System

Abstract

A system is provided.SOLUTION: This system is provided with a means for acquiring the ability and desire of an employee by using interactive artificial intelligence, a means for proposing personnel arrangement corresponding to the ability and desire of the employee based on the acquired data, a means for evaluating the effect of the proposed personnel arrangement, and a means for continuously improving the precision of the personnel arrangement based on the evaluated result.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] Companies often face the problem of mismatched personnel placement due to an inability to properly understand employees' abilities and aspirations. This reduces the organization's competitiveness and productivity. Furthermore, current methods make it difficult to gain a deep understanding of employees' abilities and aspirations, making it difficult to achieve optimal personnel placement. Therefore, the present invention aims to solve the problem of improving the productivity and efficiency of the entire organization by obtaining a detailed understanding of employees' abilities and aspirations and proposing optimal personnel placement based on that understanding. [Means for solving the problem]

[0005] The present invention is a system that includes a means for acquiring employee abilities and preferences using interactive artificial intelligence, a means for proposing personnel placements based on the acquired data in accordance with the employees' abilities and preferences, a means for evaluating the effectiveness of the proposed personnel placements, and a means for continuously improving the accuracy of personnel placements based on the evaluation results. This enables personalized personnel placements based on employees' abilities and preferences, thereby improving the competitiveness and productivity of organizations. Furthermore, by collecting detailed employee skills and preferences and generating a personnel chart that visually displays them, even more accurate personnel placement proposals can be realized.

[0006] "Interactive artificial intelligence" is a computer system that collects information through natural dialogue with the user and makes appropriate decisions and suggestions based on that information.

[0007] "Employee capabilities" refers to the technical skills, knowledge, experience, and expertise possessed by individual employees, and is a concept that encompasses all qualities that are useful in performing work.

[0008] "Employee preferences" refers to an individual employee's personal intentions and requests regarding their future career prospects, job preferences, or working style.

[0009] "Staffing" is the process of assigning each employee in an organization to the most suitable job or role, and is an operation that takes into account the organization's requirements and the employees' abilities and wishes.

[0010] "Proposed Staffing" means a proposal aimed at assigning employees to specific tasks or roles based on their capabilities and preferences.

[0011] "Means for evaluating effectiveness" refers to methods and systems for measuring employee performance and organizational efficiency after personnel assignment and determining whether the assignment was appropriate.

[0012] A "visually displayed human resources chart" is a concept that refers to materials and tools that organize information such as an employee's skills, experience, and desired job, and display it in an easy-to-read, visual format. [Brief explanation of the drawings]

[0013] [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

[0014] 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.

[0015] First, the terms used in the following description will be explained.

[0016] 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).

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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."

[0021] [First embodiment]

[0022] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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."

[0034] The system of the present invention uses interactive artificial intelligence to collect information about employee capabilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. In the system of the present invention, the roles of the server, terminals, and users are clearly defined, and each role performs the specific processing shown below.

[0035] Main processing flow

[0036] 1. User Registration and Login

[0037] The server stores the user's basic information in a database and authenticates the user when they log in via a terminal. When logging in for the first time, the user enters their basic information (name, employee ID, password, etc.) into the terminal.

[0038] As an example of system operation, when a user enters their name, employee number, and password and clicks the login button, the terminal sends this information to the server, which then verifies the information and allows the user to log in.

[0039] 2. Starting an interactive session

[0040] The user initiates a session with the conversational AI via their device, which causes the server to call up the generative AI model and prepare the conversational environment.

[0041] For example, to start a conversation session, you click the "Start conversation" button. The device sends this request to the server, which then launches the generated AI.

[0042] 3. Confirm your skills and aspirations

[0043] The generation AI (in the server) asks the user questions to collect information about the user's skill set, work experience, desired work, etc. This dialogue provides detailed data.

[0044] For example, if the user answers "I'm good at data analysis and project management," the device sends this answer to the server, which records it in a database.

[0045] 4. Data organization and analysis

[0046] The server analyzes the data obtained from the conversation and generates a talent profile based on the employee's abilities and aspirations. The data is organized and profiled in real time.

[0047] For example, the server analyzes an employee's past performance and skill level and draws a conclusion such as "he has extensive experience as a data scientist."

[0048] 5. Creation of personnel records

[0049] The server then uses the organized data to create a visually displayed personnel chart, detailing the employee's skill level, desired job role, past performance, and more.

[0050] For example, the server organizes employees' skill maps and work histories and provides them to the human resources department in a visual format.

[0051] 6. Generating placement proposals

[0052] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[0053] As a specific example, the server generates suggestions such as "Qualified to lead new data analysis project" and displays them to employees.

[0054] 7. Proposal presentation and follow-up

[0055] The device presents the user with placement suggestions from the server, which the user can review, approve, or add comments to. The server then receives feedback and adjusts the suggestions as needed.

[0056] For example, after a user approves a placement proposal, the server makes the final decision and updates the database. After placement, a performance evaluation system continuously monitors performance and reevaluates the placement if necessary.

[0057] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[0058] The processing flow will be explained below.

[0059] Step 1: Log in and register

[0060] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[0061] The terminal transmits the user's input information to the server.

[0062] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[0063] Step 2: Starting an interactive session

[0064] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0065] The terminal sends this request to the server.

[0066] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0067] Step 3: Identify your skills and aspirations

[0068] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0069] The user answers on the device, "I'm good at data analysis and project management."

[0070] The terminal sends the user's answer to the server.

[0071] The server stores this response in a database.

[0072] Step 4: Collect and organize detailed data

[0073] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[0074] The user answers these questions and provides information through the terminal.

[0075] The terminal sends each response data to the server, which records them in a database.

[0076] Step 5: Analyze the data and generate a personnel chart

[0077] The server sends the collected data to an analysis engine to organize employees' abilities and aspirations.

[0078] The analysis engine (on the server) analyzes the data and extracts and organizes information such as skill level, desired job, and years of experience.

[0079] The server generates a visually displayable personnel chart based on the organized data.

[0080] Step 6: Generate placement proposals

[0081] The server compares the generated personnel chart with the current status requirements data of the organization.

[0082] The server generates optimal placement proposals based on the employee's skill set, work experience, and preferences.

[0083] The terminal displays the placement proposal to the user.

[0084] Step 7: Review and feedback on the proposal

[0085] The user checks the placement proposal through the terminal and approves or adds comments.

[0086] The terminal sends the user's feedback to the server.

[0087] The server reevaluates based on the feedback it receives and readjusts its placement proposals as needed.

[0088] Step 8: Finalize and follow up

[0089] The server finalizes the placement based on the user's final approval and updates the database.

[0090] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[0091] In this way, the system of the present invention can grasp the user's skill set and preferences in detail through dialogue with them, and propose optimal staffing. Each step works in conjunction with the other to achieve high accuracy and efficiency.

[0092] Example 1

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

[0094] To accurately understand employee capabilities and aspirations and improve productivity and efficiency across the organization, it is necessary to effectively collect and analyze employee information and propose optimal personnel placements. However, manual data collection and analysis requires time and effort and increases the risk of human error. Furthermore, to continuously propose appropriate placements, it is necessary to keep employee profiles up to date based on the collected information and to evaluate performance after placement. These challenges need to be resolved.

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

[0096] In this invention, the server includes means for storing basic information of the user in a database and performing authentication, means for starting an interactive session using a generative AI model, and means for collecting the user's skill set, work experience, and desired work content using the generative AI model, thereby enabling accurate and rapid understanding of employee capabilities and preferences and achieving optimal personnel allocation.

[0097] A "server" is a computer system that processes information, stores and manages data, and provides services to other devices and software.

[0098] A "terminal" is a computer or device that a user operates and that sends input information to a server.

[0099] A "user" is a person who uses this system, and is primarily a person who plays the role of an employee.

[0100] A "generative AI model" is an artificial intelligence model that analyzes user input using natural language processing and an interactive interface and generates appropriate responses.

[0101] A "database" is a collection of data used to systematically store and manage user information, dialogue content, profile information, and other data.

[0102] "Authentication" is the process of checking passwords and IDs to verify that a user is a legitimate user.

[0103] A "dialogue session" is a series of conversations that take place between a user and a system using a generative AI model.

[0104] A "skill set" is a collection of skills and abilities that a user possesses.

[0105] "Work experience" refers to the experience a user has had in the past in relation to jobs or roles.

[0106] A "profile" is information that compiles a user's skills, work experience, desired work content, and so on.

[0107] A "talent chart" is a document or digital report that visually displays a user's skills, work experience, and desired job duties.

[0108] "Placement proposal" is information that is generated based on the user profile and the requirements of the organization to propose the most suitable job title and placement.

[0109] "Feedback" refers to opinions and comments provided by users regarding placement proposals.

[0110] "Performance appraisal" is the process of evaluating an employee's job performance after being assigned.

[0111] The system of the present invention uses interactive artificial intelligence to collect information about employees' abilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. The system of the present invention includes a server, a terminal, and a user, each of which plays a role and performs the specific processing described below.

[0112] First, the server stores the user's basic information in a database and is equipped with a means of authentication. When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal, and the terminal sends this information to the server. The server stores the received information in a database and generates authentication information.

[0113] When the user logs in again, they enter their employee ID and password into the terminal, which then sends the login information to the server. The server compares the information with the database and, if authentication is successful, starts a session. This allows the server to manage the user's basic information and ensure security through the authentication process.

[0114] Next, the user has a means to start a dialogue session via the terminal. For example, when the user clicks a "Start dialogue" button, the terminal sends a "Start dialogue" request to the server, and the server calls the generative AI model. This generative AI model uses natural language processing to engage in a dialogue with the user.

[0115] In a dialogue session, the generative AI model has a means of collecting information from the user, such as their skill set, work experience, and desired work content. Specifically, the generative AI displays the question, "What are your best skills?" The user inputs the answer and sends it via their device to the server, which then records the received information in a database.

[0116] The server has the means to generate employee profiles based on the collected information and store them in a database. The information in the database is aggregated and analyzed to profile employees based on their skill levels and desired jobs. The profiling results can be expressed as, for example, "Employee 1234 demonstrates high performance as a data scientist."

[0117] The server then creates a personnel chart based on the analysis results. This chart visually displays the user's skill set, work experience, and aspirations, and is provided to the HR department in a visual format. The server also outputs this chart data in JSON format, which can be displayed in a web interface.

[0118] Furthermore, the server is equipped with a means for comparing the personnel chart with the current requirements data and generating optimal placement proposals. For example, it uses an algorithm to calculate a "skill matching score" and propose optimal positions and projects. Specifically, it generates a proposal in the form of "Employee 1234 is suitable as the leader of a new data analysis project."

[0119] Finally, the device presents the placement proposal received from the server to the user and has a means for receiving feedback. The user reviews the proposal and either approves or adds their opinion, and the device then sends it to the server. The server receives and analyzes the feedback and runs the placement algorithm again. Even after placement, the performance evaluation system continuously monitors employee performance and reevaluates placement as necessary.

[0120] For example, you might use the following prompt:

[0121] "What is your best skill?"

[0122] "Please tell us about your previous work experience."

[0123] What position and duties do you desire?

[0124] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1:

[0127] User Registration and Login

[0128] When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal.

[0129] The terminal sends this information to the server.

[0130] The server stores the received information in a database and generates authentication information.

[0131] When logging in again, the user enters their employee ID and password into the terminal, and the terminal sends the login information to the server.

[0132] The server checks the information against the database and starts the session if authentication is successful.

[0133] Specifically, the server compares the entered password with the hash value in the database, and if they match, it issues a session ID.

[0134] Input: User's basic information (name, employee ID, password)

[0135] Output: Authentication information, session ID

[0136] Step 2:

[0137] Starting an interactive session

[0138] The user clicks the "Start conversation" button on the terminal interface.

[0139] The terminal sends a "start conversation" request to the server.

[0140] The server invokes the generative AI model and prepares the dialogue session.

[0141] A generative AI model (e.g., OpenAI GPT-3) is launched and a session ID is generated.

[0142] As a specific operation, the user presses the "Start dialogue" button on the dialogue box.

[0143] Input: Dialogue-initiating request

[0144] Output: Session ID, launch of the generative AI model

[0145] Step 3:

[0146] Checking skills and aspirations

[0147] The generative AI (in the server) asks the user questions about their skill set, work experience, and desired job duties.

[0148] The user inputs a response to this and transmits it to the server via the terminal.

[0149] As a specific example, the generative AI asks, "What are your best skills?" and the user answers, "Data analysis and project management."

[0150] The terminal sends the user's answer to the server, which records it in a database.

[0151] Input: User's answers (skill set, work experience, desired content)

[0152] Output: Information recorded in the database

[0153] Step 4:

[0154] Data organization and analysis

[0155] The server creates a profile of the employee based on the data collected during the interaction.

[0156] The information in the database is aggregated to profile employees' skill levels and desired jobs.

[0157] For example, the server may derive a conclusion such as "Employee 1234 is demonstrating high performance as a data scientist."

[0158] Input: Collected data in the database

[0159] Output: Employee profile, skill level assessment

[0160] Step 5:

[0161] Creation of personnel records

[0162] The server creates a personnel chart based on the analysis results.

[0163] The chart visually displays the user's skill set, work experience, and preferences.

[0164] Specifically, the server outputs the medical record data in JSON format and displays it on a web interface.

[0165] Input: Employee profile, analysis results

[0166] Output: Visually displayable personnel chart

[0167] Step 6:

[0168] Generate placement proposals

[0169] The server compares the personnel chart with the current requirements data and automatically generates optimal placement proposals.

[0170] As a concrete example, an algorithm is used to calculate a "skill matching score" and suggest the most suitable job title and project.

[0171] Specifically, it states, "Employee 1234 is a good candidate to lead a new data analysis project."

[0172] Input: Human resource chart, current requirements data

[0173] Output: Placement proposal

[0174] Step 7:

[0175] Presenting proposals and gathering feedback

[0176] The terminal presents the placement proposal received from the server to the user.

[0177] Users can review the suggestions and approve or add their comments.

[0178] When the user provides feedback, the terminal sends it to the server.

[0179] A specific action is for the user to click the "Approve" button or enter feedback in the comment field.

[0180] The server receives feedback, readjusts its suggestions as needed, and updates its database.

[0181] Input: User feedback

[0182] Output: revised placement proposal, updated database

[0183] Step 8:

[0184] Ongoing performance evaluation

[0185] After deployment, the server evaluates and monitors the employee's performance through the performance monitoring system.

[0186] If necessary, the server reevaluates the placement and makes optimizations.

[0187] Specifically, the server collects performance data and provides feedback based on the analysis results.

[0188] Input: Performance data

[0189] Output: Ongoing performance evaluation, recalibrated placement proposals

[0190] (Application example 1)

[0191] 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."

[0192] With conventional personnel allocation systems, it was difficult to accurately grasp employees' abilities and preferences and to allocate them optimally based on them. Furthermore, in a diverse environment like a factory, real-time data collection and response is required, but there was a lack of efficient ways to do this. As a result, employees' performance could not be maximized, hindering improvements in productivity throughout the organization.

[0193] 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.

[0194] In this invention, the server includes a means for acquiring employee abilities and preferences using interactive artificial intelligence, a means for proposing personnel allocation according to the employee abilities and preferences based on the acquired data, and a means for evaluating the effectiveness of the proposed personnel allocation, thereby enabling a detailed understanding of employee skills and preferences and optimizing personnel allocation within the factory.

[0195] "Conversational AI" is an AI system that has the ability to collect and provide information through dialogue with employees.

[0196] "Employee capabilities" refers to an employee's skill set, work experience, qualifications, etc.

[0197] "Hope" refers to the type of work and working style that employees would like to do.

[0198] "Means of acquisition" refers to the processes and techniques used to collect information from employees.

[0199] "Data" refers to the collection of collected information about employees, such as their skill sets and preferences.

[0200] "Proposal means" refers to the processes and techniques for calculating and presenting optimal personnel placement based on collected data.

[0201] "Means of evaluation" refers to the processes and techniques used to measure how effective the proposed staffing arrangements are in practice.

[0202] "Effects" refers to concrete results, such as increased productivity and employee satisfaction, that result from the proposed personnel allocation.

[0203] "Industrial facility" refers to manufacturing sites, including factories and production lines.

[0204] An "operating robot" refers to a machine that operates within a factory and automatically performs specific tasks or operations.

[0205] "Installation" refers to the process of incorporating software or applications into a particular device.

[0206] A "wearable device" refers to an electronic device that is worn on the body, and examples include smart glasses and head-mounted displays.

[0207] "Visually displaying" refers to presenting collected data on a screen or display in a format that is easy for humans to understand.

[0208] A "human resources chart" refers to a document or data set that organizes and visually displays information about an employee's abilities and aspirations.

[0209] The system of the present invention utilizes interactive artificial intelligence to collect information on employee abilities and preferences using robots and wearable devices operating within industrial facilities, and then proposes and evaluates optimal personnel placement based on this information. Each component of the system and its operation are described below.

[0210] System Configuration

[0211] The system consists of the following components:

[0212] Server: The main processing unit containing the database and generative AI models.

[0213] Wearable devices: Smart glasses or head-mounted displays worn by employees.

[0214] Robots operating in industrial facilities: devices equipped with conversational artificial intelligence that interact with employees.

[0215] Hardware and Software Used

[0216] Hardware:

[0217] Wearable devices (smart glasses, head-mounted displays)

[0218] Robots in factories (with interactive functions)

[0219] software:

[0220] Flask (Python web framework)

[0221] SQLite (lightweight database)

[0222] OpenAI API (Interactive Generative AI Model)

[0223] Data processing and calculation

[0224] Data collection and dialogue initiation

[0225] A user (employee) logs into the system via a wearable device and initiates a session with the conversational AI. The server receives this request, launches the generative AI model, and sets up the conversational environment.

[0226] Confirm your skills and expectations

[0227] The generative AI model asks employees questions to gather information about their skill sets, work experience, and desired work. For example, if a user answers, "I'm good at data analysis and project management," the device sends this response to a server, which then records this data in a database.

[0228] Data organization and personnel chart creation

[0229] The server analyzes the data obtained through the conversation and generates a talent chart based on the employee's abilities and aspirations. This chart is displayed visually and can be reviewed by both HR personnel and the employee themselves. For example, it displays the employee's skill map and work history.

[0230] Generating and evaluating placement proposals

[0231] The server compares the personnel records with the current requirements data within the industrial facility and generates optimal placement proposals. The proposals include specific departments, positions, and project assignments. For example, the server might suggest to an employee, "You would be ideal as the leader of a new data analysis project."

[0232] Examples and prompts

[0233] As a concrete example, here are some examples of prompts that employees use when interacting with conversational AI:

[0234] Prompt statement:

[0235] "Please suggest the best placement based on the employee's skills: they are good at data analysis and project management, and their desire: they want to participate in more advanced projects."

[0236] In this way, the system of the present invention can provide detailed insight into employee skills and preferences, enabling efficient staffing within industrial facilities, which will improve productivity and employee satisfaction throughout the organization.

[0237] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0238] Step 1:

[0239] A user puts on a wearable device and logs in to the system by entering basic information such as their name, employee ID, and password. The device sends this input data to the server, which then compares it with a database to authenticate the user. If authentication is successful, the user is allowed to log in and can use the system.

[0240] Input: User's basic information (name, employee ID, password)

[0241] Data processing: The server checks the input data against the database

[0242] Output: Login allowed or denied

[0243] Specific operation: The user enters the required information into the wearable device interface and presses the login button. The device sends the data to the server, which then references the database to perform authentication.

[0244] Step 2:

[0245] After logging in, the user clicks the "Start Dialogue" button on the device interface. The device sends a request to the server to launch the conversational AI, and the server calls the generative AI model to set up the conversational environment. The generative AI model is then ready to start a conversation with the user.

[0246] Input: Request to start a conversation

[0247] Data processing: The server launches the generative AI model and sets up the dialogue environment.

[0248] Output: Conversational AI is launched and ready for interaction

[0249] Specific operation: The user clicks the "Start dialogue" button on the device, and the device sends a request to the server. The server then launches the generative AI model and prepares the dialogue environment.

[0250] Step 3:

[0251] The generative AI model asks the user questions about their skill set, work experience, and desired job duties. The user then enters their answers through dialogue, and the device sends these answers to the server, which stores them in a database in real time.

[0252] Input: User response data (skill set, work experience, desired work)

[0253] Data processing: The server takes in the response data and stores it in a database

[0254] Output: Saved database records

[0255] How it works: The generating AI displays a question on the screen, and the user answers by voice or text. The device sends the data to the server, which stores it in a database.

[0256] Step 4:

[0257] The server analyzes the user information stored in the database and generates a personnel chart based on the employee's abilities and preferences. The generated personnel chart is organized in a visually displayable format.

[0258] Input: User information stored in the database

[0259] Data processing: Server performs analysis and profiling

[0260] Output: Visually displayable personnel chart

[0261] What it does: The server retrieves data from the database and uses specialized algorithms to profile employees' abilities and aspirations, creating a talent chart.

