System

Abstract

A system is provided.SOLUTION: A system comprising: means for inputting a desired work content, place of work, and skill; server means for receiving information input by a user; means for analyzing the received information and listing appropriate departments using a generated AI; means for transmitting information on a proposed department to the user; and means for acquiring detailed information on a department in which the user is interested and providing the user with the information.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] This document describes the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] When employees feel unsuited for a particular job, they tend to turn to external job search sites to find information, increasing the risk of losing talented personnel to the company. Furthermore, departments often lack personnel with specific skill sets, and relying on external job search sites and job advertisements incurs significant costs and risks. Therefore, a system that can optimally match personnel within a company is needed. [Means for solving the problem]

[0006] This invention aims to support appropriate employee placement and job changes / transfers within a company and prevent the loss of human resources by using a system that includes a means for inputting desired job content, work location, and skills, a server means for receiving the data input by the user, a means for analyzing the received data and using a generation AI to list appropriate departments, a means for sending information about the proposed departments to the user, and a means for obtaining detailed information about departments the user is interested in and providing it to the user.Furthermore, by adding a means for obtaining past job change / transfer data and having the generation AI learn from that data to improve analytical accuracy, a means for a company's department staff member to input job information and send it to a server, and a means for storing the transmitted job information in a database and using it for the next user's matching process, more efficient human resource placement can be achieved.

[0007] The "desired input means" is an interface for the user to input the desired work content, work location, and skills.

[0008] The "server means" is a server that receives and processes data entered by a user.

[0009] "Generative AI" is an artificial intelligence that learns from past data and suggests the most suitable department based on the user's preferences.

[0010] The "proposal method" is a method of analyzing the received data and using a generation AI to list and propose appropriate departments to the user.

[0011] The "information transmission means" is a means for transmitting information about the proposed department to the user's terminal.

[0012] The "detailed information acquisition means" is a means for acquiring detailed information about a department in which the user is interested and providing the information to the user.

[0013] A "database" is a data storage area that stores specific information and allows it to be searched and retrieved as needed.

[0014] The "job information input means" is an interface for company department personnel to input job information.

[0015] The "storage means" is a means for storing the transmitted job information in a database. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

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

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

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

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0031] 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.

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

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] The present invention is embodied in a system for supporting employee job changes and transfers within a company. This system analyzes input preference data and suggests appropriate departments, thereby optimizing personnel allocation within the company and preventing personnel from leaving the company. A specific embodiment of this system is described below.

[0038] User preference input

[0039] Terminal

[0040] Users use their own terminals to input their desired job duties, work location, and skills. This input is done through a dedicated interface, and the terminal accepts it and sends it to the server.

[0041] Data analysis and matching

[0042] server

[0043] The server receives the user's desired data sent from the device. The received data is stored in a database and analyzed. The server then launches a generation AI, which uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[0044] Displaying the proposed results

[0045] server

[0046] Based on the analysis results of the generative AI, the server organizes the information of the proposed department. The organized information is sent to the user's device and made available for viewing.

[0047] Terminal

[0048] The user's device receives the proposed results sent from the server and displays them for easy viewing, allowing the user to select the department that best suits them from the list of proposed departments.

[0049] Providing more information

[0050] User

[0051] Once the user selects a department of interest, they can request more information through their terminal.

[0052] Terminal

[0053] The terminal transmits the user's request to the server.

[0054] server

[0055] The server receives the request, retrieves the details of the relevant department from the database, and sends the details back to the terminal.

[0056] Terminal

[0057] The terminal displays the detailed information sent from the server to the user, who can then decide which department is most suitable for them.

[0058] Job posting by department

[0059] Terminal

[0060] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[0061] server

[0062] The server stores the entered job information in a database and can use it to match the desired data entered by the user next time.

[0063] Specific examples

[0064] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze the corresponding department based on past data and suggest "Department A: Software development," "Department B: Data analysis team," etc. The user becomes interested in "Department A" and requests more information. In response to this request, the server can provide information about "Department A," such as detailed job description, recruitment requirements, work location, and skill requirements.

[0065] Furthermore, when a department employee enters new job information, they enter information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java," and register it on the server. This information will be used for the next request from the user.

[0066] The above is a specific embodiment of the present invention. This system allows for efficient personnel matching within a company, preventing the loss of personnel to the outside. It also has the advantage for employees that they can be assigned to work that best suits them.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] User

[0070] Users use their own terminals to input the desired job duties, work location, and skills.

[0071] Step 2:

[0072] Terminal

[0073] The terminal accepts the input desired data. When the user clicks the "Submit" button, the input data is sent to the server.

[0074] Step 3:

[0075] server

[0076] The server receives the user's desired data sent from the terminal and stores the received data in a database.

[0077] Step 4:

[0078] server

[0079] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers and the user's desired data. The AI ​​then creates a list of departments that best match the user's desired conditions.

[0080] Step 5:

[0081] server

[0082] Based on the analysis results, the server organizes department information to suggest to the user.

[0083] Step 6:

[0084] server

[0085] The organized proposal result data is sent to the user's terminal.

[0086] Step 7:

[0087] Terminal

[0088] The terminal receives the data of the proposal results sent from the server and displays it to the user, who can then check the list of proposed departments.

[0089] Step 8:

[0090] User

[0091] The user selects the department of interest and requests detailed information on the terminal.

[0092] Step 9:

[0093] Terminal

[0094] The terminal sends a request to the server for detailed information about the department selected by the user.

[0095] Step 10:

[0096] server

[0097] The server receives the request and retrieves the details of the relevant department from the database.

[0098] Step 11:

[0099] server

[0100] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[0101] Step 12:

[0102] Terminal

[0103] The terminal receives the detailed information sent from the server and displays it to the user, who can then check the details and decide which department is best for him or her.

[0104] Step 13:

[0105] Terminal

[0106] Company department staff use a dedicated interface to enter job information.

[0107] Step 14:

[0108] Terminal

[0109] The entered job information is sent from the terminal to the server.

[0110] Step 15:

[0111] server

[0112] The server receives the entered job information and stores it in a database.

[0113] These steps allow employees to efficiently find the right department for them and companies to optimize their internal talent matching.

[0114] Example 1

[0115] 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."

[0116] By supporting appropriate employee job changes and transfers within a company, companies need to optimize their internal human resource allocation, maintain employee motivation, and prevent talent loss. By suggesting the optimal department based on employees' desired work content, work location, and skills, and providing detailed information, companies need to help employees find the work that best suits them. They also need a system that allows each department within a company to efficiently register new job information and use it for matching.

[0117] 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.

[0118] In this invention, the server includes a means for inputting desired job content, work location, and skills, a means for receiving data input by the user, a means for analyzing the received data and using a generative AI model to list appropriate departments, a means for transmitting information about the proposed departments to the user, a means for a company department staff member to input job information and transmit it to the server, a means for saving the sent job information in a database, and a means for using the saved job information for the next user matching process. This allows the generative AI model to suggest the most suitable department based on the desired information input by the user and provide detailed information about the department, thereby optimizing human resource allocation within the company and improving employee satisfaction. Furthermore, the ability for company department staff to efficiently register and use job information improves the accuracy and efficiency of human resource matching.

[0119] The "means for inputting desired work content, work location, and skills" is an interface for a user to input the desired work content, work location, and skill information.

[0120] The "server means for receiving data entered by a user" is a function of the server for receiving data entered from a user's terminal and recording it in a database.

[0121] "Means for analyzing received data and using a generative AI model to list appropriate departments" refers to a process that uses a generative AI model based on received data to select the department that best matches the user's preferences.

[0122] The "means for sending information about the proposed department to the user" is a server function for sending information about the optimal department suggested by the generative AI model to the user's terminal.

[0123] The "means for obtaining detailed information about a department in which the user is interested and providing it to the user" is a process for obtaining detailed information about a department selected by the user from a database and providing it to the user.

[0124] The "means for a company departmental employee to input job information and send it to the server" is an interface that allows a departmental employee to input new job information and send that information to the server.

[0125] The "means for saving submitted job information in a database" is a function of the server for saving job information submitted by department personnel of a company in a database.

[0126] The "means for utilizing the saved job information in the next matching process by the user" is a process for utilizing the saved job information in the next matching process by the user with desired data.

[0127] "Means of acquiring past job change and transfer data and having the generative AI model learn from that data to improve the accuracy of analysis" refers to a process of collecting data on past job changes and transfers and having the generative AI model learn from that data to improve the accuracy of analysis.

[0128] This invention is a system for supporting employee job changes and transfers within a company, and is implemented in the following steps: The system optimizes the allocation of personnel within a company by inputting the user's desired work content, work location, and skills, and then proposing the most suitable department based on that information and providing detailed information.

[0129] Hardware and Software Configuration

[0130] This system is implemented in a network environment that includes user devices, a server, and a database. User devices are assumed to be PCs, smartphones, tablets, etc., and operations are performed through a dedicated web interface called "Employee Shift Portal." A database (PostgreSQL) is connected to the server, which implements the generative AI model "HR-MatchAI." The server is operated through a web application framework (e.g., Django or Flask).

[0131] User preference input

[0132] Users access the Employee Shift Portal using their own devices and enter their desired work content, work location, and skills through the interface. This input data is sent to the server by the device.

[0133] Examples:

[0134] The user enters "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis."

[0135] Example of prompt: The user enters information according to the instructions: "Please enter the job description, location, and skills you are interested in (e.g., software development, Tokyo, Python)."

[0136] Data analysis and matching

[0137] The server receives the user's desired data sent from the device and stores it in a database. It then launches the generative AI model "HR-MatchAI" and uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[0138] Examples:

[0139] The generative AI model suggests the most suitable department, such as "Department A: Software Development" or "Department B: Data Analysis Team."

[0140] Example of prompt sentence: The server follows the instruction "Analyze the user's desired data and suggest the most suitable department," and lists departments.

[0141] Displaying the proposed results

[0142] Based on the analysis results of the generative AI model, the server formats the information on the proposed departments and sends it to the user's device, which displays the received proposals on a dedicated interface, and the user can select the department that best suits them from the list of proposed departments.

[0143] Examples:

[0144] The proposed departments "Department A: Software Development" and "Department B: Data Analysis Team" are displayed on the terminal.

[0145] Example of prompt sentence: The terminal follows the instruction to display the suggestion results to the user and prompt them to select a department on the screen.

[0146] Providing more information

[0147] When a user selects a department of interest and sends a request for detailed information, the device sends this request to the server. The server retrieves the details of the department from the database and sends them back to the user's device. The device displays the received details, and the user can use this information to decide which department is most suitable for them.

[0148] Examples:

[0149] The user clicks the detailed information button for "Department A" and the detailed information is displayed on the terminal screen.

[0150] Example of prompt sentence: The server follows the instruction "Please provide details about Department A" and sends the details.

[0151] Job posting by department

[0152] A company's department staff uses the dedicated web interface "HR-Manager Suite" to enter new job information and send it to the server, which then stores the received information in a database and uses it to match the user's desired data next time.

[0153] Examples:

[0154] The department staff member enters "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" and submits the form.

[0155] Example of prompt: The person in charge will follow the instructions to "Register new job information" and enter the information.

[0156] The above is an embodiment of the present invention. This system makes it easy to optimize personnel allocation within a company, enabling the right person to be placed in the right position and reducing the company's employee turnover rate.

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1:

[0159] Input desired data by the user

[0160] Users input their desired work content, work location, and skills. This data is input through the "Employee Shift Portal," and the information entered through a dedicated interface is sent to the terminal.

[0161] Input: Desired job description, location, skills (e.g. software development, Tokyo, Python)

[0162] Output: The input data is saved in the form

[0163] Specific behavior:

[0164] The user enters "Job Description: Software Development", "Work Location: Tokyo", and "Skills: Python, Data Analysis" and clicks the submit button.

[0165] Step 2:

[0166] Receiving and storing user preference data

[0167] The terminal receives the desired data entered by the user, performs form validation (checks the input data), and if there are no errors, sends the data to the server.

[0168] Input: Data entered by the user into the terminal

[0169] Output: Data sent to the server

[0170] Specific behavior:

[0171] The terminal receives the data entered by the user, for example, "software development, Tokyo, Python, data analysis," and sends it to the server.

[0172] Step 3:

[0173] Analyzing and storing user preference data

[0174] The server receives the user's desired data sent from the device, stores the received data in a PostgreSQL database, and prepares it for analysis.

[0175] Input: Desired data sent from the terminal

[0176] Output: Data stored in the database

[0177] Specific behavior:

[0178] The server stores the received data "Software Development, Tokyo, Python, Data Analysis" in a database.

[0179] Step 4:

[0180] Matching analysis using generative AI models

[0181] The server launches the generative AI model "HR-MatchAI" based on the saved user preference data. The generative AI model learns from past job change and transfer data and lists the departments that best match the preference data.

[0182] Input: User preference data stored in the database

[0183] Output: A list of the best departments (e.g., Department A, Department B)

[0184] Specific behavior:

[0185] The generative AI model analyzes "software development, Tokyo, Python, data analysis" and suggests "Department A: software development" and "Department B: data analysis team."

[0186] Step 5:

[0187] Formatting and sending the proposal results

[0188] The server formats the analysis results from the generative AI model and sends the proposed department information to the user's device.

[0189] Input: A list of optimal departments generated by a generative AI model

[0190] Output: Proposal result data sent to the user's device

[0191] Specific behavior:

[0192] The server sends "Department A: Software Development" and "Department B: Data Analysis Team" to the user's device.

[0193] Step 6:

[0194] Displaying the proposed results

[0195] The terminal receives the proposal result data sent from the server and displays it on a dedicated interface.

[0196] Input: Proposal result data sent from the server

[0197] Output: Suggestion results displayed on the user's device

[0198] Specific behavior:

[0199] "Department A: Software Development" and "Department B: Data Analysis Team" will be displayed on the user's device.

[0200] Step 7:

[0201] Request more information

[0202] The user selects the department they are interested in and submits a request for more information.

[0203] Input: User requests more information

[0204] Output: Request data from the terminal to the server

[0205] Specific behavior:

[0206] The user clicks the detailed information button for "Department A," and the terminal sends the request to the server.

[0207] Step 8:

[0208] Get and send details

[0209] The server receives the detailed information request, retrieves the detailed information for the relevant department from the database, and sends the retrieved detailed information back to the user's terminal.

[0210] Input:Detailed information request data

[0211] Output: Retrieved details

[0212] Specific behavior:

[0213] The server retrieves detailed information about "Department A" from the database and sends it to the user's terminal.

[0214] Step 9:

[0215] Viewing detailed information

[0216] The terminal receives the detailed information sent from the server and displays it to the user, who can then use this information to decide which department is most suitable for them.

[0217] Input: Details sent from the server

[0218] Output: Detailed information displayed on the user's terminal

[0219] Specific behavior:

[0220] Detailed information about "Department A" is displayed on the user's device.

[0221] (Application example 1)

[0222] 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."

[0223] Conventional in-house human resource allocation systems often fail to properly reflect employee preferences, and are not effective enough in preventing the loss of human resources to other companies. Furthermore, optimization of the placement and roles of robots in factories is primarily managed manually, making efficient operation difficult. Furthermore, the inability to fully utilize past data creates issues with the accuracy of placement proposals. For these reasons, there was a need for a system that could simultaneously optimize the placement of both in-house human resources and in-factory robots, and make more efficient and accurate proposals.

[0224] 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.

[0225] In this invention, the server includes: means for inputting desired work content, work location, and skills; server means for receiving the data input by the user; means for analyzing the received data and using a generation AI to list appropriate departments; means for sending information about the proposed departments to the user; means for obtaining detailed information about departments in which the user is interested and providing it to the user; means for inputting robot specifications, roles, placement, and skills; and means for analyzing the robot specifications, roles, placement, and skills and using a generation AI to propose appropriate placement. This makes it possible to simultaneously optimize personnel placement within a company and robot placement within a factory, thereby improving employee satisfaction and improving corporate operational efficiency.

[0226] The "desired work content" is the job content that an employee or user desires to be in charge of.

[0227] "Work location" is the location or area where an employee or user wants to work.

[0228] "Skills" refer to the techniques and knowledge possessed by employees or users, and indicate the abilities required to perform specific tasks.

[0229] A "means" is a method or device used to achieve a particular purpose.

[0230] "User" means an individual or employee who uses this system.

[0231] A "server" is a computer system that receives information from users and performs analytical processing.

[0232] "Generative AI" is artificial intelligence that analyzes data and generates optimal suggestions and lists.

[0233] "Listing" means displaying the best options in a list format based on specific conditions.

[0234] A "robot" is a mechanical device designed to perform a specific task in a factory.

[0235] "Deployment" refers to the allocation of specific people or machines to appropriate locations.

[0236] "Specifications" are detailed information that indicates the specifications and capabilities of a robot or machine.

[0237] A "role" refers to the tasks or responsibilities that an employee or robot should perform.

[0238] The present invention is implemented as a system for optimizing the allocation of employees and robots in a factory within a company. This system is composed of the following components:

[0239] User preference input

[0240] Terminal

[0241] Users use their own devices to input their desired work content, work location, and skills. This input data is sent to the server via the device. Factory robot specifications, current role, placement, and skill sets are also input from the device and sent to the server.

[0242] Data analysis and matching

[0243] server

[0244] The server receives data sent from users and factory robots. The received data is stored in a database. The server then launches a generative AI model (Recommendation AI) that lists appropriate departments and optimal robot placements based on the user's desired data and robot data. The generative AI model learns from past job change and transfer data and past robot placement data to improve its analysis accuracy.

[0245] Displaying the proposed results

[0246] server

[0247] Based on the analysis results of the generation AI, the server organizes the proposed department and robot placement information, which is then sent to the terminal.

[0248] Terminal

[0249] The user's terminal receives and displays the proposed results sent from the server, and the user or factory manager can select an appropriate department or robot placement from the proposed list.

[0250] Providing more information

[0251] User

[0252] Once the user or factory manager selects a proposal that interests them, they can request more information.

[0253] Terminal

[0254] The terminal sends a request to the server, which retrieves the detailed information and sends it back to the terminal.

[0255] Terminal

[0256] The terminal displays detailed information received from the server, allowing users and factory managers to make decisions.

[0257] Registering department and robot placement information

[0258] Terminal

[0259] Company department staff and factory managers enter new job information and robot placement information through a dedicated interface and send it to the server.

[0260] server

[0261] The server stores the transmitted information in a database and uses it for the next analysis.

[0262] As a specific example, if a user inputs "Desired work content: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze this and suggest "Department A: Software development," "Department B: Data analysis team," etc. On the other hand, if a factory manager inputs "Robot ID: robot_001," "Specs: Type-A," "Current role: Assembly," "Location: Line-1," and "Skills: welding, bolting," the generation AI will make a "New placement proposal: Maintenance." This allows for the optimization of departments and placements.

[0263] Example prompt sentence:

[0264] Please recommend the best placement and role based on the following robot data:

[0265] Specs: Type-A

[0266] Current role: Assembly

[0267] Current location: Line-1

[0268] Skill Set: ["welding", "bolting"]

[0269] This system makes it possible to simultaneously optimize the allocation of personnel within a company and the allocation of robots within a factory, which is expected to improve the operational efficiency of a company and employee satisfaction.

