System

Abstract

A system is provided.SOLUTION: A system comprising: means for collecting employee information; means for analyzing the collected employee information; means for suggesting an optimal department or task to an employee based on an analysis result; means for providing a suggestion result to a company manager; and means for collecting feedback from the employee and adjusting a generative AI model.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] In order for companies to make the most of their employees' skills and experience, it is important to assign the right people to the right departments and tasks. However, currently, personnel selection is often based on individual biases and limited information, resulting in the issue of not being able to achieve optimal personnel assignments. This can lead to the risk that employees will not be able to fully utilize their abilities, leading to a decline in the company's overall performance. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that includes a means for collecting employee data, a means for analyzing the collected employee data, a means for proposing optimal departments and tasks for employees based on the analysis results, a means for providing the proposal results to a company manager, and a means for collecting feedback from employees and adjusting a generative AI model. This enables unbiased, data-driven personnel allocation, allowing employees to maximize their capabilities.

[0006] 1. "Employee data" refers to information about employees, such as their resume, experience, achievements, desired department / qualifications, strengths / weaknesses, personality, and family circumstances.

[0007] 2. "Means" refers to a method, device, or process used to achieve a particular purpose.

[0008] 3. "Means of collection" refers to the interface or mechanism used to collect information from employees and store it in a database.

[0009] 4. "Means of analysis" refers to the process or system that evaluates and analyzes collected data using generative AI models.

[0010] 5. "Means for making proposals based on analysis results" refers to the process or mechanism for presenting optimal departments and operations to employees and corporate managers based on analysis results.

[0011] 6. "Means for providing proposal results" refers to the interface or mechanism for communicating optimal personnel placement proposals to corporate managers.

[0012] 7. "Feedback collection methods" refers to the process or mechanisms for collecting employee ratings and opinions and storing them in a database.

[0013] 8. "Generative AI model" refers to an algorithm or system that uses artificial intelligence technology to analyze data and suggest optimal departments or tasks. [Brief explanation of the drawings]

[0014] [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

[0015] 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.

[0016] First, the terms used in the following description will be explained.

[0017] 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).

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 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.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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."

[0035] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their terminals, and the data is sent to a server for analysis. The analysis results are then provided to employees and company managers as suggestions.

[0036] System Configuration

[0037] The system consists of the following main components:

[0038] 1. Terminal

[0039] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[0040] 2. Server

[0041] The server receives the data sent by employees and stores it in a database, which accumulates data on all employees.

[0042] The server analyzes the data using a generative AI model, which evaluates each employee's skill set and career path to determine the department and job that best suits them.

[0043] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[0044] Program processing flow (natural language explanation)

[0045] 1. Data entry (terminal)

[0046] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0047] 2. Data transmission (terminal)

[0048] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[0049] 3. Data storage (server)

[0050] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0051] 4. Data analysis (server)

[0052] The server analyzes all employee data stored in a database using a generative AI model, evaluating each employee's skill set and career path, and assigning them the most suitable department or job.

[0053] 5. Proposal Generation and Submission (Server)

[0054] The server generates a list of proposals for optimal departments and tasks based on the analysis results, which are sent to both employees and company administrators and can be viewed on their own devices.

[0055] 6. Displaying Suggestions (Device)

[0056] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[0057] 7. Feedback collection (device, server)

[0058] Users can input feedback on the suggestions and send it to the server via their device, which then stores the collected feedback in a database and uses it to refine the generative AI model.

[0059] Specific examples

[0060] Example 1: Employee A's career change

[0061] 1. Enter information

[0062] Employee A enters his / her achievements in the sales department and marketing-related skill information into the terminal and sends it.

[0063] 2. Data transmission and storage

[0064] The terminal sends the data to a server, which stores the information in a database.

[0065] 3. Data Analysis

[0066] The server analyzes employee A's data using generative AI and determines that a career change to the marketing department would be optimal for him.

[0067] 4. Departmental proposals and feedback

[0068] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback on whether it matches his or her preferences.

[0069] effect

[0070] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[0071] The processing flow will be explained below.

[0072] Step 1: Data Entry (User)

[0073] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0074] Step 2: Send data (terminal)

[0075] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[0076] Step 3: Data storage (server)

[0077] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0078] Step 4: Data collection and cleaning (server)

[0079] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[0080] Step 5: Analysis by Generative AI (Server)

[0081] The server then uses a generative AI model to analyze the cleaned data, which then evaluates and scores each employee's skill set and career path.

[0082] Step 6: Generate optimal placement plan (server)

[0083] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[0084] Step 7: Sending the proposal list (server)

[0085] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[0086] Step 8: Receive and display the proposal list (terminal)

[0087] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[0088] Step 9: User evaluation and feedback

[0089] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[0090] Step 10: Send feedback (device)

[0091] The device sends the feedback entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[0092] Step 11: Aggregating and Reflecting Feedback (Server)

[0093] The server aggregates the received feedback and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[0094] Example 1

[0095] 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."

[0096] Conventional personnel allocation systems have difficulty in properly evaluating employees' skills and preferences and suggesting the most suitable departments and tasks based on those evaluations. Furthermore, there is a lack of means to effectively collect feedback from employees and improve the system based on that feedback, making it difficult to contribute to improving the performance of the entire company.

[0097] 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.

[0098] In this invention, the server includes means for providing a terminal for inputting employee information, means for formally verifying the employee data input from the terminal and transmitting it to the server after ensuring security, means for saving the received data in a database, means for analyzing the data of all employees saved in the database using a generative AI model and scoring the optimal department and job, means for generating and providing a list of suggestions to employees and company managers based on the analysis results, and means for collecting feedback on the suggestions and using it to adjust the generative AI model. This enables data-driven and less biased personnel allocation, provides an environment where employees can maximize their abilities, and contributes to improving overall company performance.

[0099] A "terminal for entering employee information" is a device that allows employees to digitally enter their resume, work experience, skill set, strengths and weaknesses, desired department and qualifications, and send the information to a server.

[0100] "Means for verifying the format of employee data entered from a terminal, ensuring security, and sending it to the server" refers to a process for verifying the format of data entered from a terminal and checking for irregularities or defects, and a mechanism for ensuring security by encrypting the data and sending it safely to the server.

[0101] "Means for storing received data in a database" refers to the process of verifying employee data received from a terminal in temporary storage and then securely storing it in a database.

[0102] "A method of analyzing the data of all employees stored in a database using a generative AI model and scoring the most suitable department or job" refers to the process of analyzing employee information stored in a database using generative AI model techniques, and then using the results to quantify and evaluate the department or job that is most suitable for the employee.

[0103] "Means for generating and providing a list of proposals to employees and company managers based on the analysis results" refers to a mechanism that creates and provides a list of proposals for optimal departments and tasks to employees and company managers based on the analysis results of the generative AI model.

[0104] "Means for collecting feedback on proposals and using it to adjust the generative AI model" refers to the process of collecting opinions and impressions on proposals from employees and company managers, and improving the analysis algorithms and evaluation criteria of the generative AI model based on this feedback.

[0105] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their devices, and the data is sent to a server for analysis. The analysis results are provided as suggestions to employees and company managers, and feedback is collected and used to adjust the generative AI model.

[0106] System Configuration

[0107] The system consists of the following main components:

[0108] 1. Terminal

[0109] Terminals provide an interface for employees to input information. Examples include devices such as PCs and tablets. Employees input information such as their resume, experience, achievements, strengths and weaknesses, desired department and qualifications, etc., and send it to the server via their terminal.

[0110] 2. Server

[0111] The server receives the data sent by employees and stores it in a database, where it is analyzed using a generative AI model.

[0112] As an analysis procedure, data of all employees stored in the database is collected and the following prompt statement is generated:

[0113] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[0114] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[0115] Specific examples

[0116] Example 1: Employee A's career change

[0117] 1. Enter information

[0118] The user, Employee A, enters his / her achievements in the sales department (e.g., "95% sales quota achievement rate over the past five years") and marketing-related skill information (e.g., "Digital marketing qualifications") into the terminal and submits it.

[0119] 2. Data transmission and storage

[0120] The terminal validates the input data of employee A and sends it to the server. The server receives the data and stores the information in a database.

[0121] 3. Data Analysis

[0122] The server collects data on employee A and inputs the following prompt sentence into the generative AI model:

[0123] "Please optimize the career path for employee ID 001. His skills are "sales" and he holds a digital marketing qualification. He would like to work in "marketing." Please suggest the most suitable department and duties."

[0124] The generative AI model performs an analysis and determines that a career change to the marketing department would be optimal for Employee A.

[0125] 4. Departmental proposals and feedback

[0126] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback, saying, "I would like to be transferred to the marketing department, but I would like to continue to utilize my sales skills."

[0127] effect

[0128] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[0129] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0130] Step 1:

[0131] The user (employee) enters information through the terminal interface, specifically, resume, work experience, skill set, strengths and weaknesses, desired department and qualifications. The input data is in form format, and required fields are checked and the data format is validated.

[0132] Input: Employee resume, work experience, skill set, strengths and weaknesses, desired department and qualifications

[0133] Output: Validated employee data

[0134] Step 2:

[0135] The terminal performs format verification on the data entered by the user, checking for invalid formats and missing fields, encrypting the data, and sending it to the server.

[0136] Input: Validated employee data

[0137] Output: Encrypted employee data

[0138] Step 3:

[0139] The server receives the data sent from the device. The received data is temporarily stored in storage and the data format is checked again. The data is then saved in the database. A notification that saving is complete is sent to the device.

[0140] Input: Encrypted employee data

[0141] Output: Employee data saved in the database, notification of saving completion

[0142] Step 4:

[0143] The server collects all employee data stored in a database and analyzes it using a generative AI model. It generates prompt sentences and inputs them into the AI ​​model for analysis.

[0144] Input: All employee data stored in a database

[0145] Output: Analysis results (scoring of optimal departments and tasks)

[0146] As an example, the server generates the following prompt:

[0147] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[0148] Step 5:

[0149] The server generates a list of proposals for employees and company administrators based on the analysis results and sends it to the terminals. The proposal list includes details of the most suitable departments and tasks.

[0150] Input: Analysis results

[0151] Output: A list of suggestions for employees and company administrators

[0152] Step 6:

[0153] The device decodes the list of suggestions received from the server and displays it to the user, who can then review the details of each suggestion and enter feedback in a form.

[0154] Input: Suggestion list

[0155] Output: A list of suggestions displayed to the user, a feedback form

[0156] Step 7:

[0157] The user (employee) inputs feedback on the received proposal and sends it to the server via their device. The server receives this feedback, stores it in a database, and uses it to adjust the generative AI model.

[0158] Input: Feedback data

[0159] Output: Feedback data stored in a database, a tuned generative AI model

[0160] (Application example 1)

[0161] 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."

[0162] While systems existed that analyzed employee data to suggest optimal departments and tasks, there was a lack of mechanisms for evaluating the operational status and performance of robots operating in factories in real time and suggesting optimal tasks and placement locations. As a result, efficient robot operation was difficult, limiting productivity improvements. Therefore, the present invention aims to solve the problem of efficient production management and work automation by expanding human resource management systems and realizing optimal placement and task suggestions for factory robots.

[0163] 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.

[0164] In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for collecting data on the operating status of robots in the factory, means for analyzing the collected robot data and proposing optimal tasks and placement locations, means for providing the proposal results to the factory manager, and means for collecting feedback from the factory manager and adjusting the generative AI model. This enables optimal placement and work proposals in real time for not only employees but also robots in the factory, thereby realizing efficient production management and work automation.

[0165] "Means for collecting employee data" refers to interfaces and devices for collecting information such as resumes, experience, achievements, desired departments and qualifications from employees.

[0166] "Means for analyzing collected employee data" refers to a device or program that has the function of analyzing collected employee data and evaluating the skills and aptitude of employees.

[0167] "Means for suggesting the most suitable department or job" refers to algorithms or systems that present the most suitable department or job to employees based on the analysis results.

[0168] The "means of providing to the company administrator" refers to the means of notifying the company administrator of the proposal results and requesting confirmation and approval.

[0169] "Means for collecting feedback from employees and adjusting the generative AI model" refers to a device or program that allows employees to input their opinions and evaluations of proposed placements and work, and then improves and adjusts the generative AI model based on that data.

[0170] The "means for collecting operational status data of robots in a factory" refers to sensors and communication modules for collecting operational status and performance data from robots operating in a factory.

[0171] "Means for analyzing collected robot data" refers to a device or program that has the function of analyzing collected robot performance data and evaluating the operating status and capabilities of the robot.

[0172] "Means for proposing optimal tasks and placement locations" refers to algorithms or systems that, based on the analysis results, propose optimal tasks and placement locations for robots in a factory.

[0173] The "means of providing to the factory manager" refers to the means of notifying the factory manager of the proposal results and requesting confirmation and approval.

[0174] The "means for collecting feedback from factory managers and adjusting the generative AI model" refers to a device or program that allows factory managers to input their opinions and evaluations of proposed layouts and work, and then improves and adjusts the generative AI model based on that data.

[0175] The system for implementing this invention collects and analyzes employee data to propose optimal departments and tasks, and collects and analyzes operational status data of robots in factories to propose optimal tasks and locations. This system includes a terminal for inputting employee information, a server for collecting and analyzing data, and smart glasses that display the analysis results.

[0176] Hardware and Software Configuration

[0177] 1. Terminal

[0178] Provides an interface for employees to enter information. Examples include PCs, tablets, and smartphones. Employees enter their resumes, experience, achievements, desired departments, and qualifications.

[0179] 2. Server

[0180] The server receives input data from employees and stores it in a database. A specific example is an AWS (registered trademark) EC2 instance.

[0181] The server analyzes employee data using a generative AI model, such as OpenAI® GPT-4®.

[0182] The server also collects and analyzes operational status data from factory robots, specifically processing the robots' sensor data and performance information using AWS Lambda.

[0183] The generated analysis results are provided as suggestions to employees and factory managers, and feedback is collected and used to adjust the AI ​​model.

[0184] 3. Smart Glasses

[0185] A device worn by a factory manager. An example is the Microsoft® HoloLens®.

[0186] The analysis results and suggestions sent from the server are displayed, and administrators can provide immediate feedback using voice input or gestures.

[0187] Process Overview

[0188] The server receives employee data, stores it in a database, and then analyzes it using a generative AI model. This allows it to evaluate each employee's skill set and career path and score them for the department or job they're best suited for. Similarly, by collecting data on the operation of robots in factories and analyzing their performance, it can suggest optimal placement and tasks.

[0189] Specific processing flow

[0190] 1. Data Entry

[0191] The user (employee) enters their resume, experience, etc. into the terminal. After completing the input, the data is sent to the server and saved in the database.

[0192] 2. Data Analysis

[0193] The server analyzes employee data stored in a database using a generative AI model, and based on the analysis results, generates a list of proposals for optimal departments and work.

[0194] 3. Providing Proposals

[0195] The list of suggestions is provided to employees and company administrators, who can view it on their own devices. Users can review the details of the suggestions and provide feedback.

[0196] 4. Factory Robot Data Analysis

[0197] The server collects data on the robot's operating status and analyzes it. The analysis results are displayed on the factory manager's smart glasses, and appropriate tasks and placement locations are suggested.

[0198] Prompt Sentence Examples

[0199] 1. Employee Career Assessment Prompt:

[0200] "Please analyze employee A's latest data and suggest the best career path for him."

[0201] 2. Robot maintenance optimization prompt:

[0202] "Please analyze the latest performance data for Robot A and suggest any necessary maintenance items. Please also provide the next maintenance schedule."

[0203] This will enable optimal employee placement and work, as well as efficient operation of robots in factories, thereby improving productivity.

[0204] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0205] Step 1:

[0206] The user (employee) enters their resume, experience, achievements, desired department and qualifications into the terminal. Once the input is complete, they click the "Send" button to send the data to the server. The input data contains text information, which the terminal encrypts before sending.

[0207] Step 2:

[0208] The terminal sends the data entered by the user to the server. The server verifies the format of the received data and stores it in a database. The data is encrypted before being sent, and is decrypted and stored on the server side. The database uses a relational database service such as Amazon RDS.

[0209] Step 3:

[0210] The server analyzes employee data stored in the database using a generative AI model (OpenAI GPT-4). Specifically, it evaluates employees' skill sets and career paths, and processes and calculates the data to score the most suitable departments and tasks. Employee resumes and experience are used as input data, and the output is a score indicating the optimal placement for each employee.

[0211] Step 4:

[0212] The server generates a list of proposals for optimal departments and tasks based on the analysis results, and provides the proposals to company administrators and employees. The results are sent to the administrators' and employees' devices and displayed in GUI format. Employees can check the proposals on their devices.