[0262] Step 5:

[0263] The server compares the personnel chart with the current requirements data within the industrial facility and generates optimal placement proposals, including specific departments, positions, and project assignments. The proposals are then sent to the terminal and presented to the user.

[0264] Input: Personnel chart and current requirements data

[0265] Data processing: The server performs matching and calculates the optimal placement

[0266] Output: Specific placement proposal

[0267] Specific operation: The server integrates the personnel chart and the factory needs to generate optimal placement proposals and send the proposals to the user's terminal.

[0268] Step 6:

[0269] The user reviews the proposed placement and provides approval or feedback. The terminal sends the user's feedback to the server, which readjusts the placement proposal as needed based on the feedback.

[0270] Input: User feedback

[0271] Data processing: The server analyzes the feedback and refines the suggestions.

[0272] Output: Reworked placement proposal or final decision

[0273] Specific operation: The user checks the placement proposal on the device and enters feedback. The device sends the feedback to the server, which then analyzes it again and updates the optimal placement proposal.

[0274] Through this process, the system will gain a detailed understanding of employees' skills and preferences, enabling efficient staffing within industrial facilities, which is expected to improve productivity and employee satisfaction across the organization.

[0275] 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.

[0276] The system of the present invention uses interactive artificial intelligence to acquire employee capabilities and preferences, and then proposes and evaluates personnel allocation based on these. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the dialogue is improved, resulting in more accurate personnel allocation. In the system of the present invention, the roles of the server, terminal, and user are clearly defined, and each performs the specific processing shown below.

[0277] Main processing flow

[0278] 1. User Registration and Login

[0279] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[0280] The terminal transmits the user's input information to the server.

[0281] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[0282] 2. Starting an interactive session

[0283] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0284] The terminal sends this request to the server.

[0285] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0286] 3. Confirm your skills and aspirations

[0287] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0288] The user answers on the device, "I'm good at data analysis and project management."

[0289] The terminal sends the user's answer to the server.

[0290] The server stores this response in a database.

[0291] 4. Sentiment analysis using an emotion engine

[0292] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[0293] For example, if a user responds "I'm a little anxious," the emotion engine will recognize the feeling of anxiety from the content of the words and the tone of voice.

[0294] 5. Tailoring questions based on user sentiment

[0295] Based on feedback from the emotion engine, the generative AI generates questions that correspond to the user's emotions. For example, if the user is feeling anxious, the generative AI can ask questions to relax them or respond in a considerate manner.

[0296] By having users answer emotionally sensitive questions, more detailed and accurate data is collected.

[0297] 6. Data organization and human resource chart generation

[0298] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[0299] For example, the server integrates the user's skill map, desired job, and emotional data to create a visualized talent chart.

[0300] 7. Generating placement proposals

[0301] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[0302] As a specific example, the server generates a suggestion such as "suitable as a leader for a data analysis project" and displays it to the user.

[0303] 8. Review and feedback on proposals

[0304] The user checks the placement proposal through the terminal and approves or adds comments.

[0305] The terminal sends the user's feedback to the server.

[0306] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[0307] 9. Final decision and follow-up

[0308] The server finalizes the placement based on the user's final approval and updates the database.

[0309] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[0310] In this way, the system of the present invention, by combining interactive AI with an emotion engine, can accurately grasp the capabilities and desires of employees in detail and propose optimal personnel allocation. Furthermore, by incorporating an emotion engine, the system can grasp the user's emotional state in real time and flexibly adjust the content of the dialogue, enabling highly accurate information collection. This is expected to improve organizational productivity and efficiency.

[0311] The processing flow will be explained below.

[0312] Step 1: Log in and register

[0313] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[0314] The terminal transmits the user's input information to the server.

[0315] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[0316] Step 2: Starting an interactive session

[0317] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0318] The terminal sends this request to the server.

[0319] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0320] Step 3: Identify your skills and aspirations

[0321] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0322] The user answers on the device, "I'm good at data analysis and project management."

[0323] The terminal sends the user's answer to the server.

[0324] The server stores this response in a database.

[0325] Step 4: Collect and organize detailed data

[0326] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[0327] The user answers these questions and provides information through the terminal.

[0328] The terminal sends each response data to the server, which records them in a database.

[0329] Step 5: Sentiment analysis with the sentiment engine

[0330] The emotion engine in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state, making it possible to grasp the emotions the user is feeling during the conversation in real time.

[0331] The emotion engine (in the server) sends the analysis results to the generation AI, which then adjusts the response to enable appropriate dialogue based on the user's emotions.

[0332] Step 6: Adjust your questions based on emotion

[0333] The generative AI dynamically adjusts the dialogue content based on the user's emotional data. For example, if the user is feeling anxious, it will ask relaxing questions, and if the user is showing interest, it will ask more in-depth questions.

[0334] The user answers tailored questions and provides further detailed data.

[0335] The terminal sends this new response data to the server, which updates the database.

[0336] Step 7: Organize data and generate personnel charts

[0337] The server sends all collected data to an analytics engine, which compiles employee skills, aspirations, and emotional state.

[0338] The analysis engine (within the server) creates a personnel chart that integrates skill level, desired job, years of experience, and emotional state.

[0339] The server prepares the generated personnel chart in a visually displayable format and stores it in a database.

[0340] Step 8: Generate placement proposals

[0341] The server compares the generated personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[0342] The terminal displays the placement proposal received from the server to the user.

[0343] Step 9: Review and feedback on proposals

[0344] The user checks the placement proposal through the terminal and approves or adds comments.

[0345] The terminal sends the user's feedback to the server.

[0346] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[0347] Step 10: Finalize and follow up

[0348] The server finalizes the placement based on the user's final approval and updates the database.

[0349] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[0350] This allows the system to understand the user's skill set and preferences in detail through dialogue, and to use the emotion engine to improve the quality of dialogue and achieve optimal staffing. Each step works in tandem to achieve high accuracy and efficiency.

[0351] Example 2

[0352] 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."

[0353] Conventional personnel placement systems are unable to fully grasp employees' abilities and preferences, making it difficult to perform optimal personnel placement. Furthermore, because they do not take the user's emotional state into account, the quality of information collection declines, resulting in inappropriate placement proposals. Furthermore, the lack of a means to organize and visually display employees' skills and preferences makes centralized information management difficult. Therefore, a new system is needed to achieve highly accurate and flexible personnel placement.

[0354] 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.

[0355] In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing personnel allocations based on the acquired data in accordance with the employee abilities and preferences; means for evaluating the effectiveness of the proposed personnel allocations; means for continuously improving the accuracy of the personnel allocations; emotion recognition means for analyzing the user's emotional state; means for adjusting the content of questions based on the analysis results of the emotion recognition means; and means for organizing the acquired data and generating a personnel chart that visually displays it. This makes it possible to accurately grasp employee abilities and preferences and improve the quality of information collection through flexible dialogue that takes the user's emotional state into consideration. Furthermore, optimal personnel allocation can be achieved based on the visually organized data, which is expected to improve organizational productivity and efficiency.

[0356] 1. "Conversational AI" refers to an AI system that has the ability to gather, understand, and respond to information through dialogue with a user.

[0357] 2. "Employee Competence" means the knowledge, skills, techniques, and experience an employee possesses to perform a job.

[0358] 3. "Employee preferences" refer to employees' current and future aspirations for their jobs, roles, work environment, career paths, etc.

[0359] 4. "Staffing" refers to the allocation of employees to appropriate positions or roles within an organization.

[0360] 5. "Recommendation" means a recommendation for determining optimal staffing based on the information collected.

[0361] 6. "Evaluation tool" means a tool or method for measuring and evaluating the effectiveness or success of a proposed staffing arrangement.

[0362] 7. "Emotion recognition means" refers to technology or methods for recognizing and analyzing a user's emotional state from the content of their dialogue, facial expressions, tone of voice, etc.

[0363] 8. "Database" means a collection system for systematically storing and managing collected data.

[0364] 9. A "human resources chart" is a document or digital content that centrally organizes and visually displays information such as an employee's abilities, aspirations, and emotional state.

[0365] 10. "Visual display" refers to the presentation of information to a user in a visual format, such as a graph or dashboard.

[0366] The system of the present invention utilizes interactive artificial intelligence and emotion recognition means to acquire employee capabilities and desires, and then proposes optimal personnel placement based on these. Specific implementation methods of this system are described in detail below.

[0367] Hardware and software used

[0368] This system uses the following main hardware and software:

[0369] 1. Server: Responsible for main data processing and database management.

[0370] 2. Terminal: Acts as a user interface, receives input from the user, and communicates with the server.

[0371] 3. Generative AI model (e.g., GPT-4): An artificial intelligence model that enables interaction with users.

[0372] 4. Emotion recognition engine: Software that analyzes the content of the user's dialogue, facial expressions, tone of voice, etc. to recognize the user's emotional state.

[0373] Processing flow and specific operations

[0374] The specific operations and techniques used in each processing step will be described.

[0375] User Registration and Login

[0376] The user uses a terminal to access the system's login screen, where they enter their name, employee ID, and password.

[0377] Example: Enter the name "Taro Tanaka", employee ID "12345", and password "password123".

[0378] The terminal transmits the user's input information to the server.

[0379] The server checks the received login information against the database and performs authentication processing.

[0380] Starting an interactive session

[0381] The user clicks the "Start Interaction" button to begin the interaction session.

[0382] Example: Clicking the "Start conversation" button.

[0383] The terminal sends this request to the server.

[0384] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0385] Verifying skills and preferences

[0386] The generating AI (in the server) asks questions to the user.

[0387] Example prompt: "What is your best skill?"

[0388] The user responds using the terminal.

[0389] Example: "I'm good at data analysis and project management."

[0390] The terminal transmits the user's answer to the server.

[0391] The server stores this response in a database.

[0392] Emotion recognition and question adjustment

[0393] The emotion recognition engine (in the server) analyzes the content of the user's dialogue, facial expressions, and tone of voice to recognize their emotional state.

[0394] Example: If a user responds, "I'm a little anxious," the emotion engine recognizes that emotion.

[0395] The generative AI adjusts the questions based on feedback from the emotion recognition engine.

[0396] Example: Asking questions to put anxious users at ease.

[0397] Data organization and personnel chart generation

[0398] The server organizes the collected data and generates a personnel chart that integrates the employee's abilities, aspirations, and emotional state.

[0399] Example: Creating a chart that visually displays a user's skill map, desired job, and emotional data.

[0400] Generate and review placement proposals

[0401] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[0402] Example: Generate suggestions such as "You're a good choice to lead a data analysis project."

[0403] The user reviews the placement proposal and approves or adds comments.

[0404] The server reevaluates the proposal and makes adjustments if necessary.

[0405] Final decision and follow-up

[0406] The server reflects the final decision in the database and monitors the employee's performance after placement.

[0407] Example: Collect performance data periodically and reassess as needed.

[0408] The system utilizes conversational artificial intelligence and an emotion recognition engine to enable users to receive accurate and user-friendly staffing recommendations, while centralized management of employee data can improve organizational productivity and efficiency.

[0409] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0410] Step 1:

[0411] User Registration and Login

[0412] A user accesses the system login screen using a terminal and enters their name, employee ID, and password.

[0413] Input: Name "Taro Tanaka", Employee ID "12345", Password "password123"

[0414] Action: Enter information on the keyboard

[0415] The terminal transmits the user's input information to the server.

[0416] Input: Information entered by the user

[0417] What happens: The "Submit" button is clicked and the information is sent to the server as a POST request

[0418] Output: User information received by the server

[0419] The server checks the received login information against the database and performs authentication processing.

[0420] Input: Received user information

[0421] How it works: The server looks up the user information in its database and checks for a matching ID and password.

[0422] Output: Authentication result (success or failure)

[0423] Step 2:

[0424] Starting an interactive session

[0425] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0426] Input: Click the "Start conversation" button

[0427] Action: Click the button with the mouse

[0428] The terminal sends this request to the server.

[0429] Input: "Start conversation" request

[0430] Action: A request is sent to the server

[0431] Output: The server receives the request

[0432] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0433] Input: "Start conversation" request

[0434] Operation: Call the API of the generative AI model and perform the necessary initial setup.

[0435] Output: Interactive environment is ready

[0436] Step 3:

[0437] Checking skills and aspirations

[0438] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0439] Input: User interaction context

[0440] How it works: Send a question prompt to a generative AI model

[0441] Output: Question text

[0442] The user answers on the device, "I'm good at data analysis and project management."

[0443] Input: Question text

[0444] Action: Enter your answer on the keyboard and click the submit button

[0445] Output: Answer text

[0446] The terminal sends the user's answer to the server.

[0447] Input: User's answer

[0448] What it does: Sends the answer text to the server as a POST request

[0449] Output: The response text received by the server

[0450] The server stores this response in a database.

[0451] Input: User answer text

[0452] What it does: Saves the answer to the user's profile information in a database

[0453] Output: Answer data stored in a database

[0454] Step 4:

[0455] Sentiment analysis with emotion engine

[0456] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[0457] Input: User interaction, facial expressions, and tone of voice

[0458] How it works: Applying text analysis, facial expression recognition, and speech analysis algorithms

[0459] Output: Recognized emotion data

[0460] Example: When a user says, "I'm a little anxious," the emotion engine recognizes anxiety.

[0461] Step 5:

[0462] Tailoring questions based on user sentiment

[0463] The generative AI generates questions based on the user's emotions based on feedback from the emotion engine.

[0464] Input: Emotion engine feedback

[0465] How it works: The AI ​​model generates the next question

[0466] Output: Adjusted question text

[0467] Example: If a user is feeling anxious, the generative AI will ask them questions to help them relax.

[0468] Users answer emotionally sensitive questions, which allows for more detailed and accurate data to be collected.

[0469] Input: Tailored Question

[0470] Action: Enter answer on keyboard and click submit button

[0471] Output: User's answer text

[0472] Step 6:

[0473] Data organization and personnel chart generation

[0474] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[0475] Input: Data on the user's abilities, desires, and emotional state

[0476] How it works: Algorithms analyze and synthesize data to generate a visualized medical record

[0477] Output: Human Resources Chart

[0478] Example: Integrating skills maps, job preferences, and sentiment data to create a visual dashboard

[0479] Step 7:

[0480] Generate placement proposals

[0481] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[0482] Input: Personnel chart, current status request data

[0483] How it works: Algorithms collate data and calculate optimal placement

[0484] Output: Placement proposal

[0485] Example: Generate a suggestion that "You are a good candidate to lead a data analysis project."

[0486] Step 8:

[0487] Review and feedback on suggestions

[0488] The user checks the placement proposal through the terminal and approves or adds comments.

[0489] Input: Proposal

[0490] Action: Review the suggestion and click the "Approve" or "Add Suggestion" button

[0491] Output: User feedback

[0492] The terminal sends the user's feedback to the server.

[0493] Input: User feedback

[0494] Behavior: Sends feedback to the server as a POST request

[0495] Output: Feedback data received by the server

[0496] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[0497] Input: User feedback

[0498] How it works: Analyzes feedback and updates suggestions based on a rating algorithm

[0499] Output: Updated placement proposal

[0500] Step 9:

[0501] Final decision and follow-up

[0502] The server finalizes the placement based on the user's final approval and updates the database.

[0503] Input: Final approved placement proposal

[0504] Action: Writes the location information to the database.

[0505] Output: Updated database

[0506] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[0507] Input: Performance data

[0508] How it works: Periodically collects performance data and re-evaluates it based on the evaluation algorithm.

[0509] Output: Improvement suggestions and reallocation decisions

[0510] (Application example 2)

[0511] 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."

[0512] In the past, employee allocation in logistics centers was primarily based on experience and intuition, which meant that efficient allocation that fully reflected employee abilities and preferences could not be achieved. Furthermore, employee emotions and psychological states were not taken into consideration, which had a negative impact on work efficiency and employee satisfaction. The present invention aims to solve these problems by providing a system that maximizes employee abilities and preferences while also taking into account their emotional states, resulting in efficient and satisfying personnel allocation.

[0513] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing staffing arrangements based on employee abilities and preferences based on the acquired data; means for evaluating the effectiveness of the proposed staffing arrangements; means for continuously improving the accuracy of staffing arrangements based on the evaluation results; means for analyzing user emotions using an emotion engine and reflecting the results in the dialogue content; and means for generating optimal staffing proposals based on the business characteristics of the application destination. This enables detailed collection of employee abilities and preferences and enables highly accurate staffing arrangements that take emotional states into account. This is also expected to improve work efficiency and employee satisfaction at logistics centers.

[0514] "Conversational AI" is an AI system that naturally interacts with users and collects necessary information.

[0515] "Employee Competence" refers to the techniques, knowledge, skills and experience possessed by an employee.

[0516] "Wishes" refer to the job duties and work content that employees desire, as well as their desired working conditions.

[0517] "Staffing" refers to assigning appropriate tasks and roles to employees based on their abilities and preferences.

[0518] An "emotion engine" is a system for analyzing a user's emotional state.

[0519] "Means for evaluating the effectiveness of the proposed personnel allocation" is a system for evaluating how effective the proposed personnel allocation was.

[0520] "Means to continuously improve the accuracy of personnel placement" refers to a system for providing feedback on evaluation results and improving the accuracy of placement from the next time onwards.

[0521] "Means for analyzing the user's emotions and reflecting the results in the content of the dialogue" refers to a mechanism in which the emotion engine recognizes the user's emotions and adjusts the content of the dialogue based on that information.

[0522] A "visual human resources chart" is a document that organizes employee abilities, aspirations, and emotional data and displays them in a graphical format.

[0523] A "logistics center" refers to a facility that stores goods, ships them, manages inventory, and handles incoming and outgoing shipments.

[0524] This invention is a system that acquires the abilities and desires of employees at a logistics center and proposes and evaluates optimal personnel allocation based on them. This system uses a combination of conversational artificial intelligence and an emotion engine to collect detailed information about employees' abilities and desires, and recognizes and reflects their emotional states to achieve highly accurate personnel allocation.

[0525] Hardware and software used

[0526] Hardware: Smartphone, Head-Mounted Display (HMD)

[0527] Software: Python, EmotionRecognition (e.g. DeepFace), OpenAI GPT

[0528] System Operation Overview

[0529] 1. User Registration and Login

[0530] Users access the system's login screen using a smartphone or HMD and enter basic information such as their name, employee ID, and password. The device sends the information to the server, which then compares it with a database and performs authentication.

[0531] 2. Starting an interactive session

[0532] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The device sends this request to the server, which then invokes a generative AI model (e.g., GPT-4) to set up the conversational environment.

[0533] 3. Confirm your skills and aspirations

[0534] The generative AI on the server asks the user questions such as, "What are your best skills?" The user answers using a device, "I'm good at data analysis and project management," and the device sends the answer to the server and stores it in a database.

[0535] 4. Sentiment analysis using an emotion engine

[0536] The emotion engine (e.g., DeepFace) in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content of the words and tone of voice.

[0537] 5. Tailoring questions based on user sentiment

[0538] The server's generation AI generates questions based on the user's emotions based on feedback from the emotion engine. For example, if the user is feeling anxious, it can ask questions to relax them or respond in a considerate manner.

[0539] 6. Data organization and human resource chart generation

[0540] The server organizes the collected data and generates a human resource chart that centrally manages employees' abilities, aspirations, and emotional states, creating a visualized human resource chart.

[0541] 7. Generating placement proposals

[0542] The server compares the talent chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[0543] 8. Review and feedback on proposals

[0544] The user can review the placement proposal through the device and approve or add their comments. The device then sends the user's feedback to the server, which then reevaluates the proposal based on the received feedback and adjusts it as necessary.

[0545] An example of a prompt sentence to be input to a specific generative AI model:

[0546] "What is your best skill?"

[0547] "What kind of work would you like done?"

[0548] This will enable efficient and highly satisfying staffing at logistics centers.

[0549] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0550] Step 1:

[0551] A user logs in to the system using a terminal. The input is the user's basic information (name, employee ID, password), and the output is the authentication result (success or failure). The terminal sends this information to the server, which then checks it against a database and performs authentication processing. If authentication is successful, a user session is started.

[0552] Step 2:

[0553] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The input is the user's request to start the session, and the output is a notification that the conversational environment has been set up. The device sends the request to the server, which then calls the generative AI model to set up the conversational environment.

[0554] Step 3:

[0555] The server uses a generative AI model to ask the user questions and obtain the user's abilities and aspirations. The input is the question generated by the generative AI model (e.g., "What are your best skills?") and the user's answer, and the output is the obtained data (skill information, desired job). The user answers using a device, and the device sends the answer to the server. The server stores this data in a database.

[0556] Step 4:

[0557] The emotion engine in the server analyzes the content of the user's dialogue and recognizes their emotional state. The input is the content of the user's dialogue and tone of voice, and the output is the analysis result of their emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content and tone of voice.