[0270] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0271] Step 1:

[0272] User data entry

[0273] The terminal allows the user to input "desired work content," "work location," and "skills." The factory manager inputs "robot specifications," "role," "placement," and "skill set." These data are entered into the input screen by the user or factory manager, and the entered data is sent from the terminal to the server.

[0274] Input: User's desired work data, robot specification data

[0275] Output: Desired data and robot data sent to the server

[0276] Step 2:

[0277] Data reception and storage by the server

[0278] The server receives the desired data and robot data sent from the device. The received data is stored in a database (e.g., MySQL, SQLite). This makes the data ready for analysis.

[0279] Input: Desired data and robot data sent from the terminal

[0280] Output: Desired data and robot data stored in a database

[0281] Step 3:

[0282] Data analysis and department / robot placement proposals

[0283] The server passes the received data to a generative AI model (Recommendation AI) for analysis. The generative AI model uses past job change and transfer data and past robot placement data to create a list of appropriate departments and optimal robot placements that match the user's desired data.

[0284] Input: Desired data and robot data stored in the database

[0285] Output: A list of department and robot placement suggestions from the generation AI

[0286] Step 4:

[0287] Organizing and sending proposal results

[0288] The server organizes the analysis results of the generated AI model, composes proposed department information and robot placement information, converts that information into a format suitable for sending to the user's device, and sends it to the device.

[0289] Input: A list of suggestions from the generative AI

[0290] Output: Formatted proposal information to send to terminal

[0291] Step 5:

[0292] Display of proposal results on user device

[0293] The terminal receives the proposed information sent from the server and displays it in an easy-to-read format for users and factory managers, who can then use this information to consider the optimal placement of themselves or the robots.

[0294] Input: Proposal information sent from the server

[0295] Output: Displayed proposal information

[0296] Step 6:

[0297] Requesting and Providing More Information

[0298] When a user or factory manager selects a department or location of interest, the terminal sends a request for detailed information to the server. The server retrieves the relevant information from the database and sends it back to the terminal. The terminal then displays the detailed information.

[0299] Input: A request for more information from the user

[0300] Output: Display of detailed information provided by the server

[0301] Step 7:

[0302] Registration of job vacancies and robot placement information

[0303] Company department staff and factory managers input new job information and robot placement information and send it from their terminals to the server, which stores it in a database and uses it for the next matching process.

[0304] Input: New job information and robot placement information

[0305] Output: Information stored in the database

[0306] The above processing steps make it possible to realize optimal allocation of personnel and robots within a company.

[0307] 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.

[0308] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. Employees input their preferences, and the system proposes appropriate departments based on those preferences. The introduction of an emotion engine analyzes the emotions expressed at the time of input, further individualizing the system.

[0309] User preference input

[0310] Terminal

[0311] Users use their own devices to input their desired job duties, work location, and skills. The device receives the input information and sends it to the server. The emotion engine then analyzes the user's facial expressions and tone of voice when inputting information to recognize the user's emotional state.

[0312] Data analysis and matching

[0313] server

[0314] The server receives the user's desired data sent from the device along with the emotion data from the emotion engine. The server stores this data in a database and activates the generation AI. The generation AI analyzes past job change and transfer data and lists the most suitable departments, taking into account the user's desired conditions and emotion data.

[0315] Displaying the proposed results

[0316] server

[0317] Based on the analysis results of the generative AI, the server organizes the department information to suggest to the user, and the organized information is sent to the user's device.

[0318] Terminal

[0319] The user's device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message according to the user's emotions.

[0320] Providing more information

[0321] User

[0322] The user selects the department of interest and requests detailed information on the terminal.

[0323] Terminal

[0324] The terminal sends a request to the server for detailed information about the department selected by the user.

[0325] server

[0326] The server receives the request, retrieves the details of the department from the database, and sends the details to the user's device.

[0327] Terminal

[0328] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[0329] Job posting by department

[0330] Terminal

[0331] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[0332] server

[0333] The server receives the job information and stores it in a database, which is used to match the user's desired data the next time they enter it.

[0334] Specific examples

[0335] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities," a department that would reduce the user's stress. Furthermore, if the emotion engine determines that the user is entering information in a relaxed state, it will make general suggestions such as "Department A: Software development" or "Department B: Data analysis team."

[0336] Additionally, when a department staff member enters new job information (job description: AI development, work location: Osaka, skills: machine learning, Java) and registers it on the server, this information will be used in the next matching process.

[0337] In this way, by combining this system with an emotion engine, it is possible to make suggestions that take into account the user's emotional state, helping employees to work in a more suitable environment.

[0338] The processing flow will be explained below.

[0339] Step 1:

[0340] User

[0341] Users use their own terminals to input the desired job duties, work location, and skills.

[0342] Step 2:

[0343] Terminal

[0344] The device accepts the input desired data, and at the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize the emotional data.

[0345] Step 3:

[0346] Terminal

[0347] When the user clicks the "send" button, the terminal transmits the input data and emotion data to the server.

[0348] Step 4:

[0349] server

[0350] The server receives the user's desired data and emotion data sent from the device, and stores the received data in a database.

[0351] Step 5:

[0352] server

[0353] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers, as well as the user's preferences and emotional data. The AI ​​then creates a list of the most suitable departments, taking into account the user's preferences and emotional data.

[0354] Step 6:

[0355] server

[0356] Based on the analysis results from the generative AI, the server organizes the department information to suggest to the user.

[0357] Step 7:

[0358] server

[0359] The organized proposal result data is sent to the user's terminal.

[0360] Step 8:

[0361] Terminal

[0362] The device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message based on the user's emotions.

[0363] Step 9:

[0364] User

[0365] The user selects the department of interest and requests detailed information on the terminal.

[0366] Step 10:

[0367] Terminal

[0368] The terminal sends a request to the server for detailed information about the department selected by the user.

[0369] Step 11:

[0370] server

[0371] The server receives the request and retrieves the details of the relevant department from the database.

[0372] Step 12:

[0373] server

[0374] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[0375] Step 13:

[0376] Terminal

[0377] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[0378] Step 14:

[0379] Terminal

[0380] Company department staff use a dedicated interface to enter job information.

[0381] Step 15:

[0382] Terminal

[0383] The entered job information is sent from the terminal to the server.

[0384] Step 16:

[0385] server

[0386] The server receives the entered job information and stores it in a database.

[0387] Specific examples

[0388] For example, if a user inputs "Desired work: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities" as a department that will reduce stress. If the emotion engine determines that the user is entering information in a relaxed state, the system will make the usual suggestions of "Department A: Software development" and "Department B: Data analysis team."

[0389] The above is a specific embodiment of a system incorporating an emotion engine. This system enables more appropriate personnel matching within a company by taking into account the emotions of users.

[0390] Example 2

[0391] 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."

[0392] Conventional personnel job change and transfer systems typically suggest an appropriate department based solely on the user's desired conditions. However, because the user's emotional state is not taken into consideration, the department that is most suitable for the user may not be suggested. This can result in a failed job change or transfer and reduced user satisfaction. The objective of this invention is to suggest a more appropriate department and improve user satisfaction by simultaneously considering the user's desired conditions and emotional state.

[0393] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0394] In this invention, the server includes a means for receiving data entered by a user and emotion data analyzed by an emotion engine, a means for storing the received data in a database and listing appropriate departments using a generative AI model, and a means for transmitting information on the proposed departments to the user, thereby enabling optimal department proposals that simultaneously consider the user's desired conditions and emotional state.

[0395] "Desired work content" refers to the work content that the user wants to undertake in their career.

[0396] "Work location" is the geographic location where a user wishes to work.

[0397] "Skills" refer to the specialized abilities and knowledge that a user has.

[0398] "Emotion data" is data relating to the emotional state of the user analyzed from facial expressions, tone of voice, etc. using an emotion engine.

[0399] "Server means" is a general term for equipment and software for receiving, processing, and storing data sent from users and emotion engines.

[0400] The "database" is a system for organizing and managing received user data and emotion data.

[0401] The "generative AI model" is an artificial intelligence model that learns from past job change and transfer data and suggests the most suitable department based on the user's desired conditions and emotional state.

[0402] The "proposal results" are information about the optimal department that the generative AI model analyzes and presents to the user.

[0403] "Detailed information" is data containing specific job descriptions, conditions, and other supplemental information about the proposed department.

[0404] "Job information" is information about available positions entered by department personnel in a company.

[0405] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. This system allows users to input their preferences, and suggests appropriate departments based on those preferences. By introducing an emotion engine, the system analyzes the emotions entered at the time of input and further individualizes the proposals.

[0406] Users use their own devices to input their desired job duties, work location, and skills. Specifically, they input "Desired job duties: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The devices are equipped with built-in cameras and microphones, which are used to capture the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data. The emotion engine uses Python's facial recognition library and voice analysis tools to recognize the user's emotional state.

[0407] The device sends the input preference data and emotion data as a single data packet to the server. The server stores the received data in a database and launches a generative AI model (using TensorFlow or PyTorch as an example). The generative AI model learns from past job change and transfer data and lists the most suitable departments based on the user's preference and emotion data.

[0408] Based on the analysis results of the generative AI model, the server organizes the department information to suggest to the user. This organization also includes supplemental information based on the user's emotional state. For example, if the user is entering information in a relaxed state, general suggestions such as "Department A: Software Development" and "Department B: Data Analysis Team" will be made, but if the user is feeling stressed, suggestions such as "Department C: Software Development with Remote Work Capability" will be made. The organized information is sent to the user's device, which receives and displays it.

[0409] To obtain detailed information about a department that interests a user, the user makes a request on their device. The server receives the request, retrieves the details of the department from the database, and sends them to the user's device. The user's device then displays this information to the user. This display also includes supplementary information and suggestions based on the user's emotional state, provided by the emotion engine.

[0410] Additionally, company department staff use a dedicated interface to enter job information and send it to the server. This job information is saved in the database and used to match the next desired data entered by a user. For example, if "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" are entered and registered on the server, this information will be used in the next matching process.

[0411] As described above, this system simultaneously considers the user's desired conditions and emotional state, and makes more appropriate suggestions, thereby improving user satisfaction.

[0412] Examples:

[0413] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the emotion engine analyzes the user's emotions in real time and determines that the user is in a relaxed state, for example. Based on this data, the generative AI model lists appropriate departments. The suggested results, "Department A: Software development" and "Department B: Data analysis team," are displayed to the user. Examples of prompt sentences are as follows:

[0414] Example prompt sentence:

[0415] Write an algorithm that will suggest an appropriate department based on the user's emotional state when they enter "Desired job: Software development", "Location: Tokyo", and "Skills: Python, data analysis".

[0416] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0417] Step 1: Enter desired conditions and emotional data

[0418] Terminal

[0419] A user logs in to a device and enters their desired job description, work location, and skills. For example, they might enter "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The device's camera and microphone capture the user's facial expressions and tone of voice, which the emotion engine analyzes to identify the user's emotional state. The input for this step is the user's desired data and real-time emotion data, and the output is a data packet containing this input.

[0420] Step 2: Sending data packets

[0421] Terminal

[0422] The user's device sends the input desired data and the emotional data analyzed by the emotion engine to the server as a single data packet. The transmitted data packet includes, for example, the user ID, desired job content, work location, skills, and emotional state. The input to this step is the data packet generated in step 1, and the output is a transmission confirmation to the server.

[0423] Step 3: Receiving and storing data

[0424] server

[0425] The server receives the data packet sent from the device. This input data is the user's desired conditions and emotional state. After receiving, the server stores this data in a database. For example, the data is stored using a database management system (e.g., MySQL or PostgreSQL). The input of this step is the data packet sent from the device, and the output is confirmation of successful storage.

[0426] Step 4: Launching the generative AI model and analyzing data

[0427] server

[0428] The server uses the stored data to launch a generative AI model. For example, an AI model using TensorFlow or PyTorch learns from past job change and transfer data and lists the most suitable departments by taking into account the user's desired conditions and emotional data. The input for this step is the stored user data and past job change and transfer data, and the output is a list of appropriate departments as a result of the analysis.

[0429] Step 5: Organize and submit your proposal

[0430] server

[0431] Based on the analysis results of the generative AI model, the server organizes department information to suggest to the user. The suggestions also include supplemental information based on the user's emotional state. The organized data is sent to the user's device. The input to this step is the analysis results of the generative AI model, and the output is the data sent to the user's device.

[0432] Step 6: Viewing the Suggestion Results

[0433] Terminal

[0434] The user's device receives the proposal results sent from the server and displays them to the user. For example, a relaxed user may be suggested "Department A: Software Development" or "Department B: Data Analysis Team," while a stressed user may be suggested "Department C: Remote Work Software Development." The input for this step is the proposal results from the server, and the output is a list of proposals displayed to the user.

[0435] Step 7: Request more information

[0436] User

[0437] The user selects the department they are interested in from the proposed departments and requests detailed information on the terminal. The input of this step is the department information selected by the user, and the output is a detailed information request.

[0438] Step 8: Get and send details

[0439] server

[0440] The server receives a detailed information request from the user and retrieves the detailed information for the relevant department from the database. It then sends the retrieved detailed information to the user's terminal. The input of this step is the detailed information request, and the output is the transmission of the detailed information to the user's terminal.

[0441] Step 9: View detailed information

[0442] Terminal

[0443] The user's device receives the detailed information sent from the server and displays it to the user. The displayed information also includes supplementary information and suggestions according to the emotional state transmitted by the emotion engine. The input of this step is the detailed information from the server, and the output is the detailed information displayed to the user.

[0444] Step 10: Fill out and submit your job posting

[0445] Terminal

[0446] A departmental employee at a company uses a dedicated interface to input new job information. The input information is sent to the server. For example, information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" is input. The input for this step is the company's job information, and the output is the transmission of the job information to the server.

[0447] Step 11: Save your job posting

[0448] server

[0449] The server receives the job information sent by the company's department staff and saves it in the database. The saved information will be used for the next matching process with the desired input data by the user. The input of this step is the submitted job information, and the output is a confirmation that the job information was successfully saved.

[0450] (Application example 2)

[0451] 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."

[0452] Conventional personnel job change and department transfer support systems often make mechanical suggestions based on user input data and are unable to consider the user's emotions or psychological state. As a result, the proposed departments and jobs do not satisfy the user psychologically, making it difficult to realize an effective transfer or job change. In addition, the job information entered by company department personnel is not provided in a form that reflects their emotions and expectations, and further improvements in matching accuracy are required.

[0453] The specification processing by the specification 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 inputting desired work content, work location, and skills, server means for receiving data input by the user, means for analyzing the received data and using a generative AI model to list appropriate departments, means for transmitting information on the proposed departments to the user, means for acquiring detailed information on departments in which the user is interested and providing it to the user, means for analyzing the user's facial expressions and voice using an emotion engine and using the emotion data for analysis, means for generating proposals according to the user's psychological state based on the analyzed emotion data, means for acquiring past job change and transfer data and having the generative AI model learn from the data to improve analysis accuracy, means for inputting prompt sentences into the generative AI model to obtain optimal analysis results, means for a company department employee to input job information and transmit it to the server, means for saving the sent job information in a database, means for using the saved job information in a matching process by the next user, and means for utilizing the emotion engine to analyze the employee's intentions and expectations when entering job information. This will enable more accurate department and job suggestions that reflect the user's emotions and psychological state.

[0454] A "user" is someone who uses the system to input their job description, work location, and skills, and receives suggestions for suitable departments and jobs.

[0455] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[0456] "Job content" refers to the type of work and specific duties desired by the user.

[0457] "Work location" refers to the location or area where the user wishes to work.

[0458] "Skills" refers to the specialized techniques, knowledge, or proficiency that a user possesses.

[0459] "Server means" means a computing device used by the system to receive, store, and analyze user-entered data and other information.

[0460] A "generative AI model" is an artificial intelligence model that suggests appropriate departments and jobs based on input data and past data.

[0461] A "prompt sentence" is a sentence that is input to a generative AI model to obtain optimal analysis results.

[0462] "Suggestion means" is a function that allows the system to send users information about appropriate departments and jobs listed by the generative AI model.

[0463] The "detailed information acquisition means" is a function for acquiring further detailed information about a proposal in which the user is interested and providing the information to the user.

[0464] "Job information" refers to the content and conditions of the job being recruited for, entered by department staff at a company.

[0465] The "analysis means" is a means for analyzing the data received by the server means and listing appropriate departments and jobs.

[0466] "Matching processing" is the process of selecting the most suitable department and job based on the user's preferences, emotional data, and past data.

[0467] The "means for analyzing intentions and expectations" is a means for using an emotion engine to recognize and analyze the intentions and expectations of company personnel who enter job information.

[0468] The present invention is a system that proposes departments and jobs with higher accuracy by taking into account the user's wishes and emotional state. By combining an emotion engine, this system improves on conventional mechanical proposals and enables proposals that correspond to the user's psychological state.

[0469] System Configuration

[0470] Hardware

[0471] The system is implemented mainly using the following hardware:

[0472] Server: A high-performance computer device that receives data, analyzes it, and sends out proposals.

[0473] User terminal: A smartphone or tablet, which is a device where users input their preferences and emotional data and receive suggested information.

[0474] software

[0475] The system is implemented using the following software:

[0476] Generative AI model: Built using TensorFlow, it lists the most suitable departments and jobs based on the user's preferences and past job change data.

[0477] Emotion analysis: Using OpenCV and AudioEmotion libraries, we analyze the user's facial expressions and voice to obtain emotional data.

[0478] Database: Using MySQL, we store user preferences and emotional data, company job information, and past job change and transfer data.

[0479] Program processing overview

[0480] Desired input

[0481] Users use their smartphones to input their desired job duties, work location, and skills. The camera also captures their facial expressions and records their voice with a microphone. The emotion engine analyzes this data and recognizes the user's emotional state.

[0482] Data reception and analysis

[0483] The server receives preference and emotion data sent from the user's device. This data is stored in a database and analyzed by a generative AI model. The generative AI model learns from past job change and transfer data to improve the accuracy of its analysis.

[0484] suggestion

[0485] The server then suggests appropriate departments and jobs to the user based on the generative AI model. This suggestion information is generated taking into account the user's emotional state. For example, if the user is feeling stressed, the server will suggest departments that will help them reduce stress.

[0486] Providing more information

[0487] If the user is interested in the proposed department or job, they can request more information. The server receives this request, retrieves the relevant details from the database, and provides them to the user.

[0488] Job information registration

[0489] A company's department staff enters job information using a dedicated interface. This job information is analyzed by the emotion engine, along with the staff's intentions and expectations, and saved in a database. The saved information is then used for the next matching process by the user.

[0490] Specific examples

[0491] For example, if a user inputs "Desired job: software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest a department that would reduce the user's stress, such as "Software development with remote work capabilities."

[0492] Prompt Sentence Examples

[0493] "Please suggest a department that allows remote work and reduces stress for users with software development and data analysis skills using Python."

[0494] In this way, the present invention is a system that realizes more accurate suggestions that take into account the user's emotional state, enabling employees to work in a more appropriate environment and improving work comfort.