[0213] Step 5:

[0214] Users (employees and managers) review the proposals and enter their feedback. The feedback includes opinions on how practical the proposals are and what areas need improvement. The terminals collect this feedback and send it to the server.

[0215] Step 6:

[0216] The server stores the collected feedback in a database and uses it to further adjust the generative AI model. The feedback is analyzed and the parameters of the generative AI model are fine-tuned to improve the accuracy of the next proposal. The input data is feedback from employees and managers, and the output data is the parameters of the adjusted AI model.

[0217] Step 7:

[0218] The server collects operational status data from the robots in the factory. The robots perform self-diagnosis and send operational status and performance data to the cloud. This data is pre-processed in real time using AWS Lambda and sent to the server.

[0219] Step 8:

[0220] The server analyzes the collected robot performance data and uses a generative AI model to evaluate the robot's operating status. The data includes sensor information and operating time, and the analysis results suggest optimal tasks and placement locations for the robot.

[0221] Step 9:

[0222] The server sends the robot's analysis results to the factory manager's smart glasses, where the manager can review the suggestions and provide real-time feedback. The smart glasses are operated through visual and voice input.

[0223] Step 10:

[0224] The server collects feedback from factory managers and adjusts the generative AI model again. Based on new performance data and feedback, the generative AI model is updated to further improve the accuracy of the next proposal. The input data is the factory managers' feedback, and the output data is an adjusted AI model.

[0225] 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.

[0226] This invention combines a system that collects employee data, analyzes it using a generative AI model, and suggests optimal departments and tasks with an emotion engine that recognizes the user's emotions. In this system, users input information using their devices, and the data is sent to a server for analysis. The analysis results are provided to users and company managers as suggestions. The emotion engine also takes the user's emotional state into account, improving the quality of the suggestions.

[0227] System Configuration

[0228] The system consists of the following main components:

[0229] 1. Terminal

[0230] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal. The terminal also incorporates an emotion engine that evaluates the user's emotions in real time as they input information.

[0231] 2. Server

[0232] The server receives the data sent by the user and stores it in a database, which accumulates data on all employees.

[0233] The server analyzes the data using a generative AI model, which evaluates and scores each employee's skill set and career path, incorporating emotional data from an emotion engine.

[0234] The server provides the analysis results to users and company administrators in the form of suggestions, and also collects user feedback to use in adjusting the generative AI model.

[0235] Program processing flow (natural language explanation)

[0236] 1. Data Entry (User)

[0237] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0238] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[0239] 2. Data transmission (terminal)

[0240] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[0241] 3. Data storage (server)

[0242] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0243] 4. Data Collection and Cleaning (Server)

[0244] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[0245] 5. Analysis by generative AI (server)

[0246] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. The analysis also incorporates emotional data provided by the emotion engine.

[0247] 6. Generating optimal placement plan (server)

[0248] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and the list of suggestions is stored in a database.

[0249] 7. Sending the proposal list (server)

[0250] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[0251] 8. Receiving and displaying the proposal list (terminal)

[0252] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[0253] 9. User Evaluation and Feedback

[0254] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[0255] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[0256] 10. Send Feedback (Device)

[0257] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[0258] 11. Feedback aggregation and reflection (server)

[0259] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[0260] Specific examples

[0261] Example 1: Employee B's career change

[0262] 1. Enter information

[0263] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[0264] 2. Data transmission and storage

[0265] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[0266] 3. Data Analysis

[0267] The server analyzes Employee B's data using generative AI and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B's stress level is low.

[0268] 4. Departmental proposals and feedback

[0269] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[0270] This system allows for highly accurate personnel allocation that takes into account both data and emotions, thereby maximizing employee performance and improving overall company performance.

[0271] The processing flow will be explained below.

[0272] Step 1: Data Entry (User)

[0273] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0274] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[0275] Step 2: Send data (terminal)

[0276] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[0277] Step 3: Data storage (server)

[0278] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0279] Step 4: Data collection and cleaning (server)

[0280] The server collects all employee data stored in the database and performs data cleaning to find any incomplete or inaccurate data, including validating the data format and removing duplicate data.

[0281] Step 5: Analysis by Generative AI (Server)

[0282] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. Emotion data provided by the emotion engine is also incorporated into the analysis.

[0283] Step 6: Generate optimal placement plan (server)

[0284] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[0285] Step 7: Sending the proposal list (server)

[0286] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[0287] Step 8: Receive and display the proposal list (terminal)

[0288] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[0289] Step 9: User evaluation and feedback

[0290] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[0291] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[0292] Step 10: Send feedback (device)

[0293] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[0294] Step 11: Aggregating and Reflecting Feedback (Server)

[0295] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[0296] Example 2

[0297] 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."

[0298] Conventional employee allocation systems only analyze collected employee data to suggest optimal departments and tasks. However, this does not take into account psychological factors such as employee emotions and stress levels, which limits the relevance of the suggestions. Furthermore, feedback may not be properly reflected, resulting in low accuracy of the generated AI model. This makes it difficult to maximize employee satisfaction and overall company efficiency.

[0299] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, an emotion engine for evaluating user emotions in real time, and means for incorporating emotion data into the analysis. This enables highly accurate placement proposals that take into account not only the skill sets of employees but also their emotions.

[0300] "Employee data" refers to information about individual employees, including their resume, experience, achievements, desired department, qualifications, etc.

[0301] A "collection means" is a method or device for obtaining employee data and entering it into the system.

[0302] "Means for analyzing" refers to a method or device for analyzing collected employee data.

[0303] The "means for proposing the most suitable department or job" is a method or device for providing an appropriate department or job to an employee based on the analysis results.

[0304] The "means for providing to the company administrator" refers to a method or device for notifying the manager of the company of the proposal results.

[0305] "Means for collecting feedback" refers to a method or device for obtaining evaluations and opinions from employees and reflecting them in the system.

[0306] A "generative AI model" is a model that uses machine learning or artificial intelligence techniques to analyze data and make predictions or suggestions.

[0307] The "emotion engine" is a technology that analyzes data such as a user's facial expressions, voice, and typing speed to assess their emotional state in real time.

[0308] "Emotion data" is information about the user's emotional state obtained by the emotion engine.

[0309] A "system" is a device or program that functions as a whole by combining multiple means.

[0310] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes optimal departments and tasks. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of the proposals is improved. This system consists of several major components.

[0311] First, the terminal provides an interface for employees to input information. This terminal is equipped with an emotion engine that evaluates the user's emotions in real time as they input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[0312] The server receives data sent by users and stores it in a database. The database serves to collect data on all employees. Once stored, the data undergoes a data cleaning process and is then analyzed by a generative AI model. The generative AI model evaluates and scores employees' skill sets and career paths. This analysis also incorporates emotional data from the emotion engine. For example, "Employee B, who has marketing-related skills and a low stress level" may be determined to be ideal for a "senior position in the marketing department."

[0313] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This list is stored in a database and sent to employees and company administrators. The proposed list is received by the device and displayed to the user. The user checks the details of the proposals and evaluates whether they match their preferences. When the user enters feedback and sends it back to the server via the device, the emotion engine also collects emotion data again.

[0314] The server aggregates user feedback and emotional data and uses it to adjust the generative AI model, which can improve the accuracy of future suggestions.

[0315] Specific examples

[0316] Example: Employee B's career change

[0317] 1. Enter information

[0318] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[0319] 2. Data transmission and storage

[0320] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[0321] 3. Data Analysis

[0322] The server analyzes Employee B's data using a generative AI model and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B is in a low stress state.

[0323] 4. Departmental proposals and feedback

[0324] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[0325] Prompt Sentence Examples

[0326] Please enter your performance in the sales department.

[0327] "Please enter your marketing-related skills"

[0328] Please enter your desired department and qualifications.

[0329] "Please select your current emotional state: low, medium, or high stress level."

[0330] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0331] Step 1:

[0332] Users enter information such as their resume, experience, strengths / weaknesses, desired department / qualifications, etc. through the device interface. Once the input is complete, they click the "Send" button to send the data to the device. At this point, the emotion engine analyzes the user's voice tone, facial expression, typing speed, etc. to evaluate their emotional state. This input data (resume, experience, emotional state) is used for subsequent processing.

[0333] Step 2:

[0334] The device sends the data and emotional state input by the user to the server. When transmitted, the data is securely transmitted using the AES encryption algorithm. The input here is the user's input data and emotional data, which are encrypted and transmitted to the server.

[0335] Step 3:

[0336] The server stores the data received from the device in a database. Once the storage is complete, the server sends a notification to the device that the storage is complete. The input data here is encrypted, and the server processes the data by decrypting it and storing it in the database.

[0337] Step 4:

[0338] The server collects all employee data stored in the database and performs a data cleaning process if there is any incomplete or inaccurate data. The input here is the raw data in the database and the output is the cleaned data. The cleaning process includes filling in missing data and standardizing the format.

[0339] Step 5:

[0340] The server uses a generative AI model to analyze the cleaned data. This analysis evaluates and scores each employee's skill set and career path. Emotional data provided by the emotion engine is also incorporated into the analysis. The input here is the cleaned data and emotional data, and the output is scored data. Specifically, the AI ​​model analyzes each skill and emotional state and calculates a score.

[0341] Step 6:

[0342] Based on the analysis results, the server generates a list of candidates for the optimal departments and tasks for each employee. This list of suggestions is stored in a database. The input here is the scoring results, and the output is a list containing optimal placement proposals.

[0343] Step 7:

[0344] The server then sends the generated proposal list to employees and company administrators. Each proposal recipient is notified. The input is the proposal list, and the output is notifications to employees and administrators. Specifically, the proposal list is sent via email or the company's internal messaging system.

[0345] Step 8:

[0346] The terminal decodes the proposal list received from the server and displays it to the user. The user reviews the details of each proposal and evaluates whether it reflects their preferences. The input here is the proposal list, and the output is the information displayed to the user.

[0347] Step 9:

[0348] The user evaluates the displayed suggestions and enters their feedback. Once the feedback is complete, they click the "Send" button to send the data to the device. The emotion engine also analyzes the user's emotional state at the time of the feedback and sends the results to the server. The input is the user's feedback and emotional data, and the output is the data to be sent to the server.

[0349] Step 10:

[0350] The device sends the feedback and emotion data entered by the user to the server. The data is checked for consistency, and if there are any problems, the device prompts the user to correct them. The input here is the feedback data, and the output is the data sent to the server.

[0351] Step 11:

[0352] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. It updates the database information based on the feedback and improves the accuracy of future suggestions. The input here is the feedback data and emotion data, and the output is an updated generative AI model. Specifically, the feedback information is reflected as learning data for the model.

[0353] (Application example 2)

[0354] 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."

[0355] Conventional employee placement systems suggest tasks based on an employee's skill set and experience, but because they do not take into account the employee's emotional state or stress level, the proposed tasks do not optimally motivate or enhance employee performance. Furthermore, fixed task suggestions can prevent individual employees from fully utilizing their talents and abilities. There is a need to solve these problems and provide an optimal task suggestion system that takes into account the emotional state of employees.

[0356] 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 collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for recognizing the emotional state of employees, and means for improving the quality of proposals by taking the emotional state into consideration. This enables highly accurate job proposals that reflect the emotional state and stress level of employees.

[0357] "Employee Data" includes an employee's resume, experience, achievements, desired department, qualifications, and other related information.

[0358] "Means of analysis" refers to the use of generative AI models to analyze collected employee data and assess skill sets and career paths.

[0359] The "means for proposing the most suitable department or job" is a means for generating a proposal for the most suitable department or job for an employee based on the analysis results.

[0360] The "means of providing the company administrator" is a means of notifying the company administrator of the proposal results and having the administrator confirm them.

[0361] "Means for collecting feedback and adjusting the generative AI model" refers to means for collecting feedback from employees and reflecting it in the generative AI model to improve the accuracy of the model.

[0362] The "means for recognizing emotional states" refers to a means for assessing an employee's emotional state in real time by analyzing their tone of voice, facial expressions, typing speed, etc.

[0363] "Means for improving the quality of proposals by taking into account emotional states" refers to a means for improving the quality of proposals by incorporating employees' emotional data into the analysis of generative AI models.

[0364] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes the most suitable department or job. This system incorporates an emotion engine that recognizes the user's emotions, improving the quality of proposals. Specific embodiments for implementation are shown below.

[0365] Hardware and software used

[0366] Terminal: A device that provides an interface for employees to input information. Smart glasses or head-mounted displays are used.

[0367] Server: The central unit that stores data, analyzes it, and generates suggestions.

[0368] Emotion engine: This engine recognizes the emotional state of the employee entering data and reflects it in the analysis. Specifically, it analyzes voice tone, facial expressions, and typing speed.

[0369] Generative AI model: An AI model used to analyze employee data and suggest the most suitable department or job.

[0370] System program processing description

[0371] The terminal provides an interface for employees to input data such as their resume, experience, achievements, desired department, qualifications, etc. The data entered by employees is analyzed in real time by an emotion engine, and their emotional state is also recorded.

[0372] The input data and emotional data are sent via a secure protocol to a server, where they are stored in a database. The server then analyzes the data using a generative AI model to assess each employee's skill set and career path. Emotional data is also incorporated into the analysis, and emotional states influence the scoring.

[0373] Based on the analysis results, the server generates the most suitable department and job suggestions for employees, and the suggested list is saved in the database and then notified to employees and company administrators.

[0374] Employees and company administrators can receive the suggestions and review specific details. Employees can then enter feedback on the suggestions. The emotion engine also evaluates the employee's emotional state when providing the feedback and collects the data. The feedback and emotion data are then sent back to the server and used to refine the generative AI model.

[0375] Specific examples

[0376] For example, consider the case of Employee A working on a factory production line. Employee A inputs his or her work experience and skill set through smart glasses, and the data is analyzed in real time by an emotion engine, which also evaluates his or her emotional state. The server then analyzes this data using a generative AI model and proposes a new, optimal production line placement for Employee A under low stress.

[0377] Prompt Sentence Examples

[0378] "Employee A's stress level has been assessed as low. We suggest that you assign him to supervisory duties on the next production line. Would you like to confirm the assignment?"

[0379] As described above, this invention is a system that can comprehensively analyze employee data and emotional states and make optimal work suggestions, thereby maximizing employee performance and improving overall work efficiency.

[0380] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0381] Step 1:

[0382] Users input information such as resume, experience, achievements, desired department, and qualifications through the terminal. The terminal collects this data and uses an emotion engine to evaluate the user's emotional state in real time as it is being input. The input data also includes emotional data such as tone of voice, facial expressions, and typing speed.

[0383] Step 2:

[0384] The terminal encrypts the collected employee data and emotion data and sends them to the server, ensuring data security. Input data includes the employee's resume and desired department, while emotion data represents the user's real-time emotional state.

[0385] Step 3:

[0386] The server receives the data sent from the terminal and stores it in a database, which collects all employee data and cleans incomplete or inaccurate data.

[0387] Step 4:

[0388] The server uses the generated AI model to analyze the stored data. It inputs the cleaned employee data and emotion data to evaluate and score the employee's skill set and career path. The analysis results are output as a scored dataset for each employee.

[0389] Step 5:

[0390] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This candidate list is created using a generative AI model and takes into account the employee's skill set and emotional data. The candidate list is output as a dataset and stored in a database.

[0391] Step 6:

[0392] The server sends the generated proposal list to employees and company administrators. The proposals are sent to the employees' and company administrators' devices as notifications and are displayed as detailed information.

[0393] Step 7:

[0394] The user checks the list of suggestions and inputs feedback. The input feedback includes requests for suggestions and revisions, as well as emotional data from the emotion engine. The feedback is then sent from the device to the server.

[0395] Step 8:

[0396] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. By analyzing the feedback data, the accuracy of the model is improved and the database is updated. As a result, the accuracy of proposals from the next time onwards is improved.

[0397] This series of steps enables the system to make highly accurate suggestions that take into account the emotional state of employees, proposing placements that are beneficial to both the company and the employees.

[0398] 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.

[0399] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0400] 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.

[0401] [Second embodiment]

[0402] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0403] 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.

[0404] 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).

[0405] 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.

[0406] 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.

[0407] 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).

[0408] 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. 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.

[0409] 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.

[0410] 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.

[0411] 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.

[0412] 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.

[0413] 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."

[0414] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their terminals, and the data is sent to a server for analysis. The analysis results are then provided to employees and company managers as suggestions.

[0415] System Configuration

[0416] The system consists of the following main components:

[0417] 1. Terminal

[0418] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[0419] 2. Server

[0420] The server receives the data sent by employees and stores it in a database, which accumulates data on all employees.