[0558] Step 5:

[0559] Based on feedback from the emotion engine, the server uses a generative AI model to generate questions that correspond to the user's emotions. The input is the analysis result of the emotion engine, and the output is a tailored question (e.g., "What do you think about working in a relaxed environment?"). This allows the user to provide more detailed and accurate information.

[0560] Step 6:

[0561] The server organizes the collected data and generates a visually displayed talent chart. The input is employee ability, preference, and emotion data, and the output is an integrated talent chart. The server consolidates this data and displays it in a visual format (e.g., skill map, preference list, emotional state).

[0562] Step 7:

[0563] The server compares the personnel chart with the business characteristics of the logistics center and generates optimal placement proposals. The input is the personnel chart and current requirements data, and the output is a specific placement proposal (e.g., "suitable as leader of the data analysis project"). The server automatically generates optimal placements based on this data.

[0564] Step 8:

[0565] The user reviews the proposed placement and provides feedback through the device. The input is the user feedback (approval or additional comments), and the output is the reevaluated placement proposal. The device sends the feedback to the server, which reevaluates the placement proposal based on the feedback and adjusts it if necessary.

[0566] The above are the specific processing steps of a system that realizes optimal personnel allocation that reflects the abilities and wishes of employees at a logistics center.

[0567] 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.

[0568] 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.

[0569] 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.

[0570] [Second embodiment]

[0571] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0572] 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.

[0573] 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).

[0574] 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.

[0575] 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.

[0576] 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).

[0577] 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.

[0578] 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.

[0579] 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.

[0580] 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.

[0581] 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.

[0582] 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."

[0583] The system of the present invention uses interactive artificial intelligence to collect information about employee capabilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. In the system of the present invention, the roles of the server, terminals, and users are clearly defined, and each role performs the specific processing shown below.

[0584] Main processing flow

[0585] 1. User Registration and Login

[0586] The server stores the user's basic information in a database and authenticates the user when they log in via a terminal. When logging in for the first time, the user enters their basic information (name, employee ID, password, etc.) into the terminal.

[0587] As an example of system operation, when a user enters their name, employee number, and password and clicks the login button, the terminal sends this information to the server, which then verifies the information and allows the user to log in.

[0588] 2. Starting an interactive session

[0589] The user initiates a session with the conversational AI via their device, which causes the server to call up the generative AI model and prepare the conversational environment.

[0590] For example, to start a conversation session, you click the "Start conversation" button. The device sends this request to the server, which then launches the generated AI.

[0591] 3. Confirm your skills and aspirations

[0592] The generation AI (in the server) asks the user questions to collect information about the user's skill set, work experience, desired work, etc. This dialogue provides detailed data.

[0593] For example, if the user answers "I'm good at data analysis and project management," the device sends this answer to the server, which records it in a database.

[0594] 4. Data organization and analysis

[0595] The server analyzes the data obtained from the conversation and generates a talent profile based on the employee's abilities and aspirations. The data is organized and profiled in real time.

[0596] For example, the server analyzes an employee's past performance and skill level and draws a conclusion such as "he has extensive experience as a data scientist."

[0597] 5. Creation of personnel records

[0598] The server then uses the organized data to create a visually displayed personnel chart, detailing the employee's skill level, desired job role, past performance, and more.

[0599] For example, the server organizes employees' skill maps and work histories and provides them to the human resources department in a visual format.

[0600] 6. Generating placement proposals

[0601] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[0602] As a specific example, the server generates suggestions such as "Qualified to lead new data analysis project" and displays them to employees.

[0603] 7. Proposal presentation and follow-up

[0604] The device presents the user with placement suggestions from the server, which the user can review, approve, or add comments to. The server then receives feedback and adjusts the suggestions as needed.

[0605] For example, after a user approves a placement proposal, the server makes the final decision and updates the database. After placement, a performance evaluation system continuously monitors performance and reevaluates the placement if necessary.

[0606] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[0607] The processing flow will be explained below.

[0608] Step 1: Log in and register

[0609] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[0610] The terminal transmits the user's input information to the server.

[0611] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[0612] Step 2: Starting an interactive session

[0613] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0614] The terminal sends this request to the server.

[0615] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0616] Step 3: Identify your skills and aspirations

[0617] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0618] The user answers on the device, "I'm good at data analysis and project management."

[0619] The terminal sends the user's answer to the server.

[0620] The server stores this response in a database.

[0621] Step 4: Collect and organize detailed data

[0622] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[0623] The user answers these questions and provides information through the terminal.

[0624] The terminal sends each response data to the server, which records them in a database.

[0625] Step 5: Analyze the data and generate a personnel chart

[0626] The server sends the collected data to an analysis engine to organize employees' abilities and aspirations.

[0627] The analysis engine (on the server) analyzes the data and extracts and organizes information such as skill level, desired job, and years of experience.

[0628] The server generates a visually displayable personnel chart based on the organized data.

[0629] Step 6: Generate placement proposals

[0630] The server compares the generated personnel chart with the current status requirements data of the organization.

[0631] The server generates optimal placement proposals based on the employee's skill set, work experience, and preferences.

[0632] The terminal displays the placement proposal to the user.

[0633] Step 7: Review and feedback on the proposal

[0634] The user checks the placement proposal through the terminal and approves or adds comments.

[0635] The terminal sends the user's feedback to the server.

[0636] The server reevaluates based on the feedback it receives and readjusts its placement proposals as needed.

[0637] Step 8: Finalize and follow up

[0638] The server finalizes the placement based on the user's final approval and updates the database.

[0639] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[0640] In this way, the system of the present invention can grasp the user's skill set and preferences in detail through dialogue with them, and propose optimal staffing. Each step works in conjunction with the other to achieve high accuracy and efficiency.

[0641] Example 1

[0642] 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."

[0643] To accurately understand employee capabilities and aspirations and improve productivity and efficiency across the organization, it is necessary to effectively collect and analyze employee information and propose optimal personnel placements. However, manual data collection and analysis requires time and effort and increases the risk of human error. Furthermore, to continuously propose appropriate placements, it is necessary to keep employee profiles up to date based on the collected information and to evaluate performance after placement. These challenges need to be resolved.

[0644] 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.

[0645] In this invention, the server includes means for storing basic information of the user in a database and performing authentication, means for starting an interactive session using a generative AI model, and means for collecting the user's skill set, work experience, and desired work content using the generative AI model, thereby enabling accurate and rapid understanding of employee capabilities and preferences and achieving optimal personnel allocation.

[0646] A "server" is a computer system that processes information, stores and manages data, and provides services to other devices and software.

[0647] A "terminal" is a computer or device that a user operates and that sends input information to a server.

[0648] A "user" is a person who uses this system, and is primarily a person who plays the role of an employee.

[0649] A "generative AI model" is an artificial intelligence model that analyzes user input using natural language processing and an interactive interface and generates appropriate responses.

[0650] A "database" is a collection of data used to systematically store and manage user information, dialogue content, profile information, and other data.

[0651] "Authentication" is the process of checking passwords and IDs to verify that a user is a legitimate user.

[0652] A "dialogue session" is a series of conversations that take place between a user and a system using a generative AI model.

[0653] A "skill set" is a collection of skills and abilities that a user possesses.

[0654] "Work experience" refers to the experience a user has had in the past in relation to jobs or roles.

[0655] A "profile" is information that compiles a user's skills, work experience, desired work content, and so on.

[0656] A "talent chart" is a document or digital report that visually displays a user's skills, work experience, and desired job duties.

[0657] "Placement proposal" is information that is generated based on the user profile and the requirements of the organization to propose the most suitable job title and placement.

[0658] "Feedback" refers to opinions and comments provided by users regarding placement proposals.

[0659] "Performance appraisal" is the process of evaluating an employee's job performance after being assigned.

[0660] The system of the present invention uses interactive artificial intelligence to collect information about employees' abilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. The system of the present invention includes a server, a terminal, and a user, each of which plays a role and performs the specific processing described below.

[0661] First, the server stores the user's basic information in a database and is equipped with a means of authentication. When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal, and the terminal sends this information to the server. The server stores the received information in a database and generates authentication information.

[0662] When the user logs in again, they enter their employee ID and password into the terminal, which then sends the login information to the server. The server compares the information with the database and, if authentication is successful, starts a session. This allows the server to manage the user's basic information and ensure security through the authentication process.

[0663] Next, the user has a means to start a dialogue session via the terminal. For example, when the user clicks a "Start dialogue" button, the terminal sends a "Start dialogue" request to the server, and the server calls the generative AI model. This generative AI model uses natural language processing to engage in a dialogue with the user.

[0664] In a dialogue session, the generative AI model has a means of collecting information from the user, such as their skill set, work experience, and desired work content. Specifically, the generative AI displays the question, "What are your best skills?" The user inputs the answer and sends it via their device to the server, which then records the received information in a database.

[0665] The server has the means to generate employee profiles based on the collected information and store them in a database. The information in the database is aggregated and analyzed to profile employees based on their skill levels and desired jobs. The profiling results can be expressed as, for example, "Employee 1234 demonstrates high performance as a data scientist."

[0666] The server then creates a personnel chart based on the analysis results. This chart visually displays the user's skill set, work experience, and aspirations, and is provided to the HR department in a visual format. The server also outputs this chart data in JSON format, which can be displayed in a web interface.

[0667] Furthermore, the server is equipped with a means for comparing the personnel chart with the current requirements data and generating optimal placement proposals. For example, it uses an algorithm to calculate a "skill matching score" and propose optimal positions and projects. Specifically, it generates a proposal in the form of "Employee 1234 is suitable as the leader of a new data analysis project."

[0668] Finally, the device presents the placement proposal received from the server to the user and has a means for receiving feedback. The user reviews the proposal and either approves or adds their opinion, and the device then sends it to the server. The server receives and analyzes the feedback and runs the placement algorithm again. Even after placement, the performance evaluation system continuously monitors employee performance and reevaluates placement as necessary.

[0669] For example, you might use the following prompt:

[0670] "What is your best skill?"

[0671] "Please tell us about your previous work experience."

[0672] What position and duties do you desire?

[0673] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[0674] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0675] Step 1:

[0676] User Registration and Login

[0677] When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal.

[0678] The terminal sends this information to the server.

[0679] The server stores the received information in a database and generates authentication information.

[0680] When logging in again, the user enters their employee ID and password into the terminal, and the terminal sends the login information to the server.

[0681] The server checks the information against the database and starts the session if authentication is successful.

[0682] Specifically, the server compares the entered password with the hash value in the database, and if they match, it issues a session ID.

[0683] Input: User's basic information (name, employee ID, password)

[0684] Output: Authentication information, session ID

[0685] Step 2:

[0686] Starting an interactive session

[0687] The user clicks the "Start conversation" button on the terminal interface.

[0688] The terminal sends a "start conversation" request to the server.

[0689] The server invokes the generative AI model and prepares the dialogue session.

[0690] A generative AI model (e.g., OpenAI GPT-3) is launched and a session ID is generated.

[0691] As a specific operation, the user presses the "Start dialogue" button on the dialogue box.

[0692] Input: Dialogue-initiating request

[0693] Output: Session ID, launch of the generative AI model

[0694] Step 3:

[0695] Checking skills and aspirations

[0696] The generative AI (in the server) asks the user questions about their skill set, work experience, and desired job duties.

[0697] The user inputs a response to this and transmits it to the server via the terminal.

[0698] As a specific example, the generative AI asks, "What are your best skills?" and the user answers, "Data analysis and project management."

[0699] The terminal sends the user's answer to the server, which records it in a database.

[0700] Input: User's answers (skill set, work experience, desired content)

[0701] Output: Information recorded in the database

[0702] Step 4:

[0703] Data organization and analysis

[0704] The server creates a profile of the employee based on the data collected during the interaction.

[0705] The information in the database is aggregated to profile employees' skill levels and desired jobs.

[0706] For example, the server may derive a conclusion such as "Employee 1234 is demonstrating high performance as a data scientist."

[0707] Input: Collected data in the database

[0708] Output: Employee profile, skill level assessment

[0709] Step 5:

[0710] Creation of personnel records

[0711] The server creates a personnel chart based on the analysis results.

[0712] The chart visually displays the user's skill set, work experience, and preferences.

[0713] Specifically, the server outputs the medical record data in JSON format and displays it on a web interface.

[0714] Input: Employee profile, analysis results

[0715] Output: Visually displayable personnel chart

[0716] Step 6:

[0717] Generate placement proposals

[0718] The server compares the personnel chart with the current requirements data and automatically generates optimal placement proposals.

[0719] As a concrete example, an algorithm is used to calculate a "skill matching score" and suggest the most suitable job title and project.

[0720] Specifically, it states, "Employee 1234 is a good candidate to lead a new data analysis project."

[0721] Input: Human resource chart, current requirements data

[0722] Output: Placement proposal

[0723] Step 7:

[0724] Presenting proposals and gathering feedback

[0725] The terminal presents the placement proposal received from the server to the user.

[0726] Users can review the suggestions and approve or add their comments.

[0727] When the user provides feedback, the terminal sends it to the server.

[0728] A specific action is for the user to click the "Approve" button or enter feedback in the comment field.

[0729] The server receives feedback, readjusts its suggestions as needed, and updates its database.

[0730] Input: User feedback

[0731] Output: revised placement proposal, updated database

[0732] Step 8:

[0733] Ongoing performance evaluation

[0734] After deployment, the server evaluates and monitors the employee's performance through the performance monitoring system.

[0735] If necessary, the server reevaluates the placement and makes optimizations.

[0736] Specifically, the server collects performance data and provides feedback based on the analysis results.

[0737] Input: Performance data

[0738] Output: Ongoing performance evaluation, recalibrated placement proposals

[0739] (Application example 1)

[0740] 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."

[0741] With conventional personnel allocation systems, it was difficult to accurately grasp employees' abilities and preferences and to allocate them optimally based on them. Furthermore, in a diverse environment like a factory, real-time data collection and response is required, but there was a lack of efficient ways to do this. As a result, employees' performance could not be maximized, hindering improvements in productivity throughout the organization.

[0742] 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.

[0743] In this invention, the server includes a means for acquiring employee abilities and preferences using interactive artificial intelligence, a means for proposing personnel allocation according to the employee abilities and preferences based on the acquired data, and a means for evaluating the effectiveness of the proposed personnel allocation, thereby enabling a detailed understanding of employee skills and preferences and optimizing personnel allocation within the factory.

[0744] "Conversational AI" is an AI system that has the ability to collect and provide information through dialogue with employees.

[0745] "Employee capabilities" refers to an employee's skill set, work experience, qualifications, etc.

[0746] "Hope" refers to the type of work and working style that employees would like to do.

[0747] "Means of acquisition" refers to the processes and techniques used to collect information from employees.

[0748] "Data" refers to the collection of collected information about employees, such as their skill sets and preferences.

[0749] "Proposal means" refers to the processes and techniques for calculating and presenting optimal personnel placement based on collected data.

[0750] "Means of evaluation" refers to the processes and techniques used to measure how effective the proposed staffing arrangements are in practice.

[0751] "Effects" refers to concrete results, such as increased productivity and employee satisfaction, that result from the proposed personnel allocation.

[0752] "Industrial facility" refers to manufacturing sites, including factories and production lines.

[0753] An "operating robot" refers to a machine that operates within a factory and automatically performs specific tasks or operations.

[0754] "Installation" refers to the process of incorporating software or applications into a particular device.

[0755] A "wearable device" refers to an electronic device that is worn on the body, and examples include smart glasses and head-mounted displays.

[0756] "Visually displaying" refers to presenting collected data on a screen or display in a format that is easy for humans to understand.

[0757] A "human resources chart" refers to a document or data set that organizes and visually displays information about an employee's abilities and aspirations.

[0758] The system of the present invention utilizes interactive artificial intelligence to collect information on employee abilities and preferences using robots and wearable devices operating within industrial facilities, and then proposes and evaluates optimal personnel placement based on this information. Each component of the system and its operation are described below.

[0759] System Configuration

[0760] The system consists of the following components:

[0761] Server: The main processing unit containing the database and generative AI models.

[0762] Wearable devices: Smart glasses or head-mounted displays worn by employees.

[0763] Robots operating in industrial facilities: devices equipped with conversational artificial intelligence that interact with employees.

[0764] Hardware and Software Used

[0765] Hardware:

[0766] Wearable devices (smart glasses, head-mounted displays)

[0767] Robots in factories (with interactive functions)

[0768] software:

[0769] Flask (Python web framework)

[0770] SQLite (lightweight database)

[0771] OpenAI API (Interactive Generative AI Model)

[0772] Data processing and calculation

[0773] Data collection and dialogue initiation

[0774] A user (employee) logs into the system via a wearable device and initiates a session with the conversational AI. The server receives this request, launches the generative AI model, and sets up the conversational environment.

[0775] Confirm your skills and expectations

[0776] The generative AI model asks employees questions to gather information about their skill sets, work experience, and desired work. For example, if a user answers, "I'm good at data analysis and project management," the device sends this response to a server, which then records this data in a database.

[0777] Data organization and personnel chart creation

[0778] The server analyzes the data obtained through the conversation and generates a talent chart based on the employee's abilities and aspirations. This chart is displayed visually and can be reviewed by both HR personnel and the employee themselves. For example, it displays the employee's skill map and work history.

[0779] Generating and evaluating placement proposals

[0780] The server compares the personnel records with the current requirements data within the industrial facility and generates optimal placement proposals. The proposals include specific departments, positions, and project assignments. For example, the server might suggest to an employee, "You would be ideal as the leader of a new data analysis project."

[0781] Examples and prompts

[0782] As a concrete example, here are some examples of prompts that employees use when interacting with conversational AI:

[0783] Prompt statement:

[0784] "Please suggest the best placement based on the employee's skills: they are good at data analysis and project management, and their desire: they want to participate in more advanced projects."

[0785] In this way, the system of the present invention can provide detailed insight into employee skills and preferences, enabling efficient staffing within industrial facilities, which will improve productivity and employee satisfaction throughout the organization.

[0786] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0787] Step 1:

[0788] A user puts on a wearable device and logs in to the system by entering basic information such as their name, employee ID, and password. The device sends this input data to the server, which then compares it with a database to authenticate the user. If authentication is successful, the user is allowed to log in and can use the system.

[0789] Input: User's basic information (name, employee ID, password)

[0790] Data processing: The server checks the input data against the database

[0791] Output: Login allowed or denied

[0792] Specific operation: The user enters the required information into the wearable device interface and presses the login button. The device sends the data to the server, which then references the database to perform authentication.

[0793] Step 2:

[0794] After logging in, the user clicks the "Start Dialogue" button on the device interface. The device sends a request to the server to launch the conversational AI, and the server calls the generative AI model to set up the conversational environment. The generative AI model is then ready to start a conversation with the user.

[0795] Input: Request to start a conversation

[0796] Data processing: The server launches the generative AI model and sets up the dialogue environment.

[0797] Output: Conversational AI is launched and ready for interaction

[0798] Specific operation: The user clicks the "Start dialogue" button on the device, and the device sends a request to the server. The server then launches the generative AI model and prepares the dialogue environment.

[0799] Step 3:

[0800] The generative AI model asks the user questions about their skill set, work experience, and desired job duties. The user then enters their answers through dialogue, and the device sends these answers to the server, which stores them in a database in real time.

[0801] Input: User response data (skill set, work experience, desired work)

[0802] Data processing: The server takes in the response data and stores it in a database

[0803] Output: Saved database records

[0804] How it works: The generating AI displays a question on the screen, and the user answers by voice or text. The device sends the data to the server, which stores it in a database.

[0805] Step 4:

[0806] The server analyzes the user information stored in the database and generates a personnel chart based on the employee's abilities and preferences. The generated personnel chart is organized in a visually displayable format.

[0807] Input: User information stored in the database

[0808] Data processing: Server performs analysis and profiling

[0809] Output: Visually displayable personnel chart

[0810] What it does: The server retrieves data from the database and uses specialized algorithms to profile employees' abilities and aspirations, creating a talent chart.

[0811] Step 5:

[0812] The server compares the personnel chart with the current requirements data within the industrial facility and generates optimal placement proposals, including specific departments, positions, and project assignments. The proposals are then sent to the terminal and presented to the user.

[0813] Input: Personnel chart and current requirements data

[0814] Data processing: The server performs matching and calculates the optimal placement

[0815] Output: Specific placement proposal

[0816] Specific operation: The server integrates the personnel chart and the factory needs to generate optimal placement proposals and send the proposals to the user's terminal.

[0817] Step 6:

[0818] The user reviews the proposed placement and provides approval or feedback. The terminal sends the user's feedback to the server, which readjusts the placement proposal as needed based on the feedback.

[0819] Input: User feedback

[0820] Data processing: The server analyzes the feedback and refines the suggestions.

[0821] Output: Reworked placement proposal or final decision

[0822] Specific operation: The user checks the placement proposal on the device and enters feedback. The device sends the feedback to the server, which then analyzes it again and updates the optimal placement proposal.

[0823] Through this process, the system will gain a detailed understanding of employees' skills and preferences, enabling efficient staffing within industrial facilities, which is expected to improve productivity and employee satisfaction across the organization.