[0495] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0496] Step 1:

[0497] The user uses the device to input the desired job duties, work location, and skills. The input data is temporarily saved on the device. In addition, the device's camera captures the user's facial expressions and records their voice with a microphone. This emotional data is also acquired on the device. After the user has completed input, the data is sent to the server.

[0498] Step 2:

[0499] The terminal transmits the user's preference data and emotion data to the server, including text data on the user's preference, work location, and skills, image data captured by the camera, and voice data recorded by the microphone.

[0500] Step 3:

[0501] The server stores the received user preference data and emotion data in a database, where each user data is associated with the other user data.

[0502] Step 4:

[0503] The server uses an emotion engine to analyze the received facial image data and voice data. It uses OpenCV to extract facial features and the AudioEmotion library to identify the voice emotion. This provides the user's emotional state as numerical data.

[0504] Step 5:

[0505] Based on the analyzed emotion data and preference data, the server uses a generative AI model to create a list of the most suitable departments and jobs. Past job change and transfer data stored in the database is also referenced, and the generative AI model improves the accuracy of the analysis. A prompt sentence is entered to have the generative AI model perform the analysis.

[0506] Step 6:

[0507] The server sends the list of suitable departments and job positions proposed by the generative AI model to the user's device, including a message based on the user's emotions.

[0508] Step 7:

[0509] The user can check the proposal results on the terminal and request detailed information about the department or job they are interested in. The terminal then sends the request to the server.

[0510] Step 8:

[0511] Based on the received request, the server retrieves detailed information about the relevant department and job from the database and sends it to the user's device. The detailed information also includes supplementary explanations based on the analysis results of the emotion engine.

[0512] Step 9:

[0513] A company's department staff uses a dedicated interface to enter new job information, which is then sent to the server.

[0514] Step 10:

[0515] The server stores the received job information in a database. It also uses an emotion engine to analyze the intentions and expectations of the person in charge and stores this information along with the job information. The stored information will be used for the next matching process by the user.

[0516] 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.

[0517] 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.

[0518] 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.

[0519] [Second embodiment]

[0520] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0521] 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.

[0522] 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).

[0523] 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.

[0524] 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.

[0525] 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).

[0526] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0527] 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.

[0528] 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.

[0529] 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.

[0530] 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.

[0531] 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."

[0532] The present invention is embodied in a system for supporting employee job changes and transfers within a company. This system analyzes input preference data and suggests appropriate departments, thereby optimizing personnel allocation within the company and preventing personnel from leaving the company. A specific embodiment of this system is described below.

[0533] User preference input

[0534] Terminal

[0535] Users use their own terminals to input their desired job duties, work location, and skills. This input is done through a dedicated interface, and the terminal accepts it and sends it to the server.

[0536] Data analysis and matching

[0537] server

[0538] The server receives the user's desired data sent from the device. The received data is stored in a database and analyzed. The server then launches a generation AI, which uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[0539] Displaying the proposed results

[0540] server

[0541] Based on the analysis results of the generative AI, the server organizes the information of the proposed department. The organized information is sent to the user's device and made available for viewing.

[0542] Terminal

[0543] The user's device receives the proposed results sent from the server and displays them for easy viewing, allowing the user to select the department that best suits them from the list of proposed departments.

[0544] Providing more information

[0545] User

[0546] Once the user selects a department of interest, they can request more information through their terminal.

[0547] Terminal

[0548] The terminal transmits the user's request to the server.

[0549] server

[0550] The server receives the request, retrieves the details of the relevant department from the database, and sends the details back to the terminal.

[0551] Terminal

[0552] The terminal displays the detailed information sent from the server to the user, who can then decide which department is most suitable for them.

[0553] Job posting by department

[0554] Terminal

[0555] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[0556] server

[0557] The server stores the entered job information in a database and can use it to match the desired data entered by the user next time.

[0558] Specific examples

[0559] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze the corresponding department based on past data and suggest "Department A: Software development," "Department B: Data analysis team," etc. The user becomes interested in "Department A" and requests more information. In response to this request, the server can provide information about "Department A," such as detailed job description, recruitment requirements, work location, and skill requirements.

[0560] Furthermore, when a department employee enters new job information, they enter information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java," and register it on the server. This information will be used for the next request from the user.

[0561] The above is a specific embodiment of the present invention. This system allows for efficient personnel matching within a company, preventing the loss of personnel to the outside. It also has the advantage for employees that they can be assigned to work that best suits them.

[0562] The processing flow will be explained below.

[0563] Step 1:

[0564] User

[0565] Users use their own terminals to input the desired job duties, work location, and skills.

[0566] Step 2:

[0567] Terminal

[0568] The terminal accepts the input desired data. When the user clicks the "Submit" button, the input data is sent to the server.

[0569] Step 3:

[0570] server

[0571] The server receives the user's desired data sent from the terminal and stores the received data in a database.

[0572] Step 4:

[0573] server

[0574] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers and the user's desired data. The AI ​​then creates a list of departments that best match the user's desired conditions.

[0575] Step 5:

[0576] server

[0577] Based on the analysis results, the server organizes department information to suggest to the user.

[0578] Step 6:

[0579] server

[0580] The organized proposal result data is sent to the user's terminal.

[0581] Step 7:

[0582] Terminal

[0583] The terminal receives the data of the proposal results sent from the server and displays it to the user, who can then check the list of proposed departments.

[0584] Step 8:

[0585] User

[0586] The user selects the department of interest and requests detailed information on the terminal.

[0587] Step 9:

[0588] Terminal

[0589] The terminal sends a request to the server for detailed information about the department selected by the user.

[0590] Step 10:

[0591] server

[0592] The server receives the request and retrieves the details of the relevant department from the database.

[0593] Step 11:

[0594] server

[0595] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[0596] Step 12:

[0597] Terminal

[0598] The terminal receives the detailed information sent from the server and displays it to the user, who can then check the details and decide which department is best for him or her.

[0599] Step 13:

[0600] Terminal

[0601] Company department staff use a dedicated interface to enter job information.

[0602] Step 14:

[0603] Terminal

[0604] The entered job information is sent from the terminal to the server.

[0605] Step 15:

[0606] server

[0607] The server receives the entered job information and stores it in a database.

[0608] These steps allow employees to efficiently find the right department for them and companies to optimize their internal talent matching.

[0609] Example 1

[0610] 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."

[0611] By supporting appropriate employee job changes and transfers within a company, companies need to optimize their internal human resource allocation, maintain employee motivation, and prevent talent loss. By suggesting the optimal department based on employees' desired work content, work location, and skills, and providing detailed information, companies need to help employees find the work that best suits them. They also need a system that allows each department within a company to efficiently register new job information and use it for matching.

[0612] 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.

[0613] In this invention, the server includes a means for inputting desired job content, work location, and skills, a means for receiving data input by the user, a means for analyzing the received data and using a generative AI model to list appropriate departments, a means for transmitting information about the proposed departments to the user, a means for a company department staff member to input job information and transmit it to the server, a means for saving the sent job information in a database, and a means for using the saved job information for the next user matching process. This allows the generative AI model to suggest the most suitable department based on the desired information input by the user and provide detailed information about the department, thereby optimizing human resource allocation within the company and improving employee satisfaction. Furthermore, the ability for company department staff to efficiently register and use job information improves the accuracy and efficiency of human resource matching.

[0614] The "means for inputting desired work content, work location, and skills" is an interface for a user to input the desired work content, work location, and skill information.

[0615] The "server means for receiving data entered by a user" is a function of the server for receiving data entered from a user's terminal and recording it in a database.

[0616] "Means for analyzing received data and using a generative AI model to list appropriate departments" refers to a process that uses a generative AI model based on received data to select the department that best matches the user's preferences.

[0617] The "means for sending information about the proposed department to the user" is a server function for sending information about the optimal department suggested by the generative AI model to the user's terminal.

[0618] The "means for obtaining detailed information about a department in which the user is interested and providing it to the user" is a process for obtaining detailed information about a department selected by the user from a database and providing it to the user.

[0619] The "means for a company departmental employee to input job information and send it to the server" is an interface that allows a departmental employee to input new job information and send that information to the server.

[0620] The "means for saving submitted job information in a database" is a function of the server for saving job information submitted by department personnel of a company in a database.

[0621] The "means for utilizing the saved job information in the next matching process by the user" is a process for utilizing the saved job information in the next matching process by the user with desired data.

[0622] "Means of acquiring past job change and transfer data and having the generative AI model learn from that data to improve the accuracy of analysis" refers to a process of collecting data on past job changes and transfers and having the generative AI model learn from that data to improve the accuracy of analysis.

[0623] This invention is a system for supporting employee job changes and transfers within a company, and is implemented in the following steps: The system optimizes the allocation of personnel within a company by inputting the user's desired work content, work location, and skills, and then proposing the most suitable department based on that information and providing detailed information.

[0624] Hardware and Software Configuration

[0625] This system is implemented in a network environment that includes user devices, a server, and a database. User devices are assumed to be PCs, smartphones, tablets, etc., and operations are performed through a dedicated web interface called "Employee Shift Portal." A database (PostgreSQL) is connected to the server, which implements the generative AI model "HR-MatchAI." The server is operated through a web application framework (e.g., Django or Flask).

[0626] User preference input

[0627] Users access the Employee Shift Portal using their own devices and enter their desired work content, work location, and skills through the interface. This input data is sent to the server by the device.

[0628] Examples:

[0629] The user enters "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis."

[0630] Example of prompt: The user enters information according to the instructions: "Please enter the job description, location, and skills you are interested in (e.g., software development, Tokyo, Python)."

[0631] Data analysis and matching

[0632] The server receives the user's desired data sent from the device and stores it in a database. It then launches the generative AI model "HR-MatchAI" and uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[0633] Examples:

[0634] The generative AI model suggests the most suitable department, such as "Department A: Software Development" or "Department B: Data Analysis Team."

[0635] Example of prompt sentence: The server follows the instruction "Analyze the user's desired data and suggest the most suitable department," and lists departments.

[0636] Displaying the proposed results

[0637] Based on the analysis results of the generative AI model, the server formats the information on the proposed departments and sends it to the user's device, which displays the received proposals on a dedicated interface, and the user can select the department that best suits them from the list of proposed departments.

[0638] Examples:

[0639] The proposed departments "Department A: Software Development" and "Department B: Data Analysis Team" are displayed on the terminal.

[0640] Example of prompt sentence: The terminal follows the instruction to display the suggestion results to the user and prompt them to select a department on the screen.

[0641] Providing more information

[0642] When a user selects a department of interest and sends a request for detailed information, the device sends this request to the server. The server retrieves the details of the department from the database and sends them back to the user's device. The device displays the received details, and the user can use this information to decide which department is most suitable for them.

[0643] Examples:

[0644] The user clicks the detailed information button for "Department A" and the detailed information is displayed on the terminal screen.

[0645] Example of prompt sentence: The server follows the instruction "Please provide details about Department A" and sends the details.

[0646] Job posting by department

[0647] A company's department staff uses the dedicated web interface "HR-Manager Suite" to enter new job information and send it to the server, which then stores the received information in a database and uses it to match the user's desired data next time.

[0648] Examples:

[0649] The department staff member enters "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" and submits the form.

[0650] Example of prompt: The person in charge will follow the instructions to "Register new job information" and enter the information.

[0651] The above is an embodiment of the present invention. This system makes it easy to optimize personnel allocation within a company, enabling the right person to be placed in the right position and reducing the company's employee turnover rate.

[0652] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0653] Step 1:

[0654] Input desired data by the user

[0655] Users input their desired work content, work location, and skills. This data is input through the "Employee Shift Portal," and the information entered through a dedicated interface is sent to the terminal.

[0656] Input: Desired job description, location, skills (e.g. software development, Tokyo, Python)

[0657] Output: The input data is saved in the form

[0658] Specific behavior:

[0659] The user enters "Job Description: Software Development", "Work Location: Tokyo", and "Skills: Python, Data Analysis" and clicks the submit button.

[0660] Step 2:

[0661] Receiving and storing user preference data

[0662] The terminal receives the desired data entered by the user, performs form validation (checks the input data), and if there are no errors, sends the data to the server.

[0663] Input: Data entered by the user into the terminal

[0664] Output: Data sent to the server

[0665] Specific behavior:

[0666] The terminal receives the data entered by the user, for example, "software development, Tokyo, Python, data analysis," and sends it to the server.

[0667] Step 3:

[0668] Analyzing and storing user preference data

[0669] The server receives the user's desired data sent from the device, stores the received data in a PostgreSQL database, and prepares it for analysis.

[0670] Input: Desired data sent from the terminal

[0671] Output: Data stored in the database

[0672] Specific behavior:

[0673] The server stores the received data "Software Development, Tokyo, Python, Data Analysis" in a database.

[0674] Step 4:

[0675] Matching analysis using generative AI models

[0676] The server launches the generative AI model "HR-MatchAI" based on the saved user preference data. The generative AI model learns from past job change and transfer data and lists the departments that best match the preference data.

[0677] Input: User preference data stored in the database

[0678] Output: A list of the best departments (e.g., Department A, Department B)

[0679] Specific behavior:

[0680] The generative AI model analyzes "software development, Tokyo, Python, data analysis" and suggests "Department A: software development" and "Department B: data analysis team."

[0681] Step 5:

[0682] Formatting and sending the proposal results

[0683] The server formats the analysis results from the generative AI model and sends the proposed department information to the user's device.

[0684] Input: A list of optimal departments generated by a generative AI model

[0685] Output: Proposal result data sent to the user's device

[0686] Specific behavior:

[0687] The server sends "Department A: Software Development" and "Department B: Data Analysis Team" to the user's device.

[0688] Step 6:

[0689] Displaying the proposed results

[0690] The terminal receives the proposal result data sent from the server and displays it on a dedicated interface.

[0691] Input: Proposal result data sent from the server

[0692] Output: Suggestion results displayed on the user's device

[0693] Specific behavior:

[0694] "Department A: Software Development" and "Department B: Data Analysis Team" will be displayed on the user's device.

[0695] Step 7:

[0696] Request more information

[0697] The user selects the department they are interested in and submits a request for more information.

[0698] Input: User requests more information

[0699] Output: Request data from the terminal to the server

[0700] Specific behavior:

[0701] The user clicks the detailed information button for "Department A," and the terminal sends the request to the server.

[0702] Step 8:

[0703] Get and send details

[0704] The server receives the detailed information request, retrieves the detailed information for the relevant department from the database, and sends the retrieved detailed information back to the user's terminal.

[0705] Input:Detailed information request data

[0706] Output: Retrieved details

[0707] Specific behavior:

[0708] The server retrieves detailed information about "Department A" from the database and sends it to the user's terminal.

[0709] Step 9:

[0710] Viewing detailed information

[0711] The terminal receives the detailed information sent from the server and displays it to the user, who can then use this information to decide which department is most suitable for them.

[0712] Input: Details sent from the server

[0713] Output: Detailed information displayed on the user's terminal

[0714] Specific behavior:

[0715] Detailed information about "Department A" is displayed on the user's device.

[0716] (Application example 1)

[0717] 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."

[0718] Conventional in-house human resource allocation systems often fail to properly reflect employee preferences, and are not effective enough in preventing the loss of human resources to other companies. Furthermore, optimization of the placement and roles of robots in factories is primarily managed manually, making efficient operation difficult. Furthermore, the inability to fully utilize past data creates issues with the accuracy of placement proposals. For these reasons, there was a need for a system that could simultaneously optimize the placement of both in-house human resources and in-factory robots, and make more efficient and accurate proposals.

[0719] 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.

[0720] In this invention, the server includes: means for inputting desired work content, work location, and skills; server means for receiving the data input by the user; means for analyzing the received data and using a generation AI to list appropriate departments; means for sending information about the proposed departments to the user; means for obtaining detailed information about departments in which the user is interested and providing it to the user; means for inputting robot specifications, roles, placement, and skills; and means for analyzing the robot specifications, roles, placement, and skills and using a generation AI to propose appropriate placement. This makes it possible to simultaneously optimize personnel placement within a company and robot placement within a factory, thereby improving employee satisfaction and improving corporate operational efficiency.

[0721] The "desired work content" is the job content that an employee or user desires to be in charge of.

[0722] "Work location" is the location or area where an employee or user wants to work.

[0723] "Skills" refer to the techniques and knowledge possessed by employees or users, and indicate the abilities required to perform specific tasks.

[0724] A "means" is a method or device used to achieve a particular purpose.

[0725] "User" means an individual or employee who uses this system.

[0726] A "server" is a computer system that receives information from users and performs analytical processing.

[0727] "Generative AI" is artificial intelligence that analyzes data and generates optimal suggestions and lists.

[0728] "Listing" means displaying the best options in a list format based on specific conditions.

[0729] A "robot" is a mechanical device designed to perform a specific task in a factory.

[0730] "Deployment" refers to the allocation of specific people or machines to appropriate locations.

[0731] "Specifications" are detailed information that indicates the specifications and capabilities of a robot or machine.

[0732] A "role" refers to the tasks or responsibilities that an employee or robot should perform.

[0733] The present invention is implemented as a system for optimizing the allocation of employees and robots in a factory within a company. This system is composed of the following components:

[0734] User preference input

[0735] Terminal

[0736] Users use their own devices to input their desired work content, work location, and skills. This input data is sent to the server via the device. Factory robot specifications, current role, placement, and skill sets are also input from the device and sent to the server.

[0737] Data analysis and matching

[0738] server

[0739] The server receives data sent from users and factory robots. The received data is stored in a database. The server then launches a generative AI model (Recommendation AI) that lists appropriate departments and optimal robot placements based on the user's desired data and robot data. The generative AI model learns from past job change and transfer data and past robot placement data to improve its analysis accuracy.

[0740] Displaying the proposed results

[0741] server

[0742] Based on the analysis results of the generation AI, the server organizes the proposed department and robot placement information, which is then sent to the terminal.

[0743] Terminal

[0744] The user's terminal receives and displays the proposed results sent from the server, and the user or factory manager can select an appropriate department or robot placement from the proposed list.

[0745] Providing more information

[0746] User

[0747] Once the user or factory manager selects a proposal that interests them, they can request more information.

[0748] Terminal

[0749] The terminal sends a request to the server, which retrieves the detailed information and sends it back to the terminal.

[0750] Terminal

[0751] The terminal displays detailed information received from the server, allowing users and factory managers to make decisions.

[0752] Registering department and robot placement information

[0753] Terminal

[0754] Company department staff and factory managers enter new job information and robot placement information through a dedicated interface and send it to the server.

[0755] server

[0756] The server stores the transmitted information in a database and uses it for the next analysis.

[0757] As a specific example, if a user inputs "Desired work content: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze this and suggest "Department A: Software development," "Department B: Data analysis team," etc. On the other hand, if a factory manager inputs "Robot ID: robot_001," "Specs: Type-A," "Current role: Assembly," "Location: Line-1," and "Skills: welding, bolting," the generation AI will make a "New placement proposal: Maintenance." This allows for the optimization of departments and placements.

[0758] Example prompt sentence:

[0759] Please recommend the best placement and role based on the following robot data:

[0760] Specs: Type-A

[0761] Current role: Assembly

[0762] Current location: Line-1

[0763] Skill Set: ["welding", "bolting"]

[0764] This system makes it possible to simultaneously optimize the allocation of personnel within a company and the allocation of robots within a factory, which is expected to improve the operational efficiency of a company and employee satisfaction.