[0421] The server analyzes the data using a generative AI model, which evaluates each employee's skill set and career path to determine the department and job that best suits them.

[0422] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[0423] Program processing flow (natural language explanation)

[0424] 1. Data entry (terminal)

[0425] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0426] 2. Data transmission (terminal)

[0427] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[0428] 3. Data storage (server)

[0429] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0430] 4. Data analysis (server)

[0431] The server analyzes all employee data stored in a database using a generative AI model, evaluating each employee's skill set and career path, and assigning them the most suitable department or job.

[0432] 5. Proposal Generation and Submission (Server)

[0433] The server generates a list of proposals for optimal departments and tasks based on the analysis results, which are sent to both employees and company administrators and can be viewed on their own devices.

[0434] 6. Displaying Suggestions (Device)

[0435] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[0436] 7. Feedback collection (device, server)

[0437] Users can input feedback on the suggestions and send it to the server via their device, which then stores the collected feedback in a database and uses it to refine the generative AI model.

[0438] Specific examples

[0439] Example 1: Employee A's career change

[0440] 1. Enter information

[0441] Employee A enters his / her achievements in the sales department and marketing-related skill information into the terminal and sends it.

[0442] 2. Data transmission and storage

[0443] The terminal sends the data to a server, which stores the information in a database.

[0444] 3. Data Analysis

[0445] The server analyzes employee A's data using generative AI and determines that a career change to the marketing department would be optimal for him.

[0446] 4. Departmental proposals and feedback

[0447] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback on whether it matches his or her preferences.

[0448] effect

[0449] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[0450] The processing flow will be explained below.

[0451] Step 1: Data Entry (User)

[0452] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0453] Step 2: Send data (terminal)

[0454] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[0455] Step 3: Data storage (server)

[0456] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0457] Step 4: Data collection and cleaning (server)

[0458] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[0459] Step 5: Analysis by Generative AI (Server)

[0460] The server then uses a generative AI model to analyze the cleaned data, which then evaluates and scores each employee's skill set and career path.

[0461] Step 6: Generate optimal placement plan (server)

[0462] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[0463] Step 7: Sending the proposal list (server)

[0464] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[0465] Step 8: Receive and display the proposal list (terminal)

[0466] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[0467] Step 9: User evaluation and feedback

[0468] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[0469] Step 10: Send feedback (device)

[0470] The device sends the feedback entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[0471] Step 11: Aggregating and Reflecting Feedback (Server)

[0472] The server aggregates the received feedback and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[0473] Example 1

[0474] 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."

[0475] Conventional personnel allocation systems have difficulty in properly evaluating employees' skills and preferences and suggesting the most suitable departments and tasks based on those evaluations. Furthermore, there is a lack of means to effectively collect feedback from employees and improve the system based on that feedback, making it difficult to contribute to improving the performance of the entire company.

[0476] 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.

[0477] In this invention, the server includes means for providing a terminal for inputting employee information, means for formally verifying the employee data input from the terminal and transmitting it to the server after ensuring security, means for saving the received data in a database, means for analyzing the data of all employees saved in the database using a generative AI model and scoring the optimal department and job, means for generating and providing a list of suggestions to employees and company managers based on the analysis results, and means for collecting feedback on the suggestions and using it to adjust the generative AI model. This enables data-driven and less biased personnel allocation, provides an environment where employees can maximize their abilities, and contributes to improving overall company performance.

[0478] A "terminal for entering employee information" is a device that allows employees to digitally enter their resume, work experience, skill set, strengths and weaknesses, desired department and qualifications, and send the information to a server.

[0479] "Means for verifying the format of employee data entered from a terminal, ensuring security, and sending it to the server" refers to a process for verifying the format of data entered from a terminal and checking for irregularities or defects, and a mechanism for ensuring security by encrypting the data and sending it safely to the server.

[0480] "Means for storing received data in a database" refers to the process of verifying employee data received from a terminal in temporary storage and then securely storing it in a database.

[0481] "A method of analyzing the data of all employees stored in a database using a generative AI model and scoring the most suitable department or job" refers to the process of analyzing employee information stored in a database using generative AI model techniques, and then using the results to quantify and evaluate the department or job that is most suitable for the employee.

[0482] "Means for generating and providing a list of proposals to employees and company managers based on the analysis results" refers to a mechanism that creates and provides a list of proposals for optimal departments and tasks to employees and company managers based on the analysis results of the generative AI model.

[0483] "Means for collecting feedback on proposals and using it to adjust the generative AI model" refers to the process of collecting opinions and impressions on proposals from employees and company managers, and improving the analysis algorithms and evaluation criteria of the generative AI model based on this feedback.

[0484] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their devices, and the data is sent to a server for analysis. The analysis results are provided as suggestions to employees and company managers, and feedback is collected and used to adjust the generative AI model.

[0485] System Configuration

[0486] The system consists of the following main components:

[0487] 1. Terminal

[0488] Terminals provide an interface for employees to input information. Examples include devices such as PCs and tablets. Employees input information such as their resume, experience, achievements, strengths and weaknesses, desired department and qualifications, etc., and send it to the server via their terminal.

[0489] 2. Server

[0490] The server receives the data sent by employees and stores it in a database, where it is analyzed using a generative AI model.

[0491] As an analysis procedure, data of all employees stored in the database is collected and the following prompt statement is generated:

[0492] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[0493] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[0494] Specific examples

[0495] Example 1: Employee A's career change

[0496] 1. Enter information

[0497] The user, Employee A, enters his / her achievements in the sales department (e.g., "95% sales quota achievement rate over the past five years") and marketing-related skill information (e.g., "Digital marketing qualifications") into the terminal and submits it.

[0498] 2. Data transmission and storage

[0499] The terminal validates the input data of employee A and sends it to the server. The server receives the data and stores the information in a database.

[0500] 3. Data Analysis

[0501] The server collects data on employee A and inputs the following prompt sentence into the generative AI model:

[0502] "Please optimize the career path for employee ID 001. His skills are "sales" and he holds a digital marketing qualification. He would like to work in "marketing." Please suggest the most suitable department and duties."

[0503] The generative AI model performs an analysis and determines that a career change to the marketing department would be optimal for Employee A.

[0504] 4. Departmental proposals and feedback

[0505] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback, saying, "I would like to be transferred to the marketing department, but I would like to continue to utilize my sales skills."

[0506] effect

[0507] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[0508] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0509] Step 1:

[0510] The user (employee) enters information through the terminal interface, specifically, resume, work experience, skill set, strengths and weaknesses, desired department and qualifications. The input data is in form format, and required fields are checked and the data format is validated.

[0511] Input: Employee resume, work experience, skill set, strengths and weaknesses, desired department and qualifications

[0512] Output: Validated employee data

[0513] Step 2:

[0514] The terminal performs format verification on the data entered by the user, checking for invalid formats and missing fields, encrypting the data, and sending it to the server.

[0515] Input: Validated employee data

[0516] Output: Encrypted employee data

[0517] Step 3:

[0518] The server receives the data sent from the device. The received data is temporarily stored in storage and the data format is checked again. The data is then saved in the database. A notification that saving is complete is sent to the device.

[0519] Input: Encrypted employee data

[0520] Output: Employee data saved in the database, notification of saving completion

[0521] Step 4:

[0522] The server collects all employee data stored in a database and analyzes it using a generative AI model. It generates prompt sentences and inputs them into the AI ​​model for analysis.

[0523] Input: All employee data stored in a database

[0524] Output: Analysis results (scoring of optimal departments and tasks)

[0525] As an example, the server generates the following prompt:

[0526] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[0527] Step 5:

[0528] The server generates a list of proposals for employees and company administrators based on the analysis results and sends it to the terminals. The proposal list includes details of the most suitable departments and tasks.

[0529] Input: Analysis results

[0530] Output: A list of suggestions for employees and company administrators

[0531] Step 6:

[0532] The device decodes the list of suggestions received from the server and displays it to the user, who can then review the details of each suggestion and enter feedback in a form.

[0533] Input: Suggestion list

[0534] Output: A list of suggestions displayed to the user, a feedback form

[0535] Step 7:

[0536] The user (employee) inputs feedback on the received proposal and sends it to the server via their device. The server receives this feedback, stores it in a database, and uses it to adjust the generative AI model.

[0537] Input: Feedback data

[0538] Output: Feedback data stored in a database, a tuned generative AI model

[0539] (Application example 1)

[0540] 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."

[0541] While systems existed that analyzed employee data to suggest optimal departments and tasks, there was a lack of mechanisms for evaluating the operational status and performance of robots operating in factories in real time and suggesting optimal tasks and placement locations. As a result, efficient robot operation was difficult, limiting productivity improvements. Therefore, the present invention aims to solve the problem of efficient production management and work automation by expanding human resource management systems and realizing optimal placement and task suggestions for factory robots.

[0542] 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.

[0543] In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for collecting data on the operating status of robots in the factory, means for analyzing the collected robot data and proposing optimal tasks and placement locations, means for providing the proposal results to the factory manager, and means for collecting feedback from the factory manager and adjusting the generative AI model. This enables optimal placement and work proposals in real time for not only employees but also robots in the factory, thereby realizing efficient production management and work automation.

[0544] "Means for collecting employee data" refers to interfaces and devices for collecting information such as resumes, experience, achievements, desired departments and qualifications from employees.

[0545] "Means for analyzing collected employee data" refers to a device or program that has the function of analyzing collected employee data and evaluating the skills and aptitude of employees.

[0546] "Means for suggesting the most suitable department or job" refers to algorithms or systems that present the most suitable department or job to employees based on the analysis results.

[0547] The "means of providing to the company administrator" refers to the means of notifying the company administrator of the proposal results and requesting confirmation and approval.

[0548] "Means for collecting feedback from employees and adjusting the generative AI model" refers to a device or program that allows employees to input their opinions and evaluations of proposed placements and work, and then improves and adjusts the generative AI model based on that data.

[0549] The "means for collecting operational status data of robots in a factory" refers to sensors and communication modules for collecting operational status and performance data from robots operating in a factory.

[0550] "Means for analyzing collected robot data" refers to a device or program that has the function of analyzing collected robot performance data and evaluating the operating status and capabilities of the robot.

[0551] "Means for proposing optimal tasks and placement locations" refers to algorithms or systems that, based on the analysis results, propose optimal tasks and placement locations for robots in a factory.

[0552] The "means of providing to the factory manager" refers to the means of notifying the factory manager of the proposal results and requesting confirmation and approval.

[0553] The "means for collecting feedback from factory managers and adjusting the generative AI model" refers to a device or program that allows factory managers to input their opinions and evaluations of proposed layouts and work, and then improves and adjusts the generative AI model based on that data.

[0554] The system for implementing this invention collects and analyzes employee data to propose optimal departments and tasks, and collects and analyzes operational status data of robots in factories to propose optimal tasks and locations. This system includes a terminal for inputting employee information, a server for collecting and analyzing data, and smart glasses that display the analysis results.

[0555] Hardware and Software Configuration

[0556] 1. Terminal

[0557] Provides an interface for employees to enter information. Examples include PCs, tablets, and smartphones. Employees enter their resumes, experience, achievements, desired departments, and qualifications.

[0558] 2. Server

[0559] The server receives input data from employees and stores it in a database. A specific example is an AWS EC2 instance.

[0560] The server analyzes employee data using a generative AI model, such as OpenAI GPT-4.

[0561] The server also collects and analyzes operational status data from factory robots, specifically processing the robots' sensor data and performance information using AWS Lambda.

[0562] The generated analysis results are provided as suggestions to employees and factory managers, and feedback is collected and used to adjust the AI ​​model.

[0563] 3. Smart Glasses

[0564] A device worn by a factory manager. An example is the Microsoft HoloLens.

[0565] The analysis results and suggestions sent from the server are displayed, and administrators can provide immediate feedback using voice input or gestures.

[0566] Process Overview

[0567] The server receives employee data, stores it in a database, and then analyzes it using a generative AI model. This allows it to evaluate each employee's skill set and career path and score them for the department or job they're best suited for. Similarly, by collecting data on the operation of robots in factories and analyzing their performance, it can suggest optimal placement and tasks.

[0568] Specific processing flow

[0569] 1. Data Entry

[0570] The user (employee) enters their resume, experience, etc. into the terminal. After completing the input, the data is sent to the server and saved in the database.

[0571] 2. Data Analysis

[0572] The server analyzes employee data stored in a database using a generative AI model, and based on the analysis results, generates a list of proposals for optimal departments and work.

[0573] 3. Providing Proposals

[0574] The list of suggestions is provided to employees and company administrators, who can view it on their own devices. Users can review the details of the suggestions and provide feedback.

[0575] 4. Factory Robot Data Analysis

[0576] The server collects data on the robot's operating status and analyzes it. The analysis results are displayed on the factory manager's smart glasses, and appropriate tasks and placement locations are suggested.

[0577] Prompt Sentence Examples

[0578] 1. Employee Career Assessment Prompt:

[0579] "Please analyze employee A's latest data and suggest the best career path for him."

[0580] 2. Robot maintenance optimization prompt:

[0581] "Please analyze the latest performance data for Robot A and suggest any necessary maintenance items. Please also provide the next maintenance schedule."

[0582] This will enable optimal employee placement and work, as well as efficient operation of robots in factories, thereby improving productivity.

[0583] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0584] Step 1:

[0585] The user (employee) enters their resume, experience, achievements, desired department and qualifications into the terminal. Once the input is complete, they click the "Send" button to send the data to the server. The input data contains text information, which the terminal encrypts before sending.

[0586] Step 2:

[0587] The terminal sends the data entered by the user to the server. The server verifies the format of the received data and stores it in a database. The data is encrypted before being sent, and is decrypted and stored on the server side. The database uses a relational database service such as Amazon RDS.

[0588] Step 3:

[0589] The server analyzes employee data stored in the database using a generative AI model (OpenAI GPT-4). Specifically, it evaluates employees' skill sets and career paths, and processes and calculates the data to score the most suitable departments and tasks. Employee resumes and experience are used as input data, and the output is a score indicating the optimal placement for each employee.

[0590] Step 4:

[0591] The server generates a list of proposals for optimal departments and tasks based on the analysis results, and provides the proposals to company administrators and employees. The results are sent to the administrators' and employees' devices and displayed in GUI format. Employees can check the proposals on their devices.

[0592] Step 5:

[0593] Users (employees and managers) review the proposals and enter their feedback. The feedback includes opinions on how practical the proposals are and what areas need improvement. The terminals collect this feedback and send it to the server.

[0594] Step 6:

[0595] The server stores the collected feedback in a database and uses it to further adjust the generative AI model. The feedback is analyzed and the parameters of the generative AI model are fine-tuned to improve the accuracy of the next proposal. The input data is feedback from employees and managers, and the output data is the parameters of the adjusted AI model.

[0596] Step 7:

[0597] The server collects operational status data from the robots in the factory. The robots perform self-diagnosis and send operational status and performance data to the cloud. This data is pre-processed in real time using AWS Lambda and sent to the server.

[0598] Step 8:

[0599] The server analyzes the collected robot performance data and uses a generative AI model to evaluate the robot's operating status. The data includes sensor information and operating time, and the analysis results suggest optimal tasks and placement locations for the robot.

[0600] Step 9:

[0601] The server sends the robot's analysis results to the factory manager's smart glasses, where the manager can review the suggestions and provide real-time feedback. The smart glasses are operated through visual and voice input.

[0602] Step 10:

[0603] The server collects feedback from factory managers and adjusts the generative AI model again. Based on new performance data and feedback, the generative AI model is updated to further improve the accuracy of the next proposal. The input data is the factory managers' feedback, and the output data is an adjusted AI model.

[0604] 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.

[0605] This invention combines a system that collects employee data, analyzes it using a generative AI model, and suggests optimal departments and tasks with an emotion engine that recognizes the user's emotions. In this system, users input information using their devices, and the data is sent to a server for analysis. The analysis results are provided to users and company managers as suggestions. The emotion engine also takes the user's emotional state into account, improving the quality of the suggestions.

[0606] System Configuration

[0607] The system consists of the following main components:

[0608] 1. Terminal

[0609] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal. The terminal also incorporates an emotion engine that evaluates the user's emotions in real time as they input information.

[0610] 2. Server

[0611] The server receives the data sent by the user and stores it in a database, which accumulates data on all employees.

[0612] The server analyzes the data using a generative AI model, which evaluates and scores each employee's skill set and career path, incorporating emotional data from an emotion engine.

[0613] The server provides the analysis results to users and company administrators in the form of suggestions, and also collects user feedback to use in adjusting the generative AI model.