[0824] 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.

[0825] The system of the present invention uses interactive artificial intelligence to acquire employee capabilities and preferences, and then proposes and evaluates personnel allocation based on these. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the dialogue is improved, resulting in more accurate personnel allocation. In the system of the present invention, the roles of the server, terminal, and user are clearly defined, and each performs the specific processing shown below.

[0826] Main processing flow

[0827] 1. User Registration and Login

[0828] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[0829] The terminal transmits the user's input information to the server.

[0830] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[0831] 2. Starting an interactive session

[0832] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0833] The terminal sends this request to the server.

[0834] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0835] 3. Confirm your skills and aspirations

[0836] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0837] The user answers on the device, "I'm good at data analysis and project management."

[0838] The terminal sends the user's answer to the server.

[0839] The server stores this response in a database.

[0840] 4. Sentiment analysis using an emotion engine

[0841] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[0842] For example, if a user responds "I'm a little anxious," the emotion engine will recognize the feeling of anxiety from the content of the words and the tone of voice.

[0843] 5. Tailoring questions based on user sentiment

[0844] Based on feedback from the emotion engine, the generative AI generates questions that correspond to the user's emotions. For example, if the user is feeling anxious, the generative AI can ask questions to relax them or respond in a considerate manner.

[0845] By having users answer emotionally sensitive questions, more detailed and accurate data is collected.

[0846] 6. Data organization and human resource chart generation

[0847] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[0848] For example, the server integrates the user's skill map, desired job, and emotional data to create a visualized talent chart.

[0849] 7. Generating placement proposals

[0850] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[0851] As a specific example, the server generates a suggestion such as "suitable as a leader for a data analysis project" and displays it to the user.

[0852] 8. Review and feedback on proposals

[0853] The user checks the placement proposal through the terminal and approves or adds comments.

[0854] The terminal sends the user's feedback to the server.

[0855] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[0856] 9. Final decision and follow-up

[0857] The server finalizes the placement based on the user's final approval and updates the database.

[0858] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[0859] In this way, the system of the present invention, by combining interactive AI with an emotion engine, can accurately grasp the capabilities and desires of employees in detail and propose optimal personnel allocation. Furthermore, by incorporating an emotion engine, the system can grasp the user's emotional state in real time and flexibly adjust the content of the dialogue, enabling highly accurate information collection. This is expected to improve organizational productivity and efficiency.

[0860] The processing flow will be explained below.

[0861] Step 1: Log in and register

[0862] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[0863] The terminal transmits the user's input information to the server.

[0864] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[0865] Step 2: Starting an interactive session

[0866] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0867] The terminal sends this request to the server.

[0868] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0869] Step 3: Identify your skills and aspirations

[0870] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0871] The user answers on the device, "I'm good at data analysis and project management."

[0872] The terminal sends the user's answer to the server.

[0873] The server stores this response in a database.

[0874] Step 4: Collect and organize detailed data

[0875] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[0876] The user answers these questions and provides information through the terminal.

[0877] The terminal sends each response data to the server, which records them in a database.

[0878] Step 5: Sentiment analysis with the sentiment engine

[0879] The emotion engine in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state, making it possible to grasp the emotions the user is feeling during the conversation in real time.

[0880] The emotion engine (in the server) sends the analysis results to the generation AI, which then adjusts the response to enable appropriate dialogue based on the user's emotions.

[0881] Step 6: Adjust your questions based on emotion

[0882] The generative AI dynamically adjusts the dialogue content based on the user's emotional data. For example, if the user is feeling anxious, it will ask relaxing questions, and if the user is showing interest, it will ask more in-depth questions.

[0883] The user answers tailored questions and provides further detailed data.

[0884] The terminal sends this new response data to the server, which updates the database.

[0885] Step 7: Organize data and generate personnel charts

[0886] The server sends all collected data to an analytics engine, which compiles employee skills, aspirations, and emotional state.

[0887] The analysis engine (within the server) creates a personnel chart that integrates skill level, desired job, years of experience, and emotional state.

[0888] The server prepares the generated personnel chart in a visually displayable format and stores it in a database.

[0889] Step 8: Generate placement proposals

[0890] The server compares the generated personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[0891] The terminal displays the placement proposal received from the server to the user.

[0892] Step 9: Review and feedback on proposals

[0893] The user checks the placement proposal through the terminal and approves or adds comments.

[0894] The terminal sends the user's feedback to the server.

[0895] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[0896] Step 10: Finalize and follow up

[0897] The server finalizes the placement based on the user's final approval and updates the database.

[0898] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[0899] This allows the system to understand the user's skill set and preferences in detail through dialogue, and to use the emotion engine to improve the quality of dialogue and achieve optimal staffing. Each step works in tandem to achieve high accuracy and efficiency.

[0900] Example 2

[0901] 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."

[0902] Conventional personnel placement systems are unable to fully grasp employees' abilities and preferences, making it difficult to perform optimal personnel placement. Furthermore, because they do not take the user's emotional state into account, the quality of information collection declines, resulting in inappropriate placement proposals. Furthermore, the lack of a means to organize and visually display employees' skills and preferences makes centralized information management difficult. Therefore, a new system is needed to achieve highly accurate and flexible personnel placement.

[0903] 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.

[0904] In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing personnel allocations based on the acquired data in accordance with the employee abilities and preferences; means for evaluating the effectiveness of the proposed personnel allocations; means for continuously improving the accuracy of the personnel allocations; emotion recognition means for analyzing the user's emotional state; means for adjusting the content of questions based on the analysis results of the emotion recognition means; and means for organizing the acquired data and generating a personnel chart that visually displays it. This makes it possible to accurately grasp employee abilities and preferences and improve the quality of information collection through flexible dialogue that takes the user's emotional state into consideration. Furthermore, optimal personnel allocation can be achieved based on the visually organized data, which is expected to improve organizational productivity and efficiency.

[0905] 1. "Conversational AI" refers to an AI system that has the ability to gather, understand, and respond to information through dialogue with a user.

[0906] 2. "Employee Competence" means the knowledge, skills, techniques, and experience an employee possesses to perform a job.

[0907] 3. "Employee preferences" refer to employees' current and future aspirations for their jobs, roles, work environment, career paths, etc.

[0908] 4. "Staffing" refers to the allocation of employees to appropriate positions or roles within an organization.

[0909] 5. "Recommendation" means a recommendation for determining optimal staffing based on the information collected.

[0910] 6. "Evaluation tool" means a tool or method for measuring and evaluating the effectiveness or success of a proposed staffing arrangement.

[0911] 7. "Emotion recognition means" refers to technology or methods for recognizing and analyzing a user's emotional state from the content of their dialogue, facial expressions, tone of voice, etc.

[0912] 8. "Database" means a collection system for systematically storing and managing collected data.

[0913] 9. A "human resources chart" is a document or digital content that centrally organizes and visually displays information such as an employee's abilities, aspirations, and emotional state.

[0914] 10. "Visual display" refers to the presentation of information to a user in a visual format, such as a graph or dashboard.

[0915] The system of the present invention utilizes interactive artificial intelligence and emotion recognition means to acquire employee capabilities and desires, and then proposes optimal personnel placement based on these. Specific implementation methods of this system are described in detail below.

[0916] Hardware and software used

[0917] This system uses the following main hardware and software:

[0918] 1. Server: Responsible for main data processing and database management.

[0919] 2. Terminal: Acts as a user interface, receives input from the user, and communicates with the server.

[0920] 3. Generative AI model (e.g., GPT-4): An artificial intelligence model that enables interaction with users.

[0921] 4. Emotion recognition engine: Software that analyzes the content of the user's dialogue, facial expressions, tone of voice, etc. to recognize the user's emotional state.

[0922] Processing flow and specific operations

[0923] The specific operations and techniques used in each processing step will be described.

[0924] User Registration and Login

[0925] The user uses a terminal to access the system's login screen, where they enter their name, employee ID, and password.

[0926] Example: Enter the name "Taro Tanaka", employee ID "12345", and password "password123".

[0927] The terminal transmits the user's input information to the server.

[0928] The server checks the received login information against the database and performs authentication processing.

[0929] Starting an interactive session

[0930] The user clicks the "Start Interaction" button to begin the interaction session.

[0931] Example: Clicking the "Start conversation" button.

[0932] The terminal sends this request to the server.

[0933] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0934] Verifying skills and preferences

[0935] The generating AI (in the server) asks questions to the user.

[0936] Example prompt: "What is your best skill?"

[0937] The user responds using the terminal.

[0938] Example: "I'm good at data analysis and project management."

[0939] The terminal transmits the user's answer to the server.

[0940] The server stores this response in a database.

[0941] Emotion recognition and question adjustment

[0942] The emotion recognition engine (in the server) analyzes the content of the user's dialogue, facial expressions, and tone of voice to recognize their emotional state.

[0943] Example: If a user responds, "I'm a little anxious," the emotion engine recognizes that emotion.

[0944] The generative AI adjusts the questions based on feedback from the emotion recognition engine.

[0945] Example: Asking questions to put anxious users at ease.

[0946] Data organization and personnel chart generation

[0947] The server organizes the collected data and generates a personnel chart that integrates the employee's abilities, aspirations, and emotional state.

[0948] Example: Creating a chart that visually displays a user's skill map, desired job, and emotional data.

[0949] Generate and review placement proposals

[0950] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[0951] Example: Generate suggestions such as "You're a good choice to lead a data analysis project."

[0952] The user reviews the placement proposal and approves or adds comments.

[0953] The server reevaluates the proposal and makes adjustments if necessary.

[0954] Final decision and follow-up

[0955] The server reflects the final decision in the database and monitors the employee's performance after placement.

[0956] Example: Collect performance data periodically and reassess as needed.

[0957] The system utilizes conversational artificial intelligence and an emotion recognition engine to enable users to receive accurate and user-friendly staffing recommendations, while centralized management of employee data can improve organizational productivity and efficiency.

[0958] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0959] Step 1:

[0960] User Registration and Login

[0961] A user accesses the system login screen using a terminal and enters their name, employee ID, and password.

[0962] Input: Name "Taro Tanaka", Employee ID "12345", Password "password123"

[0963] Action: Enter information on the keyboard

[0964] The terminal transmits the user's input information to the server.

[0965] Input: Information entered by the user

[0966] What happens: The "Submit" button is clicked and the information is sent to the server as a POST request

[0967] Output: User information received by the server

[0968] The server checks the received login information against the database and performs authentication processing.

[0969] Input: Received user information

[0970] How it works: The server looks up the user information in its database and checks for a matching ID and password.

[0971] Output: Authentication result (success or failure)

[0972] Step 2:

[0973] Starting an interactive session

[0974] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[0975] Input: Click the "Start conversation" button

[0976] Action: Click the button with the mouse

[0977] The terminal sends this request to the server.

[0978] Input: "Start conversation" request

[0979] Action: A request is sent to the server

[0980] Output: The server receives the request

[0981] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[0982] Input: "Start conversation" request

[0983] Operation: Call the API of the generative AI model and perform the necessary initial setup.

[0984] Output: Interactive environment is ready

[0985] Step 3:

[0986] Checking skills and aspirations

[0987] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[0988] Input: User interaction context

[0989] How it works: Send a question prompt to a generative AI model

[0990] Output: Question text

[0991] The user answers on the device, "I'm good at data analysis and project management."

[0992] Input: Question text

[0993] Action: Enter your answer on the keyboard and click the submit button

[0994] Output: Answer text

[0995] The terminal sends the user's answer to the server.

[0996] Input: User's answer

[0997] What it does: Sends the answer text to the server as a POST request

[0998] Output: The response text received by the server

[0999] The server stores this response in a database.

[1000] Input: User answer text

[1001] What it does: Saves the answer to the user's profile information in a database

[1002] Output: Answer data stored in a database

[1003] Step 4:

[1004] Sentiment analysis with emotion engine

[1005] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[1006] Input: User interaction, facial expressions, and tone of voice

[1007] How it works: Applying text analysis, facial expression recognition, and speech analysis algorithms

[1008] Output: Recognized emotion data

[1009] Example: When a user says, "I'm a little anxious," the emotion engine recognizes anxiety.

[1010] Step 5:

[1011] Tailoring questions based on user sentiment

[1012] The generative AI generates questions based on the user's emotions based on feedback from the emotion engine.

[1013] Input: Emotion engine feedback

[1014] How it works: The AI ​​model generates the next question

[1015] Output: Adjusted question text

[1016] Example: If a user is feeling anxious, the generative AI will ask them questions to help them relax.

[1017] Users answer emotionally sensitive questions, which allows for more detailed and accurate data to be collected.

[1018] Input: Tailored Question

[1019] Action: Enter answer on keyboard and click submit button

[1020] Output: User's answer text

[1021] Step 6:

[1022] Data organization and personnel chart generation

[1023] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[1024] Input: Data on the user's abilities, desires, and emotional state

[1025] How it works: Algorithms analyze and synthesize data to generate a visualized medical record

[1026] Output: Human Resources Chart

[1027] Example: Integrating skills maps, job preferences, and sentiment data to create a visual dashboard

[1028] Step 7:

[1029] Generate placement proposals

[1030] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[1031] Input: Personnel chart, current status request data

[1032] How it works: Algorithms collate data and calculate optimal placement

[1033] Output: Placement proposal

[1034] Example: Generate a suggestion that "You are a good candidate to lead a data analysis project."

[1035] Step 8:

[1036] Review and feedback on suggestions

[1037] The user checks the placement proposal through the terminal and approves or adds comments.

[1038] Input: Proposal

[1039] Action: Review the suggestion and click the "Approve" or "Add Suggestion" button

[1040] Output: User feedback

[1041] The terminal sends the user's feedback to the server.

[1042] Input: User feedback

[1043] Behavior: Sends feedback to the server as a POST request

[1044] Output: Feedback data received by the server

[1045] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[1046] Input: User feedback

[1047] How it works: Analyzes feedback and updates suggestions based on a rating algorithm

[1048] Output: Updated placement proposal

[1049] Step 9:

[1050] Final decision and follow-up

[1051] The server finalizes the placement based on the user's final approval and updates the database.

[1052] Input: Final approved placement proposal

[1053] Action: Writes the location information to the database.

[1054] Output: Updated database

[1055] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1056] Input: Performance data

[1057] How it works: Periodically collects performance data and re-evaluates it based on the evaluation algorithm.

[1058] Output: Improvement suggestions and reallocation decisions

[1059] (Application example 2)

[1060] 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."

[1061] In the past, employee allocation in logistics centers was primarily based on experience and intuition, which meant that efficient allocation that fully reflected employee abilities and preferences could not be achieved. Furthermore, employee emotions and psychological states were not taken into consideration, which had a negative impact on work efficiency and employee satisfaction. The present invention aims to solve these problems by providing a system that maximizes employee abilities and preferences while also taking into account their emotional states, resulting in efficient and satisfying personnel allocation.

[1062] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing staffing arrangements based on employee abilities and preferences based on the acquired data; means for evaluating the effectiveness of the proposed staffing arrangements; means for continuously improving the accuracy of staffing arrangements based on the evaluation results; means for analyzing user emotions using an emotion engine and reflecting the results in the dialogue content; and means for generating optimal staffing proposals based on the business characteristics of the application destination. This enables detailed collection of employee abilities and preferences and enables highly accurate staffing arrangements that take emotional states into account. This is also expected to improve work efficiency and employee satisfaction at logistics centers.

[1063] "Conversational AI" is an AI system that naturally interacts with users and collects necessary information.

[1064] "Employee Competence" refers to the techniques, knowledge, skills and experience possessed by an employee.

[1065] "Wishes" refer to the job duties and work content that employees desire, as well as their desired working conditions.

[1066] "Staffing" refers to assigning appropriate tasks and roles to employees based on their abilities and preferences.

[1067] An "emotion engine" is a system for analyzing a user's emotional state.

[1068] "Means for evaluating the effectiveness of the proposed personnel allocation" is a system for evaluating how effective the proposed personnel allocation was.

[1069] "Means to continuously improve the accuracy of personnel placement" refers to a system for providing feedback on evaluation results and improving the accuracy of placement from the next time onwards.

[1070] "Means for analyzing the user's emotions and reflecting the results in the content of the dialogue" refers to a mechanism in which the emotion engine recognizes the user's emotions and adjusts the content of the dialogue based on that information.

[1071] A "visual human resources chart" is a document that organizes employee abilities, aspirations, and emotional data and displays them in a graphical format.

[1072] A "logistics center" refers to a facility that stores goods, ships them, manages inventory, and handles incoming and outgoing shipments.

[1073] This invention is a system that acquires the abilities and desires of employees at a logistics center and proposes and evaluates optimal personnel allocation based on them. This system uses a combination of conversational artificial intelligence and an emotion engine to collect detailed information about employees' abilities and desires, and recognizes and reflects their emotional states to achieve highly accurate personnel allocation.

[1074] Hardware and software used

[1075] Hardware: Smartphone, Head-Mounted Display (HMD)

[1076] Software: Python, EmotionRecognition (e.g. DeepFace), OpenAI GPT

[1077] System Operation Overview

[1078] 1. User Registration and Login

[1079] Users access the system's login screen using a smartphone or HMD and enter basic information such as their name, employee ID, and password. The device sends the information to the server, which then compares it with a database and performs authentication.

[1080] 2. Starting an interactive session

[1081] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The device sends this request to the server, which then invokes a generative AI model (e.g., GPT-4) to set up the conversational environment.

[1082] 3. Confirm your skills and aspirations

[1083] The generative AI on the server asks the user questions such as, "What are your best skills?" The user answers using a device, "I'm good at data analysis and project management," and the device sends the answer to the server and stores it in a database.

[1084] 4. Sentiment analysis using an emotion engine

[1085] The emotion engine (e.g., DeepFace) in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content of the words and tone of voice.

[1086] 5. Tailoring questions based on user sentiment

[1087] The server's generation AI generates questions based on the user's emotions based on feedback from the emotion engine. For example, if the user is feeling anxious, it can ask questions to relax them or respond in a considerate manner.

[1088] 6. Data organization and human resource chart generation

[1089] The server organizes the collected data and generates a human resource chart that centrally manages employees' abilities, aspirations, and emotional states, creating a visualized human resource chart.

[1090] 7. Generating placement proposals

[1091] The server compares the talent chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1092] 8. Review and feedback on proposals

[1093] The user can review the placement proposal through the device and approve or add their comments. The device then sends the user's feedback to the server, which then reevaluates the proposal based on the received feedback and adjusts it as necessary.

[1094] An example of a prompt sentence to be input to a specific generative AI model:

[1095] "What is your best skill?"

[1096] "What kind of work would you like done?"

[1097] This will enable efficient and highly satisfying staffing at logistics centers.

[1098] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1099] Step 1:

[1100] A user logs in to the system using a terminal. The input is the user's basic information (name, employee ID, password), and the output is the authentication result (success or failure). The terminal sends this information to the server, which then checks it against a database and performs authentication processing. If authentication is successful, a user session is started.

[1101] Step 2:

[1102] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The input is the user's request to start the session, and the output is a notification that the conversational environment has been set up. The device sends the request to the server, which then calls the generative AI model to set up the conversational environment.

[1103] Step 3:

[1104] The server uses a generative AI model to ask the user questions and obtain the user's abilities and aspirations. The input is the question generated by the generative AI model (e.g., "What are your best skills?") and the user's answer, and the output is the obtained data (skill information, desired job). The user answers using a device, and the device sends the answer to the server. The server stores this data in a database.

[1105] Step 4:

[1106] The emotion engine in the server analyzes the content of the user's dialogue and recognizes their emotional state. The input is the content of the user's dialogue and tone of voice, and the output is the analysis result of their emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content and tone of voice.

[1107] Step 5:

[1108] Based on feedback from the emotion engine, the server uses a generative AI model to generate questions that correspond to the user's emotions. The input is the analysis result of the emotion engine, and the output is a tailored question (e.g., "What do you think about working in a relaxed environment?"). This allows the user to provide more detailed and accurate information.

[1109] Step 6:

[1110] The server organizes the collected data and generates a visually displayed talent chart. The input is employee ability, preference, and emotion data, and the output is an integrated talent chart. The server consolidates this data and displays it in a visual format (e.g., skill map, preference list, emotional state).

[1111] Step 7:

[1112] The server compares the personnel chart with the business characteristics of the logistics center and generates optimal placement proposals. The input is the personnel chart and current requirements data, and the output is a specific placement proposal (e.g., "suitable as leader of the data analysis project"). The server automatically generates optimal placements based on this data.

[1113] Step 8:

[1114] The user reviews the proposed placement and provides feedback through the device. The input is the user feedback (approval or additional comments), and the output is the reevaluated placement proposal. The device sends the feedback to the server, which reevaluates the placement proposal based on the feedback and adjusts it if necessary.

[1115] The above are the specific processing steps of a system that realizes optimal personnel allocation that reflects the abilities and wishes of employees at a logistics center.

[1116] 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.

[1117] 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.

[1118] 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.