[0765] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0766] Step 1:

[0767] User data entry

[0768] The terminal allows the user to input "desired work content," "work location," and "skills." The factory manager inputs "robot specifications," "role," "placement," and "skill set." These data are entered into the input screen by the user or factory manager, and the entered data is sent from the terminal to the server.

[0769] Input: User's desired work data, robot specification data

[0770] Output: Desired data and robot data sent to the server

[0771] Step 2:

[0772] Data reception and storage by the server

[0773] The server receives the desired data and robot data sent from the device. The received data is stored in a database (e.g., MySQL, SQLite). This makes the data ready for analysis.

[0774] Input: Desired data and robot data sent from the terminal

[0775] Output: Desired data and robot data stored in a database

[0776] Step 3:

[0777] Data analysis and department / robot placement proposals

[0778] The server passes the received data to a generative AI model (Recommendation AI) for analysis. The generative AI model uses past job change and transfer data and past robot placement data to create a list of appropriate departments and optimal robot placements that match the user's desired data.

[0779] Input: Desired data and robot data stored in the database

[0780] Output: A list of department and robot placement suggestions from the generation AI

[0781] Step 4:

[0782] Organizing and sending proposal results

[0783] The server organizes the analysis results of the generated AI model, composes proposed department information and robot placement information, converts that information into a format suitable for sending to the user's device, and sends it to the device.

[0784] Input: A list of suggestions from the generative AI

[0785] Output: Formatted proposal information to send to terminal

[0786] Step 5:

[0787] Display of proposal results on user device

[0788] The terminal receives the proposed information sent from the server and displays it in an easy-to-read format for users and factory managers, who can then use this information to consider the optimal placement of themselves or the robots.

[0789] Input: Proposal information sent from the server

[0790] Output: Displayed proposal information

[0791] Step 6:

[0792] Requesting and Providing More Information

[0793] When a user or factory manager selects a department or location of interest, the terminal sends a request for detailed information to the server. The server retrieves the relevant information from the database and sends it back to the terminal. The terminal then displays the detailed information.

[0794] Input: A request for more information from the user

[0795] Output: Display of detailed information provided by the server

[0796] Step 7:

[0797] Registration of job vacancies and robot placement information

[0798] Company department staff and factory managers input new job information and robot placement information and send it from their terminals to the server, which stores it in a database and uses it for the next matching process.

[0799] Input: New job information and robot placement information

[0800] Output: Information stored in the database

[0801] The above processing steps make it possible to realize optimal allocation of personnel and robots within a company.

[0802] 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.

[0803] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. Employees input their preferences, and the system proposes appropriate departments based on those preferences. The introduction of an emotion engine analyzes the emotions expressed at the time of input, further individualizing the system.

[0804] User preference input

[0805] Terminal

[0806] Users use their own devices to input their desired job duties, work location, and skills. The device receives the input information and sends it to the server. The emotion engine then analyzes the user's facial expressions and tone of voice when inputting information to recognize the user's emotional state.

[0807] Data analysis and matching

[0808] server

[0809] The server receives the user's desired data sent from the device along with the emotion data from the emotion engine. The server stores this data in a database and activates the generation AI. The generation AI analyzes past job change and transfer data and lists the most suitable departments, taking into account the user's desired conditions and emotion data.

[0810] Displaying the proposed results

[0811] server

[0812] Based on the analysis results of the generative AI, the server organizes the department information to suggest to the user, and the organized information is sent to the user's device.

[0813] Terminal

[0814] The user's device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message according to the user's emotions.

[0815] Providing more information

[0816] User

[0817] The user selects the department of interest and requests detailed information on the terminal.

[0818] Terminal

[0819] The terminal sends a request to the server for detailed information about the department selected by the user.

[0820] server

[0821] The server receives the request, retrieves the details of the department from the database, and sends the details to the user's device.

[0822] Terminal

[0823] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[0824] Job posting by department

[0825] Terminal

[0826] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[0827] server

[0828] The server receives the job information and stores it in a database, which is used to match the user's desired data the next time they enter it.

[0829] Specific examples

[0830] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities," a department that would reduce the user's stress. Furthermore, if the emotion engine determines that the user is entering information in a relaxed state, it will make general suggestions such as "Department A: Software development" or "Department B: Data analysis team."

[0831] Additionally, when a department staff member enters new job information (job description: AI development, work location: Osaka, skills: machine learning, Java) and registers it on the server, this information will be used in the next matching process.

[0832] In this way, by combining this system with an emotion engine, it is possible to make suggestions that take into account the user's emotional state, helping employees to work in a more suitable environment.

[0833] The processing flow will be explained below.

[0834] Step 1:

[0835] User

[0836] Users use their own terminals to input the desired job duties, work location, and skills.

[0837] Step 2:

[0838] Terminal

[0839] The device accepts the input desired data, and at the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize the emotional data.

[0840] Step 3:

[0841] Terminal

[0842] When the user clicks the "send" button, the terminal transmits the input data and emotion data to the server.

[0843] Step 4:

[0844] server

[0845] The server receives the user's desired data and emotion data sent from the device, and stores the received data in a database.

[0846] Step 5:

[0847] server

[0848] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers, as well as the user's preferences and emotional data. The AI ​​then creates a list of the most suitable departments, taking into account the user's preferences and emotional data.

[0849] Step 6:

[0850] server

[0851] Based on the analysis results from the generative AI, the server organizes the department information to suggest to the user.

[0852] Step 7:

[0853] server

[0854] The organized proposal result data is sent to the user's terminal.

[0855] Step 8:

[0856] Terminal

[0857] The device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message based on the user's emotions.

[0858] Step 9:

[0859] User

[0860] The user selects the department of interest and requests detailed information on the terminal.

[0861] Step 10:

[0862] Terminal

[0863] The terminal sends a request to the server for detailed information about the department selected by the user.

[0864] Step 11:

[0865] server

[0866] The server receives the request and retrieves the details of the relevant department from the database.

[0867] Step 12:

[0868] server

[0869] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[0870] Step 13:

[0871] Terminal

[0872] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[0873] Step 14:

[0874] Terminal

[0875] Company department staff use a dedicated interface to enter job information.

[0876] Step 15:

[0877] Terminal

[0878] The entered job information is sent from the terminal to the server.

[0879] Step 16:

[0880] server

[0881] The server receives the entered job information and stores it in a database.

[0882] Specific examples

[0883] For example, if a user inputs "Desired work: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities" as a department that will reduce stress. If the emotion engine determines that the user is entering information in a relaxed state, the system will make the usual suggestions of "Department A: Software development" and "Department B: Data analysis team."

[0884] The above is a specific embodiment of a system incorporating an emotion engine. This system enables more appropriate personnel matching within a company by taking into account the emotions of users.

[0885] Example 2

[0886] 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."

[0887] Conventional personnel job change and transfer systems typically suggest an appropriate department based solely on the user's desired conditions. However, because the user's emotional state is not taken into consideration, the department that is most suitable for the user may not be suggested. This can result in a failed job change or transfer and reduced user satisfaction. The objective of this invention is to suggest a more appropriate department and improve user satisfaction by simultaneously considering the user's desired conditions and emotional state.

[0888] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0889] In this invention, the server includes a means for receiving data entered by a user and emotion data analyzed by an emotion engine, a means for storing the received data in a database and listing appropriate departments using a generative AI model, and a means for transmitting information on the proposed departments to the user, thereby enabling optimal department proposals that simultaneously consider the user's desired conditions and emotional state.

[0890] "Desired work content" refers to the work content that the user wants to undertake in their career.

[0891] "Work location" is the geographic location where a user wishes to work.

[0892] "Skills" refer to the specialized abilities and knowledge that a user has.

[0893] "Emotion data" is data relating to the emotional state of the user analyzed from facial expressions, tone of voice, etc. using an emotion engine.

[0894] "Server means" is a general term for equipment and software for receiving, processing, and storing data sent from users and emotion engines.

[0895] The "database" is a system for organizing and managing received user data and emotion data.

[0896] The "generative AI model" is an artificial intelligence model that learns from past job change and transfer data and suggests the most suitable department based on the user's desired conditions and emotional state.

[0897] The "proposal results" are information about the optimal department that the generative AI model analyzes and presents to the user.

[0898] "Detailed information" is data containing specific job descriptions, conditions, and other supplemental information about the proposed department.

[0899] "Job information" is information about available positions entered by department personnel in a company.

[0900] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. This system allows users to input their preferences, and suggests appropriate departments based on those preferences. By introducing an emotion engine, the system analyzes the emotions entered at the time of input and further individualizes the proposals.

[0901] Users use their own devices to input their desired job duties, work location, and skills. Specifically, they input "Desired job duties: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The devices are equipped with built-in cameras and microphones, which are used to capture the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data. The emotion engine uses Python's facial recognition library and voice analysis tools to recognize the user's emotional state.

[0902] The device sends the input preference data and emotion data as a single data packet to the server. The server stores the received data in a database and launches a generative AI model (using TensorFlow or PyTorch as an example). The generative AI model learns from past job change and transfer data and lists the most suitable departments based on the user's preference and emotion data.

[0903] Based on the analysis results of the generative AI model, the server organizes the department information to suggest to the user. This organization also includes supplemental information based on the user's emotional state. For example, if the user is entering information in a relaxed state, general suggestions such as "Department A: Software Development" and "Department B: Data Analysis Team" will be made, but if the user is feeling stressed, suggestions such as "Department C: Software Development with Remote Work Capability" will be made. The organized information is sent to the user's device, which receives and displays it.

[0904] To obtain detailed information about a department that interests a user, the user makes a request on their device. The server receives the request, retrieves the details of the department from the database, and sends them to the user's device. The user's device then displays this information to the user. This display also includes supplementary information and suggestions based on the user's emotional state, provided by the emotion engine.

[0905] Additionally, company department staff use a dedicated interface to enter job information and send it to the server. This job information is saved in the database and used to match the next desired data entered by a user. For example, if "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" are entered and registered on the server, this information will be used in the next matching process.

[0906] As described above, this system simultaneously considers the user's desired conditions and emotional state, and makes more appropriate suggestions, thereby improving user satisfaction.

[0907] Examples:

[0908] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the emotion engine analyzes the user's emotions in real time and determines that the user is in a relaxed state, for example. Based on this data, the generative AI model lists appropriate departments. The suggested results, "Department A: Software development" and "Department B: Data analysis team," are displayed to the user. Examples of prompt sentences are as follows:

[0909] Example prompt sentence:

[0910] Write an algorithm that will suggest an appropriate department based on the user's emotional state when they enter "Desired job: Software development", "Location: Tokyo", and "Skills: Python, data analysis".

[0911] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0912] Step 1: Enter desired conditions and emotional data

[0913] Terminal

[0914] A user logs in to a device and enters their desired job description, work location, and skills. For example, they might enter "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The device's camera and microphone capture the user's facial expressions and tone of voice, which the emotion engine analyzes to identify the user's emotional state. The input for this step is the user's desired data and real-time emotion data, and the output is a data packet containing this input.

[0915] Step 2: Sending data packets

[0916] Terminal

[0917] The user's device sends the input desired data and the emotional data analyzed by the emotion engine to the server as a single data packet. The transmitted data packet includes, for example, the user ID, desired job content, work location, skills, and emotional state. The input to this step is the data packet generated in step 1, and the output is a transmission confirmation to the server.

[0918] Step 3: Receiving and storing data

[0919] server

[0920] The server receives the data packet sent from the device. This input data is the user's desired conditions and emotional state. After receiving, the server stores this data in a database. For example, the data is stored using a database management system (e.g., MySQL or PostgreSQL). The input of this step is the data packet sent from the device, and the output is confirmation of successful storage.

[0921] Step 4: Launching the generative AI model and analyzing data

[0922] server

[0923] The server uses the stored data to launch a generative AI model. For example, an AI model using TensorFlow or PyTorch learns from past job change and transfer data and lists the most suitable departments by taking into account the user's desired conditions and emotional data. The input for this step is the stored user data and past job change and transfer data, and the output is a list of appropriate departments as a result of the analysis.

[0924] Step 5: Organize and submit your proposal

[0925] server

[0926] Based on the analysis results of the generative AI model, the server organizes department information to suggest to the user. The suggestions also include supplemental information based on the user's emotional state. The organized data is sent to the user's device. The input to this step is the analysis results of the generative AI model, and the output is the data sent to the user's device.

[0927] Step 6: Viewing the Suggestion Results

[0928] Terminal

[0929] The user's device receives the proposal results sent from the server and displays them to the user. For example, a relaxed user may be suggested "Department A: Software Development" or "Department B: Data Analysis Team," while a stressed user may be suggested "Department C: Remote Work Software Development." The input for this step is the proposal results from the server, and the output is a list of proposals displayed to the user.

[0930] Step 7: Request more information

[0931] User

[0932] The user selects the department they are interested in from the proposed departments and requests detailed information on the terminal. The input of this step is the department information selected by the user, and the output is a detailed information request.

[0933] Step 8: Get and send details

[0934] server

[0935] The server receives a detailed information request from the user and retrieves the detailed information for the relevant department from the database. It then sends the retrieved detailed information to the user's terminal. The input of this step is the detailed information request, and the output is the transmission of the detailed information to the user's terminal.

[0936] Step 9: View detailed information

[0937] Terminal

[0938] The user's device receives the detailed information sent from the server and displays it to the user. The displayed information also includes supplementary information and suggestions according to the emotional state transmitted by the emotion engine. The input of this step is the detailed information from the server, and the output is the detailed information displayed to the user.

[0939] Step 10: Fill out and submit your job posting

[0940] Terminal

[0941] A departmental employee at a company uses a dedicated interface to input new job information. The input information is sent to the server. For example, information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" is input. The input for this step is the company's job information, and the output is the transmission of the job information to the server.

[0942] Step 11: Save your job posting

[0943] server

[0944] The server receives the job information sent by the company's department staff and saves it in the database. The saved information will be used for the next matching process with the desired input data by the user. The input of this step is the submitted job information, and the output is a confirmation that the job information was successfully saved.

[0945] (Application example 2)

[0946] 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."

[0947] Conventional personnel job change and department transfer support systems often make mechanical suggestions based on user input data and are unable to consider the user's emotions or psychological state. As a result, the proposed departments and jobs do not satisfy the user psychologically, making it difficult to realize an effective transfer or job change. In addition, the job information entered by company department personnel is not provided in a form that reflects their emotions and expectations, and further improvements in matching accuracy are required.

[0948] The specification processing by the specification 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 inputting desired work content, work location, and skills, server means for receiving data input by the user, means for analyzing the received data and using a generative AI model to list appropriate departments, means for transmitting information on the proposed departments to the user, means for acquiring detailed information on departments in which the user is interested and providing it to the user, means for analyzing the user's facial expressions and voice using an emotion engine and using the emotion data for analysis, means for generating proposals according to the user's psychological state based on the analyzed emotion data, means for acquiring past job change and transfer data and having the generative AI model learn from the data to improve analysis accuracy, means for inputting prompt sentences into the generative AI model to obtain optimal analysis results, means for a company department employee to input job information and transmit it to the server, means for saving the sent job information in a database, means for using the saved job information in a matching process by the next user, and means for utilizing the emotion engine to analyze the employee's intentions and expectations when entering job information. This will enable more accurate department and job suggestions that reflect the user's emotions and psychological state.

[0949] A "user" is someone who uses the system to input their job description, work location, and skills, and receives suggestions for suitable departments and jobs.

[0950] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[0951] "Job content" refers to the type of work and specific duties desired by the user.

[0952] "Work location" refers to the location or area where the user wishes to work.

[0953] "Skills" refers to the specialized techniques, knowledge, or proficiency that a user possesses.

[0954] "Server means" means a computing device used by the system to receive, store, and analyze user-entered data and other information.

[0955] A "generative AI model" is an artificial intelligence model that suggests appropriate departments and jobs based on input data and past data.

[0956] A "prompt sentence" is a sentence that is input to a generative AI model to obtain optimal analysis results.

[0957] "Suggestion means" is a function that allows the system to send users information about appropriate departments and jobs listed by the generative AI model.

[0958] The "detailed information acquisition means" is a function for acquiring further detailed information about a proposal in which the user is interested and providing the information to the user.

[0959] "Job information" refers to the content and conditions of the job being recruited for, entered by department staff at a company.

[0960] The "analysis means" is a means for analyzing the data received by the server means and listing appropriate departments and jobs.

[0961] "Matching processing" is the process of selecting the most suitable department and job based on the user's preferences, emotional data, and past data.

[0962] The "means for analyzing intentions and expectations" is a means for using an emotion engine to recognize and analyze the intentions and expectations of company personnel who enter job information.

[0963] The present invention is a system that proposes departments and jobs with higher accuracy by taking into account the user's wishes and emotional state. By combining an emotion engine, this system improves on conventional mechanical proposals and enables proposals that correspond to the user's psychological state.

[0964] System Configuration

[0965] Hardware

[0966] The system is implemented mainly using the following hardware:

[0967] Server: A high-performance computer device that receives data, analyzes it, and sends out proposals.

[0968] User terminal: A smartphone or tablet, which is a device where users input their preferences and emotional data and receive suggested information.

[0969] software

[0970] The system is implemented using the following software:

[0971] Generative AI model: Built using TensorFlow, it lists the most suitable departments and jobs based on the user's preferences and past job change data.

[0972] Emotion analysis: Using OpenCV and AudioEmotion libraries, we analyze the user's facial expressions and voice to obtain emotional data.

[0973] Database: Using MySQL, we store user preferences and emotional data, company job information, and past job change and transfer data.

[0974] Program processing overview

[0975] Desired input

[0976] Users use their smartphones to input their desired job duties, work location, and skills. The camera also captures their facial expressions and records their voice with a microphone. The emotion engine analyzes this data and recognizes the user's emotional state.

[0977] Data reception and analysis

[0978] The server receives preference and emotion data sent from the user's device. This data is stored in a database and analyzed by a generative AI model. The generative AI model learns from past job change and transfer data to improve the accuracy of its analysis.

[0979] suggestion

[0980] The server then suggests appropriate departments and jobs to the user based on the generative AI model. This suggestion information is generated taking into account the user's emotional state. For example, if the user is feeling stressed, the server will suggest departments that will help them reduce stress.

[0981] Providing more information

[0982] If the user is interested in the proposed department or job, they can request more information. The server receives this request, retrieves the relevant details from the database, and provides them to the user.

[0983] Job information registration

[0984] A company's department staff enters job information using a dedicated interface. This job information is analyzed by the emotion engine, along with the staff's intentions and expectations, and saved in a database. The saved information is then used for the next matching process by the user.

[0985] Specific examples

[0986] For example, if a user inputs "Desired job: software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest a department that would reduce the user's stress, such as "Software development with remote work capabilities."

[0987] Prompt Sentence Examples

[0988] "Please suggest a department that allows remote work and reduces stress for users with software development and data analysis skills using Python."

[0989] In this way, the present invention is a system that realizes more accurate suggestions that take into account the user's emotional state, enabling employees to work in a more appropriate environment and improving work comfort.