[0614] Program processing flow (natural language explanation)

[0615] 1. Data Entry (User)

[0616] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0617] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[0618] 2. Data transmission (terminal)

[0619] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[0620] 3. Data storage (server)

[0621] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0622] 4. Data Collection and Cleaning (Server)

[0623] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[0624] 5. Analysis by generative AI (server)

[0625] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. The analysis also incorporates emotional data provided by the emotion engine.

[0626] 6. Generating optimal placement plan (server)

[0627] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and the list of suggestions is stored in a database.

[0628] 7. Sending the proposal list (server)

[0629] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[0630] 8. Receiving and displaying the proposal list (terminal)

[0631] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[0632] 9. User Evaluation and Feedback

[0633] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[0634] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[0635] 10. Send Feedback (Device)

[0636] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[0637] 11. Feedback aggregation and reflection (server)

[0638] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[0639] Specific examples

[0640] Example 1: Employee B's career change

[0641] 1. Enter information

[0642] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[0643] 2. Data transmission and storage

[0644] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[0645] 3. Data Analysis

[0646] The server analyzes Employee B's data using generative AI and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B's stress level is low.

[0647] 4. Departmental proposals and feedback

[0648] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[0649] This system allows for highly accurate personnel allocation that takes into account both data and emotions, thereby maximizing employee performance and improving overall company performance.

[0650] The processing flow will be explained below.

[0651] Step 1: Data Entry (User)

[0652] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0653] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[0654] Step 2: Send data (terminal)

[0655] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[0656] Step 3: Data storage (server)

[0657] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0658] Step 4: Data collection and cleaning (server)

[0659] The server collects all employee data stored in the database and performs data cleaning to find any incomplete or inaccurate data, including validating the data format and removing duplicate data.

[0660] Step 5: Analysis by Generative AI (Server)

[0661] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. Emotion data provided by the emotion engine is also incorporated into the analysis.

[0662] Step 6: Generate optimal placement plan (server)

[0663] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[0664] Step 7: Sending the proposal list (server)

[0665] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[0666] Step 8: Receive and display the proposal list (terminal)

[0667] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[0668] Step 9: User evaluation and feedback

[0669] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[0670] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[0671] Step 10: Send feedback (device)

[0672] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[0673] Step 11: Aggregating and Reflecting Feedback (Server)

[0674] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[0675] Example 2

[0676] 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."

[0677] Conventional employee allocation systems only analyze collected employee data to suggest optimal departments and tasks. However, this does not take into account psychological factors such as employee emotions and stress levels, which limits the relevance of the suggestions. Furthermore, feedback may not be properly reflected, resulting in low accuracy of the generated AI model. This makes it difficult to maximize employee satisfaction and overall company efficiency.

[0678] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, an emotion engine for evaluating user emotions in real time, and means for incorporating emotion data into the analysis. This enables highly accurate placement proposals that take into account not only the skill sets of employees but also their emotions.

[0679] "Employee data" refers to information about individual employees, including their resume, experience, achievements, desired department, qualifications, etc.

[0680] A "collection means" is a method or device for obtaining employee data and entering it into the system.

[0681] "Means for analyzing" refers to a method or device for analyzing collected employee data.

[0682] The "means for proposing the most suitable department or job" is a method or device for providing an appropriate department or job to an employee based on the analysis results.

[0683] The "means for providing to the company administrator" refers to a method or device for notifying the manager of the company of the proposal results.

[0684] "Means for collecting feedback" refers to a method or device for obtaining evaluations and opinions from employees and reflecting them in the system.

[0685] A "generative AI model" is a model that uses machine learning or artificial intelligence techniques to analyze data and make predictions or suggestions.

[0686] The "emotion engine" is a technology that analyzes data such as a user's facial expressions, voice, and typing speed to assess their emotional state in real time.

[0687] "Emotion data" is information about the user's emotional state obtained by the emotion engine.

[0688] A "system" is a device or program that functions as a whole by combining multiple means.

[0689] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes optimal departments and tasks. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of the proposals is improved. This system consists of several major components.

[0690] First, the terminal provides an interface for employees to input information. This terminal is equipped with an emotion engine that evaluates the user's emotions in real time as they input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[0691] The server receives data sent by users and stores it in a database. The database serves to collect data on all employees. Once stored, the data undergoes a data cleaning process and is then analyzed by a generative AI model. The generative AI model evaluates and scores employees' skill sets and career paths. This analysis also incorporates emotional data from the emotion engine. For example, "Employee B, who has marketing-related skills and a low stress level" may be determined to be ideal for a "senior position in the marketing department."

[0692] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This list is stored in a database and sent to employees and company administrators. The proposed list is received by the device and displayed to the user. The user checks the details of the proposals and evaluates whether they match their preferences. When the user enters feedback and sends it back to the server via the device, the emotion engine also collects emotion data again.

[0693] The server aggregates user feedback and emotional data and uses it to adjust the generative AI model, which can improve the accuracy of future suggestions.

[0694] Specific examples

[0695] Example: Employee B's career change

[0696] 1. Enter information

[0697] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[0698] 2. Data transmission and storage

[0699] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[0700] 3. Data Analysis

[0701] The server analyzes Employee B's data using a generative AI model and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B is in a low stress state.

[0702] 4. Departmental proposals and feedback

[0703] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[0704] Prompt Sentence Examples

[0705] Please enter your performance in the sales department.

[0706] "Please enter your marketing-related skills"

[0707] Please enter your desired department and qualifications.

[0708] "Please select your current emotional state: low, medium, or high stress level."

[0709] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0710] Step 1:

[0711] Users enter information such as their resume, experience, strengths / weaknesses, desired department / qualifications, etc. through the device interface. Once the input is complete, they click the "Send" button to send the data to the device. At this point, the emotion engine analyzes the user's voice tone, facial expression, typing speed, etc. to evaluate their emotional state. This input data (resume, experience, emotional state) is used for subsequent processing.

[0712] Step 2:

[0713] The device sends the data and emotional state input by the user to the server. When transmitted, the data is securely transmitted using the AES encryption algorithm. The input here is the user's input data and emotional data, which are encrypted and transmitted to the server.

[0714] Step 3:

[0715] The server stores the data received from the device in a database. Once the storage is complete, the server sends a notification to the device that the storage is complete. The input data here is encrypted, and the server processes the data by decrypting it and storing it in the database.

[0716] Step 4:

[0717] The server collects all employee data stored in the database and performs a data cleaning process if there is any incomplete or inaccurate data. The input here is the raw data in the database and the output is the cleaned data. The cleaning process includes filling in missing data and standardizing the format.

[0718] Step 5:

[0719] The server uses a generative AI model to analyze the cleaned data. This analysis evaluates and scores each employee's skill set and career path. Emotional data provided by the emotion engine is also incorporated into the analysis. The input here is the cleaned data and emotional data, and the output is scored data. Specifically, the AI ​​model analyzes each skill and emotional state and calculates a score.

[0720] Step 6:

[0721] Based on the analysis results, the server generates a list of candidates for the optimal departments and tasks for each employee. This list of suggestions is stored in a database. The input here is the scoring results, and the output is a list containing optimal placement proposals.

[0722] Step 7:

[0723] The server then sends the generated proposal list to employees and company administrators. Each proposal recipient is notified. The input is the proposal list, and the output is notifications to employees and administrators. Specifically, the proposal list is sent via email or the company's internal messaging system.

[0724] Step 8:

[0725] The terminal decodes the proposal list received from the server and displays it to the user. The user reviews the details of each proposal and evaluates whether it reflects their preferences. The input here is the proposal list, and the output is the information displayed to the user.

[0726] Step 9:

[0727] The user evaluates the displayed suggestions and enters their feedback. Once the feedback is complete, they click the "Send" button to send the data to the device. The emotion engine also analyzes the user's emotional state at the time of the feedback and sends the results to the server. The input is the user's feedback and emotional data, and the output is the data to be sent to the server.

[0728] Step 10:

[0729] The device sends the feedback and emotion data entered by the user to the server. The data is checked for consistency, and if there are any problems, the device prompts the user to correct them. The input here is the feedback data, and the output is the data sent to the server.

[0730] Step 11:

[0731] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. It updates the database information based on the feedback and improves the accuracy of future suggestions. The input here is the feedback data and emotion data, and the output is an updated generative AI model. Specifically, the feedback information is reflected as learning data for the model.

[0732] (Application example 2)

[0733] 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."

[0734] Conventional employee placement systems suggest tasks based on an employee's skill set and experience, but because they do not take into account the employee's emotional state or stress level, the proposed tasks do not optimally motivate or enhance employee performance. Furthermore, fixed task suggestions can prevent individual employees from fully utilizing their talents and abilities. There is a need to solve these problems and provide an optimal task suggestion system that takes into account the emotional state of employees.

[0735] 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 collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for recognizing the emotional state of employees, and means for improving the quality of proposals by taking the emotional state into consideration. This enables highly accurate job proposals that reflect the emotional state and stress level of employees.

[0736] "Employee Data" includes an employee's resume, experience, achievements, desired department, qualifications, and other related information.

[0737] "Means of analysis" refers to the use of generative AI models to analyze collected employee data and assess skill sets and career paths.

[0738] The "means for proposing the most suitable department or job" is a means for generating a proposal for the most suitable department or job for an employee based on the analysis results.

[0739] The "means of providing the company administrator" is a means of notifying the company administrator of the proposal results and having the administrator confirm them.

[0740] "Means for collecting feedback and adjusting the generative AI model" refers to means for collecting feedback from employees and reflecting it in the generative AI model to improve the accuracy of the model.

[0741] The "means for recognizing emotional states" refers to a means for assessing an employee's emotional state in real time by analyzing their tone of voice, facial expressions, typing speed, etc.

[0742] "Means for improving the quality of proposals by taking into account emotional states" refers to a means for improving the quality of proposals by incorporating employees' emotional data into the analysis of generative AI models.

[0743] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes the most suitable department or job. This system incorporates an emotion engine that recognizes the user's emotions, improving the quality of proposals. Specific embodiments for implementation are shown below.

[0744] Hardware and software used

[0745] Terminal: A device that provides an interface for employees to input information. Smart glasses or head-mounted displays are used.

[0746] Server: The central unit that stores data, analyzes it, and generates suggestions.

[0747] Emotion engine: This engine recognizes the emotional state of the employee entering data and reflects it in the analysis. Specifically, it analyzes voice tone, facial expressions, and typing speed.

[0748] Generative AI model: An AI model used to analyze employee data and suggest the most suitable department or job.

[0749] System program processing description

[0750] The terminal provides an interface for employees to input data such as their resume, experience, achievements, desired department, qualifications, etc. The data entered by employees is analyzed in real time by an emotion engine, and their emotional state is also recorded.

[0751] The input data and emotional data are sent via a secure protocol to a server, where they are stored in a database. The server then analyzes the data using a generative AI model to assess each employee's skill set and career path. Emotional data is also incorporated into the analysis, and emotional states influence the scoring.

[0752] Based on the analysis results, the server generates the most suitable department and job suggestions for employees, and the suggested list is saved in the database and then notified to employees and company administrators.

[0753] Employees and company administrators can receive the suggestions and review specific details. Employees can then enter feedback on the suggestions. The emotion engine also evaluates the employee's emotional state when providing the feedback and collects the data. The feedback and emotion data are then sent back to the server and used to refine the generative AI model.

[0754] Specific examples

[0755] For example, consider the case of Employee A working on a factory production line. Employee A inputs his or her work experience and skill set through smart glasses, and the data is analyzed in real time by an emotion engine, which also evaluates his or her emotional state. The server then analyzes this data using a generative AI model and proposes a new, optimal production line placement for Employee A under low stress.

[0756] Prompt Sentence Examples

[0757] "Employee A's stress level has been assessed as low. We suggest that you assign him to supervisory duties on the next production line. Would you like to confirm the assignment?"

[0758] As described above, this invention is a system that can comprehensively analyze employee data and emotional states and make optimal work suggestions, thereby maximizing employee performance and improving overall work efficiency.

[0759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0760] Step 1:

[0761] Users input information such as resume, experience, achievements, desired department, and qualifications through the terminal. The terminal collects this data and uses an emotion engine to evaluate the user's emotional state in real time as it is being input. The input data also includes emotional data such as tone of voice, facial expressions, and typing speed.

[0762] Step 2:

[0763] The terminal encrypts the collected employee data and emotion data and sends them to the server, ensuring data security. Input data includes the employee's resume and desired department, while emotion data represents the user's real-time emotional state.

[0764] Step 3:

[0765] The server receives the data sent from the terminal and stores it in a database, which collects all employee data and cleans incomplete or inaccurate data.

[0766] Step 4:

[0767] The server uses the generated AI model to analyze the stored data. It inputs the cleaned employee data and emotion data to evaluate and score the employee's skill set and career path. The analysis results are output as a scored dataset for each employee.

[0768] Step 5:

[0769] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This candidate list is created using a generative AI model and takes into account the employee's skill set and emotional data. The candidate list is output as a dataset and stored in a database.

[0770] Step 6:

[0771] The server sends the generated proposal list to employees and company administrators. The proposals are sent to the employees' and company administrators' devices as notifications and are displayed as detailed information.

[0772] Step 7:

[0773] The user checks the list of suggestions and inputs feedback. The input feedback includes requests for suggestions and revisions, as well as emotional data from the emotion engine. The feedback is then sent from the device to the server.

[0774] Step 8:

[0775] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. By analyzing the feedback data, the accuracy of the model is improved and the database is updated. As a result, the accuracy of proposals from the next time onwards is improved.

[0776] This series of steps enables the system to make highly accurate suggestions that take into account the emotional state of employees, proposing placements that are beneficial to both the company and the employees.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] [Third embodiment]

[0781] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0782] 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.

[0783] 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).

[0784] 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.

[0785] 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.

[0786] 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).

[0787] 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. 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] 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."

[0793] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their terminals, and the data is sent to a server for analysis. The analysis results are then provided to employees and company managers as suggestions.

[0794] System Configuration

[0795] The system consists of the following main components:

[0796] 1. Terminal

[0797] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[0798] 2. Server

[0799] The server receives the data sent by employees and stores it in a database, which accumulates data on all employees.

[0800] The server analyzes the data using a generative AI model, which evaluates each employee's skill set and career path to determine the department and job that best suits them.

[0801] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[0802] Program processing flow (natural language explanation)

[0803] 1. Data entry (terminal)

[0804] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0805] 2. Data transmission (terminal)

[0806] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[0807] 3. Data storage (server)

[0808] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0809] 4. Data analysis (server)

[0810] The server analyzes all employee data stored in a database using a generative AI model, evaluating each employee's skill set and career path, and assigning them the most suitable department or job.

[0811] 5. Proposal Generation and Submission (Server)

[0812] The server generates a list of proposals for optimal departments and tasks based on the analysis results, which are sent to both employees and company administrators and can be viewed on their own devices.

[0813] 6. Displaying Suggestions (Device)

[0814] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[0815] 7. Feedback collection (device, server)

[0816] Users can input feedback on the suggestions and send it to the server via their device, which then stores the collected feedback in a database and uses it to refine the generative AI model.

[0817] Specific examples

[0818] Example 1: Employee A's career change

[0819] 1. Enter information

[0820] Employee A enters his / her achievements in the sales department and marketing-related skill information into the terminal and sends it.

[0821] 2. Data transmission and storage

[0822] The terminal sends the data to a server, which stores the information in a database.

[0823] 3. Data Analysis

[0824] The server analyzes employee A's data using generative AI and determines that a career change to the marketing department would be optimal for him.

[0825] 4. Departmental proposals and feedback

[0826] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback on whether it matches his or her preferences.

[0827] effect

[0828] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[0829] The processing flow will be explained below.

[0830] Step 1: Data Entry (User)

[0831] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0832] Step 2: Send data (terminal)

[0833] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[0834] Step 3: Data storage (server)

[0835] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[0836] Step 4: Data collection and cleaning (server)

[0837] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[0838] Step 5: Analysis by Generative AI (Server)

[0839] The server then uses a generative AI model to analyze the cleaned data, which then evaluates and scores each employee's skill set and career path.

[0840] Step 6: Generate optimal placement plan (server)

[0841] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[0842] Step 7: Sending the proposal list (server)

[0843] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[0844] Step 8: Receive and display the proposal list (terminal)

[0845] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[0846] Step 9: User evaluation and feedback

[0847] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[0848] Step 10: Send feedback (device)

[0849] The device sends the feedback entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[0850] Step 11: Aggregating and Reflecting Feedback (Server)

[0851] The server aggregates the received feedback and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[0852] Example 1

[0853] 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."