[1119] [Third embodiment]

[1120] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1121] 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.

[1122] 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).

[1123] 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.

[1124] 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.

[1125] 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).

[1126] 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.

[1127] 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.

[1128] 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.

[1129] 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.

[1130] 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.

[1131] 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."

[1132] The system of the present invention uses interactive artificial intelligence to collect information about employee capabilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. In the system of the present invention, the roles of the server, terminals, and users are clearly defined, and each role performs the specific processing shown below.

[1133] Main processing flow

[1134] 1. User Registration and Login

[1135] The server stores the user's basic information in a database and authenticates the user when they log in via a terminal. When logging in for the first time, the user enters their basic information (name, employee ID, password, etc.) into the terminal.

[1136] As an example of system operation, when a user enters their name, employee number, and password and clicks the login button, the terminal sends this information to the server, which then verifies the information and allows the user to log in.

[1137] 2. Starting an interactive session

[1138] The user initiates a session with the conversational AI via their device, which causes the server to call up the generative AI model and prepare the conversational environment.

[1139] For example, to start a conversation session, you click the "Start conversation" button. The device sends this request to the server, which then launches the generated AI.

[1140] 3. Confirm your skills and aspirations

[1141] The generation AI (in the server) asks the user questions to collect information about the user's skill set, work experience, desired work, etc. This dialogue provides detailed data.

[1142] For example, if the user answers "I'm good at data analysis and project management," the device sends this answer to the server, which records it in a database.

[1143] 4. Data organization and analysis

[1144] The server analyzes the data obtained from the conversation and generates a talent profile based on the employee's abilities and aspirations. The data is organized and profiled in real time.

[1145] For example, the server analyzes an employee's past performance and skill level and draws a conclusion such as "he has extensive experience as a data scientist."

[1146] 5. Creation of personnel records

[1147] The server then uses the organized data to create a visually displayed personnel chart, detailing the employee's skill level, desired job role, past performance, and more.

[1148] For example, the server organizes employees' skill maps and work histories and provides them to the human resources department in a visual format.

[1149] 6. Generating placement proposals

[1150] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1151] As a specific example, the server generates suggestions such as "Qualified to lead new data analysis project" and displays them to employees.

[1152] 7. Proposal presentation and follow-up

[1153] The device presents the user with placement suggestions from the server, which the user can review, approve, or add comments to. The server then receives feedback and adjusts the suggestions as needed.

[1154] For example, after a user approves a placement proposal, the server makes the final decision and updates the database. After placement, a performance evaluation system continuously monitors performance and reevaluates the placement if necessary.

[1155] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[1156] The processing flow will be explained below.

[1157] Step 1: Log in and register

[1158] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[1159] The terminal transmits the user's input information to the server.

[1160] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[1161] Step 2: Starting an interactive session

[1162] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[1163] The terminal sends this request to the server.

[1164] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1165] Step 3: Identify your skills and aspirations

[1166] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[1167] The user answers on the device, "I'm good at data analysis and project management."

[1168] The terminal sends the user's answer to the server.

[1169] The server stores this response in a database.

[1170] Step 4: Collect and organize detailed data

[1171] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[1172] The user answers these questions and provides information through the terminal.

[1173] The terminal sends each response data to the server, which records them in a database.

[1174] Step 5: Analyze the data and generate a personnel chart

[1175] The server sends the collected data to an analysis engine to organize employees' abilities and aspirations.

[1176] The analysis engine (on the server) analyzes the data and extracts and organizes information such as skill level, desired job, and years of experience.

[1177] The server generates a visually displayable personnel chart based on the organized data.

[1178] Step 6: Generate placement proposals

[1179] The server compares the generated personnel chart with the current status requirements data of the organization.

[1180] The server generates optimal placement proposals based on the employee's skill set, work experience, and preferences.

[1181] The terminal displays the placement proposal to the user.

[1182] Step 7: Review and feedback on the proposal

[1183] The user checks the placement proposal through the terminal and approves or adds comments.

[1184] The terminal sends the user's feedback to the server.

[1185] The server reevaluates based on the feedback it receives and readjusts its placement proposals as needed.

[1186] Step 8: Finalize and follow up

[1187] The server finalizes the placement based on the user's final approval and updates the database.

[1188] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1189] In this way, the system of the present invention can grasp the user's skill set and preferences in detail through dialogue with them, and propose optimal staffing. Each step works in conjunction with the other to achieve high accuracy and efficiency.

[1190] Example 1

[1191] 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."

[1192] To accurately understand employee capabilities and aspirations and improve productivity and efficiency across the organization, it is necessary to effectively collect and analyze employee information and propose optimal personnel placements. However, manual data collection and analysis requires time and effort and increases the risk of human error. Furthermore, to continuously propose appropriate placements, it is necessary to keep employee profiles up to date based on the collected information and to evaluate performance after placement. These challenges need to be resolved.

[1193] 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.

[1194] In this invention, the server includes means for storing basic information of the user in a database and performing authentication, means for starting an interactive session using a generative AI model, and means for collecting the user's skill set, work experience, and desired work content using the generative AI model, thereby enabling accurate and rapid understanding of employee capabilities and preferences and achieving optimal personnel allocation.

[1195] A "server" is a computer system that processes information, stores and manages data, and provides services to other devices and software.

[1196] A "terminal" is a computer or device that a user operates and that sends input information to a server.

[1197] A "user" is a person who uses this system, and is primarily a person who plays the role of an employee.

[1198] A "generative AI model" is an artificial intelligence model that analyzes user input using natural language processing and an interactive interface and generates appropriate responses.

[1199] A "database" is a collection of data used to systematically store and manage user information, dialogue content, profile information, and other data.

[1200] "Authentication" is the process of checking passwords and IDs to verify that a user is a legitimate user.

[1201] A "dialogue session" is a series of conversations that take place between a user and a system using a generative AI model.

[1202] A "skill set" is a collection of skills and abilities that a user possesses.

[1203] "Work experience" refers to the experience a user has had in the past in relation to jobs or roles.

[1204] A "profile" is information that compiles a user's skills, work experience, desired work content, and so on.

[1205] A "talent chart" is a document or digital report that visually displays a user's skills, work experience, and desired job duties.

[1206] "Placement proposal" is information that is generated based on the user profile and the requirements of the organization to propose the most suitable job title and placement.

[1207] "Feedback" refers to opinions and comments provided by users regarding placement proposals.

[1208] "Performance appraisal" is the process of evaluating an employee's job performance after being assigned.

[1209] The system of the present invention uses interactive artificial intelligence to collect information about employees' abilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. The system of the present invention includes a server, a terminal, and a user, each of which plays a role and performs the specific processing described below.

[1210] First, the server stores the user's basic information in a database and is equipped with a means of authentication. When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal, and the terminal sends this information to the server. The server stores the received information in a database and generates authentication information.

[1211] When the user logs in again, they enter their employee ID and password into the terminal, which then sends the login information to the server. The server compares the information with the database and, if authentication is successful, starts a session. This allows the server to manage the user's basic information and ensure security through the authentication process.

[1212] Next, the user has a means to start a dialogue session via the terminal. For example, when the user clicks a "Start dialogue" button, the terminal sends a "Start dialogue" request to the server, and the server calls the generative AI model. This generative AI model uses natural language processing to engage in a dialogue with the user.

[1213] In a dialogue session, the generative AI model has a means of collecting information from the user, such as their skill set, work experience, and desired work content. Specifically, the generative AI displays the question, "What are your best skills?" The user inputs the answer and sends it via their device to the server, which then records the received information in a database.

[1214] The server has the means to generate employee profiles based on the collected information and store them in a database. The information in the database is aggregated and analyzed to profile employees based on their skill levels and desired jobs. The profiling results can be expressed as, for example, "Employee 1234 demonstrates high performance as a data scientist."

[1215] The server then creates a personnel chart based on the analysis results. This chart visually displays the user's skill set, work experience, and aspirations, and is provided to the HR department in a visual format. The server also outputs this chart data in JSON format, which can be displayed in a web interface.

[1216] Furthermore, the server is equipped with a means for comparing the personnel chart with the current requirements data and generating optimal placement proposals. For example, it uses an algorithm to calculate a "skill matching score" and propose optimal positions and projects. Specifically, it generates a proposal in the form of "Employee 1234 is suitable as the leader of a new data analysis project."

[1217] Finally, the device presents the placement proposal received from the server to the user and has a means for receiving feedback. The user reviews the proposal and either approves or adds their opinion, and the device then sends it to the server. The server receives and analyzes the feedback and runs the placement algorithm again. Even after placement, the performance evaluation system continuously monitors employee performance and reevaluates placement as necessary.

[1218] For example, you might use the following prompt:

[1219] "What is your best skill?"

[1220] "Please tell us about your previous work experience."

[1221] What position and duties do you desire?

[1222] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[1223] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1224] Step 1:

[1225] User Registration and Login

[1226] When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal.

[1227] The terminal sends this information to the server.

[1228] The server stores the received information in a database and generates authentication information.

[1229] When logging in again, the user enters their employee ID and password into the terminal, and the terminal sends the login information to the server.

[1230] The server checks the information against the database and starts the session if authentication is successful.

[1231] Specifically, the server compares the entered password with the hash value in the database, and if they match, it issues a session ID.

[1232] Input: User's basic information (name, employee ID, password)

[1233] Output: Authentication information, session ID

[1234] Step 2:

[1235] Starting an interactive session

[1236] The user clicks the "Start conversation" button on the terminal interface.

[1237] The terminal sends a "start conversation" request to the server.

[1238] The server invokes the generative AI model and prepares the dialogue session.

[1239] A generative AI model (e.g., OpenAI GPT-3) is launched and a session ID is generated.

[1240] As a specific operation, the user presses the "Start dialogue" button on the dialogue box.

[1241] Input: Dialogue-initiating request

[1242] Output: Session ID, launch of the generative AI model

[1243] Step 3:

[1244] Checking skills and aspirations

[1245] The generative AI (in the server) asks the user questions about their skill set, work experience, and desired job duties.

[1246] The user inputs a response to this and transmits it to the server via the terminal.

[1247] As a specific example, the generative AI asks, "What are your best skills?" and the user answers, "Data analysis and project management."

[1248] The terminal sends the user's answer to the server, which records it in a database.

[1249] Input: User's answers (skill set, work experience, desired content)

[1250] Output: Information recorded in the database

[1251] Step 4:

[1252] Data organization and analysis

[1253] The server creates a profile of the employee based on the data collected during the interaction.

[1254] The information in the database is aggregated to profile employees' skill levels and desired jobs.

[1255] For example, the server may derive a conclusion such as "Employee 1234 is demonstrating high performance as a data scientist."

[1256] Input: Collected data in the database

[1257] Output: Employee profile, skill level assessment

[1258] Step 5:

[1259] Creation of personnel records

[1260] The server creates a personnel chart based on the analysis results.

[1261] The chart visually displays the user's skill set, work experience, and preferences.

[1262] Specifically, the server outputs the medical record data in JSON format and displays it on a web interface.

[1263] Input: Employee profile, analysis results

[1264] Output: Visually displayable personnel chart

[1265] Step 6:

[1266] Generate placement proposals

[1267] The server compares the personnel chart with the current requirements data and automatically generates optimal placement proposals.

[1268] As a concrete example, an algorithm is used to calculate a "skill matching score" and suggest the most suitable job title and project.

[1269] Specifically, it states, "Employee 1234 is a good candidate to lead a new data analysis project."

[1270] Input: Human resource chart, current requirements data

[1271] Output: Placement proposal

[1272] Step 7:

[1273] Presenting proposals and gathering feedback

[1274] The terminal presents the placement proposal received from the server to the user.

[1275] Users can review the suggestions and approve or add their comments.

[1276] When the user provides feedback, the terminal sends it to the server.

[1277] A specific action is for the user to click the "Approve" button or enter feedback in the comment field.

[1278] The server receives feedback, readjusts its suggestions as needed, and updates its database.

[1279] Input: User feedback

[1280] Output: revised placement proposal, updated database

[1281] Step 8:

[1282] Ongoing performance evaluation

[1283] After deployment, the server evaluates and monitors the employee's performance through the performance monitoring system.

[1284] If necessary, the server reevaluates the placement and makes optimizations.

[1285] Specifically, the server collects performance data and provides feedback based on the analysis results.

[1286] Input: Performance data

[1287] Output: Ongoing performance evaluation, recalibrated placement proposals

[1288] (Application example 1)

[1289] 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."

[1290] With conventional personnel allocation systems, it was difficult to accurately grasp employees' abilities and preferences and to allocate them optimally based on them. Furthermore, in a diverse environment like a factory, real-time data collection and response is required, but there was a lack of efficient ways to do this. As a result, employees' performance could not be maximized, hindering improvements in productivity throughout the organization.

[1291] 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.

[1292] In this invention, the server includes a means for acquiring employee abilities and preferences using interactive artificial intelligence, a means for proposing personnel allocation according to the employee abilities and preferences based on the acquired data, and a means for evaluating the effectiveness of the proposed personnel allocation, thereby enabling a detailed understanding of employee skills and preferences and optimizing personnel allocation within the factory.

[1293] "Conversational AI" is an AI system that has the ability to collect and provide information through dialogue with employees.

[1294] "Employee capabilities" refers to an employee's skill set, work experience, qualifications, etc.

[1295] "Hope" refers to the type of work and working style that employees would like to do.

[1296] "Means of acquisition" refers to the processes and techniques used to collect information from employees.

[1297] "Data" refers to the collection of collected information about employees, such as their skill sets and preferences.

[1298] "Proposal means" refers to the processes and techniques for calculating and presenting optimal personnel placement based on collected data.

[1299] "Means of evaluation" refers to the processes and techniques used to measure how effective the proposed staffing arrangements are in practice.

[1300] "Effects" refers to concrete results, such as increased productivity and employee satisfaction, that result from the proposed personnel allocation.

[1301] "Industrial facility" refers to manufacturing sites, including factories and production lines.

[1302] An "operating robot" refers to a machine that operates within a factory and automatically performs specific tasks or operations.

[1303] "Installation" refers to the process of incorporating software or applications into a particular device.

[1304] A "wearable device" refers to an electronic device that is worn on the body, and examples include smart glasses and head-mounted displays.

[1305] "Visually displaying" refers to presenting collected data on a screen or display in a format that is easy for humans to understand.

[1306] A "human resources chart" refers to a document or data set that organizes and visually displays information about an employee's abilities and aspirations.

[1307] The system of the present invention utilizes interactive artificial intelligence to collect information on employee abilities and preferences using robots and wearable devices operating within industrial facilities, and then proposes and evaluates optimal personnel placement based on this information. Each component of the system and its operation are described below.

[1308] System Configuration

[1309] The system consists of the following components:

[1310] Server: The main processing unit containing the database and generative AI models.

[1311] Wearable devices: Smart glasses or head-mounted displays worn by employees.

[1312] Robots operating in industrial facilities: devices equipped with conversational artificial intelligence that interact with employees.

[1313] Hardware and Software Used

[1314] Hardware:

[1315] Wearable devices (smart glasses, head-mounted displays)

[1316] Robots in factories (with interactive functions)

[1317] software:

[1318] Flask (Python web framework)

[1319] SQLite (lightweight database)

[1320] OpenAI API (Interactive Generative AI Model)

[1321] Data processing and calculation

[1322] Data collection and dialogue initiation

[1323] A user (employee) logs into the system via a wearable device and initiates a session with the conversational AI. The server receives this request, launches the generative AI model, and sets up the conversational environment.

[1324] Confirm your skills and expectations

[1325] The generative AI model asks employees questions to gather information about their skill sets, work experience, and desired work. For example, if a user answers, "I'm good at data analysis and project management," the device sends this response to a server, which then records this data in a database.

[1326] Data organization and personnel chart creation

[1327] The server analyzes the data obtained through the conversation and generates a talent chart based on the employee's abilities and aspirations. This chart is displayed visually and can be reviewed by both HR personnel and the employee themselves. For example, it displays the employee's skill map and work history.

[1328] Generating and evaluating placement proposals

[1329] The server compares the personnel records with the current requirements data within the industrial facility and generates optimal placement proposals. The proposals include specific departments, positions, and project assignments. For example, the server might suggest to an employee, "You would be ideal as the leader of a new data analysis project."

[1330] Examples and prompts

[1331] As a concrete example, here are some examples of prompts that employees use when interacting with conversational AI:

[1332] Prompt statement:

[1333] "Please suggest the best placement based on the employee's skills: they are good at data analysis and project management, and their desire: they want to participate in more advanced projects."

[1334] In this way, the system of the present invention can provide detailed insight into employee skills and preferences, enabling efficient staffing within industrial facilities, which will improve productivity and employee satisfaction throughout the organization.

[1335] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1336] Step 1:

[1337] A user puts on a wearable device and logs in to the system by entering basic information such as their name, employee ID, and password. The device sends this input data to the server, which then compares it with a database to authenticate the user. If authentication is successful, the user is allowed to log in and can use the system.

[1338] Input: User's basic information (name, employee ID, password)

[1339] Data processing: The server checks the input data against the database

[1340] Output: Login allowed or denied

[1341] Specific operation: The user enters the required information into the wearable device interface and presses the login button. The device sends the data to the server, which then references the database to perform authentication.

[1342] Step 2:

[1343] After logging in, the user clicks the "Start Dialogue" button on the device interface. The device sends a request to the server to launch the conversational AI, and the server calls the generative AI model to set up the conversational environment. The generative AI model is then ready to start a conversation with the user.

[1344] Input: Request to start a conversation

[1345] Data processing: The server launches the generative AI model and sets up the dialogue environment.

[1346] Output: Conversational AI is launched and ready for interaction

[1347] Specific operation: The user clicks the "Start dialogue" button on the device, and the device sends a request to the server. The server then launches the generative AI model and prepares the dialogue environment.

[1348] Step 3:

[1349] The generative AI model asks the user questions about their skill set, work experience, and desired job duties. The user then enters their answers through dialogue, and the device sends these answers to the server, which stores them in a database in real time.

[1350] Input: User response data (skill set, work experience, desired work)

[1351] Data processing: The server takes in the response data and stores it in a database

[1352] Output: Saved database records

[1353] How it works: The generating AI displays a question on the screen, and the user answers by voice or text. The device sends the data to the server, which stores it in a database.

[1354] Step 4:

[1355] The server analyzes the user information stored in the database and generates a personnel chart based on the employee's abilities and preferences. The generated personnel chart is organized in a visually displayable format.

[1356] Input: User information stored in the database

[1357] Data processing: Server performs analysis and profiling

[1358] Output: Visually displayable personnel chart

[1359] What it does: The server retrieves data from the database and uses specialized algorithms to profile employees' abilities and aspirations, creating a talent chart.

[1360] Step 5:

[1361] The server compares the personnel chart with the current requirements data within the industrial facility and generates optimal placement proposals, including specific departments, positions, and project assignments. The proposals are then sent to the terminal and presented to the user.

[1362] Input: Personnel chart and current requirements data

[1363] Data processing: The server performs matching and calculates the optimal placement

[1364] Output: Specific placement proposal

[1365] Specific operation: The server integrates the personnel chart and the factory needs to generate optimal placement proposals and send the proposals to the user's terminal.

[1366] Step 6:

[1367] The user reviews the proposed placement and provides approval or feedback. The terminal sends the user's feedback to the server, which readjusts the placement proposal as needed based on the feedback.

[1368] Input: User feedback

[1369] Data processing: The server analyzes the feedback and refines the suggestions.

[1370] Output: Reworked placement proposal or final decision

[1371] Specific operation: The user checks the placement proposal on the device and enters feedback. The device sends the feedback to the server, which then analyzes it again and updates the optimal placement proposal.

[1372] Through this process, the system will gain a detailed understanding of employees' skills and preferences, enabling efficient staffing within industrial facilities, which is expected to improve productivity and employee satisfaction across the organization.

[1373] 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.

[1374] The system of the present invention uses interactive artificial intelligence to acquire employee capabilities and preferences, and then proposes and evaluates personnel allocation based on these. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the dialogue is improved, resulting in more accurate personnel allocation. In the system of the present invention, the roles of the server, terminal, and user are clearly defined, and each performs the specific processing shown below.

[1375] Main processing flow

[1376] 1. User Registration and Login

[1377] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[1378] The terminal transmits the user's input information to the server.

[1379] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[1380] 2. Starting an interactive session

[1381] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[1382] The terminal sends this request to the server.

[1383] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1384] 3. Confirm your skills and aspirations

[1385] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[1386] The user answers on the device, "I'm good at data analysis and project management."

[1387] The terminal sends the user's answer to the server.

[1388] The server stores this response in a database.

[1389] 4. Sentiment analysis using an emotion engine

[1390] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[1391] For example, if a user responds "I'm a little anxious," the emotion engine will recognize the feeling of anxiety from the content of the words and the tone of voice.

[1392] 5. Tailoring questions based on user sentiment

[1393] Based on feedback from the emotion engine, the generative AI generates questions that correspond to the user's emotions. For example, if the user is feeling anxious, the generative AI can ask questions to relax them or respond in a considerate manner.