[0990] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0991] Step 1:

[0992] The user uses the device to input the desired job duties, work location, and skills. The input data is temporarily saved on the device. In addition, the device's camera captures the user's facial expressions and records their voice with a microphone. This emotional data is also acquired on the device. After the user has completed input, the data is sent to the server.

[0993] Step 2:

[0994] The terminal transmits the user's preference data and emotion data to the server, including text data on the user's preference, work location, and skills, image data captured by the camera, and voice data recorded by the microphone.

[0995] Step 3:

[0996] The server stores the received user preference data and emotion data in a database, where each user data is associated with the other user data.

[0997] Step 4:

[0998] The server uses an emotion engine to analyze the received facial image data and voice data. It uses OpenCV to extract facial features and the AudioEmotion library to identify the voice emotion. This provides the user's emotional state as numerical data.

[0999] Step 5:

[1000] Based on the analyzed emotion data and preference data, the server uses a generative AI model to create a list of the most suitable departments and jobs. Past job change and transfer data stored in the database is also referenced, and the generative AI model improves the accuracy of the analysis. A prompt sentence is entered to have the generative AI model perform the analysis.

[1001] Step 6:

[1002] The server sends the list of suitable departments and job positions proposed by the generative AI model to the user's device, including a message based on the user's emotions.

[1003] Step 7:

[1004] The user can check the proposal results on the terminal and request detailed information about the department or job they are interested in. The terminal then sends the request to the server.

[1005] Step 8:

[1006] Based on the received request, the server retrieves detailed information about the relevant department and job from the database and sends it to the user's device. The detailed information also includes supplementary explanations based on the analysis results of the emotion engine.

[1007] Step 9:

[1008] A company's department staff uses a dedicated interface to enter new job information, which is then sent to the server.

[1009] Step 10:

[1010] The server stores the received job information in a database. It also uses an emotion engine to analyze the intentions and expectations of the person in charge and stores this information along with the job information. The stored information will be used for the next matching process by the user.

[1011] 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.

[1012] 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.

[1013] 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.

[1014] [Third embodiment]

[1015] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1016] 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.

[1017] 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).

[1018] 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.

[1019] 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.

[1020] 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).

[1021] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1022] 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.

[1023] 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.

[1024] 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.

[1025] 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.

[1026] 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."

[1027] The present invention is embodied in a system for supporting employee job changes and transfers within a company. This system analyzes input preference data and suggests appropriate departments, thereby optimizing personnel allocation within the company and preventing personnel from leaving the company. A specific embodiment of this system is described below.

[1028] User preference input

[1029] Terminal

[1030] Users use their own terminals to input their desired job duties, work location, and skills. This input is done through a dedicated interface, and the terminal accepts it and sends it to the server.

[1031] Data analysis and matching

[1032] server

[1033] The server receives the user's desired data sent from the device. The received data is stored in a database and analyzed. The server then launches a generation AI, which uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[1034] Displaying the proposed results

[1035] server

[1036] Based on the analysis results of the generative AI, the server organizes the information of the proposed department. The organized information is sent to the user's device and made available for viewing.

[1037] Terminal

[1038] The user's device receives the proposed results sent from the server and displays them for easy viewing, allowing the user to select the department that best suits them from the list of proposed departments.

[1039] Providing more information

[1040] User

[1041] Once the user selects a department of interest, they can request more information through their terminal.

[1042] Terminal

[1043] The terminal transmits the user's request to the server.

[1044] server

[1045] The server receives the request, retrieves the details of the relevant department from the database, and sends the details back to the terminal.

[1046] Terminal

[1047] The terminal displays the detailed information sent from the server to the user, who can then decide which department is most suitable for them.

[1048] Job posting by department

[1049] Terminal

[1050] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[1051] server

[1052] The server stores the entered job information in a database and can use it to match the desired data entered by the user next time.

[1053] Specific examples

[1054] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze the corresponding department based on past data and suggest "Department A: Software development," "Department B: Data analysis team," etc. The user becomes interested in "Department A" and requests more information. In response to this request, the server can provide information about "Department A," such as detailed job description, recruitment requirements, work location, and skill requirements.

[1055] Furthermore, when a department employee enters new job information, they enter information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java," and register it on the server. This information will be used for the next request from the user.

[1056] The above is a specific embodiment of the present invention. This system allows for efficient personnel matching within a company, preventing the loss of personnel to the outside. It also has the advantage for employees that they can be assigned to work that best suits them.

[1057] The processing flow will be explained below.

[1058] Step 1:

[1059] User

[1060] Users use their own terminals to input the desired job duties, work location, and skills.

[1061] Step 2:

[1062] Terminal

[1063] The terminal accepts the input desired data. When the user clicks the "Submit" button, the input data is sent to the server.

[1064] Step 3:

[1065] server

[1066] The server receives the user's desired data sent from the terminal and stores the received data in a database.

[1067] Step 4:

[1068] server

[1069] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers and the user's desired data. The AI ​​then creates a list of departments that best match the user's desired conditions.

[1070] Step 5:

[1071] server

[1072] Based on the analysis results, the server organizes department information to suggest to the user.

[1073] Step 6:

[1074] server

[1075] The organized proposal result data is sent to the user's terminal.

[1076] Step 7:

[1077] Terminal

[1078] The terminal receives the data of the proposal results sent from the server and displays it to the user, who can then check the list of proposed departments.

[1079] Step 8:

[1080] User

[1081] The user selects the department of interest and requests detailed information on the terminal.

[1082] Step 9:

[1083] Terminal

[1084] The terminal sends a request to the server for detailed information about the department selected by the user.

[1085] Step 10:

[1086] server

[1087] The server receives the request and retrieves the details of the relevant department from the database.

[1088] Step 11:

[1089] server

[1090] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[1091] Step 12:

[1092] Terminal

[1093] The terminal receives the detailed information sent from the server and displays it to the user, who can then check the details and decide which department is best for him or her.

[1094] Step 13:

[1095] Terminal

[1096] Company department staff use a dedicated interface to enter job information.

[1097] Step 14:

[1098] Terminal

[1099] The entered job information is sent from the terminal to the server.

[1100] Step 15:

[1101] server

[1102] The server receives the entered job information and stores it in a database.

[1103] These steps allow employees to efficiently find the right department for them and companies to optimize their internal talent matching.

[1104] Example 1

[1105] 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."

[1106] By supporting appropriate employee job changes and transfers within a company, companies need to optimize their internal human resource allocation, maintain employee motivation, and prevent talent loss. By suggesting the optimal department based on employees' desired work content, work location, and skills, and providing detailed information, companies need to help employees find the work that best suits them. They also need a system that allows each department within a company to efficiently register new job information and use it for matching.

[1107] 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.

[1108] In this invention, the server includes a means for inputting desired job content, work location, and skills, a means for receiving data input by the user, a means for analyzing the received data and using a generative AI model to list appropriate departments, a means for transmitting information about the proposed departments to the user, a means for a company department staff member to input job information and transmit it to the server, a means for saving the sent job information in a database, and a means for using the saved job information for the next user matching process. This allows the generative AI model to suggest the most suitable department based on the desired information input by the user and provide detailed information about the department, thereby optimizing human resource allocation within the company and improving employee satisfaction. Furthermore, the ability for company department staff to efficiently register and use job information improves the accuracy and efficiency of human resource matching.

[1109] The "means for inputting desired work content, work location, and skills" is an interface for a user to input the desired work content, work location, and skill information.

[1110] The "server means for receiving data entered by a user" is a function of the server for receiving data entered from a user's terminal and recording it in a database.

[1111] "Means for analyzing received data and using a generative AI model to list appropriate departments" refers to a process that uses a generative AI model based on received data to select the department that best matches the user's preferences.

[1112] The "means for sending information about the proposed department to the user" is a server function for sending information about the optimal department suggested by the generative AI model to the user's terminal.

[1113] The "means for obtaining detailed information about a department in which the user is interested and providing it to the user" is a process for obtaining detailed information about a department selected by the user from a database and providing it to the user.

[1114] The "means for a company departmental employee to input job information and send it to the server" is an interface that allows a departmental employee to input new job information and send that information to the server.

[1115] The "means for saving submitted job information in a database" is a function of the server for saving job information submitted by department personnel of a company in a database.

[1116] The "means for utilizing the saved job information in the next matching process by the user" is a process for utilizing the saved job information in the next matching process by the user with desired data.

[1117] "Means of acquiring past job change and transfer data and having the generative AI model learn from that data to improve the accuracy of analysis" refers to a process of collecting data on past job changes and transfers and having the generative AI model learn from that data to improve the accuracy of analysis.

[1118] This invention is a system for supporting employee job changes and transfers within a company, and is implemented in the following steps: The system optimizes the allocation of personnel within a company by inputting the user's desired work content, work location, and skills, and then proposing the most suitable department based on that information and providing detailed information.

[1119] Hardware and Software Configuration

[1120] This system is implemented in a network environment that includes user devices, a server, and a database. User devices are assumed to be PCs, smartphones, tablets, etc., and operations are performed through a dedicated web interface called "Employee Shift Portal." A database (PostgreSQL) is connected to the server, which implements the generative AI model "HR-MatchAI." The server is operated through a web application framework (e.g., Django or Flask).

[1121] User preference input

[1122] Users access the Employee Shift Portal using their own devices and enter their desired work content, work location, and skills through the interface. This input data is sent to the server by the device.

[1123] Examples:

[1124] The user enters "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis."

[1125] Example of prompt: The user enters information according to the instructions: "Please enter the job description, location, and skills you are interested in (e.g., software development, Tokyo, Python)."

[1126] Data analysis and matching

[1127] The server receives the user's desired data sent from the device and stores it in a database. It then launches the generative AI model "HR-MatchAI" and uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[1128] Examples:

[1129] The generative AI model suggests the most suitable department, such as "Department A: Software Development" or "Department B: Data Analysis Team."

[1130] Example of prompt sentence: The server follows the instruction "Analyze the user's desired data and suggest the most suitable department," and lists departments.

[1131] Displaying the proposed results

[1132] Based on the analysis results of the generative AI model, the server formats the information on the proposed departments and sends it to the user's device, which displays the received proposals on a dedicated interface, and the user can select the department that best suits them from the list of proposed departments.

[1133] Examples:

[1134] The proposed departments "Department A: Software Development" and "Department B: Data Analysis Team" are displayed on the terminal.

[1135] Example of prompt sentence: The terminal follows the instruction to display the suggestion results to the user and prompt them to select a department on the screen.

[1136] Providing more information

[1137] When a user selects a department of interest and sends a request for detailed information, the device sends this request to the server. The server retrieves the details of the department from the database and sends them back to the user's device. The device displays the received details, and the user can use this information to decide which department is most suitable for them.

[1138] Examples:

[1139] The user clicks the detailed information button for "Department A" and the detailed information is displayed on the terminal screen.

[1140] Example of prompt sentence: The server follows the instruction "Please provide details about Department A" and sends the details.

[1141] Job posting by department

[1142] A company's department staff uses the dedicated web interface "HR-Manager Suite" to enter new job information and send it to the server, which then stores the received information in a database and uses it to match the user's desired data next time.

[1143] Examples:

[1144] The department staff member enters "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" and submits the form.

[1145] Example of prompt: The person in charge will follow the instructions to "Register new job information" and enter the information.

[1146] The above is an embodiment of the present invention. This system makes it easy to optimize personnel allocation within a company, enabling the right person to be placed in the right position and reducing the company's employee turnover rate.

[1147] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1148] Step 1:

[1149] Input desired data by the user

[1150] Users input their desired work content, work location, and skills. This data is input through the "Employee Shift Portal," and the information entered through a dedicated interface is sent to the terminal.

[1151] Input: Desired job description, location, skills (e.g. software development, Tokyo, Python)

[1152] Output: The input data is saved in the form

[1153] Specific behavior:

[1154] The user enters "Job Description: Software Development", "Work Location: Tokyo", and "Skills: Python, Data Analysis" and clicks the submit button.

[1155] Step 2:

[1156] Receiving and storing user preference data

[1157] The terminal receives the desired data entered by the user, performs form validation (checks the input data), and if there are no errors, sends the data to the server.

[1158] Input: Data entered by the user into the terminal

[1159] Output: Data sent to the server

[1160] Specific behavior:

[1161] The terminal receives the data entered by the user, for example, "software development, Tokyo, Python, data analysis," and sends it to the server.

[1162] Step 3:

[1163] Analyzing and storing user preference data

[1164] The server receives the user's desired data sent from the device, stores the received data in a PostgreSQL database, and prepares it for analysis.

[1165] Input: Desired data sent from the terminal

[1166] Output: Data stored in the database

[1167] Specific behavior:

[1168] The server stores the received data "Software Development, Tokyo, Python, Data Analysis" in a database.

[1169] Step 4:

[1170] Matching analysis using generative AI models

[1171] The server launches the generative AI model "HR-MatchAI" based on the saved user preference data. The generative AI model learns from past job change and transfer data and lists the departments that best match the preference data.

[1172] Input: User preference data stored in the database

[1173] Output: A list of the best departments (e.g., Department A, Department B)

[1174] Specific behavior:

[1175] The generative AI model analyzes "software development, Tokyo, Python, data analysis" and suggests "Department A: software development" and "Department B: data analysis team."

[1176] Step 5:

[1177] Formatting and sending the proposal results

[1178] The server formats the analysis results from the generative AI model and sends the proposed department information to the user's device.

[1179] Input: A list of optimal departments generated by a generative AI model

[1180] Output: Proposal result data sent to the user's device

[1181] Specific behavior:

[1182] The server sends "Department A: Software Development" and "Department B: Data Analysis Team" to the user's device.

[1183] Step 6:

[1184] Displaying the proposed results

[1185] The terminal receives the proposal result data sent from the server and displays it on a dedicated interface.

[1186] Input: Proposal result data sent from the server

[1187] Output: Suggestion results displayed on the user's device

[1188] Specific behavior:

[1189] "Department A: Software Development" and "Department B: Data Analysis Team" will be displayed on the user's device.

[1190] Step 7:

[1191] Request more information

[1192] The user selects the department they are interested in and submits a request for more information.

[1193] Input: User requests more information

[1194] Output: Request data from the terminal to the server

[1195] Specific behavior:

[1196] The user clicks the detailed information button for "Department A," and the terminal sends the request to the server.

[1197] Step 8:

[1198] Get and send details

[1199] The server receives the detailed information request, retrieves the detailed information for the relevant department from the database, and sends the retrieved detailed information back to the user's terminal.

[1200] Input:Detailed information request data

[1201] Output: Retrieved details

[1202] Specific behavior:

[1203] The server retrieves detailed information about "Department A" from the database and sends it to the user's terminal.

[1204] Step 9:

[1205] Viewing detailed information

[1206] The terminal receives the detailed information sent from the server and displays it to the user, who can then use this information to decide which department is most suitable for them.

[1207] Input: Details sent from the server

[1208] Output: Detailed information displayed on the user's terminal

[1209] Specific behavior:

[1210] Detailed information about "Department A" is displayed on the user's device.

[1211] (Application example 1)

[1212] 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."

[1213] Conventional in-house human resource allocation systems often fail to properly reflect employee preferences, and are not effective enough in preventing the loss of human resources to other companies. Furthermore, optimization of the placement and roles of robots in factories is primarily managed manually, making efficient operation difficult. Furthermore, the inability to fully utilize past data creates issues with the accuracy of placement proposals. For these reasons, there was a need for a system that could simultaneously optimize the placement of both in-house human resources and in-factory robots, and make more efficient and accurate proposals.

[1214] 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.

[1215] In this invention, the server includes: means for inputting desired work content, work location, and skills; server means for receiving the data input by the user; means for analyzing the received data and using a generation AI to list appropriate departments; means for sending information about the proposed departments to the user; means for obtaining detailed information about departments in which the user is interested and providing it to the user; means for inputting robot specifications, roles, placement, and skills; and means for analyzing the robot specifications, roles, placement, and skills and using a generation AI to propose appropriate placement. This makes it possible to simultaneously optimize personnel placement within a company and robot placement within a factory, thereby improving employee satisfaction and improving corporate operational efficiency.

[1216] The "desired work content" is the job content that an employee or user desires to be in charge of.

[1217] "Work location" is the location or area where an employee or user wants to work.

[1218] "Skills" refer to the techniques and knowledge possessed by employees or users, and indicate the abilities required to perform specific tasks.

[1219] A "means" is a method or device used to achieve a particular purpose.

[1220] "User" means an individual or employee who uses this system.

[1221] A "server" is a computer system that receives information from users and performs analytical processing.

[1222] "Generative AI" is artificial intelligence that analyzes data and generates optimal suggestions and lists.

[1223] "Listing" means displaying the best options in a list format based on specific conditions.

[1224] A "robot" is a mechanical device designed to perform a specific task in a factory.

[1225] "Deployment" refers to the allocation of specific people or machines to appropriate locations.

[1226] "Specifications" are detailed information that indicates the specifications and capabilities of a robot or machine.

[1227] A "role" refers to the tasks or responsibilities that an employee or robot should perform.

[1228] The present invention is implemented as a system for optimizing the allocation of employees and robots in a factory within a company. This system is composed of the following components:

[1229] User preference input

[1230] Terminal

[1231] Users use their own devices to input their desired work content, work location, and skills. This input data is sent to the server via the device. Factory robot specifications, current role, placement, and skill sets are also input from the device and sent to the server.

[1232] Data analysis and matching

[1233] server

[1234] The server receives data sent from users and factory robots. The received data is stored in a database. The server then launches a generative AI model (Recommendation AI) that lists appropriate departments and optimal robot placements based on the user's desired data and robot data. The generative AI model learns from past job change and transfer data and past robot placement data to improve its analysis accuracy.

[1235] Displaying the proposed results

[1236] server

[1237] Based on the analysis results of the generation AI, the server organizes the proposed department and robot placement information, which is then sent to the terminal.

[1238] Terminal

[1239] The user's terminal receives and displays the proposed results sent from the server, and the user or factory manager can select an appropriate department or robot placement from the proposed list.

[1240] Providing more information

[1241] User

[1242] Once the user or factory manager selects a proposal that interests them, they can request more information.

[1243] Terminal

[1244] The terminal sends a request to the server, which retrieves the detailed information and sends it back to the terminal.

[1245] Terminal

[1246] The terminal displays detailed information received from the server, allowing users and factory managers to make decisions.

[1247] Registering department and robot placement information

[1248] Terminal

[1249] Company department staff and factory managers enter new job information and robot placement information through a dedicated interface and send it to the server.

[1250] server

[1251] The server stores the transmitted information in a database and uses it for the next analysis.

[1252] As a specific example, if a user inputs "Desired work content: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze this and suggest "Department A: Software development," "Department B: Data analysis team," etc. On the other hand, if a factory manager inputs "Robot ID: robot_001," "Specs: Type-A," "Current role: Assembly," "Location: Line-1," and "Skills: welding, bolting," the generation AI will make a "New placement proposal: Maintenance." This allows for the optimization of departments and placements.