[0854] Conventional personnel allocation systems have difficulty in properly evaluating employees' skills and preferences and suggesting the most suitable departments and tasks based on those evaluations. Furthermore, there is a lack of means to effectively collect feedback from employees and improve the system based on that feedback, making it difficult to contribute to improving the performance of the entire company.

[0855] 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.

[0856] In this invention, the server includes means for providing a terminal for inputting employee information, means for formally verifying the employee data input from the terminal and transmitting it to the server after ensuring security, means for saving the received data in a database, means for analyzing the data of all employees saved in the database using a generative AI model and scoring the optimal department and job, means for generating and providing a list of suggestions to employees and company managers based on the analysis results, and means for collecting feedback on the suggestions and using it to adjust the generative AI model. This enables data-driven and less biased personnel allocation, provides an environment where employees can maximize their abilities, and contributes to improving overall company performance.

[0857] A "terminal for entering employee information" is a device that allows employees to digitally enter their resume, work experience, skill set, strengths and weaknesses, desired department and qualifications, and send the information to a server.

[0858] "Means for verifying the format of employee data entered from a terminal, ensuring security, and sending it to the server" refers to a process for verifying the format of data entered from a terminal and checking for irregularities or defects, and a mechanism for ensuring security by encrypting the data and sending it safely to the server.

[0859] "Means for storing received data in a database" refers to the process of verifying employee data received from a terminal in temporary storage and then securely storing it in a database.

[0860] "A method of analyzing the data of all employees stored in a database using a generative AI model and scoring the most suitable department or job" refers to the process of analyzing employee information stored in a database using generative AI model techniques, and then using the results to quantify and evaluate the department or job that is most suitable for the employee.

[0861] "Means for generating and providing a list of proposals to employees and company managers based on the analysis results" refers to a mechanism that creates and provides a list of proposals for optimal departments and tasks to employees and company managers based on the analysis results of the generative AI model.

[0862] "Means for collecting feedback on proposals and using it to adjust the generative AI model" refers to the process of collecting opinions and impressions on proposals from employees and company managers, and improving the analysis algorithms and evaluation criteria of the generative AI model based on this feedback.

[0863] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their devices, and the data is sent to a server for analysis. The analysis results are provided as suggestions to employees and company managers, and feedback is collected and used to adjust the generative AI model.

[0864] System Configuration

[0865] The system consists of the following main components:

[0866] 1. Terminal

[0867] Terminals provide an interface for employees to input information. Examples include devices such as PCs and tablets. Employees input information such as their resume, experience, achievements, strengths and weaknesses, desired department and qualifications, etc., and send it to the server via their terminal.

[0868] 2. Server

[0869] The server receives the data sent by employees and stores it in a database, where it is analyzed using a generative AI model.

[0870] As an analysis procedure, data of all employees stored in the database is collected and the following prompt statement is generated:

[0871] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[0872] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[0873] Specific examples

[0874] Example 1: Employee A's career change

[0875] 1. Enter information

[0876] The user, Employee A, enters his / her achievements in the sales department (e.g., "95% sales quota achievement rate over the past five years") and marketing-related skill information (e.g., "Digital marketing qualifications") into the terminal and submits it.

[0877] 2. Data transmission and storage

[0878] The terminal validates the input data of employee A and sends it to the server. The server receives the data and stores the information in a database.

[0879] 3. Data Analysis

[0880] The server collects data on employee A and inputs the following prompt sentence into the generative AI model:

[0881] "Please optimize the career path for employee ID 001. His skills are "sales" and he holds a digital marketing qualification. He would like to work in "marketing." Please suggest the most suitable department and duties."

[0882] The generative AI model performs an analysis and determines that a career change to the marketing department would be optimal for Employee A.

[0883] 4. Departmental proposals and feedback

[0884] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback, saying, "I would like to be transferred to the marketing department, but I would like to continue to utilize my sales skills."

[0885] effect

[0886] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[0887] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0888] Step 1:

[0889] The user (employee) enters information through the terminal interface, specifically, resume, work experience, skill set, strengths and weaknesses, desired department and qualifications. The input data is in form format, and required fields are checked and the data format is validated.

[0890] Input: Employee resume, work experience, skill set, strengths and weaknesses, desired department and qualifications

[0891] Output: Validated employee data

[0892] Step 2:

[0893] The terminal performs format verification on the data entered by the user, checking for invalid formats and missing fields, encrypting the data, and sending it to the server.

[0894] Input: Validated employee data

[0895] Output: Encrypted employee data

[0896] Step 3:

[0897] The server receives the data sent from the device. The received data is temporarily stored in storage and the data format is checked again. The data is then saved in the database. A notification that saving is complete is sent to the device.

[0898] Input: Encrypted employee data

[0899] Output: Employee data saved in the database, notification of saving completion

[0900] Step 4:

[0901] The server collects all employee data stored in a database and analyzes it using a generative AI model. It generates prompt sentences and inputs them into the AI ​​model for analysis.

[0902] Input: All employee data stored in a database

[0903] Output: Analysis results (scoring of optimal departments and tasks)

[0904] As an example, the server generates the following prompt:

[0905] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[0906] Step 5:

[0907] The server generates a list of proposals for employees and company administrators based on the analysis results and sends it to the terminals. The proposal list includes details of the most suitable departments and tasks.

[0908] Input: Analysis results

[0909] Output: A list of suggestions for employees and company administrators

[0910] Step 6:

[0911] The device decodes the list of suggestions received from the server and displays it to the user, who can then review the details of each suggestion and enter feedback in a form.

[0912] Input: Suggestion list

[0913] Output: A list of suggestions displayed to the user, a feedback form

[0914] Step 7:

[0915] The user (employee) inputs feedback on the received proposal and sends it to the server via their device. The server receives this feedback, stores it in a database, and uses it to adjust the generative AI model.

[0916] Input: Feedback data

[0917] Output: Feedback data stored in a database, a tuned generative AI model

[0918] (Application example 1)

[0919] 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."

[0920] While systems existed that analyzed employee data to suggest optimal departments and tasks, there was a lack of mechanisms for evaluating the operational status and performance of robots operating in factories in real time and suggesting optimal tasks and placement locations. As a result, efficient robot operation was difficult, limiting productivity improvements. Therefore, the present invention aims to solve the problem of efficient production management and work automation by expanding human resource management systems and realizing optimal placement and task suggestions for factory robots.

[0921] 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.

[0922] In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for collecting data on the operating status of robots in the factory, means for analyzing the collected robot data and proposing optimal tasks and placement locations, means for providing the proposal results to the factory manager, and means for collecting feedback from the factory manager and adjusting the generative AI model. This enables optimal placement and work proposals in real time for not only employees but also robots in the factory, thereby realizing efficient production management and work automation.

[0923] "Means for collecting employee data" refers to interfaces and devices for collecting information such as resumes, experience, achievements, desired departments and qualifications from employees.

[0924] "Means for analyzing collected employee data" refers to a device or program that has the function of analyzing collected employee data and evaluating the skills and aptitude of employees.

[0925] "Means for suggesting the most suitable department or job" refers to algorithms or systems that present the most suitable department or job to employees based on the analysis results.

[0926] The "means of providing to the company administrator" refers to the means of notifying the company administrator of the proposal results and requesting confirmation and approval.

[0927] "Means for collecting feedback from employees and adjusting the generative AI model" refers to a device or program that allows employees to input their opinions and evaluations of proposed placements and work, and then improves and adjusts the generative AI model based on that data.

[0928] The "means for collecting operational status data of robots in a factory" refers to sensors and communication modules for collecting operational status and performance data from robots operating in a factory.

[0929] "Means for analyzing collected robot data" refers to a device or program that has the function of analyzing collected robot performance data and evaluating the operating status and capabilities of the robot.

[0930] "Means for proposing optimal tasks and placement locations" refers to algorithms or systems that, based on the analysis results, propose optimal tasks and placement locations for robots in a factory.

[0931] The "means of providing to the factory manager" refers to the means of notifying the factory manager of the proposal results and requesting confirmation and approval.

[0932] The "means for collecting feedback from factory managers and adjusting the generative AI model" refers to a device or program that allows factory managers to input their opinions and evaluations of proposed layouts and work, and then improves and adjusts the generative AI model based on that data.

[0933] The system for implementing this invention collects and analyzes employee data to propose optimal departments and tasks, and collects and analyzes operational status data of robots in factories to propose optimal tasks and locations. This system includes a terminal for inputting employee information, a server for collecting and analyzing data, and smart glasses that display the analysis results.

[0934] Hardware and Software Configuration

[0935] 1. Terminal

[0936] Provides an interface for employees to enter information. Examples include PCs, tablets, and smartphones. Employees enter their resumes, experience, achievements, desired departments, and qualifications.

[0937] 2. Server

[0938] The server receives input data from employees and stores it in a database. A specific example is an AWS EC2 instance.

[0939] The server analyzes employee data using a generative AI model, such as OpenAI GPT-4.

[0940] The server also collects and analyzes operational status data from factory robots, specifically processing the robots' sensor data and performance information using AWS Lambda.

[0941] The generated analysis results are provided as suggestions to employees and factory managers, and feedback is collected and used to adjust the AI ​​model.

[0942] 3. Smart Glasses

[0943] A device worn by a factory manager. An example is the Microsoft HoloLens.

[0944] The analysis results and suggestions sent from the server are displayed, and administrators can provide immediate feedback using voice input or gestures.

[0945] Process Overview

[0946] The server receives employee data, stores it in a database, and then analyzes it using a generative AI model. This allows it to evaluate each employee's skill set and career path and score them for the department or job they're best suited for. Similarly, by collecting data on the operation of robots in factories and analyzing their performance, it can suggest optimal placement and tasks.

[0947] Specific processing flow

[0948] 1. Data Entry

[0949] The user (employee) enters their resume, experience, etc. into the terminal. After completing the input, the data is sent to the server and saved in the database.

[0950] 2. Data Analysis

[0951] The server analyzes employee data stored in a database using a generative AI model, and based on the analysis results, generates a list of proposals for optimal departments and work.

[0952] 3. Providing Proposals

[0953] The list of suggestions is provided to employees and company administrators, who can view it on their own devices. Users can review the details of the suggestions and provide feedback.

[0954] 4. Factory Robot Data Analysis

[0955] The server collects data on the robot's operating status and analyzes it. The analysis results are displayed on the factory manager's smart glasses, and appropriate tasks and placement locations are suggested.

[0956] Prompt Sentence Examples

[0957] 1. Employee Career Assessment Prompt:

[0958] "Please analyze employee A's latest data and suggest the best career path for him."

[0959] 2. Robot maintenance optimization prompt:

[0960] "Please analyze the latest performance data for Robot A and suggest any necessary maintenance items. Please also provide the next maintenance schedule."

[0961] This will enable optimal employee placement and work, as well as efficient operation of robots in factories, thereby improving productivity.

[0962] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0963] Step 1:

[0964] The user (employee) enters their resume, experience, achievements, desired department and qualifications into the terminal. Once the input is complete, they click the "Send" button to send the data to the server. The input data contains text information, which the terminal encrypts before sending.

[0965] Step 2:

[0966] The terminal sends the data entered by the user to the server. The server verifies the format of the received data and stores it in a database. The data is encrypted before being sent, and is decrypted and stored on the server side. The database uses a relational database service such as Amazon RDS.

[0967] Step 3:

[0968] The server analyzes employee data stored in the database using a generative AI model (OpenAI GPT-4). Specifically, it evaluates employees' skill sets and career paths, and processes and calculates the data to score the most suitable departments and tasks. Employee resumes and experience are used as input data, and the output is a score indicating the optimal placement for each employee.

[0969] Step 4:

[0970] The server generates a list of proposals for optimal departments and tasks based on the analysis results, and provides the proposals to company administrators and employees. The results are sent to the administrators' and employees' devices and displayed in GUI format. Employees can check the proposals on their devices.

[0971] Step 5:

[0972] Users (employees and managers) review the proposals and enter their feedback. The feedback includes opinions on how practical the proposals are and what areas need improvement. The terminals collect this feedback and send it to the server.

[0973] Step 6:

[0974] The server stores the collected feedback in a database and uses it to further adjust the generative AI model. The feedback is analyzed and the parameters of the generative AI model are fine-tuned to improve the accuracy of the next proposal. The input data is feedback from employees and managers, and the output data is the parameters of the adjusted AI model.

[0975] Step 7:

[0976] The server collects operational status data from the robots in the factory. The robots perform self-diagnosis and send operational status and performance data to the cloud. This data is pre-processed in real time using AWS Lambda and sent to the server.

[0977] Step 8:

[0978] The server analyzes the collected robot performance data and uses a generative AI model to evaluate the robot's operating status. The data includes sensor information and operating time, and the analysis results suggest optimal tasks and placement locations for the robot.

[0979] Step 9:

[0980] The server sends the robot's analysis results to the factory manager's smart glasses, where the manager can review the suggestions and provide real-time feedback. The smart glasses are operated through visual and voice input.

[0981] Step 10:

[0982] The server collects feedback from factory managers and adjusts the generative AI model again. Based on new performance data and feedback, the generative AI model is updated to further improve the accuracy of the next proposal. The input data is the factory managers' feedback, and the output data is an adjusted AI model.

[0983] 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.

[0984] This invention combines a system that collects employee data, analyzes it using a generative AI model, and suggests optimal departments and tasks with an emotion engine that recognizes the user's emotions. In this system, users input information using their devices, and the data is sent to a server for analysis. The analysis results are provided to users and company managers as suggestions. The emotion engine also takes the user's emotional state into account, improving the quality of the suggestions.

[0985] System Configuration

[0986] The system consists of the following main components:

[0987] 1. Terminal

[0988] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal. The terminal also incorporates an emotion engine that evaluates the user's emotions in real time as they input information.

[0989] 2. Server

[0990] The server receives the data sent by the user and stores it in a database, which accumulates data on all employees.

[0991] The server analyzes the data using a generative AI model, which evaluates and scores each employee's skill set and career path, incorporating emotional data from an emotion engine.

[0992] The server provides the analysis results to users and company administrators in the form of suggestions, and also collects user feedback to use in adjusting the generative AI model.

[0993] Program processing flow (natural language explanation)

[0994] 1. Data Entry (User)

[0995] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[0996] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[0997] 2. Data transmission (terminal)

[0998] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[0999] 3. Data storage (server)

[1000] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[1001] 4. Data Collection and Cleaning (Server)

[1002] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[1003] 5. Analysis by generative AI (server)

[1004] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. The analysis also incorporates emotional data provided by the emotion engine.

[1005] 6. Generating optimal placement plan (server)

[1006] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and the list of suggestions is stored in a database.

[1007] 7. Sending the proposal list (server)

[1008] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[1009] 8. Receiving and displaying the proposal list (terminal)

[1010] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[1011] 9. User Evaluation and Feedback

[1012] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[1013] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[1014] 10. Send Feedback (Device)

[1015] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[1016] 11. Feedback aggregation and reflection (server)

[1017] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[1018] Specific examples

[1019] Example 1: Employee B's career change

[1020] 1. Enter information

[1021] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[1022] 2. Data transmission and storage

[1023] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[1024] 3. Data Analysis

[1025] The server analyzes Employee B's data using generative AI and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B's stress level is low.

[1026] 4. Departmental proposals and feedback

[1027] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[1028] This system allows for highly accurate personnel allocation that takes into account both data and emotions, thereby maximizing employee performance and improving overall company performance.

[1029] The processing flow will be explained below.

[1030] Step 1: Data Entry (User)

[1031] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[1032] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[1033] Step 2: Send data (terminal)

[1034] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[1035] Step 3: Data storage (server)

[1036] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[1037] Step 4: Data collection and cleaning (server)

[1038] The server collects all employee data stored in the database and performs data cleaning to find any incomplete or inaccurate data, including validating the data format and removing duplicate data.

[1039] Step 5: Analysis by Generative AI (Server)

[1040] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. Emotion data provided by the emotion engine is also incorporated into the analysis.

[1041] Step 6: Generate optimal placement plan (server)

[1042] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[1043] Step 7: Sending the proposal list (server)

[1044] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[1045] Step 8: Receive and display the proposal list (terminal)

[1046] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[1047] Step 9: User evaluation and feedback

[1048] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[1049] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[1050] Step 10: Send feedback (device)

[1051] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[1052] Step 11: Aggregating and Reflecting Feedback (Server)

[1053] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[1054] Example 2

[1055] 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."