[1394] By having users answer emotionally sensitive questions, more detailed and accurate data is collected.

[1395] 6. Data organization and human resource chart generation

[1396] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[1397] For example, the server integrates the user's skill map, desired job, and emotional data to create a visualized talent chart.

[1398] 7. Generating placement proposals

[1399] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1400] As a specific example, the server generates a suggestion such as "suitable as a leader for a data analysis project" and displays it to the user.

[1401] 8. Review and feedback on proposals

[1402] The user checks the placement proposal through the terminal and approves or adds comments.

[1403] The terminal sends the user's feedback to the server.

[1404] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[1405] 9. Final decision and follow-up

[1406] The server finalizes the placement based on the user's final approval and updates the database.

[1407] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1408] In this way, the system of the present invention, by combining interactive AI with an emotion engine, can accurately grasp the capabilities and desires of employees in detail and propose optimal personnel allocation. Furthermore, by incorporating an emotion engine, the system can grasp the user's emotional state in real time and flexibly adjust the content of the dialogue, enabling highly accurate information collection. This is expected to improve organizational productivity and efficiency.

[1409] The processing flow will be explained below.

[1410] Step 1: Log in and register

[1411] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[1412] The terminal transmits the user's input information to the server.

[1413] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[1414] Step 2: Starting an interactive session

[1415] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[1416] The terminal sends this request to the server.

[1417] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1418] Step 3: Identify your skills and aspirations

[1419] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[1420] The user answers on the device, "I'm good at data analysis and project management."

[1421] The terminal sends the user's answer to the server.

[1422] The server stores this response in a database.

[1423] Step 4: Collect and organize detailed data

[1424] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[1425] The user answers these questions and provides information through the terminal.

[1426] The terminal sends each response data to the server, which records them in a database.

[1427] Step 5: Sentiment analysis with the sentiment engine

[1428] The emotion engine in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state, making it possible to grasp the emotions the user is feeling during the conversation in real time.

[1429] The emotion engine (in the server) sends the analysis results to the generation AI, which then adjusts the response to enable appropriate dialogue based on the user's emotions.

[1430] Step 6: Adjust your questions based on emotion

[1431] The generative AI dynamically adjusts the dialogue content based on the user's emotional data. For example, if the user is feeling anxious, it will ask relaxing questions, and if the user is showing interest, it will ask more in-depth questions.

[1432] The user answers tailored questions and provides further detailed data.

[1433] The terminal sends this new response data to the server, which updates the database.

[1434] Step 7: Organize data and generate personnel charts

[1435] The server sends all collected data to an analytics engine, which compiles employee skills, aspirations, and emotional state.

[1436] The analysis engine (within the server) creates a personnel chart that integrates skill level, desired job, years of experience, and emotional state.

[1437] The server prepares the generated personnel chart in a visually displayable format and stores it in a database.

[1438] Step 8: Generate placement proposals

[1439] The server compares the generated personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1440] The terminal displays the placement proposal received from the server to the user.

[1441] Step 9: Review and feedback on proposals

[1442] The user checks the placement proposal through the terminal and approves or adds comments.

[1443] The terminal sends the user's feedback to the server.

[1444] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[1445] Step 10: Finalize and follow up

[1446] The server finalizes the placement based on the user's final approval and updates the database.

[1447] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1448] This allows the system to understand the user's skill set and preferences in detail through dialogue, and to use the emotion engine to improve the quality of dialogue and achieve optimal staffing. Each step works in tandem to achieve high accuracy and efficiency.

[1449] Example 2

[1450] 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."

[1451] Conventional personnel placement systems are unable to fully grasp employees' abilities and preferences, making it difficult to perform optimal personnel placement. Furthermore, because they do not take the user's emotional state into account, the quality of information collection declines, resulting in inappropriate placement proposals. Furthermore, the lack of a means to organize and visually display employees' skills and preferences makes centralized information management difficult. Therefore, a new system is needed to achieve highly accurate and flexible personnel placement.

[1452] 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.

[1453] In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing personnel allocations based on the acquired data in accordance with the employee abilities and preferences; means for evaluating the effectiveness of the proposed personnel allocations; means for continuously improving the accuracy of the personnel allocations; emotion recognition means for analyzing the user's emotional state; means for adjusting the content of questions based on the analysis results of the emotion recognition means; and means for organizing the acquired data and generating a personnel chart that visually displays it. This makes it possible to accurately grasp employee abilities and preferences and improve the quality of information collection through flexible dialogue that takes the user's emotional state into consideration. Furthermore, optimal personnel allocation can be achieved based on the visually organized data, which is expected to improve organizational productivity and efficiency.

[1454] 1. "Conversational AI" refers to an AI system that has the ability to gather, understand, and respond to information through dialogue with a user.

[1455] 2. "Employee Competence" means the knowledge, skills, techniques, and experience an employee possesses to perform a job.

[1456] 3. "Employee preferences" refer to employees' current and future aspirations for their jobs, roles, work environment, career paths, etc.

[1457] 4. "Staffing" refers to the allocation of employees to appropriate positions or roles within an organization.

[1458] 5. "Recommendation" means a recommendation for determining optimal staffing based on the information collected.

[1459] 6. "Evaluation tool" means a tool or method for measuring and evaluating the effectiveness or success of a proposed staffing arrangement.

[1460] 7. "Emotion recognition means" refers to technology or methods for recognizing and analyzing a user's emotional state from the content of their dialogue, facial expressions, tone of voice, etc.

[1461] 8. "Database" means a collection system for systematically storing and managing collected data.

[1462] 9. A "human resources chart" is a document or digital content that centrally organizes and visually displays information such as an employee's abilities, aspirations, and emotional state.

[1463] 10. "Visual display" refers to the presentation of information to a user in a visual format, such as a graph or dashboard.

[1464] The system of the present invention utilizes interactive artificial intelligence and emotion recognition means to acquire employee capabilities and desires, and then proposes optimal personnel placement based on these. Specific implementation methods of this system are described in detail below.

[1465] Hardware and software used

[1466] This system uses the following main hardware and software:

[1467] 1. Server: Responsible for main data processing and database management.

[1468] 2. Terminal: Acts as a user interface, receives input from the user, and communicates with the server.

[1469] 3. Generative AI model (e.g., GPT-4): An artificial intelligence model that enables interaction with users.

[1470] 4. Emotion recognition engine: Software that analyzes the content of the user's dialogue, facial expressions, tone of voice, etc. to recognize the user's emotional state.

[1471] Processing flow and specific operations

[1472] The specific operations and techniques used in each processing step will be described.

[1473] User Registration and Login

[1474] The user uses a terminal to access the system's login screen, where they enter their name, employee ID, and password.

[1475] Example: Enter the name "Taro Tanaka", employee ID "12345", and password "password123".

[1476] The terminal transmits the user's input information to the server.

[1477] The server checks the received login information against the database and performs authentication processing.

[1478] Starting an interactive session

[1479] The user clicks the "Start Interaction" button to begin the interaction session.

[1480] Example: Clicking the "Start conversation" button.

[1481] The terminal sends this request to the server.

[1482] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1483] Verifying skills and preferences

[1484] The generating AI (in the server) asks questions to the user.

[1485] Example prompt: "What is your best skill?"

[1486] The user responds using the terminal.

[1487] Example: "I'm good at data analysis and project management."

[1488] The terminal transmits the user's answer to the server.

[1489] The server stores this response in a database.

[1490] Emotion recognition and question adjustment

[1491] The emotion recognition engine (in the server) analyzes the content of the user's dialogue, facial expressions, and tone of voice to recognize their emotional state.

[1492] Example: If a user responds, "I'm a little anxious," the emotion engine recognizes that emotion.

[1493] The generative AI adjusts the questions based on feedback from the emotion recognition engine.

[1494] Example: Asking questions to put anxious users at ease.

[1495] Data organization and personnel chart generation

[1496] The server organizes the collected data and generates a personnel chart that integrates the employee's abilities, aspirations, and emotional state.

[1497] Example: Creating a chart that visually displays a user's skill map, desired job, and emotional data.

[1498] Generate and review placement proposals

[1499] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[1500] Example: Generate suggestions such as "You're a good choice to lead a data analysis project."

[1501] The user reviews the placement proposal and approves or adds comments.

[1502] The server reevaluates the proposal and makes adjustments if necessary.

[1503] Final decision and follow-up

[1504] The server reflects the final decision in the database and monitors the employee's performance after placement.

[1505] Example: Collect performance data periodically and reassess as needed.

[1506] The system utilizes conversational artificial intelligence and an emotion recognition engine to enable users to receive accurate and user-friendly staffing recommendations, while centralized management of employee data can improve organizational productivity and efficiency.

[1507] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1508] Step 1:

[1509] User Registration and Login

[1510] A user accesses the system login screen using a terminal and enters their name, employee ID, and password.

[1511] Input: Name "Taro Tanaka", Employee ID "12345", Password "password123"

[1512] Action: Enter information on the keyboard

[1513] The terminal transmits the user's input information to the server.

[1514] Input: Information entered by the user

[1515] What happens: The "Submit" button is clicked and the information is sent to the server as a POST request

[1516] Output: User information received by the server

[1517] The server checks the received login information against the database and performs authentication processing.

[1518] Input: Received user information

[1519] How it works: The server looks up the user information in its database and checks for a matching ID and password.

[1520] Output: Authentication result (success or failure)

[1521] Step 2:

[1522] Starting an interactive session

[1523] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[1524] Input: Click the "Start conversation" button

[1525] Action: Click the button with the mouse

[1526] The terminal sends this request to the server.

[1527] Input: "Start conversation" request

[1528] Action: A request is sent to the server

[1529] Output: The server receives the request

[1530] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1531] Input: "Start conversation" request

[1532] Operation: Call the API of the generative AI model and perform the necessary initial setup.

[1533] Output: Interactive environment is ready

[1534] Step 3:

[1535] Checking skills and aspirations

[1536] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[1537] Input: User interaction context

[1538] How it works: Send a question prompt to a generative AI model

[1539] Output: Question text

[1540] The user answers on the device, "I'm good at data analysis and project management."

[1541] Input: Question text

[1542] Action: Enter your answer on the keyboard and click the submit button

[1543] Output: Answer text

[1544] The terminal sends the user's answer to the server.

[1545] Input: User's answer

[1546] What it does: Sends the answer text to the server as a POST request

[1547] Output: The response text received by the server

[1548] The server stores this response in a database.

[1549] Input: User answer text

[1550] What it does: Saves the answer to the user's profile information in a database

[1551] Output: Answer data stored in a database

[1552] Step 4:

[1553] Sentiment analysis with emotion engine

[1554] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[1555] Input: User interaction, facial expressions, and tone of voice

[1556] How it works: Applying text analysis, facial expression recognition, and speech analysis algorithms

[1557] Output: Recognized emotion data

[1558] Example: When a user says, "I'm a little anxious," the emotion engine recognizes anxiety.

[1559] Step 5:

[1560] Tailoring questions based on user sentiment

[1561] The generative AI generates questions based on the user's emotions based on feedback from the emotion engine.

[1562] Input: Emotion engine feedback

[1563] How it works: The AI ​​model generates the next question

[1564] Output: Adjusted question text

[1565] Example: If a user is feeling anxious, the generative AI will ask them questions to help them relax.

[1566] Users answer emotionally sensitive questions, which allows for more detailed and accurate data to be collected.

[1567] Input: Tailored Question

[1568] Action: Enter answer on keyboard and click submit button

[1569] Output: User's answer text

[1570] Step 6:

[1571] Data organization and personnel chart generation

[1572] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[1573] Input: Data on the user's abilities, desires, and emotional state

[1574] How it works: Algorithms analyze and synthesize data to generate a visualized medical record

[1575] Output: Human Resources Chart

[1576] Example: Integrating skills maps, job preferences, and sentiment data to create a visual dashboard

[1577] Step 7:

[1578] Generate placement proposals

[1579] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[1580] Input: Personnel chart, current status request data

[1581] How it works: Algorithms collate data and calculate optimal placement

[1582] Output: Placement proposal

[1583] Example: Generate a suggestion that "You are a good candidate to lead a data analysis project."

[1584] Step 8:

[1585] Review and feedback on suggestions

[1586] The user checks the placement proposal through the terminal and approves or adds comments.

[1587] Input: Proposal

[1588] Action: Review the suggestion and click the "Approve" or "Add Suggestion" button

[1589] Output: User feedback

[1590] The terminal sends the user's feedback to the server.

[1591] Input: User feedback

[1592] Behavior: Sends feedback to the server as a POST request

[1593] Output: Feedback data received by the server

[1594] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[1595] Input: User feedback

[1596] How it works: Analyzes feedback and updates suggestions based on a rating algorithm

[1597] Output: Updated placement proposal

[1598] Step 9:

[1599] Final decision and follow-up

[1600] The server finalizes the placement based on the user's final approval and updates the database.

[1601] Input: Final approved placement proposal

[1602] Action: Writes the location information to the database.

[1603] Output: Updated database

[1604] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1605] Input: Performance data

[1606] How it works: Periodically collects performance data and re-evaluates it based on the evaluation algorithm.

[1607] Output: Improvement suggestions and reallocation decisions

[1608] (Application example 2)

[1609] 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."

[1610] In the past, employee allocation in logistics centers was primarily based on experience and intuition, which meant that efficient allocation that fully reflected employee abilities and preferences could not be achieved. Furthermore, employee emotions and psychological states were not taken into consideration, which had a negative impact on work efficiency and employee satisfaction. The present invention aims to solve these problems by providing a system that maximizes employee abilities and preferences while also taking into account their emotional states, resulting in efficient and satisfying personnel allocation.

[1611] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing staffing arrangements based on employee abilities and preferences based on the acquired data; means for evaluating the effectiveness of the proposed staffing arrangements; means for continuously improving the accuracy of staffing arrangements based on the evaluation results; means for analyzing user emotions using an emotion engine and reflecting the results in the dialogue content; and means for generating optimal staffing proposals based on the business characteristics of the application destination. This enables detailed collection of employee abilities and preferences and enables highly accurate staffing arrangements that take emotional states into account. This is also expected to improve work efficiency and employee satisfaction at logistics centers.

[1612] "Conversational AI" is an AI system that naturally interacts with users and collects necessary information.

[1613] "Employee Competence" refers to the techniques, knowledge, skills and experience possessed by an employee.

[1614] "Wishes" refer to the job duties and work content that employees desire, as well as their desired working conditions.

[1615] "Staffing" refers to assigning appropriate tasks and roles to employees based on their abilities and preferences.

[1616] An "emotion engine" is a system for analyzing a user's emotional state.

[1617] "Means for evaluating the effectiveness of the proposed personnel allocation" is a system for evaluating how effective the proposed personnel allocation was.

[1618] "Means to continuously improve the accuracy of personnel placement" refers to a system for providing feedback on evaluation results and improving the accuracy of placement from the next time onwards.

[1619] "Means for analyzing the user's emotions and reflecting the results in the content of the dialogue" refers to a mechanism in which the emotion engine recognizes the user's emotions and adjusts the content of the dialogue based on that information.

[1620] A "visual human resources chart" is a document that organizes employee abilities, aspirations, and emotional data and displays them in a graphical format.

[1621] A "logistics center" refers to a facility that stores goods, ships them, manages inventory, and handles incoming and outgoing shipments.

[1622] This invention is a system that acquires the abilities and desires of employees at a logistics center and proposes and evaluates optimal personnel allocation based on them. This system uses a combination of conversational artificial intelligence and an emotion engine to collect detailed information about employees' abilities and desires, and recognizes and reflects their emotional states to achieve highly accurate personnel allocation.

[1623] Hardware and software used

[1624] Hardware: Smartphone, Head-Mounted Display (HMD)

[1625] Software: Python, EmotionRecognition (e.g. DeepFace), OpenAI GPT

[1626] System Operation Overview

[1627] 1. User Registration and Login

[1628] Users access the system's login screen using a smartphone or HMD and enter basic information such as their name, employee ID, and password. The device sends the information to the server, which then compares it with a database and performs authentication.

[1629] 2. Starting an interactive session

[1630] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The device sends this request to the server, which then invokes a generative AI model (e.g., GPT-4) to set up the conversational environment.

[1631] 3. Confirm your skills and aspirations

[1632] The generative AI on the server asks the user questions such as, "What are your best skills?" The user answers using a device, "I'm good at data analysis and project management," and the device sends the answer to the server and stores it in a database.

[1633] 4. Sentiment analysis using an emotion engine

[1634] The emotion engine (e.g., DeepFace) in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content of the words and tone of voice.

[1635] 5. Tailoring questions based on user sentiment

[1636] The server's generation AI generates questions based on the user's emotions based on feedback from the emotion engine. For example, if the user is feeling anxious, it can ask questions to relax them or respond in a considerate manner.

[1637] 6. Data organization and human resource chart generation

[1638] The server organizes the collected data and generates a human resource chart that centrally manages employees' abilities, aspirations, and emotional states, creating a visualized human resource chart.

[1639] 7. Generating placement proposals

[1640] The server compares the talent chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1641] 8. Review and feedback on proposals

[1642] The user can review the placement proposal through the device and approve or add their comments. The device then sends the user's feedback to the server, which then reevaluates the proposal based on the received feedback and adjusts it as necessary.

[1643] An example of a prompt sentence to be input to a specific generative AI model:

[1644] "What is your best skill?"

[1645] "What kind of work would you like done?"

[1646] This will enable efficient and highly satisfying staffing at logistics centers.

[1647] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1648] Step 1:

[1649] A user logs in to the system using a terminal. The input is the user's basic information (name, employee ID, password), and the output is the authentication result (success or failure). The terminal sends this information to the server, which then checks it against a database and performs authentication processing. If authentication is successful, a user session is started.

[1650] Step 2:

[1651] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The input is the user's request to start the session, and the output is a notification that the conversational environment has been set up. The device sends the request to the server, which then calls the generative AI model to set up the conversational environment.

[1652] Step 3:

[1653] The server uses a generative AI model to ask the user questions and obtain the user's abilities and aspirations. The input is the question generated by the generative AI model (e.g., "What are your best skills?") and the user's answer, and the output is the obtained data (skill information, desired job). The user answers using a device, and the device sends the answer to the server. The server stores this data in a database.

[1654] Step 4:

[1655] The emotion engine in the server analyzes the content of the user's dialogue and recognizes their emotional state. The input is the content of the user's dialogue and tone of voice, and the output is the analysis result of their emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content and tone of voice.

[1656] Step 5:

[1657] Based on feedback from the emotion engine, the server uses a generative AI model to generate questions that correspond to the user's emotions. The input is the analysis result of the emotion engine, and the output is a tailored question (e.g., "What do you think about working in a relaxed environment?"). This allows the user to provide more detailed and accurate information.

[1658] Step 6:

[1659] The server organizes the collected data and generates a visually displayed talent chart. The input is employee ability, preference, and emotion data, and the output is an integrated talent chart. The server consolidates this data and displays it in a visual format (e.g., skill map, preference list, emotional state).

[1660] Step 7:

[1661] The server compares the personnel chart with the business characteristics of the logistics center and generates optimal placement proposals. The input is the personnel chart and current requirements data, and the output is a specific placement proposal (e.g., "suitable as leader of the data analysis project"). The server automatically generates optimal placements based on this data.

[1662] Step 8:

[1663] The user reviews the proposed placement and provides feedback through the device. The input is the user feedback (approval or additional comments), and the output is the reevaluated placement proposal. The device sends the feedback to the server, which reevaluates the placement proposal based on the feedback and adjusts it if necessary.

[1664] The above are the specific processing steps of a system that realizes optimal personnel allocation that reflects the abilities and wishes of employees at a logistics center.

[1665] 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.

[1666] 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.

[1667] 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.

[1668] [Fourth embodiment]

[1669] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1670] 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.

[1671] 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).

[1672] 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.

[1673] 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.

[1674] 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).

[1675] 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.

[1676] 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.

[1677] 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.

[1678] 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.

[1679] 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.

[1680] 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.

[1681] 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."

[1682] The system of the present invention uses interactive artificial intelligence to collect information about employee capabilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. In the system of the present invention, the roles of the server, terminals, and users are clearly defined, and each role performs the specific processing shown below.

[1683] Main processing flow

[1684] 1. User Registration and Login

[1685] The server stores the user's basic information in a database and authenticates the user when they log in via a terminal. When logging in for the first time, the user enters their basic information (name, employee ID, password, etc.) into the terminal.

[1686] As an example of system operation, when a user enters their name, employee number, and password and clicks the login button, the terminal sends this information to the server, which then verifies the information and allows the user to log in.

[1687] 2. Starting an interactive session

[1688] The user initiates a session with the conversational AI via their device, which causes the server to call up the generative AI model and prepare the conversational environment.

[1689] For example, to start a conversation session, you click the "Start conversation" button. The device sends this request to the server, which then launches the generated AI.