[1253] Example prompt sentence:

[1254] Please recommend the best placement and role based on the following robot data:

[1255] Specs: Type-A

[1256] Current role: Assembly

[1257] Current location: Line-1

[1258] Skill Set: ["welding", "bolting"]

[1259] This system makes it possible to simultaneously optimize the allocation of personnel within a company and the allocation of robots within a factory, which is expected to improve the operational efficiency of a company and employee satisfaction.

[1260] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1261] Step 1:

[1262] User data entry

[1263] The terminal allows the user to input "desired work content," "work location," and "skills." The factory manager inputs "robot specifications," "role," "placement," and "skill set." These data are entered into the input screen by the user or factory manager, and the entered data is sent from the terminal to the server.

[1264] Input: User's desired work data, robot specification data

[1265] Output: Desired data and robot data sent to the server

[1266] Step 2:

[1267] Data reception and storage by the server

[1268] The server receives the desired data and robot data sent from the device. The received data is stored in a database (e.g., MySQL, SQLite). This makes the data ready for analysis.

[1269] Input: Desired data and robot data sent from the terminal

[1270] Output: Desired data and robot data stored in a database

[1271] Step 3:

[1272] Data analysis and department / robot placement proposals

[1273] The server passes the received data to a generative AI model (Recommendation AI) for analysis. The generative AI model uses past job change and transfer data and past robot placement data to create a list of appropriate departments and optimal robot placements that match the user's desired data.

[1274] Input: Desired data and robot data stored in the database

[1275] Output: A list of department and robot placement suggestions from the generation AI

[1276] Step 4:

[1277] Organizing and sending proposal results

[1278] The server organizes the analysis results of the generated AI model, composes proposed department information and robot placement information, converts that information into a format suitable for sending to the user's device, and sends it to the device.

[1279] Input: A list of suggestions from the generative AI

[1280] Output: Formatted proposal information to send to terminal

[1281] Step 5:

[1282] Display of proposal results on user device

[1283] The terminal receives the proposed information sent from the server and displays it in an easy-to-read format for users and factory managers, who can then use this information to consider the optimal placement of themselves or the robots.

[1284] Input: Proposal information sent from the server

[1285] Output: Displayed proposal information

[1286] Step 6:

[1287] Requesting and Providing More Information

[1288] When a user or factory manager selects a department or location of interest, the terminal sends a request for detailed information to the server. The server retrieves the relevant information from the database and sends it back to the terminal. The terminal then displays the detailed information.

[1289] Input: A request for more information from the user

[1290] Output: Display of detailed information provided by the server

[1291] Step 7:

[1292] Registration of job vacancies and robot placement information

[1293] Company department staff and factory managers input new job information and robot placement information and send it from their terminals to the server, which stores it in a database and uses it for the next matching process.

[1294] Input: New job information and robot placement information

[1295] Output: Information stored in the database

[1296] The above processing steps make it possible to realize optimal allocation of personnel and robots within a company.

[1297] 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.

[1298] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. Employees input their preferences, and the system proposes appropriate departments based on those preferences. The introduction of an emotion engine analyzes the emotions expressed at the time of input, further individualizing the system.

[1299] User preference input

[1300] Terminal

[1301] Users use their own devices to input their desired job duties, work location, and skills. The device receives the input information and sends it to the server. The emotion engine then analyzes the user's facial expressions and tone of voice when inputting information to recognize the user's emotional state.

[1302] Data analysis and matching

[1303] server

[1304] The server receives the user's desired data sent from the device along with the emotion data from the emotion engine. The server stores this data in a database and activates the generation AI. The generation AI analyzes past job change and transfer data and lists the most suitable departments, taking into account the user's desired conditions and emotion data.

[1305] Displaying the proposed results

[1306] server

[1307] Based on the analysis results of the generative AI, the server organizes the department information to suggest to the user, and the organized information is sent to the user's device.

[1308] Terminal

[1309] The user's device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message according to the user's emotions.

[1310] Providing more information

[1311] User

[1312] The user selects the department of interest and requests detailed information on the terminal.

[1313] Terminal

[1314] The terminal sends a request to the server for detailed information about the department selected by the user.

[1315] server

[1316] The server receives the request, retrieves the details of the department from the database, and sends the details to the user's device.

[1317] Terminal

[1318] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[1319] Job posting by department

[1320] Terminal

[1321] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[1322] server

[1323] The server receives the job information and stores it in a database, which is used to match the user's desired data the next time they enter it.

[1324] Specific examples

[1325] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities," a department that would reduce the user's stress. Furthermore, if the emotion engine determines that the user is entering information in a relaxed state, it will make general suggestions such as "Department A: Software development" or "Department B: Data analysis team."

[1326] Additionally, when a department staff member enters new job information (job description: AI development, work location: Osaka, skills: machine learning, Java) and registers it on the server, this information will be used in the next matching process.

[1327] In this way, by combining this system with an emotion engine, it is possible to make suggestions that take into account the user's emotional state, helping employees to work in a more suitable environment.

[1328] The processing flow will be explained below.

[1329] Step 1:

[1330] User

[1331] Users use their own terminals to input the desired job duties, work location, and skills.

[1332] Step 2:

[1333] Terminal

[1334] The device accepts the input desired data, and at the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize the emotional data.

[1335] Step 3:

[1336] Terminal

[1337] When the user clicks the "send" button, the terminal transmits the input data and emotion data to the server.

[1338] Step 4:

[1339] server

[1340] The server receives the user's desired data and emotion data sent from the device, and stores the received data in a database.

[1341] Step 5:

[1342] server

[1343] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers, as well as the user's preferences and emotional data. The AI ​​then creates a list of the most suitable departments, taking into account the user's preferences and emotional data.

[1344] Step 6:

[1345] server

[1346] Based on the analysis results from the generative AI, the server organizes the department information to suggest to the user.

[1347] Step 7:

[1348] server

[1349] The organized proposal result data is sent to the user's terminal.

[1350] Step 8:

[1351] Terminal

[1352] The device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message based on the user's emotions.

[1353] Step 9:

[1354] User

[1355] The user selects the department of interest and requests detailed information on the terminal.

[1356] Step 10:

[1357] Terminal

[1358] The terminal sends a request to the server for detailed information about the department selected by the user.

[1359] Step 11:

[1360] server

[1361] The server receives the request and retrieves the details of the relevant department from the database.

[1362] Step 12:

[1363] server

[1364] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[1365] Step 13:

[1366] Terminal

[1367] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[1368] Step 14:

[1369] Terminal

[1370] Company department staff use a dedicated interface to enter job information.

[1371] Step 15:

[1372] Terminal

[1373] The entered job information is sent from the terminal to the server.

[1374] Step 16:

[1375] server

[1376] The server receives the entered job information and stores it in a database.

[1377] Specific examples

[1378] For example, if a user inputs "Desired work: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities" as a department that will reduce stress. If the emotion engine determines that the user is entering information in a relaxed state, the system will make the usual suggestions of "Department A: Software development" and "Department B: Data analysis team."

[1379] The above is a specific embodiment of a system incorporating an emotion engine. This system enables more appropriate personnel matching within a company by taking into account the emotions of users.

[1380] Example 2

[1381] 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."

[1382] Conventional personnel job change and transfer systems typically suggest an appropriate department based solely on the user's desired conditions. However, because the user's emotional state is not taken into consideration, the department that is most suitable for the user may not be suggested. This can result in a failed job change or transfer and reduced user satisfaction. The objective of this invention is to suggest a more appropriate department and improve user satisfaction by simultaneously considering the user's desired conditions and emotional state.

[1383] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1384] In this invention, the server includes a means for receiving data entered by a user and emotion data analyzed by an emotion engine, a means for storing the received data in a database and listing appropriate departments using a generative AI model, and a means for transmitting information on the proposed departments to the user, thereby enabling optimal department proposals that simultaneously consider the user's desired conditions and emotional state.

[1385] "Desired work content" refers to the work content that the user wants to undertake in their career.

[1386] "Work location" is the geographic location where a user wishes to work.

[1387] "Skills" refer to the specialized abilities and knowledge that a user has.

[1388] "Emotion data" is data relating to the emotional state of the user analyzed from facial expressions, tone of voice, etc. using an emotion engine.

[1389] "Server means" is a general term for equipment and software for receiving, processing, and storing data sent from users and emotion engines.

[1390] The "database" is a system for organizing and managing received user data and emotion data.

[1391] The "generative AI model" is an artificial intelligence model that learns from past job change and transfer data and suggests the most suitable department based on the user's desired conditions and emotional state.

[1392] The "proposal results" are information about the optimal department that the generative AI model analyzes and presents to the user.

[1393] "Detailed information" is data containing specific job descriptions, conditions, and other supplemental information about the proposed department.

[1394] "Job information" is information about available positions entered by department personnel in a company.

[1395] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. This system allows users to input their preferences, and suggests appropriate departments based on those preferences. By introducing an emotion engine, the system analyzes the emotions entered at the time of input and further individualizes the proposals.

[1396] Users use their own devices to input their desired job duties, work location, and skills. Specifically, they input "Desired job duties: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The devices are equipped with built-in cameras and microphones, which are used to capture the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data. The emotion engine uses Python's facial recognition library and voice analysis tools to recognize the user's emotional state.

[1397] The device sends the input preference data and emotion data as a single data packet to the server. The server stores the received data in a database and launches a generative AI model (using TensorFlow or PyTorch as an example). The generative AI model learns from past job change and transfer data and lists the most suitable departments based on the user's preference and emotion data.

[1398] Based on the analysis results of the generative AI model, the server organizes the department information to suggest to the user. This organization also includes supplemental information based on the user's emotional state. For example, if the user is entering information in a relaxed state, general suggestions such as "Department A: Software Development" and "Department B: Data Analysis Team" will be made, but if the user is feeling stressed, suggestions such as "Department C: Software Development with Remote Work Capability" will be made. The organized information is sent to the user's device, which receives and displays it.

[1399] To obtain detailed information about a department that interests a user, the user makes a request on their device. The server receives the request, retrieves the details of the department from the database, and sends them to the user's device. The user's device then displays this information to the user. This display also includes supplementary information and suggestions based on the user's emotional state, provided by the emotion engine.

[1400] Additionally, company department staff use a dedicated interface to enter job information and send it to the server. This job information is saved in the database and used to match the next desired data entered by a user. For example, if "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" are entered and registered on the server, this information will be used in the next matching process.

[1401] As described above, this system simultaneously considers the user's desired conditions and emotional state, and makes more appropriate suggestions, thereby improving user satisfaction.

[1402] Examples:

[1403] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the emotion engine analyzes the user's emotions in real time and determines that the user is in a relaxed state, for example. Based on this data, the generative AI model lists appropriate departments. The suggested results, "Department A: Software development" and "Department B: Data analysis team," are displayed to the user. Examples of prompt sentences are as follows:

[1404] Example prompt sentence:

[1405] Write an algorithm that will suggest an appropriate department based on the user's emotional state when they enter "Desired job: Software development", "Location: Tokyo", and "Skills: Python, data analysis".

[1406] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1407] Step 1: Enter desired conditions and emotional data

[1408] Terminal

[1409] A user logs in to a device and enters their desired job description, work location, and skills. For example, they might enter "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The device's camera and microphone capture the user's facial expressions and tone of voice, which the emotion engine analyzes to identify the user's emotional state. The input for this step is the user's desired data and real-time emotion data, and the output is a data packet containing this input.

[1410] Step 2: Sending data packets

[1411] Terminal

[1412] The user's device sends the input desired data and the emotional data analyzed by the emotion engine to the server as a single data packet. The transmitted data packet includes, for example, the user ID, desired job content, work location, skills, and emotional state. The input to this step is the data packet generated in step 1, and the output is a transmission confirmation to the server.

[1413] Step 3: Receiving and storing data

[1414] server

[1415] The server receives the data packet sent from the device. This input data is the user's desired conditions and emotional state. After receiving, the server stores this data in a database. For example, the data is stored using a database management system (e.g., MySQL or PostgreSQL). The input of this step is the data packet sent from the device, and the output is confirmation of successful storage.

[1416] Step 4: Launching the generative AI model and analyzing data

[1417] server

[1418] The server uses the stored data to launch a generative AI model. For example, an AI model using TensorFlow or PyTorch learns from past job change and transfer data and lists the most suitable departments by taking into account the user's desired conditions and emotional data. The input for this step is the stored user data and past job change and transfer data, and the output is a list of appropriate departments as a result of the analysis.

[1419] Step 5: Organize and submit your proposal

[1420] server

[1421] Based on the analysis results of the generative AI model, the server organizes department information to suggest to the user. The suggestions also include supplemental information based on the user's emotional state. The organized data is sent to the user's device. The input to this step is the analysis results of the generative AI model, and the output is the data sent to the user's device.

[1422] Step 6: Viewing the Suggestion Results

[1423] Terminal

[1424] The user's device receives the proposal results sent from the server and displays them to the user. For example, a relaxed user may be suggested "Department A: Software Development" or "Department B: Data Analysis Team," while a stressed user may be suggested "Department C: Remote Work Software Development." The input for this step is the proposal results from the server, and the output is a list of proposals displayed to the user.

[1425] Step 7: Request more information

[1426] User

[1427] The user selects the department they are interested in from the proposed departments and requests detailed information on the terminal. The input of this step is the department information selected by the user, and the output is a detailed information request.

[1428] Step 8: Get and send details

[1429] server

[1430] The server receives a detailed information request from the user and retrieves the detailed information for the relevant department from the database. It then sends the retrieved detailed information to the user's terminal. The input of this step is the detailed information request, and the output is the transmission of the detailed information to the user's terminal.

[1431] Step 9: View detailed information

[1432] Terminal

[1433] The user's device receives the detailed information sent from the server and displays it to the user. The displayed information also includes supplementary information and suggestions according to the emotional state transmitted by the emotion engine. The input of this step is the detailed information from the server, and the output is the detailed information displayed to the user.

[1434] Step 10: Fill out and submit your job posting

[1435] Terminal

[1436] A departmental employee at a company uses a dedicated interface to input new job information. The input information is sent to the server. For example, information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" is input. The input for this step is the company's job information, and the output is the transmission of the job information to the server.

[1437] Step 11: Save your job posting

[1438] server

[1439] The server receives the job information sent by the company's department staff and saves it in the database. The saved information will be used for the next matching process with the desired input data by the user. The input of this step is the submitted job information, and the output is a confirmation that the job information was successfully saved.

[1440] (Application example 2)

[1441] 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."

[1442] Conventional personnel job change and department transfer support systems often make mechanical suggestions based on user input data and are unable to consider the user's emotions or psychological state. As a result, the proposed departments and jobs do not satisfy the user psychologically, making it difficult to realize an effective transfer or job change. In addition, the job information entered by company department personnel is not provided in a form that reflects their emotions and expectations, and further improvements in matching accuracy are required.

[1443] The specification processing by the specification 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 inputting desired work content, work location, and skills, server means for receiving data input by the user, means for analyzing the received data and using a generative AI model to list appropriate departments, means for transmitting information on the proposed departments to the user, means for acquiring detailed information on departments in which the user is interested and providing it to the user, means for analyzing the user's facial expressions and voice using an emotion engine and using the emotion data for analysis, means for generating proposals according to the user's psychological state based on the analyzed emotion data, means for acquiring past job change and transfer data and having the generative AI model learn from the data to improve analysis accuracy, means for inputting prompt sentences into the generative AI model to obtain optimal analysis results, means for a company department employee to input job information and transmit it to the server, means for saving the sent job information in a database, means for using the saved job information in a matching process by the next user, and means for utilizing the emotion engine to analyze the employee's intentions and expectations when entering job information. This will enable more accurate department and job suggestions that reflect the user's emotions and psychological state.

[1444] A "user" is someone who uses the system to input their job description, work location, and skills, and receives suggestions for suitable departments and jobs.

[1445] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[1446] "Job content" refers to the type of work and specific duties desired by the user.

[1447] "Work location" refers to the location or area where the user wishes to work.

[1448] "Skills" refers to the specialized techniques, knowledge, or proficiency that a user possesses.

[1449] "Server means" means a computing device used by the system to receive, store, and analyze user-entered data and other information.

[1450] A "generative AI model" is an artificial intelligence model that suggests appropriate departments and jobs based on input data and past data.

[1451] A "prompt sentence" is a sentence that is input to a generative AI model to obtain optimal analysis results.

[1452] "Suggestion means" is a function that allows the system to send users information about appropriate departments and jobs listed by the generative AI model.

[1453] The "detailed information acquisition means" is a function for acquiring further detailed information about a proposal in which the user is interested and providing the information to the user.

[1454] "Job information" refers to the content and conditions of the job being recruited for, entered by department staff at a company.

[1455] The "analysis means" is a means for analyzing the data received by the server means and listing appropriate departments and jobs.

[1456] "Matching processing" is the process of selecting the most suitable department and job based on the user's preferences, emotional data, and past data.

[1457] The "means for analyzing intentions and expectations" is a means for using an emotion engine to recognize and analyze the intentions and expectations of company personnel who enter job information.

[1458] The present invention is a system that proposes departments and jobs with higher accuracy by taking into account the user's wishes and emotional state. By combining an emotion engine, this system improves on conventional mechanical proposals and enables proposals that correspond to the user's psychological state.

[1459] System Configuration

[1460] Hardware

[1461] The system is implemented mainly using the following hardware:

[1462] Server: A high-performance computer device that receives data, analyzes it, and sends out proposals.

[1463] User terminal: A smartphone or tablet, which is a device where users input their preferences and emotional data and receive suggested information.

[1464] software

[1465] The system is implemented using the following software:

[1466] Generative AI model: Built using TensorFlow, it lists the most suitable departments and jobs based on the user's preferences and past job change data.

[1467] Emotion analysis: Using OpenCV and AudioEmotion libraries, we analyze the user's facial expressions and voice to obtain emotional data.

[1468] Database: Using MySQL, we store user preferences and emotional data, company job information, and past job change and transfer data.

[1469] Program processing overview

[1470] Desired input

[1471] Users use their smartphones to input their desired job duties, work location, and skills. The camera also captures their facial expressions and records their voice with a microphone. The emotion engine analyzes this data and recognizes the user's emotional state.

[1472] Data reception and analysis

[1473] The server receives preference and emotion data sent from the user's device. This data is stored in a database and analyzed by a generative AI model. The generative AI model learns from past job change and transfer data to improve the accuracy of its analysis.

[1474] suggestion

[1475] The server then suggests appropriate departments and jobs to the user based on the generative AI model. This suggestion information is generated taking into account the user's emotional state. For example, if the user is feeling stressed, the server will suggest departments that will help them reduce stress.

[1476] Providing more information

[1477] If the user is interested in the proposed department or job, they can request more information. The server receives this request, retrieves the relevant details from the database, and provides them to the user.

[1478] Job information registration

[1479] A company's department staff enters job information using a dedicated interface. This job information is analyzed by the emotion engine, along with the staff's intentions and expectations, and saved in a database. The saved information is then used for the next matching process by the user.

[1480] Specific examples

[1481] For example, if a user inputs "Desired job: software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest a department that would reduce the user's stress, such as "Software development with remote work capabilities."

[1482] Prompt Sentence Examples

[1483] "Please suggest a department that allows remote work and reduces stress for users with software development and data analysis skills using Python."

[1484] In this way, the present invention is a system that realizes more accurate suggestions that take into account the user's emotional state, enabling employees to work in a more appropriate environment and improving work comfort.