[1056] Conventional employee allocation systems only analyze collected employee data to suggest optimal departments and tasks. However, this does not take into account psychological factors such as employee emotions and stress levels, which limits the relevance of the suggestions. Furthermore, feedback may not be properly reflected, resulting in low accuracy of the generated AI model. This makes it difficult to maximize employee satisfaction and overall company efficiency.

[1057] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, an emotion engine for evaluating user emotions in real time, and means for incorporating emotion data into the analysis. This enables highly accurate placement proposals that take into account not only the skill sets of employees but also their emotions.

[1058] "Employee data" refers to information about individual employees, including their resume, experience, achievements, desired department, qualifications, etc.

[1059] A "collection means" is a method or device for obtaining employee data and entering it into the system.

[1060] "Means for analyzing" refers to a method or device for analyzing collected employee data.

[1061] The "means for proposing the most suitable department or job" is a method or device for providing an appropriate department or job to an employee based on the analysis results.

[1062] The "means for providing to the company administrator" refers to a method or device for notifying the manager of the company of the proposal results.

[1063] "Means for collecting feedback" refers to a method or device for obtaining evaluations and opinions from employees and reflecting them in the system.

[1064] A "generative AI model" is a model that uses machine learning or artificial intelligence techniques to analyze data and make predictions or suggestions.

[1065] The "emotion engine" is a technology that analyzes data such as a user's facial expressions, voice, and typing speed to assess their emotional state in real time.

[1066] "Emotion data" is information about the user's emotional state obtained by the emotion engine.

[1067] A "system" is a device or program that functions as a whole by combining multiple means.

[1068] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes optimal departments and tasks. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of the proposals is improved. This system consists of several major components.

[1069] First, the terminal provides an interface for employees to input information. This terminal is equipped with an emotion engine that evaluates the user's emotions in real time as they input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[1070] The server receives data sent by users and stores it in a database. The database serves to collect data on all employees. Once stored, the data undergoes a data cleaning process and is then analyzed by a generative AI model. The generative AI model evaluates and scores employees' skill sets and career paths. This analysis also incorporates emotional data from the emotion engine. For example, "Employee B, who has marketing-related skills and a low stress level" may be determined to be ideal for a "senior position in the marketing department."

[1071] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This list is stored in a database and sent to employees and company administrators. The proposed list is received by the device and displayed to the user. The user checks the details of the proposals and evaluates whether they match their preferences. When the user enters feedback and sends it back to the server via the device, the emotion engine also collects emotion data again.

[1072] The server aggregates user feedback and emotional data and uses it to adjust the generative AI model, which can improve the accuracy of future suggestions.

[1073] Specific examples

[1074] Example: Employee B's career change

[1075] 1. Enter information

[1076] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[1077] 2. Data transmission and storage

[1078] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[1079] 3. Data Analysis

[1080] The server analyzes Employee B's data using a generative AI model and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B is in a low stress state.

[1081] 4. Departmental proposals and feedback

[1082] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[1083] Prompt Sentence Examples

[1084] Please enter your performance in the sales department.

[1085] "Please enter your marketing-related skills"

[1086] Please enter your desired department and qualifications.

[1087] "Please select your current emotional state: low, medium, or high stress level."

[1088] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1089] Step 1:

[1090] Users enter information such as their resume, experience, strengths / weaknesses, desired department / qualifications, etc. through the device interface. Once the input is complete, they click the "Send" button to send the data to the device. At this point, the emotion engine analyzes the user's voice tone, facial expression, typing speed, etc. to evaluate their emotional state. This input data (resume, experience, emotional state) is used for subsequent processing.

[1091] Step 2:

[1092] The device sends the data and emotional state input by the user to the server. When transmitted, the data is securely transmitted using the AES encryption algorithm. The input here is the user's input data and emotional data, which are encrypted and transmitted to the server.

[1093] Step 3:

[1094] The server stores the data received from the device in a database. Once the storage is complete, the server sends a notification to the device that the storage is complete. The input data here is encrypted, and the server processes the data by decrypting it and storing it in the database.

[1095] Step 4:

[1096] The server collects all employee data stored in the database and performs a data cleaning process if there is any incomplete or inaccurate data. The input here is the raw data in the database and the output is the cleaned data. The cleaning process includes filling in missing data and standardizing the format.

[1097] Step 5:

[1098] The server uses a generative AI model to analyze the cleaned data. This analysis evaluates and scores each employee's skill set and career path. Emotional data provided by the emotion engine is also incorporated into the analysis. The input here is the cleaned data and emotional data, and the output is scored data. Specifically, the AI ​​model analyzes each skill and emotional state and calculates a score.

[1099] Step 6:

[1100] Based on the analysis results, the server generates a list of candidates for the optimal departments and tasks for each employee. This list of suggestions is stored in a database. The input here is the scoring results, and the output is a list containing optimal placement proposals.

[1101] Step 7:

[1102] The server then sends the generated proposal list to employees and company administrators. Each proposal recipient is notified. The input is the proposal list, and the output is notifications to employees and administrators. Specifically, the proposal list is sent via email or the company's internal messaging system.

[1103] Step 8:

[1104] The terminal decodes the proposal list received from the server and displays it to the user. The user reviews the details of each proposal and evaluates whether it reflects their preferences. The input here is the proposal list, and the output is the information displayed to the user.

[1105] Step 9:

[1106] The user evaluates the displayed suggestions and enters their feedback. Once the feedback is complete, they click the "Send" button to send the data to the device. The emotion engine also analyzes the user's emotional state at the time of the feedback and sends the results to the server. The input is the user's feedback and emotional data, and the output is the data to be sent to the server.

[1107] Step 10:

[1108] The device sends the feedback and emotion data entered by the user to the server. The data is checked for consistency, and if there are any problems, the device prompts the user to correct them. The input here is the feedback data, and the output is the data sent to the server.

[1109] Step 11:

[1110] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. It updates the database information based on the feedback and improves the accuracy of future suggestions. The input here is the feedback data and emotion data, and the output is an updated generative AI model. Specifically, the feedback information is reflected as learning data for the model.

[1111] (Application example 2)

[1112] 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."

[1113] Conventional employee placement systems suggest tasks based on an employee's skill set and experience, but because they do not take into account the employee's emotional state or stress level, the proposed tasks do not optimally motivate or enhance employee performance. Furthermore, fixed task suggestions can prevent individual employees from fully utilizing their talents and abilities. There is a need to solve these problems and provide an optimal task suggestion system that takes into account the emotional state of employees.

[1114] 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 collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for recognizing the emotional state of employees, and means for improving the quality of proposals by taking the emotional state into consideration. This enables highly accurate job proposals that reflect the emotional state and stress level of employees.

[1115] "Employee Data" includes an employee's resume, experience, achievements, desired department, qualifications, and other related information.

[1116] "Means of analysis" refers to the use of generative AI models to analyze collected employee data and assess skill sets and career paths.

[1117] The "means for proposing the most suitable department or job" is a means for generating a proposal for the most suitable department or job for an employee based on the analysis results.

[1118] The "means of providing the company administrator" is a means of notifying the company administrator of the proposal results and having the administrator confirm them.

[1119] "Means for collecting feedback and adjusting the generative AI model" refers to means for collecting feedback from employees and reflecting it in the generative AI model to improve the accuracy of the model.

[1120] The "means for recognizing emotional states" refers to a means for assessing an employee's emotional state in real time by analyzing their tone of voice, facial expressions, typing speed, etc.

[1121] "Means for improving the quality of proposals by taking into account emotional states" refers to a means for improving the quality of proposals by incorporating employees' emotional data into the analysis of generative AI models.

[1122] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes the most suitable department or job. This system incorporates an emotion engine that recognizes the user's emotions, improving the quality of proposals. Specific embodiments for implementation are shown below.

[1123] Hardware and software used

[1124] Terminal: A device that provides an interface for employees to input information. Smart glasses or head-mounted displays are used.

[1125] Server: The central unit that stores data, analyzes it, and generates suggestions.

[1126] Emotion engine: This engine recognizes the emotional state of the employee entering data and reflects it in the analysis. Specifically, it analyzes voice tone, facial expressions, and typing speed.

[1127] Generative AI model: An AI model used to analyze employee data and suggest the most suitable department or job.

[1128] System program processing description

[1129] The terminal provides an interface for employees to input data such as their resume, experience, achievements, desired department, qualifications, etc. The data entered by employees is analyzed in real time by an emotion engine, and their emotional state is also recorded.

[1130] The input data and emotional data are sent via a secure protocol to a server, where they are stored in a database. The server then analyzes the data using a generative AI model to assess each employee's skill set and career path. Emotional data is also incorporated into the analysis, and emotional states influence the scoring.

[1131] Based on the analysis results, the server generates the most suitable department and job suggestions for employees, and the suggested list is saved in the database and then notified to employees and company administrators.

[1132] Employees and company administrators can receive the suggestions and review specific details. Employees can then enter feedback on the suggestions. The emotion engine also evaluates the employee's emotional state when providing the feedback and collects the data. The feedback and emotion data are then sent back to the server and used to refine the generative AI model.

[1133] Specific examples

[1134] For example, consider the case of Employee A working on a factory production line. Employee A inputs his or her work experience and skill set through smart glasses, and the data is analyzed in real time by an emotion engine, which also evaluates his or her emotional state. The server then analyzes this data using a generative AI model and proposes a new, optimal production line placement for Employee A under low stress.

[1135] Prompt Sentence Examples

[1136] "Employee A's stress level has been assessed as low. We suggest that you assign him to supervisory duties on the next production line. Would you like to confirm the assignment?"

[1137] As described above, this invention is a system that can comprehensively analyze employee data and emotional states and make optimal work suggestions, thereby maximizing employee performance and improving overall work efficiency.

[1138] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1139] Step 1:

[1140] Users input information such as resume, experience, achievements, desired department, and qualifications through the terminal. The terminal collects this data and uses an emotion engine to evaluate the user's emotional state in real time as it is being input. The input data also includes emotional data such as tone of voice, facial expressions, and typing speed.

[1141] Step 2:

[1142] The terminal encrypts the collected employee data and emotion data and sends them to the server, ensuring data security. Input data includes the employee's resume and desired department, while emotion data represents the user's real-time emotional state.

[1143] Step 3:

[1144] The server receives the data sent from the terminal and stores it in a database, which collects all employee data and cleans incomplete or inaccurate data.

[1145] Step 4:

[1146] The server uses the generated AI model to analyze the stored data. It inputs the cleaned employee data and emotion data to evaluate and score the employee's skill set and career path. The analysis results are output as a scored dataset for each employee.

[1147] Step 5:

[1148] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This candidate list is created using a generative AI model and takes into account the employee's skill set and emotional data. The candidate list is output as a dataset and stored in a database.

[1149] Step 6:

[1150] The server sends the generated proposal list to employees and company administrators. The proposals are sent to the employees' and company administrators' devices as notifications and are displayed as detailed information.

[1151] Step 7:

[1152] The user checks the list of suggestions and inputs feedback. The input feedback includes requests for suggestions and revisions, as well as emotional data from the emotion engine. The feedback is then sent from the device to the server.

[1153] Step 8:

[1154] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. By analyzing the feedback data, the accuracy of the model is improved and the database is updated. As a result, the accuracy of proposals from the next time onwards is improved.

[1155] This series of steps enables the system to make highly accurate suggestions that take into account the emotional state of employees, proposing placements that are beneficial to both the company and the employees.

[1156] 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.

[1157] 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.

[1158] 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.

[1159] [Fourth embodiment]

[1160] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1161] 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.

[1162] 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).

[1163] 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.

[1164] 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.

[1165] 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).

[1166] 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. 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.

[1167] 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.

[1168] 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.

[1169] 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.

[1170] 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.

[1171] 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.

[1172] 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."

[1173] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their terminals, and the data is sent to a server for analysis. The analysis results are then provided to employees and company managers as suggestions.

[1174] System Configuration

[1175] The system consists of the following main components:

[1176] 1. Terminal

[1177] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[1178] 2. Server

[1179] The server receives the data sent by employees and stores it in a database, which accumulates data on all employees.

[1180] The server analyzes the data using a generative AI model, which evaluates each employee's skill set and career path to determine the department and job that best suits them.

[1181] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[1182] Program processing flow (natural language explanation)

[1183] 1. Data entry (terminal)

[1184] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[1185] 2. Data transmission (terminal)

[1186] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[1187] 3. Data storage (server)

[1188] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[1189] 4. Data analysis (server)

[1190] The server analyzes all employee data stored in a database using a generative AI model, evaluating each employee's skill set and career path, and assigning them the most suitable department or job.

[1191] 5. Proposal Generation and Submission (Server)

[1192] The server generates a list of proposals for optimal departments and tasks based on the analysis results, which are sent to both employees and company administrators and can be viewed on their own devices.

[1193] 6. Displaying Suggestions (Device)

[1194] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[1195] 7. Feedback collection (device, server)

[1196] Users can input feedback on the suggestions and send it to the server via their device, which then stores the collected feedback in a database and uses it to refine the generative AI model.

[1197] Specific examples

[1198] Example 1: Employee A's career change

[1199] 1. Enter information

[1200] Employee A enters his / her achievements in the sales department and marketing-related skill information into the terminal and sends it.

[1201] 2. Data transmission and storage

[1202] The terminal sends the data to a server, which stores the information in a database.

[1203] 3. Data Analysis

[1204] The server analyzes employee A's data using generative AI and determines that a career change to the marketing department would be optimal for him.

[1205] 4. Departmental proposals and feedback

[1206] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback on whether it matches his or her preferences.

[1207] effect

[1208] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[1209] The processing flow will be explained below.

[1210] Step 1: Data Entry (User)

[1211] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[1212] Step 2: Send data (terminal)

[1213] The terminal verifies the format of the data entered by the user and sends it to the server, where it is encrypted to ensure security.

[1214] Step 3: Data storage (server)

[1215] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[1216] Step 4: Data collection and cleaning (server)

[1217] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[1218] Step 5: Analysis by Generative AI (Server)

[1219] The server then uses a generative AI model to analyze the cleaned data, which then evaluates and scores each employee's skill set and career path.

[1220] Step 6: Generate optimal placement plan (server)

[1221] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[1222] Step 7: Sending the proposal list (server)

[1223] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[1224] Step 8: Receive and display the proposal list (terminal)

[1225] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[1226] Step 9: User evaluation and feedback

[1227] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[1228] Step 10: Send feedback (device)

[1229] The device sends the feedback entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[1230] Step 11: Aggregating and Reflecting Feedback (Server)

[1231] The server aggregates the received feedback and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[1232] Example 1

[1233] 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."

[1234] Conventional personnel allocation systems have difficulty in properly evaluating employees' skills and preferences and suggesting the most suitable departments and tasks based on those evaluations. Furthermore, there is a lack of means to effectively collect feedback from employees and improve the system based on that feedback, making it difficult to contribute to improving the performance of the entire company.

[1235] 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.

[1236] In this invention, the server includes means for providing a terminal for inputting employee information, means for formally verifying the employee data input from the terminal and transmitting it to the server after ensuring security, means for saving the received data in a database, means for analyzing the data of all employees saved in the database using a generative AI model and scoring the optimal department and job, means for generating and providing a list of suggestions to employees and company managers based on the analysis results, and means for collecting feedback on the suggestions and using it to adjust the generative AI model. This enables data-driven and less biased personnel allocation, provides an environment where employees can maximize their abilities, and contributes to improving overall company performance.

[1237] A "terminal for entering employee information" is a device that allows employees to digitally enter their resume, work experience, skill set, strengths and weaknesses, desired department and qualifications, and send the information to a server.

[1238] "Means for verifying the format of employee data entered from a terminal, ensuring security, and sending it to the server" refers to a process for verifying the format of data entered from a terminal and checking for irregularities or defects, and a mechanism for ensuring security by encrypting the data and sending it safely to the server.

[1239] "Means for storing received data in a database" refers to the process of verifying employee data received from a terminal in temporary storage and then securely storing it in a database.

[1240] "A method of analyzing the data of all employees stored in a database using a generative AI model and scoring the most suitable department or job" refers to the process of analyzing employee information stored in a database using generative AI model techniques, and then using the results to quantify and evaluate the department or job that is most suitable for the employee.

[1241] "Means for generating and providing a list of proposals to employees and company managers based on the analysis results" refers to a mechanism that creates and provides a list of proposals for optimal departments and tasks to employees and company managers based on the analysis results of the generative AI model.

[1242] "Means for collecting feedback on proposals and using it to adjust the generative AI model" refers to the process of collecting opinions and impressions on proposals from employees and company managers, and improving the analysis algorithms and evaluation criteria of the generative AI model based on this feedback.