[1690] 3. Confirm your skills and aspirations

[1691] The generation AI (in the server) asks the user questions to collect information about the user's skill set, work experience, desired work, etc. This dialogue provides detailed data.

[1692] For example, if the user answers "I'm good at data analysis and project management," the device sends this answer to the server, which records it in a database.

[1693] 4. Data organization and analysis

[1694] The server analyzes the data obtained from the conversation and generates a talent profile based on the employee's abilities and aspirations. The data is organized and profiled in real time.

[1695] For example, the server analyzes an employee's past performance and skill level and draws a conclusion such as "he has extensive experience as a data scientist."

[1696] 5. Creation of personnel records

[1697] The server then uses the organized data to create a visually displayed personnel chart, detailing the employee's skill level, desired job role, past performance, and more.

[1698] For example, the server organizes employees' skill maps and work histories and provides them to the human resources department in a visual format.

[1699] 6. Generating placement proposals

[1700] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1701] As a specific example, the server generates suggestions such as "Qualified to lead new data analysis project" and displays them to employees.

[1702] 7. Proposal presentation and follow-up

[1703] The device presents the user with placement suggestions from the server, which the user can review, approve, or add comments to. The server then receives feedback and adjusts the suggestions as needed.

[1704] For example, after a user approves a placement proposal, the server makes the final decision and updates the database. After placement, a performance evaluation system continuously monitors performance and reevaluates the placement if necessary.

[1705] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[1706] The processing flow will be explained below.

[1707] Step 1: Log in and register

[1708] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[1709] The terminal transmits the user's input information to the server.

[1710] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[1711] Step 2: Starting an interactive session

[1712] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[1713] The terminal sends this request to the server.

[1714] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1715] Step 3: Identify your skills and aspirations

[1716] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[1717] The user answers on the device, "I'm good at data analysis and project management."

[1718] The terminal sends the user's answer to the server.

[1719] The server stores this response in a database.

[1720] Step 4: Collect and organize detailed data

[1721] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[1722] The user answers these questions and provides information through the terminal.

[1723] The terminal sends each response data to the server, which records them in a database.

[1724] Step 5: Analyze the data and generate a personnel chart

[1725] The server sends the collected data to an analysis engine to organize employees' abilities and aspirations.

[1726] The analysis engine (on the server) analyzes the data and extracts and organizes information such as skill level, desired job, and years of experience.

[1727] The server generates a visually displayable personnel chart based on the organized data.

[1728] Step 6: Generate placement proposals

[1729] The server compares the generated personnel chart with the current status requirements data of the organization.

[1730] The server generates optimal placement proposals based on the employee's skill set, work experience, and preferences.

[1731] The terminal displays the placement proposal to the user.

[1732] Step 7: Review and feedback on the proposal

[1733] The user checks the placement proposal through the terminal and approves or adds comments.

[1734] The terminal sends the user's feedback to the server.

[1735] The server reevaluates based on the feedback it receives and readjusts its placement proposals as needed.

[1736] Step 8: Finalize and follow up

[1737] The server finalizes the placement based on the user's final approval and updates the database.

[1738] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1739] In this way, the system of the present invention can grasp the user's skill set and preferences in detail through dialogue with them, and propose optimal staffing. Each step works in conjunction with the other to achieve high accuracy and efficiency.

[1740] Example 1

[1741] 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."

[1742] To accurately understand employee capabilities and aspirations and improve productivity and efficiency across the organization, it is necessary to effectively collect and analyze employee information and propose optimal personnel placements. However, manual data collection and analysis requires time and effort and increases the risk of human error. Furthermore, to continuously propose appropriate placements, it is necessary to keep employee profiles up to date based on the collected information and to evaluate performance after placement. These challenges need to be resolved.

[1743] 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.

[1744] In this invention, the server includes means for storing basic information of the user in a database and performing authentication, means for starting an interactive session using a generative AI model, and means for collecting the user's skill set, work experience, and desired work content using the generative AI model, thereby enabling accurate and rapid understanding of employee capabilities and preferences and achieving optimal personnel allocation.

[1745] A "server" is a computer system that processes information, stores and manages data, and provides services to other devices and software.

[1746] A "terminal" is a computer or device that a user operates and that sends input information to a server.

[1747] A "user" is a person who uses this system, and is primarily a person who plays the role of an employee.

[1748] A "generative AI model" is an artificial intelligence model that analyzes user input using natural language processing and an interactive interface and generates appropriate responses.

[1749] A "database" is a collection of data used to systematically store and manage user information, dialogue content, profile information, and other data.

[1750] "Authentication" is the process of checking passwords and IDs to verify that a user is a legitimate user.

[1751] A "dialogue session" is a series of conversations that take place between a user and a system using a generative AI model.

[1752] A "skill set" is a collection of skills and abilities that a user possesses.

[1753] "Work experience" refers to the experience a user has had in the past in relation to jobs or roles.

[1754] A "profile" is information that compiles a user's skills, work experience, desired work content, and so on.

[1755] A "talent chart" is a document or digital report that visually displays a user's skills, work experience, and desired job duties.

[1756] "Placement proposal" is information that is generated based on the user profile and the requirements of the organization to propose the most suitable job title and placement.

[1757] "Feedback" refers to opinions and comments provided by users regarding placement proposals.

[1758] "Performance appraisal" is the process of evaluating an employee's job performance after being assigned.

[1759] The system of the present invention uses interactive artificial intelligence to collect information about employees' abilities and preferences, and then proposes and evaluates optimal personnel placement based on this information. The system of the present invention includes a server, a terminal, and a user, each of which plays a role and performs the specific processing described below.

[1760] First, the server stores the user's basic information in a database and is equipped with a means of authentication. When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal, and the terminal sends this information to the server. The server stores the received information in a database and generates authentication information.

[1761] When the user logs in again, they enter their employee ID and password into the terminal, which then sends the login information to the server. The server compares the information with the database and, if authentication is successful, starts a session. This allows the server to manage the user's basic information and ensure security through the authentication process.

[1762] Next, the user has a means to start a dialogue session via the terminal. For example, when the user clicks a "Start dialogue" button, the terminal sends a "Start dialogue" request to the server, and the server calls the generative AI model. This generative AI model uses natural language processing to engage in a dialogue with the user.

[1763] In a dialogue session, the generative AI model has a means of collecting information from the user, such as their skill set, work experience, and desired work content. Specifically, the generative AI displays the question, "What are your best skills?" The user inputs the answer and sends it via their device to the server, which then records the received information in a database.

[1764] The server has the means to generate employee profiles based on the collected information and store them in a database. The information in the database is aggregated and analyzed to profile employees based on their skill levels and desired jobs. The profiling results can be expressed as, for example, "Employee 1234 demonstrates high performance as a data scientist."

[1765] The server then creates a personnel chart based on the analysis results. This chart visually displays the user's skill set, work experience, and aspirations, and is provided to the HR department in a visual format. The server also outputs this chart data in JSON format, which can be displayed in a web interface.

[1766] Furthermore, the server is equipped with a means for comparing the personnel chart with the current requirements data and generating optimal placement proposals. For example, it uses an algorithm to calculate a "skill matching score" and propose optimal positions and projects. Specifically, it generates a proposal in the form of "Employee 1234 is suitable as the leader of a new data analysis project."

[1767] Finally, the device presents the placement proposal received from the server to the user and has a means for receiving feedback. The user reviews the proposal and either approves or adds their opinion, and the device then sends it to the server. The server receives and analyzes the feedback and runs the placement algorithm again. Even after placement, the performance evaluation system continuously monitors employee performance and reevaluates placement as necessary.

[1768] For example, you might use the following prompt:

[1769] "What is your best skill?"

[1770] "Please tell us about your previous work experience."

[1771] What position and duties do you desire?

[1772] As a result, the system of the present invention provides a detailed understanding of employees' abilities and desires and provides specific means for realizing optimal personnel allocation, which is expected to result in improved productivity and efficiency throughout the organization.

[1773] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1774] Step 1:

[1775] User Registration and Login

[1776] When a user logs in for the first time, they enter their basic information (name, employee ID, password) into the terminal.

[1777] The terminal sends this information to the server.

[1778] The server stores the received information in a database and generates authentication information.

[1779] When logging in again, the user enters their employee ID and password into the terminal, and the terminal sends the login information to the server.

[1780] The server checks the information against the database and starts the session if authentication is successful.

[1781] Specifically, the server compares the entered password with the hash value in the database, and if they match, it issues a session ID.

[1782] Input: User's basic information (name, employee ID, password)

[1783] Output: Authentication information, session ID

[1784] Step 2:

[1785] Starting an interactive session

[1786] The user clicks the "Start conversation" button on the terminal interface.

[1787] The terminal sends a "start conversation" request to the server.

[1788] The server invokes the generative AI model and prepares the dialogue session.

[1789] A generative AI model (e.g., OpenAI GPT-3) is launched and a session ID is generated.

[1790] As a specific operation, the user presses the "Start dialogue" button on the dialogue box.

[1791] Input: Dialogue-initiating request

[1792] Output: Session ID, launch of the generative AI model

[1793] Step 3:

[1794] Checking skills and aspirations

[1795] The generative AI (in the server) asks the user questions about their skill set, work experience, and desired job duties.

[1796] The user inputs a response to this and transmits it to the server via the terminal.

[1797] As a specific example, the generative AI asks, "What are your best skills?" and the user answers, "Data analysis and project management."

[1798] The terminal sends the user's answer to the server, which records it in a database.

[1799] Input: User's answers (skill set, work experience, desired content)

[1800] Output: Information recorded in the database

[1801] Step 4:

[1802] Data organization and analysis

[1803] The server creates a profile of the employee based on the data collected during the interaction.

[1804] The information in the database is aggregated to profile employees' skill levels and desired jobs.

[1805] For example, the server may derive a conclusion such as "Employee 1234 is demonstrating high performance as a data scientist."

[1806] Input: Collected data in the database

[1807] Output: Employee profile, skill level assessment

[1808] Step 5:

[1809] Creation of personnel records

[1810] The server creates a personnel chart based on the analysis results.

[1811] The chart visually displays the user's skill set, work experience, and preferences.

[1812] Specifically, the server outputs the medical record data in JSON format and displays it on a web interface.

[1813] Input: Employee profile, analysis results

[1814] Output: Visually displayable personnel chart

[1815] Step 6:

[1816] Generate placement proposals

[1817] The server compares the personnel chart with the current requirements data and automatically generates optimal placement proposals.

[1818] As a concrete example, an algorithm is used to calculate a "skill matching score" and suggest the most suitable job title and project.

[1819] Specifically, it states, "Employee 1234 is a good candidate to lead a new data analysis project."

[1820] Input: Human resource chart, current requirements data

[1821] Output: Placement proposal

[1822] Step 7:

[1823] Presenting proposals and gathering feedback

[1824] The terminal presents the placement proposal received from the server to the user.

[1825] Users can review the suggestions and approve or add their comments.

[1826] When the user provides feedback, the terminal sends it to the server.

[1827] A specific action is for the user to click the "Approve" button or enter feedback in the comment field.

[1828] The server receives feedback, readjusts its suggestions as needed, and updates its database.

[1829] Input: User feedback

[1830] Output: revised placement proposal, updated database

[1831] Step 8:

[1832] Ongoing performance evaluation

[1833] After deployment, the server evaluates and monitors the employee's performance through the performance monitoring system.

[1834] If necessary, the server reevaluates the placement and makes optimizations.

[1835] Specifically, the server collects performance data and provides feedback based on the analysis results.

[1836] Input: Performance data

[1837] Output: Ongoing performance evaluation, recalibrated placement proposals

[1838] (Application example 1)

[1839] 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."

[1840] With conventional personnel allocation systems, it was difficult to accurately grasp employees' abilities and preferences and to allocate them optimally based on them. Furthermore, in a diverse environment like a factory, real-time data collection and response is required, but there was a lack of efficient ways to do this. As a result, employees' performance could not be maximized, hindering improvements in productivity throughout the organization.

[1841] 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.

[1842] In this invention, the server includes a means for acquiring employee abilities and preferences using interactive artificial intelligence, a means for proposing personnel allocation according to the employee abilities and preferences based on the acquired data, and a means for evaluating the effectiveness of the proposed personnel allocation, thereby enabling a detailed understanding of employee skills and preferences and optimizing personnel allocation within the factory.

[1843] "Conversational AI" is an AI system that has the ability to collect and provide information through dialogue with employees.

[1844] "Employee capabilities" refers to an employee's skill set, work experience, qualifications, etc.

[1845] "Hope" refers to the type of work and working style that employees would like to do.

[1846] "Means of acquisition" refers to the processes and techniques used to collect information from employees.

[1847] "Data" refers to the collection of collected information about employees, such as their skill sets and preferences.

[1848] "Proposal means" refers to the processes and techniques for calculating and presenting optimal personnel placement based on collected data.

[1849] "Means of evaluation" refers to the processes and techniques used to measure how effective the proposed staffing arrangements are in practice.

[1850] "Effects" refers to concrete results, such as increased productivity and employee satisfaction, that result from the proposed personnel allocation.

[1851] "Industrial facility" refers to manufacturing sites, including factories and production lines.

[1852] An "operating robot" refers to a machine that operates within a factory and automatically performs specific tasks or operations.

[1853] "Installation" refers to the process of incorporating software or applications into a particular device.

[1854] A "wearable device" refers to an electronic device that is worn on the body, and examples include smart glasses and head-mounted displays.

[1855] "Visually displaying" refers to presenting collected data on a screen or display in a format that is easy for humans to understand.

[1856] A "human resources chart" refers to a document or data set that organizes and visually displays information about an employee's abilities and aspirations.

[1857] The system of the present invention utilizes interactive artificial intelligence to collect information on employee abilities and preferences using robots and wearable devices operating within industrial facilities, and then proposes and evaluates optimal personnel placement based on this information. Each component of the system and its operation are described below.

[1858] System Configuration

[1859] The system consists of the following components:

[1860] Server: The main processing unit containing the database and generative AI models.

[1861] Wearable devices: Smart glasses or head-mounted displays worn by employees.

[1862] Robots operating in industrial facilities: devices equipped with conversational artificial intelligence that interact with employees.

[1863] Hardware and Software Used

[1864] Hardware:

[1865] Wearable devices (smart glasses, head-mounted displays)

[1866] Robots in factories (with interactive functions)

[1867] software:

[1868] Flask (Python web framework)

[1869] SQLite (lightweight database)

[1870] OpenAI API (Interactive Generative AI Model)

[1871] Data processing and calculation

[1872] Data collection and dialogue initiation

[1873] A user (employee) logs into the system via a wearable device and initiates a session with the conversational AI. The server receives this request, launches the generative AI model, and sets up the conversational environment.

[1874] Confirm your skills and expectations

[1875] The generative AI model asks employees questions to gather information about their skill sets, work experience, and desired work. For example, if a user answers, "I'm good at data analysis and project management," the device sends this response to a server, which then records this data in a database.

[1876] Data organization and personnel chart creation

[1877] The server analyzes the data obtained through the conversation and generates a talent chart based on the employee's abilities and aspirations. This chart is displayed visually and can be reviewed by both HR personnel and the employee themselves. For example, it displays the employee's skill map and work history.

[1878] Generating and evaluating placement proposals

[1879] The server compares the personnel records with the current requirements data within the industrial facility and generates optimal placement proposals. The proposals include specific departments, positions, and project assignments. For example, the server might suggest to an employee, "You would be ideal as the leader of a new data analysis project."

[1880] Examples and prompts

[1881] As a concrete example, here are some examples of prompts that employees use when interacting with conversational AI:

[1882] Prompt statement:

[1883] "Please suggest the best placement based on the employee's skills: they are good at data analysis and project management, and their desire: they want to participate in more advanced projects."

[1884] In this way, the system of the present invention can provide detailed insight into employee skills and preferences, enabling efficient staffing within industrial facilities, which will improve productivity and employee satisfaction throughout the organization.

[1885] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1886] Step 1:

[1887] A user puts on a wearable device and logs in to the system by entering basic information such as their name, employee ID, and password. The device sends this input data to the server, which then compares it with a database to authenticate the user. If authentication is successful, the user is allowed to log in and can use the system.

[1888] Input: User's basic information (name, employee ID, password)

[1889] Data processing: The server checks the input data against the database

[1890] Output: Login allowed or denied

[1891] Specific operation: The user enters the required information into the wearable device interface and presses the login button. The device sends the data to the server, which then references the database to perform authentication.

[1892] Step 2:

[1893] After logging in, the user clicks the "Start Dialogue" button on the device interface. The device sends a request to the server to launch the conversational AI, and the server calls the generative AI model to set up the conversational environment. The generative AI model is then ready to start a conversation with the user.

[1894] Input: Request to start a conversation

[1895] Data processing: The server launches the generative AI model and sets up the dialogue environment.

[1896] Output: Conversational AI is launched and ready for interaction

[1897] Specific operation: The user clicks the "Start dialogue" button on the device, and the device sends a request to the server. The server then launches the generative AI model and prepares the dialogue environment.

[1898] Step 3:

[1899] The generative AI model asks the user questions about their skill set, work experience, and desired job duties. The user then enters their answers through dialogue, and the device sends these answers to the server, which stores them in a database in real time.

[1900] Input: User response data (skill set, work experience, desired work)

[1901] Data processing: The server takes in the response data and stores it in a database

[1902] Output: Saved database records

[1903] How it works: The generating AI displays a question on the screen, and the user answers by voice or text. The device sends the data to the server, which stores it in a database.

[1904] Step 4:

[1905] The server analyzes the user information stored in the database and generates a personnel chart based on the employee's abilities and preferences. The generated personnel chart is organized in a visually displayable format.

[1906] Input: User information stored in the database

[1907] Data processing: Server performs analysis and profiling

[1908] Output: Visually displayable personnel chart

[1909] What it does: The server retrieves data from the database and uses specialized algorithms to profile employees' abilities and aspirations, creating a talent chart.

[1910] Step 5:

[1911] The server compares the personnel chart with the current requirements data within the industrial facility and generates optimal placement proposals, including specific departments, positions, and project assignments. The proposals are then sent to the terminal and presented to the user.

[1912] Input: Personnel chart and current requirements data

[1913] Data processing: The server performs matching and calculates the optimal placement

[1914] Output: Specific placement proposal

[1915] Specific operation: The server integrates the personnel chart and the factory needs to generate optimal placement proposals and send the proposals to the user's terminal.

[1916] Step 6:

[1917] The user reviews the proposed placement and provides approval or feedback. The terminal sends the user's feedback to the server, which readjusts the placement proposal as needed based on the feedback.

[1918] Input: User feedback

[1919] Data processing: The server analyzes the feedback and refines the suggestions.

[1920] Output: Reworked placement proposal or final decision

[1921] Specific operation: The user checks the placement proposal on the device and enters feedback. The device sends the feedback to the server, which then analyzes it again and updates the optimal placement proposal.

[1922] Through this process, the system will gain a detailed understanding of employees' skills and preferences, enabling efficient staffing within industrial facilities, which is expected to improve productivity and employee satisfaction across the organization.

[1923] 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.

[1924] The system of the present invention uses interactive artificial intelligence to acquire employee capabilities and preferences, and then proposes and evaluates personnel allocation based on these. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the dialogue is improved, resulting in more accurate personnel allocation. In the system of the present invention, the roles of the server, terminal, and user are clearly defined, and each performs the specific processing shown below.

[1925] Main processing flow

[1926] 1. User Registration and Login

[1927] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[1928] The terminal transmits the user's input information to the server.

[1929] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[1930] 2. Starting an interactive session

[1931] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[1932] The terminal sends this request to the server.

[1933] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1934] 3. Confirm your skills and aspirations

[1935] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[1936] The user answers on the device, "I'm good at data analysis and project management."

[1937] The terminal sends the user's answer to the server.

[1938] The server stores this response in a database.

[1939] 4. Sentiment analysis using an emotion engine

[1940] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[1941] For example, if a user responds "I'm a little anxious," the emotion engine will recognize the feeling of anxiety from the content of the words and the tone of voice.

[1942] 5. Tailoring questions based on user sentiment

[1943] Based on feedback from the emotion engine, the generative AI generates questions that correspond to the user's emotions. For example, if the user is feeling anxious, the generative AI can ask questions to relax them or respond in a considerate manner.

[1944] By having users answer emotionally sensitive questions, more detailed and accurate data is collected.

[1945] 6. Data organization and human resource chart generation

[1946] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[1947] For example, the server integrates the user's skill map, desired job, and emotional data to create a visualized talent chart.

[1948] 7. Generating placement proposals

[1949] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1950] As a specific example, the server generates a suggestion such as "suitable as a leader for a data analysis project" and displays it to the user.

[1951] 8. Review and feedback on proposals

[1952] The user checks the placement proposal through the terminal and approves or adds comments.

[1953] The terminal sends the user's feedback to the server.

[1954] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[1955] 9. Final decision and follow-up

[1956] The server finalizes the placement based on the user's final approval and updates the database.