[1485] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1486] Step 1:

[1487] The user uses the device to input the desired job duties, work location, and skills. The input data is temporarily saved on the device. In addition, the device's camera captures the user's facial expressions and records their voice with a microphone. This emotional data is also acquired on the device. After the user has completed input, the data is sent to the server.

[1488] Step 2:

[1489] The terminal transmits the user's preference data and emotion data to the server, including text data on the user's preference, work location, and skills, image data captured by the camera, and voice data recorded by the microphone.

[1490] Step 3:

[1491] The server stores the received user preference data and emotion data in a database, where each user data is associated with the other user data.

[1492] Step 4:

[1493] The server uses an emotion engine to analyze the received facial image data and voice data. It uses OpenCV to extract facial features and the AudioEmotion library to identify the voice emotion. This provides the user's emotional state as numerical data.

[1494] Step 5:

[1495] Based on the analyzed emotion data and preference data, the server uses a generative AI model to create a list of the most suitable departments and jobs. Past job change and transfer data stored in the database is also referenced, and the generative AI model improves the accuracy of the analysis. A prompt sentence is entered to have the generative AI model perform the analysis.

[1496] Step 6:

[1497] The server sends the list of suitable departments and job positions proposed by the generative AI model to the user's device, including a message based on the user's emotions.

[1498] Step 7:

[1499] The user can check the proposal results on the terminal and request detailed information about the department or job they are interested in. The terminal then sends the request to the server.

[1500] Step 8:

[1501] Based on the received request, the server retrieves detailed information about the relevant department and job from the database and sends it to the user's device. The detailed information also includes supplementary explanations based on the analysis results of the emotion engine.

[1502] Step 9:

[1503] A company's department staff uses a dedicated interface to enter new job information, which is then sent to the server.

[1504] Step 10:

[1505] The server stores the received job information in a database. It also uses an emotion engine to analyze the intentions and expectations of the person in charge and stores this information along with the job information. The stored information will be used for the next matching process by the user.

[1506] 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.

[1507] 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.

[1508] 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.

[1509] [Fourth embodiment]

[1510] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1511] 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.

[1512] 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).

[1513] 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.

[1514] 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.

[1515] 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).

[1516] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1517] 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.

[1518] 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.

[1519] 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.

[1520] 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.

[1521] 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.

[1522] 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."

[1523] The present invention is embodied in a system for supporting employee job changes and transfers within a company. This system analyzes input preference data and suggests appropriate departments, thereby optimizing personnel allocation within the company and preventing personnel from leaving the company. A specific embodiment of this system is described below.

[1524] User preference input

[1525] Terminal

[1526] Users use their own terminals to input their desired job duties, work location, and skills. This input is done through a dedicated interface, and the terminal accepts it and sends it to the server.

[1527] Data analysis and matching

[1528] server

[1529] The server receives the user's desired data sent from the device. The received data is stored in a database and analyzed. The server then launches a generation AI, which uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[1530] Displaying the proposed results

[1531] server

[1532] Based on the analysis results of the generative AI, the server organizes the information of the proposed department. The organized information is sent to the user's device and made available for viewing.

[1533] Terminal

[1534] The user's device receives the proposed results sent from the server and displays them for easy viewing, allowing the user to select the department that best suits them from the list of proposed departments.

[1535] Providing more information

[1536] User

[1537] Once the user selects a department of interest, they can request more information through their terminal.

[1538] Terminal

[1539] The terminal transmits the user's request to the server.

[1540] server

[1541] The server receives the request, retrieves the details of the relevant department from the database, and sends the details back to the terminal.

[1542] Terminal

[1543] The terminal displays the detailed information sent from the server to the user, who can then decide which department is most suitable for them.

[1544] Job posting by department

[1545] Terminal

[1546] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[1547] server

[1548] The server stores the entered job information in a database and can use it to match the desired data entered by the user next time.

[1549] Specific examples

[1550] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze the corresponding department based on past data and suggest "Department A: Software development," "Department B: Data analysis team," etc. The user becomes interested in "Department A" and requests more information. In response to this request, the server can provide information about "Department A," such as detailed job description, recruitment requirements, work location, and skill requirements.

[1551] Furthermore, when a department employee enters new job information, they enter information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java," and register it on the server. This information will be used for the next request from the user.

[1552] The above is a specific embodiment of the present invention. This system allows for efficient personnel matching within a company, preventing the loss of personnel to the outside. It also has the advantage for employees that they can be assigned to work that best suits them.

[1553] The processing flow will be explained below.

[1554] Step 1:

[1555] User

[1556] Users use their own terminals to input the desired job duties, work location, and skills.

[1557] Step 2:

[1558] Terminal

[1559] The terminal accepts the input desired data. When the user clicks the "Submit" button, the input data is sent to the server.

[1560] Step 3:

[1561] server

[1562] The server receives the user's desired data sent from the terminal and stores the received data in a database.

[1563] Step 4:

[1564] server

[1565] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers and the user's desired data. The AI ​​then creates a list of departments that best match the user's desired conditions.

[1566] Step 5:

[1567] server

[1568] Based on the analysis results, the server organizes department information to suggest to the user.

[1569] Step 6:

[1570] server

[1571] The organized proposal result data is sent to the user's terminal.

[1572] Step 7:

[1573] Terminal

[1574] The terminal receives the data of the proposal results sent from the server and displays it to the user, who can then check the list of proposed departments.

[1575] Step 8:

[1576] User

[1577] The user selects the department of interest and requests detailed information on the terminal.

[1578] Step 9:

[1579] Terminal

[1580] The terminal sends a request to the server for detailed information about the department selected by the user.

[1581] Step 10:

[1582] server

[1583] The server receives the request and retrieves the details of the relevant department from the database.

[1584] Step 11:

[1585] server

[1586] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[1587] Step 12:

[1588] Terminal

[1589] The terminal receives the detailed information sent from the server and displays it to the user, who can then check the details and decide which department is best for him or her.

[1590] Step 13:

[1591] Terminal

[1592] Company department staff use a dedicated interface to enter job information.

[1593] Step 14:

[1594] Terminal

[1595] The entered job information is sent from the terminal to the server.

[1596] Step 15:

[1597] server

[1598] The server receives the entered job information and stores it in a database.

[1599] These steps allow employees to efficiently find the right department for them and companies to optimize their internal talent matching.

[1600] Example 1

[1601] 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."

[1602] By supporting appropriate employee job changes and transfers within a company, companies need to optimize their internal human resource allocation, maintain employee motivation, and prevent talent loss. By suggesting the optimal department based on employees' desired work content, work location, and skills, and providing detailed information, companies need to help employees find the work that best suits them. They also need a system that allows each department within a company to efficiently register new job information and use it for matching.

[1603] 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.

[1604] In this invention, the server includes a means for inputting desired job content, work location, and skills, a means for receiving data input by the user, a means for analyzing the received data and using a generative AI model to list appropriate departments, a means for transmitting information about the proposed departments to the user, a means for a company department staff member to input job information and transmit it to the server, a means for saving the sent job information in a database, and a means for using the saved job information for the next user matching process. This allows the generative AI model to suggest the most suitable department based on the desired information input by the user and provide detailed information about the department, thereby optimizing human resource allocation within the company and improving employee satisfaction. Furthermore, the ability for company department staff to efficiently register and use job information improves the accuracy and efficiency of human resource matching.

[1605] The "means for inputting desired work content, work location, and skills" is an interface for a user to input the desired work content, work location, and skill information.

[1606] The "server means for receiving data entered by a user" is a function of the server for receiving data entered from a user's terminal and recording it in a database.

[1607] "Means for analyzing received data and using a generative AI model to list appropriate departments" refers to a process that uses a generative AI model based on received data to select the department that best matches the user's preferences.

[1608] The "means for sending information about the proposed department to the user" is a server function for sending information about the optimal department suggested by the generative AI model to the user's terminal.

[1609] The "means for obtaining detailed information about a department in which the user is interested and providing it to the user" is a process for obtaining detailed information about a department selected by the user from a database and providing it to the user.

[1610] The "means for a company departmental employee to input job information and send it to the server" is an interface that allows a departmental employee to input new job information and send that information to the server.

[1611] The "means for saving submitted job information in a database" is a function of the server for saving job information submitted by department personnel of a company in a database.

[1612] The "means for utilizing the saved job information in the next matching process by the user" is a process for utilizing the saved job information in the next matching process by the user with desired data.

[1613] "Means of acquiring past job change and transfer data and having the generative AI model learn from that data to improve the accuracy of analysis" refers to a process of collecting data on past job changes and transfers and having the generative AI model learn from that data to improve the accuracy of analysis.

[1614] This invention is a system for supporting employee job changes and transfers within a company, and is implemented in the following steps: The system optimizes the allocation of personnel within a company by inputting the user's desired work content, work location, and skills, and then proposing the most suitable department based on that information and providing detailed information.

[1615] Hardware and Software Configuration

[1616] This system is implemented in a network environment that includes user devices, a server, and a database. User devices are assumed to be PCs, smartphones, tablets, etc., and operations are performed through a dedicated web interface called "Employee Shift Portal." A database (PostgreSQL) is connected to the server, which implements the generative AI model "HR-MatchAI." The server is operated through a web application framework (e.g., Django or Flask).

[1617] User preference input

[1618] Users access the Employee Shift Portal using their own devices and enter their desired work content, work location, and skills through the interface. This input data is sent to the server by the device.

[1619] Examples:

[1620] The user enters "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis."

[1621] Example of prompt: The user enters information according to the instructions: "Please enter the job description, location, and skills you are interested in (e.g., software development, Tokyo, Python)."

[1622] Data analysis and matching

[1623] The server receives the user's desired data sent from the device and stores it in a database. It then launches the generative AI model "HR-MatchAI" and uses the results of learning from past job changes and transfer data to create a list of the optimal departments that match the user's desired data.

[1624] Examples:

[1625] The generative AI model suggests the most suitable department, such as "Department A: Software Development" or "Department B: Data Analysis Team."

[1626] Example of prompt sentence: The server follows the instruction "Analyze the user's desired data and suggest the most suitable department," and lists departments.

[1627] Displaying the proposed results

[1628] Based on the analysis results of the generative AI model, the server formats the information on the proposed departments and sends it to the user's device, which displays the received proposals on a dedicated interface, and the user can select the department that best suits them from the list of proposed departments.

[1629] Examples:

[1630] The proposed departments "Department A: Software Development" and "Department B: Data Analysis Team" are displayed on the terminal.

[1631] Example of prompt sentence: The terminal follows the instruction to display the suggestion results to the user and prompt them to select a department on the screen.

[1632] Providing more information

[1633] When a user selects a department of interest and sends a request for detailed information, the device sends this request to the server. The server retrieves the details of the department from the database and sends them back to the user's device. The device displays the received details, and the user can use this information to decide which department is most suitable for them.

[1634] Examples:

[1635] The user clicks the detailed information button for "Department A" and the detailed information is displayed on the terminal screen.

[1636] Example of prompt sentence: The server follows the instruction "Please provide details about Department A" and sends the details.

[1637] Job posting by department

[1638] A company's department staff uses the dedicated web interface "HR-Manager Suite" to enter new job information and send it to the server, which then stores the received information in a database and uses it to match the user's desired data next time.

[1639] Examples:

[1640] The department staff member enters "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" and submits the form.

[1641] Example of prompt: The person in charge will follow the instructions to "Register new job information" and enter the information.

[1642] The above is an embodiment of the present invention. This system makes it easy to optimize personnel allocation within a company, enabling the right person to be placed in the right position and reducing the company's employee turnover rate.

[1643] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1644] Step 1:

[1645] Input desired data by the user

[1646] Users input their desired work content, work location, and skills. This data is input through the "Employee Shift Portal," and the information entered through a dedicated interface is sent to the terminal.

[1647] Input: Desired job description, location, skills (e.g. software development, Tokyo, Python)

[1648] Output: The input data is saved in the form

[1649] Specific behavior:

[1650] The user enters "Job Description: Software Development", "Work Location: Tokyo", and "Skills: Python, Data Analysis" and clicks the submit button.

[1651] Step 2:

[1652] Receiving and storing user preference data

[1653] The terminal receives the desired data entered by the user, performs form validation (checks the input data), and if there are no errors, sends the data to the server.

[1654] Input: Data entered by the user into the terminal

[1655] Output: Data sent to the server

[1656] Specific behavior:

[1657] The terminal receives the data entered by the user, for example, "software development, Tokyo, Python, data analysis," and sends it to the server.

[1658] Step 3:

[1659] Analyzing and storing user preference data

[1660] The server receives the user's desired data sent from the device, stores the received data in a PostgreSQL database, and prepares it for analysis.

[1661] Input: Desired data sent from the terminal

[1662] Output: Data stored in the database

[1663] Specific behavior:

[1664] The server stores the received data "Software Development, Tokyo, Python, Data Analysis" in a database.

[1665] Step 4:

[1666] Matching analysis using generative AI models

[1667] The server launches the generative AI model "HR-MatchAI" based on the saved user preference data. The generative AI model learns from past job change and transfer data and lists the departments that best match the preference data.

[1668] Input: User preference data stored in the database

[1669] Output: A list of the best departments (e.g., Department A, Department B)

[1670] Specific behavior:

[1671] The generative AI model analyzes "software development, Tokyo, Python, data analysis" and suggests "Department A: software development" and "Department B: data analysis team."

[1672] Step 5:

[1673] Formatting and sending the proposal results

[1674] The server formats the analysis results from the generative AI model and sends the proposed department information to the user's device.

[1675] Input: A list of optimal departments generated by a generative AI model

[1676] Output: Proposal result data sent to the user's device

[1677] Specific behavior:

[1678] The server sends "Department A: Software Development" and "Department B: Data Analysis Team" to the user's device.

[1679] Step 6:

[1680] Displaying the proposed results

[1681] The terminal receives the proposal result data sent from the server and displays it on a dedicated interface.

[1682] Input: Proposal result data sent from the server

[1683] Output: Suggestion results displayed on the user's device

[1684] Specific behavior:

[1685] "Department A: Software Development" and "Department B: Data Analysis Team" will be displayed on the user's device.

[1686] Step 7:

[1687] Request more information

[1688] The user selects the department they are interested in and submits a request for more information.

[1689] Input: User requests more information

[1690] Output: Request data from the terminal to the server

[1691] Specific behavior:

[1692] The user clicks the detailed information button for "Department A," and the terminal sends the request to the server.

[1693] Step 8:

[1694] Get and send details

[1695] The server receives the detailed information request, retrieves the detailed information for the relevant department from the database, and sends the retrieved detailed information back to the user's terminal.

[1696] Input:Detailed information request data

[1697] Output: Retrieved details

[1698] Specific behavior:

[1699] The server retrieves detailed information about "Department A" from the database and sends it to the user's terminal.

[1700] Step 9:

[1701] Viewing detailed information

[1702] The terminal receives the detailed information sent from the server and displays it to the user, who can then use this information to decide which department is most suitable for them.

[1703] Input: Details sent from the server

[1704] Output: Detailed information displayed on the user's terminal

[1705] Specific behavior:

[1706] Detailed information about "Department A" is displayed on the user's device.

[1707] (Application example 1)

[1708] 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."

[1709] Conventional in-house human resource allocation systems often fail to properly reflect employee preferences, and are not effective enough in preventing the loss of human resources to other companies. Furthermore, optimization of the placement and roles of robots in factories is primarily managed manually, making efficient operation difficult. Furthermore, the inability to fully utilize past data creates issues with the accuracy of placement proposals. For these reasons, there was a need for a system that could simultaneously optimize the placement of both in-house human resources and in-factory robots, and make more efficient and accurate proposals.

[1710] 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.

[1711] In this invention, the server includes: means for inputting desired work content, work location, and skills; server means for receiving the data input by the user; means for analyzing the received data and using a generation AI to list appropriate departments; means for sending information about the proposed departments to the user; means for obtaining detailed information about departments in which the user is interested and providing it to the user; means for inputting robot specifications, roles, placement, and skills; and means for analyzing the robot specifications, roles, placement, and skills and using a generation AI to propose appropriate placement. This makes it possible to simultaneously optimize personnel placement within a company and robot placement within a factory, thereby improving employee satisfaction and improving corporate operational efficiency.

[1712] The "desired work content" is the job content that an employee or user desires to be in charge of.

[1713] "Work location" is the location or area where an employee or user wants to work.

[1714] "Skills" refer to the techniques and knowledge possessed by employees or users, and indicate the abilities required to perform specific tasks.

[1715] A "means" is a method or device used to achieve a particular purpose.

[1716] "User" means an individual or employee who uses this system.

[1717] A "server" is a computer system that receives information from users and performs analytical processing.

[1718] "Generative AI" is artificial intelligence that analyzes data and generates optimal suggestions and lists.

[1719] "Listing" means displaying the best options in a list format based on specific conditions.

[1720] A "robot" is a mechanical device designed to perform a specific task in a factory.

[1721] "Deployment" refers to the allocation of specific people or machines to appropriate locations.

[1722] "Specifications" are detailed information that indicates the specifications and capabilities of a robot or machine.

[1723] A "role" refers to the tasks or responsibilities that an employee or robot should perform.

[1724] The present invention is implemented as a system for optimizing the allocation of employees and robots in a factory within a company. This system is composed of the following components:

[1725] User preference input

[1726] Terminal

[1727] Users use their own devices to input their desired work content, work location, and skills. This input data is sent to the server via the device. Factory robot specifications, current role, placement, and skill sets are also input from the device and sent to the server.

[1728] Data analysis and matching

[1729] server

[1730] The server receives data sent from users and factory robots. The received data is stored in a database. The server then launches a generative AI model (Recommendation AI) that lists appropriate departments and optimal robot placements based on the user's desired data and robot data. The generative AI model learns from past job change and transfer data and past robot placement data to improve its analysis accuracy.

[1731] Displaying the proposed results

[1732] server

[1733] Based on the analysis results of the generation AI, the server organizes the proposed department and robot placement information, which is then sent to the terminal.

[1734] Terminal

[1735] The user's terminal receives and displays the proposed results sent from the server, and the user or factory manager can select an appropriate department or robot placement from the proposed list.

[1736] Providing more information

[1737] User

[1738] Once the user or factory manager selects a proposal that interests them, they can request more information.

[1739] Terminal

[1740] The terminal sends a request to the server, which retrieves the detailed information and sends it back to the terminal.

[1741] Terminal

[1742] The terminal displays detailed information received from the server, allowing users and factory managers to make decisions.

[1743] Registering department and robot placement information

[1744] Terminal

[1745] Company department staff and factory managers enter new job information and robot placement information through a dedicated interface and send it to the server.

[1746] server

[1747] The server stores the transmitted information in a database and uses it for the next analysis.

[1748] As a specific example, if a user inputs "Desired work content: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the generation AI will analyze this and suggest "Department A: Software development," "Department B: Data analysis team," etc. On the other hand, if a factory manager inputs "Robot ID: robot_001," "Specs: Type-A," "Current role: Assembly," "Location: Line-1," and "Skills: welding, bolting," the generation AI will make a "New placement proposal: Maintenance." This allows for the optimization of departments and placements.