[1243] This invention is a system that collects employee data, analyzes it, and suggests appropriate departments and tasks. Employees input information using their devices, and the data is sent to a server for analysis. The analysis results are provided as suggestions to employees and company managers, and feedback is collected and used to adjust the generative AI model.

[1244] System Configuration

[1245] The system consists of the following main components:

[1246] 1. Terminal

[1247] Terminals provide an interface for employees to input information. Examples include devices such as PCs and tablets. Employees input information such as their resume, experience, achievements, strengths and weaknesses, desired department and qualifications, etc., and send it to the server via their terminal.

[1248] 2. Server

[1249] The server receives the data sent by employees and stores it in a database, where it is analyzed using a generative AI model.

[1250] As an analysis procedure, data of all employees stored in the database is collected and the following prompt statement is generated:

[1251] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[1252] The server provides the analysis results to employees and company administrators in the form of suggestions, and also collects feedback from employees to use in adjusting the generative AI model.

[1253] Specific examples

[1254] Example 1: Employee A's career change

[1255] 1. Enter information

[1256] The user, Employee A, enters his / her achievements in the sales department (e.g., "95% sales quota achievement rate over the past five years") and marketing-related skill information (e.g., "Digital marketing qualifications") into the terminal and submits it.

[1257] 2. Data transmission and storage

[1258] The terminal validates the input data of employee A and sends it to the server. The server receives the data and stores the information in a database.

[1259] 3. Data Analysis

[1260] The server collects data on employee A and inputs the following prompt sentence into the generative AI model:

[1261] "Please optimize the career path for employee ID 001. His skills are "sales" and he holds a digital marketing qualification. He would like to work in "marketing." Please suggest the most suitable department and duties."

[1262] The generative AI model performs an analysis and determines that a career change to the marketing department would be optimal for Employee A.

[1263] 4. Departmental proposals and feedback

[1264] The server proposes a position in the marketing department to employee A. Employee A reviews the proposal and enters feedback, saying, "I would like to be transferred to the marketing department, but I would like to continue to utilize my sales skills."

[1265] effect

[1266] This system enables data-driven, bias-free personnel allocation, providing an environment where employees can maximize their capabilities, while also contributing to improving the performance of the entire company.

[1267] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1268] Step 1:

[1269] The user (employee) enters information through the terminal interface, specifically, resume, work experience, skill set, strengths and weaknesses, desired department and qualifications. The input data is in form format, and required fields are checked and the data format is validated.

[1270] Input: Employee resume, work experience, skill set, strengths and weaknesses, desired department and qualifications

[1271] Output: Validated employee data

[1272] Step 2:

[1273] The terminal performs format verification on the data entered by the user, checking for invalid formats and missing fields, encrypting the data, and sending it to the server.

[1274] Input: Validated employee data

[1275] Output: Encrypted employee data

[1276] Step 3:

[1277] The server receives the data sent from the device. The received data is temporarily stored in storage and the data format is checked again. The data is then saved in the database. A notification that saving is complete is sent to the device.

[1278] Input: Encrypted employee data

[1279] Output: Employee data saved in the database, notification of saving completion

[1280] Step 4:

[1281] The server collects all employee data stored in a database and analyzes it using a generative AI model. It generates prompt sentences and inputs them into the AI ​​model for analysis.

[1282] Input: All employee data stored in a database

[1283] Output: Analysis results (scoring of optimal departments and tasks)

[1284] As an example, the server generates the following prompt:

[1285] "Please optimize the career path for the following employee: Employee ID 123, skills A, B, C, desired department X. Please suggest the best department and job."

[1286] Step 5:

[1287] The server generates a list of proposals for employees and company administrators based on the analysis results and sends it to the terminals. The proposal list includes details of the most suitable departments and tasks.

[1288] Input: Analysis results

[1289] Output: A list of suggestions for employees and company administrators

[1290] Step 6:

[1291] The device decodes the list of suggestions received from the server and displays it to the user, who can then review the details of each suggestion and enter feedback in a form.

[1292] Input: Suggestion list

[1293] Output: A list of suggestions displayed to the user, a feedback form

[1294] Step 7:

[1295] The user (employee) inputs feedback on the received proposal and sends it to the server via their device. The server receives this feedback, stores it in a database, and uses it to adjust the generative AI model.

[1296] Input: Feedback data

[1297] Output: Feedback data stored in a database, a tuned generative AI model

[1298] (Application example 1)

[1299] 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."

[1300] While systems existed that analyzed employee data to suggest optimal departments and tasks, there was a lack of mechanisms for evaluating the operational status and performance of robots operating in factories in real time and suggesting optimal tasks and placement locations. As a result, efficient robot operation was difficult, limiting productivity improvements. Therefore, the present invention aims to solve the problem of efficient production management and work automation by expanding human resource management systems and realizing optimal placement and task suggestions for factory robots.

[1301] 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.

[1302] In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for collecting data on the operating status of robots in the factory, means for analyzing the collected robot data and proposing optimal tasks and placement locations, means for providing the proposal results to the factory manager, and means for collecting feedback from the factory manager and adjusting the generative AI model. This enables optimal placement and work proposals in real time for not only employees but also robots in the factory, thereby realizing efficient production management and work automation.

[1303] "Means for collecting employee data" refers to interfaces and devices for collecting information such as resumes, experience, achievements, desired departments and qualifications from employees.

[1304] "Means for analyzing collected employee data" refers to a device or program that has the function of analyzing collected employee data and evaluating the skills and aptitude of employees.

[1305] "Means for suggesting the most suitable department or job" refers to algorithms or systems that present the most suitable department or job to employees based on the analysis results.

[1306] The "means of providing to the company administrator" refers to the means of notifying the company administrator of the proposal results and requesting confirmation and approval.

[1307] "Means for collecting feedback from employees and adjusting the generative AI model" refers to a device or program that allows employees to input their opinions and evaluations of proposed placements and work, and then improves and adjusts the generative AI model based on that data.

[1308] The "means for collecting operational status data of robots in a factory" refers to sensors and communication modules for collecting operational status and performance data from robots operating in a factory.

[1309] "Means for analyzing collected robot data" refers to a device or program that has the function of analyzing collected robot performance data and evaluating the operating status and capabilities of the robot.

[1310] "Means for proposing optimal tasks and placement locations" refers to algorithms or systems that, based on the analysis results, propose optimal tasks and placement locations for robots in a factory.

[1311] The "means of providing to the factory manager" refers to the means of notifying the factory manager of the proposal results and requesting confirmation and approval.

[1312] The "means for collecting feedback from factory managers and adjusting the generative AI model" refers to a device or program that allows factory managers to input their opinions and evaluations of proposed layouts and work, and then improves and adjusts the generative AI model based on that data.

[1313] The system for implementing this invention collects and analyzes employee data to propose optimal departments and tasks, and collects and analyzes operational status data of robots in factories to propose optimal tasks and locations. This system includes a terminal for inputting employee information, a server for collecting and analyzing data, and smart glasses that display the analysis results.

[1314] Hardware and Software Configuration

[1315] 1. Terminal

[1316] Provides an interface for employees to enter information. Examples include PCs, tablets, and smartphones. Employees enter their resumes, experience, achievements, desired departments, and qualifications.

[1317] 2. Server

[1318] The server receives input data from employees and stores it in a database. A specific example is an AWS EC2 instance.

[1319] The server analyzes employee data using a generative AI model, such as OpenAI GPT-4.

[1320] The server also collects and analyzes operational status data from factory robots, specifically processing the robots' sensor data and performance information using AWS Lambda.

[1321] The generated analysis results are provided as suggestions to employees and factory managers, and feedback is collected and used to adjust the AI ​​model.

[1322] 3. Smart Glasses

[1323] A device worn by a factory manager. An example is the Microsoft HoloLens.

[1324] The analysis results and suggestions sent from the server are displayed, and administrators can provide immediate feedback using voice input or gestures.

[1325] Process Overview

[1326] The server receives employee data, stores it in a database, and then analyzes it using a generative AI model. This allows it to evaluate each employee's skill set and career path and score them for the department or job they're best suited for. Similarly, by collecting data on the operation of robots in factories and analyzing their performance, it can suggest optimal placement and tasks.

[1327] Specific processing flow

[1328] 1. Data Entry

[1329] The user (employee) enters their resume, experience, etc. into the terminal. After completing the input, the data is sent to the server and saved in the database.

[1330] 2. Data Analysis

[1331] The server analyzes employee data stored in a database using a generative AI model, and based on the analysis results, generates a list of proposals for optimal departments and work.

[1332] 3. Providing Proposals

[1333] The list of suggestions is provided to employees and company administrators, who can view it on their own devices. Users can review the details of the suggestions and provide feedback.

[1334] 4. Factory Robot Data Analysis

[1335] The server collects data on the robot's operating status and analyzes it. The analysis results are displayed on the factory manager's smart glasses, and appropriate tasks and placement locations are suggested.

[1336] Prompt Sentence Examples

[1337] 1. Employee Career Assessment Prompt:

[1338] "Please analyze employee A's latest data and suggest the best career path for him."

[1339] 2. Robot maintenance optimization prompt:

[1340] "Please analyze the latest performance data for Robot A and suggest any necessary maintenance items. Please also provide the next maintenance schedule."

[1341] This will enable optimal employee placement and work, as well as efficient operation of robots in factories, thereby improving productivity.

[1342] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1343] Step 1:

[1344] The user (employee) enters their resume, experience, achievements, desired department and qualifications into the terminal. Once the input is complete, they click the "Send" button to send the data to the server. The input data contains text information, which the terminal encrypts before sending.

[1345] Step 2:

[1346] The terminal sends the data entered by the user to the server. The server verifies the format of the received data and stores it in a database. The data is encrypted before being sent, and is decrypted and stored on the server side. The database uses a relational database service such as Amazon RDS.

[1347] Step 3:

[1348] The server analyzes employee data stored in the database using a generative AI model (OpenAI GPT-4). Specifically, it evaluates employees' skill sets and career paths, and processes and calculates the data to score the most suitable departments and tasks. Employee resumes and experience are used as input data, and the output is a score indicating the optimal placement for each employee.

[1349] Step 4:

[1350] The server generates a list of proposals for optimal departments and tasks based on the analysis results, and provides the proposals to company administrators and employees. The results are sent to the administrators' and employees' devices and displayed in GUI format. Employees can check the proposals on their devices.

[1351] Step 5:

[1352] Users (employees and managers) review the proposals and enter their feedback. The feedback includes opinions on how practical the proposals are and what areas need improvement. The terminals collect this feedback and send it to the server.

[1353] Step 6:

[1354] The server stores the collected feedback in a database and uses it to further adjust the generative AI model. The feedback is analyzed and the parameters of the generative AI model are fine-tuned to improve the accuracy of the next proposal. The input data is feedback from employees and managers, and the output data is the parameters of the adjusted AI model.

[1355] Step 7:

[1356] The server collects operational status data from the robots in the factory. The robots perform self-diagnosis and send operational status and performance data to the cloud. This data is pre-processed in real time using AWS Lambda and sent to the server.

[1357] Step 8:

[1358] The server analyzes the collected robot performance data and uses a generative AI model to evaluate the robot's operating status. The data includes sensor information and operating time, and the analysis results suggest optimal tasks and placement locations for the robot.

[1359] Step 9:

[1360] The server sends the robot's analysis results to the factory manager's smart glasses, where the manager can review the suggestions and provide real-time feedback. The smart glasses are operated through visual and voice input.

[1361] Step 10:

[1362] The server collects feedback from factory managers and adjusts the generative AI model again. Based on new performance data and feedback, the generative AI model is updated to further improve the accuracy of the next proposal. The input data is the factory managers' feedback, and the output data is an adjusted AI model.

[1363] 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.

[1364] This invention combines a system that collects employee data, analyzes it using a generative AI model, and suggests optimal departments and tasks with an emotion engine that recognizes the user's emotions. In this system, users input information using their devices, and the data is sent to a server for analysis. The analysis results are provided to users and company managers as suggestions. The emotion engine also takes the user's emotional state into account, improving the quality of the suggestions.

[1365] System Configuration

[1366] The system consists of the following main components:

[1367] 1. Terminal

[1368] The terminal provides an interface for employees to input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal. The terminal also incorporates an emotion engine that evaluates the user's emotions in real time as they input information.

[1369] 2. Server

[1370] The server receives the data sent by the user and stores it in a database, which accumulates data on all employees.

[1371] The server analyzes the data using a generative AI model, which evaluates and scores each employee's skill set and career path, incorporating emotional data from an emotion engine.

[1372] The server provides the analysis results to users and company administrators in the form of suggestions, and also collects user feedback to use in adjusting the generative AI model.

[1373] Program processing flow (natural language explanation)

[1374] 1. Data Entry (User)

[1375] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[1376] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[1377] 2. Data transmission (terminal)

[1378] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[1379] 3. Data storage (server)

[1380] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[1381] 4. Data Collection and Cleaning (Server)

[1382] The server collects all employee data stored in the database and performs data cleaning if there is any incomplete or inaccurate data.

[1383] 5. Analysis by generative AI (server)

[1384] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. The analysis also incorporates emotional data provided by the emotion engine.

[1385] 6. Generating optimal placement plan (server)

[1386] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and the list of suggestions is stored in a database.

[1387] 7. Sending the proposal list (server)

[1388] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[1389] 8. Receiving and displaying the proposal list (terminal)

[1390] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the details of each proposal and evaluate whether it reflects their preferences.

[1391] 9. User Evaluation and Feedback

[1392] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[1393] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[1394] 10. Send Feedback (Device)

[1395] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[1396] 11. Feedback aggregation and reflection (server)

[1397] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[1398] Specific examples

[1399] Example 1: Employee B's career change

[1400] 1. Enter information

[1401] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[1402] 2. Data transmission and storage

[1403] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[1404] 3. Data Analysis

[1405] The server analyzes Employee B's data using generative AI and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B's stress level is low.

[1406] 4. Departmental proposals and feedback

[1407] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[1408] This system allows for highly accurate personnel allocation that takes into account both data and emotions, thereby maximizing employee performance and improving overall company performance.

[1409] The processing flow will be explained below.

[1410] Step 1: Data Entry (User)

[1411] The user (employee) enters information such as resume, experience, strengths / weaknesses, desired department, qualifications, etc. through the terminal interface. After completing the input, the user clicks the "Submit" button to send the data to the terminal.

[1412] The emotion engine analyzes the user's tone of voice, facial expression, typing speed, etc. when typing to assess their emotional state.

[1413] Step 2: Send data (terminal)

[1414] The device transmits the data and emotional state input by the user to the server, where the data is encrypted to ensure security.

[1415] Step 3: Data storage (server)

[1416] The server saves the data received from the device in a database. After saving is complete, it sends a save completion notification to the device.

[1417] Step 4: Data collection and cleaning (server)

[1418] The server collects all employee data stored in the database and performs data cleaning to find any incomplete or inaccurate data, including validating the data format and removing duplicate data.

[1419] Step 5: Analysis by Generative AI (Server)

[1420] The server then analyzes the cleaned data using a generative AI model, which evaluates and scores each employee's skill set and career path. Emotion data provided by the emotion engine is also incorporated into the analysis.

[1421] Step 6: Generate optimal placement plan (server)

[1422] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee, and stores this list in a database.

[1423] Step 7: Sending the proposal list (server)

[1424] The server sends the generated proposal list to employees and company administrators, and each proposal recipient is notified.

[1425] Step 8: Receive and display the proposal list (terminal)

[1426] The terminal decodes the proposal list received from the server and displays it to the user, who can then check the detailed information of each proposal.

[1427] Step 9: User evaluation and feedback

[1428] The user evaluates the displayed suggestions and enters feedback. Once the feedback is complete, the user clicks the "Submit" button to send the data to the terminal.

[1429] The emotion engine also analyzes the user's emotional state at the time of feedback and sends the results to the server.

[1430] Step 10: Send feedback (device)

[1431] The device sends the feedback and emotion data entered by the user to the server, which checks the integrity of the data and prompts the user to correct any problems.

[1432] Step 11: Aggregating and Reflecting Feedback (Server)

[1433] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model, updating the database based on the feedback to improve the accuracy of future suggestions.

[1434] Example 2

[1435] 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."

[1436] Conventional employee allocation systems only analyze collected employee data to suggest optimal departments and tasks. However, this does not take into account psychological factors such as employee emotions and stress levels, which limits the relevance of the suggestions. Furthermore, feedback may not be properly reflected, resulting in low accuracy of the generated AI model. This makes it difficult to maximize employee satisfaction and overall company efficiency.

[1437] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, an emotion engine for evaluating user emotions in real time, and means for incorporating emotion data into the analysis. This enables highly accurate placement proposals that take into account not only the skill sets of employees but also their emotions.