[1957] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1958] In this way, the system of the present invention, by combining interactive AI with an emotion engine, can accurately grasp the capabilities and desires of employees in detail and propose optimal personnel allocation. Furthermore, by incorporating an emotion engine, the system can grasp the user's emotional state in real time and flexibly adjust the content of the dialogue, enabling highly accurate information collection. This is expected to improve organizational productivity and efficiency.

[1959] The processing flow will be explained below.

[1960] Step 1: Log in and register

[1961] A user uses a terminal to access the system's login screen and enters basic information such as name, employee ID, and password.

[1962] The terminal transmits the user's input information to the server.

[1963] The server checks the received login information against the database and performs authentication. If authentication is successful, the login is permitted and the user session begins.

[1964] Step 2: Starting an interactive session

[1965] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[1966] The terminal sends this request to the server.

[1967] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[1968] Step 3: Identify your skills and aspirations

[1969] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[1970] The user answers on the device, "I'm good at data analysis and project management."

[1971] The terminal sends the user's answer to the server.

[1972] The server stores this response in a database.

[1973] Step 4: Collect and organize detailed data

[1974] The generation AI (in the server) asks multiple questions in succession to collect details about the user's skill set, work experience, and desired job duties.

[1975] The user answers these questions and provides information through the terminal.

[1976] The terminal sends each response data to the server, which records them in a database.

[1977] Step 5: Sentiment analysis with the sentiment engine

[1978] The emotion engine in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state, making it possible to grasp the emotions the user is feeling during the conversation in real time.

[1979] The emotion engine (in the server) sends the analysis results to the generation AI, which then adjusts the response to enable appropriate dialogue based on the user's emotions.

[1980] Step 6: Adjust your questions based on emotion

[1981] The generative AI dynamically adjusts the dialogue content based on the user's emotional data. For example, if the user is feeling anxious, it will ask relaxing questions, and if the user is showing interest, it will ask more in-depth questions.

[1982] The user answers tailored questions and provides further detailed data.

[1983] The terminal sends this new response data to the server, which updates the database.

[1984] Step 7: Organize data and generate personnel charts

[1985] The server sends all collected data to an analytics engine, which compiles employee skills, aspirations, and emotional state.

[1986] The analysis engine (within the server) creates a personnel chart that integrates skill level, desired job, years of experience, and emotional state.

[1987] The server prepares the generated personnel chart in a visually displayable format and stores it in a database.

[1988] Step 8: Generate placement proposals

[1989] The server compares the generated personnel chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[1990] The terminal displays the placement proposal received from the server to the user.

[1991] Step 9: Review and feedback on proposals

[1992] The user checks the placement proposal through the terminal and approves or adds comments.

[1993] The terminal sends the user's feedback to the server.

[1994] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[1995] Step 10: Finalize and follow up

[1996] The server finalizes the placement based on the user's final approval and updates the database.

[1997] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[1998] This allows the system to understand the user's skill set and preferences in detail through dialogue, and to use the emotion engine to improve the quality of dialogue and achieve optimal staffing. Each step works in tandem to achieve high accuracy and efficiency.

[1999] Example 2

[2000] 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."

[2001] Conventional personnel placement systems are unable to fully grasp employees' abilities and preferences, making it difficult to perform optimal personnel placement. Furthermore, because they do not take the user's emotional state into account, the quality of information collection declines, resulting in inappropriate placement proposals. Furthermore, the lack of a means to organize and visually display employees' skills and preferences makes centralized information management difficult. Therefore, a new system is needed to achieve highly accurate and flexible personnel placement.

[2002] 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.

[2003] In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing personnel allocations based on the acquired data in accordance with the employee abilities and preferences; means for evaluating the effectiveness of the proposed personnel allocations; means for continuously improving the accuracy of the personnel allocations; emotion recognition means for analyzing the user's emotional state; means for adjusting the content of questions based on the analysis results of the emotion recognition means; and means for organizing the acquired data and generating a personnel chart that visually displays it. This makes it possible to accurately grasp employee abilities and preferences and improve the quality of information collection through flexible dialogue that takes the user's emotional state into consideration. Furthermore, optimal personnel allocation can be achieved based on the visually organized data, which is expected to improve organizational productivity and efficiency.

[2004] 1. "Conversational AI" refers to an AI system that has the ability to gather, understand, and respond to information through dialogue with a user.

[2005] 2. "Employee Competence" means the knowledge, skills, techniques, and experience an employee possesses to perform a job.

[2006] 3. "Employee preferences" refer to employees' current and future aspirations for their jobs, roles, work environment, career paths, etc.

[2007] 4. "Staffing" refers to the allocation of employees to appropriate positions or roles within an organization.

[2008] 5. "Recommendation" means a recommendation for determining optimal staffing based on the information collected.

[2009] 6. "Evaluation tool" means a tool or method for measuring and evaluating the effectiveness or success of a proposed staffing arrangement.

[2010] 7. "Emotion recognition means" refers to technology or methods for recognizing and analyzing a user's emotional state from the content of their dialogue, facial expressions, tone of voice, etc.

[2011] 8. "Database" means a collection system for systematically storing and managing collected data.

[2012] 9. A "human resources chart" is a document or digital content that centrally organizes and visually displays information such as an employee's abilities, aspirations, and emotional state.

[2013] 10. "Visual display" refers to the presentation of information to a user in a visual format, such as a graph or dashboard.

[2014] The system of the present invention utilizes interactive artificial intelligence and emotion recognition means to acquire employee capabilities and desires, and then proposes optimal personnel placement based on these. Specific implementation methods of this system are described in detail below.

[2015] Hardware and software used

[2016] This system uses the following main hardware and software:

[2017] 1. Server: Responsible for main data processing and database management.

[2018] 2. Terminal: Acts as a user interface, receives input from the user, and communicates with the server.

[2019] 3. Generative AI model (e.g., GPT-4): An artificial intelligence model that enables interaction with users.

[2020] 4. Emotion recognition engine: Software that analyzes the content of the user's dialogue, facial expressions, tone of voice, etc. to recognize the user's emotional state.

[2021] Processing flow and specific operations

[2022] The specific operations and techniques used in each processing step will be described.

[2023] User Registration and Login

[2024] The user uses a terminal to access the system's login screen, where they enter their name, employee ID, and password.

[2025] Example: Enter the name "Taro Tanaka", employee ID "12345", and password "password123".

[2026] The terminal transmits the user's input information to the server.

[2027] The server checks the received login information against the database and performs authentication processing.

[2028] Starting an interactive session

[2029] The user clicks the "Start Interaction" button to begin the interaction session.

[2030] Example: Clicking the "Start conversation" button.

[2031] The terminal sends this request to the server.

[2032] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[2033] Verifying skills and preferences

[2034] The generating AI (in the server) asks questions to the user.

[2035] Example prompt: "What is your best skill?"

[2036] The user responds using the terminal.

[2037] Example: "I'm good at data analysis and project management."

[2038] The terminal transmits the user's answer to the server.

[2039] The server stores this response in a database.

[2040] Emotion recognition and question adjustment

[2041] The emotion recognition engine (in the server) analyzes the content of the user's dialogue, facial expressions, and tone of voice to recognize their emotional state.

[2042] Example: If a user responds, "I'm a little anxious," the emotion engine recognizes that emotion.

[2043] The generative AI adjusts the questions based on feedback from the emotion recognition engine.

[2044] Example: Asking questions to put anxious users at ease.

[2045] Data organization and personnel chart generation

[2046] The server organizes the collected data and generates a personnel chart that integrates the employee's abilities, aspirations, and emotional state.

[2047] Example: Creating a chart that visually displays a user's skill map, desired job, and emotional data.

[2048] Generate and review placement proposals

[2049] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[2050] Example: Generate suggestions such as "You're a good choice to lead a data analysis project."

[2051] The user reviews the placement proposal and approves or adds comments.

[2052] The server reevaluates the proposal and makes adjustments if necessary.

[2053] Final decision and follow-up

[2054] The server reflects the final decision in the database and monitors the employee's performance after placement.

[2055] Example: Collect performance data periodically and reassess as needed.

[2056] The system utilizes conversational artificial intelligence and an emotion recognition engine to enable users to receive accurate and user-friendly staffing recommendations, while centralized management of employee data can improve organizational productivity and efficiency.

[2057] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2058] Step 1:

[2059] User Registration and Login

[2060] A user accesses the system login screen using a terminal and enters their name, employee ID, and password.

[2061] Input: Name "Taro Tanaka", Employee ID "12345", Password "password123"

[2062] Action: Enter information on the keyboard

[2063] The terminal transmits the user's input information to the server.

[2064] Input: Information entered by the user

[2065] What happens: The "Submit" button is clicked and the information is sent to the server as a POST request

[2066] Output: User information received by the server

[2067] The server checks the received login information against the database and performs authentication processing.

[2068] Input: Received user information

[2069] How it works: The server looks up the user information in its database and checks for a matching ID and password.

[2070] Output: Authentication result (success or failure)

[2071] Step 2:

[2072] Starting an interactive session

[2073] To start a session with the conversational AI, the user clicks the "Start conversation" button on their device.

[2074] Input: Click the "Start conversation" button

[2075] Action: Click the button with the mouse

[2076] The terminal sends this request to the server.

[2077] Input: "Start conversation" request

[2078] Action: A request is sent to the server

[2079] Output: The server receives the request

[2080] The server invokes a generative AI model (e.g., GPT-4) and sets up the interactive environment.

[2081] Input: "Start conversation" request

[2082] Operation: Call the API of the generative AI model and perform the necessary initial setup.

[2083] Output: Interactive environment is ready

[2084] Step 3:

[2085] Checking skills and aspirations

[2086] The generating AI (in the server) asks the user questions such as, "What is your best skill?"

[2087] Input: User interaction context

[2088] How it works: Send a question prompt to a generative AI model

[2089] Output: Question text

[2090] The user answers on the device, "I'm good at data analysis and project management."

[2091] Input: Question text

[2092] Action: Enter your answer on the keyboard and click the submit button

[2093] Output: Answer text

[2094] The terminal sends the user's answer to the server.

[2095] Input: User's answer

[2096] What it does: Sends the answer text to the server as a POST request

[2097] Output: The response text received by the server

[2098] The server stores this response in a database.

[2099] Input: User answer text

[2100] What it does: Saves the answer to the user's profile information in a database

[2101] Output: Answer data stored in a database

[2102] Step 4:

[2103] Sentiment analysis with emotion engine

[2104] The emotion engine (inside the server) analyzes the content of the user's conversation, facial expressions, tone of voice, etc., and recognizes the user's emotional state.

[2105] Input: User interaction, facial expressions, and tone of voice

[2106] How it works: Applying text analysis, facial expression recognition, and speech analysis algorithms

[2107] Output: Recognized emotion data

[2108] Example: When a user says, "I'm a little anxious," the emotion engine recognizes anxiety.

[2109] Step 5:

[2110] Tailoring questions based on user sentiment

[2111] The generative AI generates questions based on the user's emotions based on feedback from the emotion engine.

[2112] Input: Emotion engine feedback

[2113] How it works: The AI ​​model generates the next question

[2114] Output: Adjusted question text

[2115] Example: If a user is feeling anxious, the generative AI will ask them questions to help them relax.

[2116] Users answer emotionally sensitive questions, which allows for more detailed and accurate data to be collected.

[2117] Input: Tailored Question

[2118] Action: Enter answer on keyboard and click submit button

[2119] Output: User's answer text

[2120] Step 6:

[2121] Data organization and personnel chart generation

[2122] The server organizes the collected data and generates a human resources chart that centrally manages employees' abilities, aspirations, and emotional states.

[2123] Input: Data on the user's abilities, desires, and emotional state

[2124] How it works: Algorithms analyze and synthesize data to generate a visualized medical record

[2125] Output: Human Resources Chart

[2126] Example: Integrating skills maps, job preferences, and sentiment data to create a visual dashboard

[2127] Step 7:

[2128] Generate placement proposals

[2129] The server compares the personnel chart with the organization's current requirements data and automatically generates optimal placement proposals.

[2130] Input: Personnel chart, current status request data

[2131] How it works: Algorithms collate data and calculate optimal placement

[2132] Output: Placement proposal

[2133] Example: Generate a suggestion that "You are a good candidate to lead a data analysis project."

[2134] Step 8:

[2135] Review and feedback on suggestions

[2136] The user checks the placement proposal through the terminal and approves or adds comments.

[2137] Input: Proposal

[2138] Action: Review the suggestion and click the "Approve" or "Add Suggestion" button

[2139] Output: User feedback

[2140] The terminal sends the user's feedback to the server.

[2141] Input: User feedback

[2142] Behavior: Sends feedback to the server as a POST request

[2143] Output: Feedback data received by the server

[2144] The server will re-evaluate based on the feedback it receives and readjust its proposals as needed.

[2145] Input: User feedback

[2146] How it works: Analyzes feedback and updates suggestions based on a rating algorithm

[2147] Output: Updated placement proposal

[2148] Step 9:

[2149] Final decision and follow-up

[2150] The server finalizes the placement based on the user's final approval and updates the database.

[2151] Input: Final approved placement proposal

[2152] Action: Writes the location information to the database.

[2153] Output: Updated database

[2154] The server will continuously monitor the performance of the employee after placement and reassess or reassign them as necessary.

[2155] Input: Performance data

[2156] How it works: Periodically collects performance data and re-evaluates it based on the evaluation algorithm.

[2157] Output: Improvement suggestions and reallocation decisions

[2158] (Application example 2)

[2159] 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."

[2160] In the past, employee allocation in logistics centers was primarily based on experience and intuition, which meant that efficient allocation that fully reflected employee abilities and preferences could not be achieved. Furthermore, employee emotions and psychological states were not taken into consideration, which had a negative impact on work efficiency and employee satisfaction. The present invention aims to solve these problems by providing a system that maximizes employee abilities and preferences while also taking into account their emotional states, resulting in efficient and satisfying personnel allocation.

[2161] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for acquiring employee abilities and preferences using interactive artificial intelligence; means for proposing staffing arrangements based on employee abilities and preferences based on the acquired data; means for evaluating the effectiveness of the proposed staffing arrangements; means for continuously improving the accuracy of staffing arrangements based on the evaluation results; means for analyzing user emotions using an emotion engine and reflecting the results in the dialogue content; and means for generating optimal staffing proposals based on the business characteristics of the application destination. This enables detailed collection of employee abilities and preferences and enables highly accurate staffing arrangements that take emotional states into account. This is also expected to improve work efficiency and employee satisfaction at logistics centers.

[2162] "Conversational AI" is an AI system that naturally interacts with users and collects necessary information.

[2163] "Employee Competence" refers to the techniques, knowledge, skills and experience possessed by an employee.

[2164] "Wishes" refer to the job duties and work content that employees desire, as well as their desired working conditions.

[2165] "Staffing" refers to assigning appropriate tasks and roles to employees based on their abilities and preferences.

[2166] An "emotion engine" is a system for analyzing a user's emotional state.

[2167] "Means for evaluating the effectiveness of the proposed personnel allocation" is a system for evaluating how effective the proposed personnel allocation was.

[2168] "Means to continuously improve the accuracy of personnel placement" refers to a system for providing feedback on evaluation results and improving the accuracy of placement from the next time onwards.

[2169] "Means for analyzing the user's emotions and reflecting the results in the content of the dialogue" refers to a mechanism in which the emotion engine recognizes the user's emotions and adjusts the content of the dialogue based on that information.

[2170] A "visual human resources chart" is a document that organizes employee abilities, aspirations, and emotional data and displays them in a graphical format.

[2171] A "logistics center" refers to a facility that stores goods, ships them, manages inventory, and handles incoming and outgoing shipments.

[2172] This invention is a system that acquires the abilities and desires of employees at a logistics center and proposes and evaluates optimal personnel allocation based on them. This system uses a combination of conversational artificial intelligence and an emotion engine to collect detailed information about employees' abilities and desires, and recognizes and reflects their emotional states to achieve highly accurate personnel allocation.

[2173] Hardware and software used

[2174] Hardware: Smartphone, Head-Mounted Display (HMD)

[2175] Software: Python, EmotionRecognition (e.g. DeepFace), OpenAI GPT

[2176] System Operation Overview

[2177] 1. User Registration and Login

[2178] Users access the system's login screen using a smartphone or HMD and enter basic information such as their name, employee ID, and password. The device sends the information to the server, which then compares it with a database and performs authentication.

[2179] 2. Starting an interactive session

[2180] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The device sends this request to the server, which then invokes a generative AI model (e.g., GPT-4) to set up the conversational environment.

[2181] 3. Confirm your skills and aspirations

[2182] The generative AI on the server asks the user questions such as, "What are your best skills?" The user answers using a device, "I'm good at data analysis and project management," and the device sends the answer to the server and stores it in a database.

[2183] 4. Sentiment analysis using an emotion engine

[2184] The emotion engine (e.g., DeepFace) in the server analyzes the content of the user's conversation, facial expressions, tone of voice, etc. to recognize the user's emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content of the words and tone of voice.

[2185] 5. Tailoring questions based on user sentiment

[2186] The server's generation AI generates questions based on the user's emotions based on feedback from the emotion engine. For example, if the user is feeling anxious, it can ask questions to relax them or respond in a considerate manner.

[2187] 6. Data organization and human resource chart generation

[2188] The server organizes the collected data and generates a human resource chart that centrally manages employees' abilities, aspirations, and emotional states, creating a visualized human resource chart.

[2189] 7. Generating placement proposals

[2190] The server compares the talent chart with the organization's current requirements data and automatically generates optimal placement proposals, including specific department, position, and project assignments.

[2191] 8. Review and feedback on proposals

[2192] The user can review the placement proposal through the device and approve or add their comments. The device then sends the user's feedback to the server, which then reevaluates the proposal based on the received feedback and adjusts it as necessary.

[2193] An example of a prompt sentence to be input to a specific generative AI model:

[2194] "What is your best skill?"

[2195] "What kind of work would you like done?"

[2196] This will enable efficient and highly satisfying staffing at logistics centers.

[2197] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2198] Step 1:

[2199] A user logs in to the system using a terminal. The input is the user's basic information (name, employee ID, password), and the output is the authentication result (success or failure). The terminal sends this information to the server, which then checks it against a database and performs authentication processing. If authentication is successful, a user session is started.

[2200] Step 2:

[2201] To start a session with a conversational AI, a user clicks the "Start conversation" button on their device. The input is the user's request to start the session, and the output is a notification that the conversational environment has been set up. The device sends the request to the server, which then calls the generative AI model to set up the conversational environment.

[2202] Step 3:

[2203] The server uses a generative AI model to ask the user questions and obtain the user's abilities and aspirations. The input is the question generated by the generative AI model (e.g., "What are your best skills?") and the user's answer, and the output is the obtained data (skill information, desired job). The user answers using a device, and the device sends the answer to the server. The server stores this data in a database.

[2204] Step 4:

[2205] The emotion engine in the server analyzes the content of the user's dialogue and recognizes their emotional state. The input is the content of the user's dialogue and tone of voice, and the output is the analysis result of their emotional state. For example, if the user responds "I'm a little anxious," the emotion engine will recognize anxiety from the content and tone of voice.

[2206] Step 5:

[2207] Based on feedback from the emotion engine, the server uses a generative AI model to generate questions that correspond to the user's emotions. The input is the analysis result of the emotion engine, and the output is a tailored question (e.g., "What do you think about working in a relaxed environment?"). This allows the user to provide more detailed and accurate information.

[2208] Step 6:

[2209] The server organizes the collected data and generates a visually displayed talent chart. The input is employee ability, preference, and emotion data, and the output is an integrated talent chart. The server consolidates this data and displays it in a visual format (e.g., skill map, preference list, emotional state).

[2210] Step 7:

[2211] The server compares the personnel chart with the business characteristics of the logistics center and generates optimal placement proposals. The input is the personnel chart and current requirements data, and the output is a specific placement proposal (e.g., "suitable as leader of the data analysis project"). The server automatically generates optimal placements based on this data.

[2212] Step 8:

[2213] The user reviews the proposed placement and provides feedback through the device. The input is the user feedback (approval or additional comments), and the output is the reevaluated placement proposal. The device sends the feedback to the server, which reevaluates the placement proposal based on the feedback and adjusts it if necessary.

[2214] The above are the specific processing steps of a system that realizes optimal personnel allocation that reflects the abilities and wishes of employees at a logistics center.

[2215] 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.

[2216] 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.

[2217] 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.

[2218] The emotion ident...

Claims

1. A means for obtaining employee capabilities and aspirations using conversational artificial intelligence; A means of proposing personnel placement based on the acquired data according to the abilities and preferences of employees; a means of evaluating the effectiveness of proposed staffing arrangements; A means of continually improving the accuracy of personnel placement based on the evaluation results; A system including:

2. 2. The system of claim 1, wherein the conversational artificial intelligence gathers detailed information about the employee's skills and aspirations through multiple questions.

3. 2. The system according to claim 1, wherein a personnel chart is generated that organizes and visually displays the abilities and desires of employees.

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