Azure, etc.).

[2087] Emotion engine: Emotion analysis software such as Affectiva SDK.

[2088] Generative AI models: such as OpenAI's ChatGPT.

[2089] Database: A database solution such as Firebase or AWS RDS.

[2090] 2. Data Collection and Storage

[2091] (1) Collecting employee skill information

[2092] Employees, who are users, enter their skill information interactively using a dedicated application on their smartphones or tablets. At this time, the emotion engine analyzes the user's emotions from their facial expressions and tone of voice. The skill and emotion information is sent to the server and stored in a database.

[2093] (2) Gathering business requirements

[2094] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. At this time, the emotion engine analyzes the leader's emotions. The business requirement information and emotion information are sent to the server and stored in a database.

[2095] 3. Data Visualization

[2096] The stored skill information and job requirements information is analyzed by the server and generated as a visualization view, which can be viewed on a smartphone or tablet, allowing employees' skills, emotional state, and job requirements to be checked at a glance.

[2097] 4. Matching and Scoring

[2098] The server retrieves employee skill information, emotional information, and job requirements information from the database and performs matching using a generative AI model. Matching scores are calculated based on a comprehensive evaluation of skill compatibility and emotional information. The final score is visualized as a dashboard and displayed to managers and HR personnel on smartphones or tablets.

[2099] 5. Proposal for optimal layout

[2100] Managers and HR personnel can receive optimal staffing recommendations based on matching scores via a smartphone or tablet application. Because these recommendations are based on comprehensive information, they can make more accurate staffing decisions than ever before.

[2101] 6. Examples of concrete examples and prompts

[2102] Examples:

[2103] Let's say there is a position in a factory department that requires "skills in electrical circuit design and mechatronics." The department leader enters that information on their smartphone, and the emotion engine analyzes the leader's high confidence. Meanwhile, employee A confidently enters that he has "skills in electrical circuit design and mechatronics." The app matches the skills with the job requirements and displays a high score.

[2104] Example prompt sentence:

[2105] "We are looking for the perfect candidate for a new project. The project requires knowledge of electrical circuit design and mechatronics. What's more important is that the candidate has confidence in these skills. Please suggest the perfect candidate."

[2106] In this way, the present invention realizes optimal personnel allocation within a company through detailed collection of employee skills and job requirements, analysis of emotional information, real-time visualization and matching scoring.

[2107] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2108] Step 1:

[2109] Employees use a dedicated application on their smartphone or tablet to interactively input their skill information. The emotion engine analyzes emotions from the employee's facial expressions and tone of voice. The input is text data, and emotions are obtained as emotion analysis data. Input: Employee's skill information and emotion information. Output: Data packet containing skill information and emotion information.

[2110] Step 2:

[2111] The skill and emotion information sent from the device is sent to the server. The server analyzes the received data and formats it as required. Data processing includes natural language analysis using a generative AI model. Input: Data packet containing skill and emotion information. Output: Formatted skill and emotion information.

[2112] Step 3:

[2113] The formatted skill information and emotion information are stored in a database by the server. The stored information includes the employee's ID, skill information, emotion information, etc. Input: Formatted skill information and emotion information. Output: Employee information stored in the database.

[2114] Step 4:

[2115] Similarly, department leaders use a dedicated application on their smartphones or tablets to interactively input business requirements. The emotion engine analyzes emotions from the leader's facial expressions and tone of voice. Input: Business requirements and emotion information. Output: Data packet containing business requirements information and emotion information.

[2116] Step 5:

[2117] The task requirement information and emotion information sent from the device are sent to the server. The server analyzes the received data and formats it into the required format. Natural language analysis is performed using a generative AI model. Input: Data packet containing task requirement information and emotion information. Output: Formatted task requirement information and emotion information.

[2118] Step 6:

[2119] The formatted business requirement information and emotion information are stored in a database by the server. The stored information includes department ID, business requirements, emotion information, etc. Input: Formatted business requirement information and emotion information. Output: Business requirement information stored in the database.

[2120] Step 7:

[2121] The server retrieves employee skill information, emotional information, and job requirement information from the database. Based on this data, it uses a generative AI model to calculate the skill compatibility and emotional match, and calculates a matching score. Input: Skill information, emotional information, and job requirement information retrieved from the database. Output: Matching score.

[2122] Step 8:

[2123] The matching scores are visualized by the server and generated as a dashboard. This dashboard is displayed to managers and HR personnel on their smartphones or tablets. Input: Matching scores. Output: Visualized dashboard.

[2124] Step 9:

[2125] Managers and HR personnel can view the dashboard via smartphone or tablet and receive optimal staffing recommendations. The recommendations are based on skill matching accuracy and sentiment information. Input: Visualized dashboard. Output: Optimal staffing recommendations.

[2126] This series of processes enables integrated management of employee skill information, emotional information, and job requirement information, enabling highly accurate personnel allocation.

[2127] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2128] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2129] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2130] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2131] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2132] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2133] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2134] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2135] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2136] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2137] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2138] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distr...

Claims

1. a generative artificial intelligence means for interactively collecting employee skills; A means of storing the collected skills in a database; A means for visualizing the stored skill information; A generative artificial intelligence means for interactively collecting job requirements for required human resources; A means for storing the collected business requirements in a database; A means for visualizing the stored business requirements; A means of comparing employee skills with job requirements and calculating a match score; A means to visualize and display matching scores A system including:

2. The system of claim 1 , further comprising means for employees to interactively input skills via a dedicated application or a web interface.

3. The system according to claim 1 , further comprising means for a department leader to interactively input business requirements via a dedicated application or a web interface.

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