[1438] "Employee data" refers to information about individual employees, including their resume, experience, achievements, desired department, qualifications, etc.

[1439] A "collection means" is a method or device for obtaining employee data and entering it into the system.

[1440] "Means for analyzing" refers to a method or device for analyzing collected employee data.

[1441] The "means for proposing the most suitable department or job" is a method or device for providing an appropriate department or job to an employee based on the analysis results.

[1442] The "means for providing to the company administrator" refers to a method or device for notifying the manager of the company of the proposal results.

[1443] "Means for collecting feedback" refers to a method or device for obtaining evaluations and opinions from employees and reflecting them in the system.

[1444] A "generative AI model" is a model that uses machine learning or artificial intelligence techniques to analyze data and make predictions or suggestions.

[1445] The "emotion engine" is a technology that analyzes data such as a user's facial expressions, voice, and typing speed to assess their emotional state in real time.

[1446] "Emotion data" is information about the user's emotional state obtained by the emotion engine.

[1447] A "system" is a device or program that functions as a whole by combining multiple means.

[1448] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes optimal departments and tasks. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the quality of the proposals is improved. This system consists of several major components.

[1449] First, the terminal provides an interface for employees to input information. This terminal is equipped with an emotion engine that evaluates the user's emotions in real time as they input information. Employees input information such as their resume, experience, achievements, desired department, and qualifications, and send it to the server via the terminal.

[1450] The server receives data sent by users and stores it in a database. The database serves to collect data on all employees. Once stored, the data undergoes a data cleaning process and is then analyzed by a generative AI model. The generative AI model evaluates and scores employees' skill sets and career paths. This analysis also incorporates emotional data from the emotion engine. For example, "Employee B, who has marketing-related skills and a low stress level" may be determined to be ideal for a "senior position in the marketing department."

[1451] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This list is stored in a database and sent to employees and company administrators. The proposed list is received by the device and displayed to the user. The user checks the details of the proposals and evaluates whether they match their preferences. When the user enters feedback and sends it back to the server via the device, the emotion engine also collects emotion data again.

[1452] The server aggregates user feedback and emotional data and uses it to adjust the generative AI model, which can improve the accuracy of future suggestions.

[1453] Specific examples

[1454] Example: Employee B's career change

[1455] 1. Enter information

[1456] Employee B enters his / her achievements in the sales department and marketing-related skill information into the terminal and submits it. At the time of input, the emotion engine evaluates Employee B’s stress level as low.

[1457] 2. Data transmission and storage

[1458] The device transmits employee B's data and emotional state to the server, which stores the information in a database.

[1459] 3. Data Analysis

[1460] The server analyzes Employee B's data using a generative AI model and determines that a career change to the marketing department would be optimal. Emotional data is also reflected in the analysis, confirming that Employee B is in a low stress state.

[1461] 4. Departmental proposals and feedback

[1462] The server proposes a position in the marketing department to employee B. Employee B reviews the proposal and enters feedback on whether it matches his or her preferences. The emotion engine again collects emotion data and analyzes the emotional state at the time of the feedback.

[1463] Prompt Sentence Examples

[1464] Please enter your performance in the sales department.

[1465] "Please enter your marketing-related skills"

[1466] Please enter your desired department and qualifications.

[1467] "Please select your current emotional state: low, medium, or high stress level."

[1468] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1469] Step 1:

[1470] Users enter information such as their resume, experience, strengths / weaknesses, desired department / qualifications, etc. through the device interface. Once the input is complete, they click the "Send" button to send the data to the device. At this point, the emotion engine analyzes the user's voice tone, facial expression, typing speed, etc. to evaluate their emotional state. This input data (resume, experience, emotional state) is used for subsequent processing.

[1471] Step 2:

[1472] The device sends the data and emotional state input by the user to the server. When transmitted, the data is securely transmitted using the AES encryption algorithm. The input here is the user's input data and emotional data, which are encrypted and transmitted to the server.

[1473] Step 3:

[1474] The server stores the data received from the device in a database. Once the storage is complete, the server sends a notification to the device that the storage is complete. The input data here is encrypted, and the server processes the data by decrypting it and storing it in the database.

[1475] Step 4:

[1476] The server collects all employee data stored in the database and performs a data cleaning process if there is any incomplete or inaccurate data. The input here is the raw data in the database and the output is the cleaned data. The cleaning process includes filling in missing data and standardizing the format.

[1477] Step 5:

[1478] The server uses a generative AI model to analyze the cleaned data. This analysis evaluates and scores each employee's skill set and career path. Emotional data provided by the emotion engine is also incorporated into the analysis. The input here is the cleaned data and emotional data, and the output is scored data. Specifically, the AI ​​model analyzes each skill and emotional state and calculates a score.

[1479] Step 6:

[1480] Based on the analysis results, the server generates a list of candidates for the optimal departments and tasks for each employee. This list of suggestions is stored in a database. The input here is the scoring results, and the output is a list containing optimal placement proposals.

[1481] Step 7:

[1482] The server then sends the generated proposal list to employees and company administrators. Each proposal recipient is notified. The input is the proposal list, and the output is notifications to employees and administrators. Specifically, the proposal list is sent via email or the company's internal messaging system.

[1483] Step 8:

[1484] The terminal decodes the proposal list received from the server and displays it to the user. The user reviews the details of each proposal and evaluates whether it reflects their preferences. The input here is the proposal list, and the output is the information displayed to the user.

[1485] Step 9:

[1486] The user evaluates the displayed suggestions and enters their feedback. Once the feedback is complete, they click the "Send" button to send the data to the device. The emotion engine also analyzes the user's emotional state at the time of the feedback and sends the results to the server. The input is the user's feedback and emotional data, and the output is the data to be sent to the server.

[1487] Step 10:

[1488] The device sends the feedback and emotion data entered by the user to the server. The data is checked for consistency, and if there are any problems, the device prompts the user to correct them. The input here is the feedback data, and the output is the data sent to the server.

[1489] Step 11:

[1490] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. It updates the database information based on the feedback and improves the accuracy of future suggestions. The input here is the feedback data and emotion data, and the output is an updated generative AI model. Specifically, the feedback information is reflected as learning data for the model.

[1491] (Application example 2)

[1492] 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."

[1493] Conventional employee placement systems suggest tasks based on an employee's skill set and experience, but because they do not take into account the employee's emotional state or stress level, the proposed tasks do not optimally motivate or enhance employee performance. Furthermore, fixed task suggestions can prevent individual employees from fully utilizing their talents and abilities. There is a need to solve these problems and provide an optimal task suggestion system that takes into account the emotional state of employees.

[1494] 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 collecting employee data, means for analyzing the collected employee data, means for proposing optimal departments and tasks for employees based on the analysis results, means for providing the proposal results to the company manager, means for collecting feedback from employees and adjusting the generative AI model, means for recognizing the emotional state of employees, and means for improving the quality of proposals by taking the emotional state into consideration. This enables highly accurate job proposals that reflect the emotional state and stress level of employees.

[1495] "Employee Data" includes an employee's resume, experience, achievements, desired department, qualifications, and other related information.

[1496] "Means of analysis" refers to the use of generative AI models to analyze collected employee data and assess skill sets and career paths.

[1497] The "means for proposing the most suitable department or job" is a means for generating a proposal for the most suitable department or job for an employee based on the analysis results.

[1498] The "means of providing the company administrator" is a means of notifying the company administrator of the proposal results and having the administrator confirm them.

[1499] "Means for collecting feedback and adjusting the generative AI model" refers to means for collecting feedback from employees and reflecting it in the generative AI model to improve the accuracy of the model.

[1500] The "means for recognizing emotional states" refers to a means for assessing an employee's emotional state in real time by analyzing their tone of voice, facial expressions, typing speed, etc.

[1501] "Means for improving the quality of proposals by taking into account emotional states" refers to a means for improving the quality of proposals by incorporating employees' emotional data into the analysis of generative AI models.

[1502] This invention is a system that collects employee data, analyzes it using a generative AI model, and proposes the most suitable department or job. This system incorporates an emotion engine that recognizes the user's emotions, improving the quality of proposals. Specific embodiments for implementation are shown below.

[1503] Hardware and software used

[1504] Terminal: A device that provides an interface for employees to input information. Smart glasses or head-mounted displays are used.

[1505] Server: The central unit that stores data, analyzes it, and generates suggestions.

[1506] Emotion engine: This engine recognizes the emotional state of the employee entering data and reflects it in the analysis. Specifically, it analyzes voice tone, facial expressions, and typing speed.

[1507] Generative AI model: An AI model used to analyze employee data and suggest the most suitable department or job.

[1508] System program processing description

[1509] The terminal provides an interface for employees to input data such as their resume, experience, achievements, desired department, qualifications, etc. The data entered by employees is analyzed in real time by an emotion engine, and their emotional state is also recorded.

[1510] The input data and emotional data are sent via a secure protocol to a server, where they are stored in a database. The server then analyzes the data using a generative AI model to assess each employee's skill set and career path. Emotional data is also incorporated into the analysis, and emotional states influence the scoring.

[1511] Based on the analysis results, the server generates the most suitable department and job suggestions for employees, and the suggested list is saved in the database and then notified to employees and company administrators.

[1512] Employees and company administrators can receive the suggestions and review specific details. Employees can then enter feedback on the suggestions. The emotion engine also evaluates the employee's emotional state when providing the feedback and collects the data. The feedback and emotion data are then sent back to the server and used to refine the generative AI model.

[1513] Specific examples

[1514] For example, consider the case of Employee A working on a factory production line. Employee A inputs his or her work experience and skill set through smart glasses, and the data is analyzed in real time by an emotion engine, which also evaluates his or her emotional state. The server then analyzes this data using a generative AI model and proposes a new, optimal production line placement for Employee A under low stress.

[1515] Prompt Sentence Examples

[1516] "Employee A's stress level has been assessed as low. We suggest that you assign him to supervisory duties on the next production line. Would you like to confirm the assignment?"

[1517] As described above, this invention is a system that can comprehensively analyze employee data and emotional states and make optimal work suggestions, thereby maximizing employee performance and improving overall work efficiency.

[1518] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1519] Step 1:

[1520] Users input information such as resume, experience, achievements, desired department, and qualifications through the terminal. The terminal collects this data and uses an emotion engine to evaluate the user's emotional state in real time as it is being input. The input data also includes emotional data such as tone of voice, facial expressions, and typing speed.

[1521] Step 2:

[1522] The terminal encrypts the collected employee data and emotion data and sends them to the server, ensuring data security. Input data includes the employee's resume and desired department, while emotion data represents the user's real-time emotional state.

[1523] Step 3:

[1524] The server receives the data sent from the terminal and stores it in a database, which collects all employee data and cleans incomplete or inaccurate data.

[1525] Step 4:

[1526] The server uses the generated AI model to analyze the stored data. It inputs the cleaned employee data and emotion data to evaluate and score the employee's skill set and career path. The analysis results are output as a scored dataset for each employee.

[1527] Step 5:

[1528] Based on the analysis results, the server generates a list of candidates for the most suitable departments and jobs for each employee. This candidate list is created using a generative AI model and takes into account the employee's skill set and emotional data. The candidate list is output as a dataset and stored in a database.

[1529] Step 6:

[1530] The server sends the generated proposal list to employees and company administrators. The proposals are sent to the employees' and company administrators' devices as notifications and are displayed as detailed information.

[1531] Step 7:

[1532] The user checks the list of suggestions and inputs feedback. The input feedback includes requests for suggestions and revisions, as well as emotional data from the emotion engine. The feedback is then sent from the device to the server.

[1533] Step 8:

[1534] The server aggregates the received feedback and emotion data and uses it to adjust the generative AI model. By analyzing the feedback data, the accuracy of the model is improved and the database is updated. As a result, the accuracy of proposals from the next time onwards is improved.

[1535] This series of steps enables the system to make highly accurate suggestions that take into account the emotional state of employees, proposing placements that are beneficial to both the company and the employees.

[1536] 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.

[1537] 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.

[1538] 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.

[1539] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1540] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1541] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1542] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1543] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1544] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1545] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1546] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1547] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1548] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1549] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[1550] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1551] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1552] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1553] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1554] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1555] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1556] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1557] The following is further disclosed regarding the above embodiment.

[1558] (Claim 1)

[1559] the means by which employee data is collected;

[1560] A means of analyzing the collected employee data;

[1561] A method to suggest the most suitable department or job for employees based on the analysis results,

[1562] A means for providing the proposal results to the company administrator;

[1563] A means to gather employee feedback and adjust generative AI models;

[1564] A system including:

[1565] (Claim 2)

[1566] The system according to claim 1, wherein the system collects employee resumes, experience, achievements, desired departments, and qualifications.

[1567] (Claim 3)

[1568] The system of claim 1, wherein the system uses a generative AI model to evaluate employee skill sets.

[1569] "Example 1"

[1570] (Claim 1)

[1571] means for providing a terminal for inputting employee information;

[1572] A means to verify the format of employee data entered from the terminal and send it to the server after ensuring security;

[1573] a means for storing the received data in a database;

[1574] A method to analyze the data of all employees stored in a database using a generative AI model and score the most suitable department or job.

[1575] A means for generating and providing a list of suggestions to employees and company administrators based on the analysis results;

[1576] A means to collect feedback on the proposals and use it to refine the generative AI model; and

[1577] A system including:

[1578] (Claim 2)

[1579] 10. The system of claim 1, wherein the system collects employee resumes, work experience, skill sets, strengths and weaknesses, and desired departments and qualifications.

[1580] (Claim 3)

[1581] The system of claim 1, wherein the system uses a generative AI model to evaluate employee skill sets and career paths.

[1582] "Application Example 1"

[1583] (Claim 1)

[1584] the means by which employee data is collected;

[1585] A means of analyzing the collected employee data;

[1586] A method to suggest the most suitable department or job for employees based on the analysis results,

[1587] A means for providing the proposal results to the company administrator;

[1588] A means to gather employee feedback and adjust generative AI models;

[1589] A means for collecting operational status data of robots in a factory;

[1590] A method for analyzing collected robot data to propose optimal tasks and placement locations, and

[1591] A means for providing the results of the proposal to the factory manager;

[1592] A means to gather feedback from factory managers and adjust the generative AI model;

[1593] A system including:

[1594] (Claim 2)

[1595] The system according to claim 1, wherein the system collects employee resumes, experience, achievements, desired departments, and qualifications.

[1596] (Claim 3)

[1597] The system of claim 1, wherein the system uses a generative AI model to evaluate employee skill sets.

[1598] "Example 2: Combining Emotion Engines"

[1599] (Claim 1)

[1600] the means by which employee data is collected;

[1601] A means of analyzing the collected employee data;

[1602] A method to suggest the most suitable department or job for employees based on the analysis results,

[1603] A means for providing the proposal results to the company administrator;

[1604] A means to gather employee feedback and adjust generative AI models;

[1605] An emotion engine that evaluates the user's emotions in real time;

[1606] a means of incorporating emotional data into the analysis;

[1607] A system including:

[1608] (Claim 2)

[1609] The system according to claim 1, wherein data on employee resumes, experience, achievements, desired departments and qualifications, and emotions are collected.

[1610] (Claim 3)

[1611] 10. The system of claim 1, wherein the system uses a generative AI model to evaluate employee skill sets and also incorporates emotional data into the analysis.

[1612] "Application example 2 when combining emotion engines"

[1613] (Claim 1)

[1614] the means by which employee data is collected;

[1615] A means of analyzing the collected employee data;

[1616] A method to suggest the most suitable department or job for employees based on the analysis results,

[1617] A means for providing the proposal results to the company administrator;

[1618] A means to gather employee feedback and adjust generative AI models;

[1619] a means of recognizing employees' emotional states;

[1620] a means of improving the quality of suggestions by taking into account emotional states;

[1621] A system including:

[1622] (Claim 2)

[1623] The system according to claim 1, wherein the system collects employee resumes, experience, achievements, desired departments, and qualifications.

[1624] (Claim 3)

[1625] 10. The system of claim 1, wherein the system uses a generative AI model to evaluate an employee's skill set and an emotion engine to consider emotional state. [Explanation of symbols]

[1626] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. the means by which employee data is collected; A means of analyzing the collected employee data; A method to suggest the most suitable department or job for employees based on the analysis results, A means for providing the proposal results to the company administrator; A means to gather employee feedback and adjust generative AI models; A system including:

2. 2. The system according to claim 1, wherein the system collects employee resumes, experience, achievements, desired departments and qualifications.

3. The system of claim 1 , wherein the system uses a generative AI model to evaluate employee skill sets.

Images