[1749] Example prompt sentence:

[1750] Please recommend the best placement and role based on the following robot data:

[1751] Specs: Type-A

[1752] Current role: Assembly

[1753] Current location: Line-1

[1754] Skill Set: ["welding", "bolting"]

[1755] This system makes it possible to simultaneously optimize the allocation of personnel within a company and the allocation of robots within a factory, which is expected to improve the operational efficiency of a company and employee satisfaction.

[1756] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1757] Step 1:

[1758] User data entry

[1759] The terminal allows the user to input "desired work content," "work location," and "skills." The factory manager inputs "robot specifications," "role," "placement," and "skill set." These data are entered into the input screen by the user or factory manager, and the entered data is sent from the terminal to the server.

[1760] Input: User's desired work data, robot specification data

[1761] Output: Desired data and robot data sent to the server

[1762] Step 2:

[1763] Data reception and storage by the server

[1764] The server receives the desired data and robot data sent from the device. The received data is stored in a database (e.g., MySQL, SQLite). This makes the data ready for analysis.

[1765] Input: Desired data and robot data sent from the terminal

[1766] Output: Desired data and robot data stored in a database

[1767] Step 3:

[1768] Data analysis and department / robot placement proposals

[1769] The server passes the received data to a generative AI model (Recommendation AI) for analysis. The generative AI model uses past job change and transfer data and past robot placement data to create a list of appropriate departments and optimal robot placements that match the user's desired data.

[1770] Input: Desired data and robot data stored in the database

[1771] Output: A list of department and robot placement suggestions from the generation AI

[1772] Step 4:

[1773] Organizing and sending proposal results

[1774] The server organizes the analysis results of the generated AI model, composes proposed department information and robot placement information, converts that information into a format suitable for sending to the user's device, and sends it to the device.

[1775] Input: A list of suggestions from the generative AI

[1776] Output: Formatted proposal information to send to terminal

[1777] Step 5:

[1778] Display of proposal results on user device

[1779] The terminal receives the proposed information sent from the server and displays it in an easy-to-read format for users and factory managers, who can then use this information to consider the optimal placement of themselves or the robots.

[1780] Input: Proposal information sent from the server

[1781] Output: Displayed proposal information

[1782] Step 6:

[1783] Requesting and Providing More Information

[1784] When a user or factory manager selects a department or location of interest, the terminal sends a request for detailed information to the server. The server retrieves the relevant information from the database and sends it back to the terminal. The terminal then displays the detailed information.

[1785] Input: A request for more information from the user

[1786] Output: Display of detailed information provided by the server

[1787] Step 7:

[1788] Registration of job vacancies and robot placement information

[1789] Company department staff and factory managers input new job information and robot placement information and send it from their terminals to the server, which stores it in a database and uses it for the next matching process.

[1790] Input: New job information and robot placement information

[1791] Output: Information stored in the database

[1792] The above processing steps make it possible to realize optimal allocation of personnel and robots within a company.

[1793] 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.

[1794] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. Employees input their preferences, and the system proposes appropriate departments based on those preferences. The introduction of an emotion engine analyzes the emotions expressed at the time of input, further individualizing the system.

[1795] User preference input

[1796] Terminal

[1797] Users use their own devices to input their desired job duties, work location, and skills. The device receives the input information and sends it to the server. The emotion engine then analyzes the user's facial expressions and tone of voice when inputting information to recognize the user's emotional state.

[1798] Data analysis and matching

[1799] server

[1800] The server receives the user's desired data sent from the device along with the emotion data from the emotion engine. The server stores this data in a database and activates the generation AI. The generation AI analyzes past job change and transfer data and lists the most suitable departments, taking into account the user's desired conditions and emotion data.

[1801] Displaying the proposed results

[1802] server

[1803] Based on the analysis results of the generative AI, the server organizes the department information to suggest to the user, and the organized information is sent to the user's device.

[1804] Terminal

[1805] The user's device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message according to the user's emotions.

[1806] Providing more information

[1807] User

[1808] The user selects the department of interest and requests detailed information on the terminal.

[1809] Terminal

[1810] The terminal sends a request to the server for detailed information about the department selected by the user.

[1811] server

[1812] The server receives the request, retrieves the details of the department from the database, and sends the details to the user's device.

[1813] Terminal

[1814] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[1815] Job posting by department

[1816] Terminal

[1817] A company's department staff uses a dedicated interface to input job information, which is then sent to the server.

[1818] server

[1819] The server receives the job information and stores it in a database, which is used to match the user's desired data the next time they enter it.

[1820] Specific examples

[1821] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities," a department that would reduce the user's stress. Furthermore, if the emotion engine determines that the user is entering information in a relaxed state, it will make general suggestions such as "Department A: Software development" or "Department B: Data analysis team."

[1822] Additionally, when a department staff member enters new job information (job description: AI development, work location: Osaka, skills: machine learning, Java) and registers it on the server, this information will be used in the next matching process.

[1823] In this way, by combining this system with an emotion engine, it is possible to make suggestions that take into account the user's emotional state, helping employees to work in a more suitable environment.

[1824] The processing flow will be explained below.

[1825] Step 1:

[1826] User

[1827] Users use their own terminals to input the desired job duties, work location, and skills.

[1828] Step 2:

[1829] Terminal

[1830] The device accepts the input desired data, and at the same time, the emotion engine analyzes the user's facial expressions and tone of voice to recognize the emotional data.

[1831] Step 3:

[1832] Terminal

[1833] When the user clicks the "send" button, the terminal transmits the input data and emotion data to the server.

[1834] Step 4:

[1835] server

[1836] The server receives the user's desired data and emotion data sent from the device, and stores the received data in a database.

[1837] Step 5:

[1838] server

[1839] The server launches the AI ​​generator, which analyzes the saved data on past job changes and transfers, as well as the user's preferences and emotional data. The AI ​​then creates a list of the most suitable departments, taking into account the user's preferences and emotional data.

[1840] Step 6:

[1841] server

[1842] Based on the analysis results from the generative AI, the server organizes the department information to suggest to the user.

[1843] Step 7:

[1844] server

[1845] The organized proposal result data is sent to the user's terminal.

[1846] Step 8:

[1847] Terminal

[1848] The device receives the data of the suggestion results sent from the server and displays them to the user, including an appropriate message based on the user's emotions.

[1849] Step 9:

[1850] User

[1851] The user selects the department of interest and requests detailed information on the terminal.

[1852] Step 10:

[1853] Terminal

[1854] The terminal sends a request to the server for detailed information about the department selected by the user.

[1855] Step 11:

[1856] server

[1857] The server receives the request and retrieves the details of the relevant department from the database.

[1858] Step 12:

[1859] server

[1860] The server that has obtained the detailed information will then retransmit it to the user's terminal.

[1861] Step 13:

[1862] Terminal

[1863] The device displays the detailed information sent from the server to the user, including supplementary information and suggestions that take the user's emotions into account.

[1864] Step 14:

[1865] Terminal

[1866] Company department staff use a dedicated interface to enter job information.

[1867] Step 15:

[1868] Terminal

[1869] The entered job information is sent from the terminal to the server.

[1870] Step 16:

[1871] server

[1872] The server receives the entered job information and stores it in a database.

[1873] Specific examples

[1874] For example, if a user inputs "Desired work: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest "Department C: Software development with remote work capabilities" as a department that will reduce stress. If the emotion engine determines that the user is entering information in a relaxed state, the system will make the usual suggestions of "Department A: Software development" and "Department B: Data analysis team."

[1875] The above is a specific embodiment of a system incorporating an emotion engine. This system enables more appropriate personnel matching within a company by taking into account the emotions of users.

[1876] Example 2

[1877] 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."

[1878] Conventional personnel job change and transfer systems typically suggest an appropriate department based solely on the user's desired conditions. However, because the user's emotional state is not taken into consideration, the department that is most suitable for the user may not be suggested. This can result in a failed job change or transfer and reduced user satisfaction. The objective of this invention is to suggest a more appropriate department and improve user satisfaction by simultaneously considering the user's desired conditions and emotional state.

[1879] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1880] In this invention, the server includes a means for receiving data entered by a user and emotion data analyzed by an emotion engine, a means for storing the received data in a database and listing appropriate departments using a generative AI model, and a means for transmitting information on the proposed departments to the user, thereby enabling optimal department proposals that simultaneously consider the user's desired conditions and emotional state.

[1881] "Desired work content" refers to the work content that the user wants to undertake in their career.

[1882] "Work location" is the geographic location where a user wishes to work.

[1883] "Skills" refer to the specialized abilities and knowledge that a user has.

[1884] "Emotion data" is data relating to the emotional state of the user analyzed from facial expressions, tone of voice, etc. using an emotion engine.

[1885] "Server means" is a general term for equipment and software for receiving, processing, and storing data sent from users and emotion engines.

[1886] The "database" is a system for organizing and managing received user data and emotion data.

[1887] The "generative AI model" is an artificial intelligence model that learns from past job change and transfer data and suggests the most suitable department based on the user's desired conditions and emotional state.

[1888] The "proposal results" are information about the optimal department that the generative AI model analyzes and presents to the user.

[1889] "Detailed information" is data containing specific job descriptions, conditions, and other supplemental information about the proposed department.

[1890] "Job information" is information about available positions entered by department personnel in a company.

[1891] This invention is a system for supporting personnel job changes and transfers within a company, and by combining it with an emotion engine that recognizes the user's emotions, it achieves more accurate matching and appropriate proposals. This system allows users to input their preferences, and suggests appropriate departments based on those preferences. By introducing an emotion engine, the system analyzes the emotions entered at the time of input and further individualizes the proposals.

[1892] Users use their own devices to input their desired job duties, work location, and skills. Specifically, they input "Desired job duties: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The devices are equipped with built-in cameras and microphones, which are used to capture the user's facial expressions and tone of voice in real time, and the emotion engine analyzes this data. The emotion engine uses Python's facial recognition library and voice analysis tools to recognize the user's emotional state.

[1893] The device sends the input preference data and emotion data as a single data packet to the server. The server stores the received data in a database and launches a generative AI model (using TensorFlow or PyTorch as an example). The generative AI model learns from past job change and transfer data and lists the most suitable departments based on the user's preference and emotion data.

[1894] Based on the analysis results of the generative AI model, the server organizes the department information to suggest to the user. This organization also includes supplemental information based on the user's emotional state. For example, if the user is entering information in a relaxed state, general suggestions such as "Department A: Software Development" and "Department B: Data Analysis Team" will be made, but if the user is feeling stressed, suggestions such as "Department C: Software Development with Remote Work Capability" will be made. The organized information is sent to the user's device, which receives and displays it.

[1895] To obtain detailed information about a department that interests a user, the user makes a request on their device. The server receives the request, retrieves the details of the department from the database, and sends them to the user's device. The user's device then displays this information to the user. This display also includes supplementary information and suggestions based on the user's emotional state, provided by the emotion engine.

[1896] Additionally, company department staff use a dedicated interface to enter job information and send it to the server. This job information is saved in the database and used to match the next desired data entered by a user. For example, if "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" are entered and registered on the server, this information will be used in the next matching process.

[1897] As described above, this system simultaneously considers the user's desired conditions and emotional state, and makes more appropriate suggestions, thereby improving user satisfaction.

[1898] Examples:

[1899] For example, if a user inputs "Desired job description: Software development," "Work location: Tokyo," and "Skills: Python, data analysis," the emotion engine analyzes the user's emotions in real time and determines that the user is in a relaxed state, for example. Based on this data, the generative AI model lists appropriate departments. The suggested results, "Department A: Software development" and "Department B: Data analysis team," are displayed to the user. Examples of prompt sentences are as follows:

[1900] Example prompt sentence:

[1901] Write an algorithm that will suggest an appropriate department based on the user's emotional state when they enter "Desired job: Software development", "Location: Tokyo", and "Skills: Python, data analysis".

[1902] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1903] Step 1: Enter desired conditions and emotional data

[1904] Terminal

[1905] A user logs in to a device and enters their desired job description, work location, and skills. For example, they might enter "Desired job description: software development," "Work location: Tokyo," and "Skills: Python, data analysis." The device's camera and microphone capture the user's facial expressions and tone of voice, which the emotion engine analyzes to identify the user's emotional state. The input for this step is the user's desired data and real-time emotion data, and the output is a data packet containing this input.

[1906] Step 2: Sending data packets

[1907] Terminal

[1908] The user's device sends the input desired data and the emotional data analyzed by the emotion engine to the server as a single data packet. The transmitted data packet includes, for example, the user ID, desired job content, work location, skills, and emotional state. The input to this step is the data packet generated in step 1, and the output is a transmission confirmation to the server.

[1909] Step 3: Receiving and storing data

[1910] server

[1911] The server receives the data packet sent from the device. This input data is the user's desired conditions and emotional state. After receiving, the server stores this data in a database. For example, the data is stored using a database management system (e.g., MySQL or PostgreSQL). The input of this step is the data packet sent from the device, and the output is confirmation of successful storage.

[1912] Step 4: Launching the generative AI model and analyzing data

[1913] server

[1914] The server uses the stored data to launch a generative AI model. For example, an AI model using TensorFlow or PyTorch learns from past job change and transfer data and lists the most suitable departments by taking into account the user's desired conditions and emotional data. The input for this step is the stored user data and past job change and transfer data, and the output is a list of appropriate departments as a result of the analysis.

[1915] Step 5: Organize and submit your proposal

[1916] server

[1917] Based on the analysis results of the generative AI model, the server organizes department information to suggest to the user. The suggestions also include supplemental information based on the user's emotional state. The organized data is sent to the user's device. The input to this step is the analysis results of the generative AI model, and the output is the data sent to the user's device.

[1918] Step 6: Viewing the Suggestion Results

[1919] Terminal

[1920] The user's device receives the proposal results sent from the server and displays them to the user. For example, a relaxed user may be suggested "Department A: Software Development" or "Department B: Data Analysis Team," while a stressed user may be suggested "Department C: Remote Work Software Development." The input for this step is the proposal results from the server, and the output is a list of proposals displayed to the user.

[1921] Step 7: Request more information

[1922] User

[1923] The user selects the department they are interested in from the proposed departments and requests detailed information on the terminal. The input of this step is the department information selected by the user, and the output is a detailed information request.

[1924] Step 8: Get and send details

[1925] server

[1926] The server receives a detailed information request from the user and retrieves the detailed information for the relevant department from the database. It then sends the retrieved detailed information to the user's terminal. The input of this step is the detailed information request, and the output is the transmission of the detailed information to the user's terminal.

[1927] Step 9: View detailed information

[1928] Terminal

[1929] The user's device receives the detailed information sent from the server and displays it to the user. The displayed information also includes supplementary information and suggestions according to the emotional state transmitted by the emotion engine. The input of this step is the detailed information from the server, and the output is the detailed information displayed to the user.

[1930] Step 10: Fill out and submit your job posting

[1931] Terminal

[1932] A departmental employee at a company uses a dedicated interface to input new job information. The input information is sent to the server. For example, information such as "Job Description: AI Development," "Work Location: Osaka," and "Skills: Machine Learning, Java" is input. The input for this step is the company's job information, and the output is the transmission of the job information to the server.

[1933] Step 11: Save your job posting

[1934] server

[1935] The server receives the job information sent by the company's department staff and saves it in the database. The saved information will be used for the next matching process with the desired input data by the user. The input of this step is the submitted job information, and the output is a confirmation that the job information was successfully saved.

[1936] (Application example 2)

[1937] 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."

[1938] Conventional personnel job change and department transfer support systems often make mechanical suggestions based on user input data and are unable to consider the user's emotions or psychological state. As a result, the proposed departments and jobs do not satisfy the user psychologically, making it difficult to realize an effective transfer or job change. In addition, the job information entered by company department personnel is not provided in a form that reflects their emotions and expectations, and further improvements in matching accuracy are required.

[1939] The specification processing by the specification 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 inputting desired work content, work location, and skills, server means for receiving data input by the user, means for analyzing the received data and using a generative AI model to list appropriate departments, means for transmitting information on the proposed departments to the user, means for acquiring detailed information on departments in which the user is interested and providing it to the user, means for analyzing the user's facial expressions and voice using an emotion engine and using the emotion data for analysis, means for generating proposals according to the user's psychological state based on the analyzed emotion data, means for acquiring past job change and transfer data and having the generative AI model learn from the data to improve analysis accuracy, means for inputting prompt sentences into the generative AI model to obtain optimal analysis results, means for a company department employee to input job information and transmit it to the server, means for saving the sent job information in a database, means for using the saved job information in a matching process by the next user, and means for utilizing the emotion engine to analyze the employee's intentions and expectations when entering job information. This will enable more accurate department and job suggestions that reflect the user's emotions and psychological state.

[1940] A "user" is someone who uses the system to input their job description, work location, and skills, and receives suggestions for suitable departments and jobs.

[1941] An "emotion engine" is a technology that analyzes a user's facial expressions and voice to recognize their emotional state.

[1942] "Job content" refers to the type of work and specific duties desired by the user.

[1943] "Work location" refers to the location or area where the user wishes to work.

[1944] "Skills" refers to the specialized techniques, knowledge, or proficiency that a user possesses.

[1945] "Server means" means a computing device used by the system to receive, store, and analyze user-entered data and other information.

[1946] A "generative AI model" is an artificial intelligence model that suggests appropriate departments and jobs based on input data and past data.

[1947] A "prompt sentence" is a sentence that is input to a generative AI model to obtain optimal analysis results.

[1948] "Suggestion means" is a function that allows the system to send users information about appropriate departments and jobs listed by the generative AI model.

[1949] The "detailed information acquisition means" is a function for acquiring further detailed information about a proposal in which the user is interested and providing the information to the user.

[1950] "Job information" refers to the content and conditions of the job being recruited for, entered by department staff at a company.

[1951] The "analysis means" is a means for analyzing the data received by the server means and listing appropriate departments and jobs.

[1952] "Matching processing" is the process of selecting the most suitable department and job based on the user's preferences, emotional data, and past data.

[1953] The "means for analyzing intentions and expectations" is a means for using an emotion engine to recognize and analyze the intentions and expectations of company personnel who enter job information.

[1954] The present invention is a system that proposes departments and jobs with higher accuracy by taking into account the user's wishes and emotional state. By combining an emotion engine, this system improves on conventional mechanical proposals and enables proposals that correspond to the user's psychological state.

[1955] System Configuration

[1956] Hardware

[1957] The system is implemented mainly using the following hardware:

[1958] Server: A high-performance computer device that receives data, analyzes it, and sends out proposals.

[1959] User terminal: A smartphone or tablet, which is a device where users input their preferences and emotional data and receive suggested information.

[1960] software

[1961] The system is implemented using the following software:

[1962] Generative AI model: Built using TensorFlow, it lists the most suitable departments and jobs based on the user's preferences and past job change data.

[1963] Emotion analysis: Using OpenCV and AudioEmotion libraries, we analyze the user's facial expressions and voice to obtain emotional data.

[1964] Database: Using MySQL, we store user preferences and emotional data, company job information, and past job change and transfer data.

[1965] Program processing overview

[1966] Desired input

[1967] Users use their smartphones to input their desired job duties, work location, and skills. The camera also captures their facial expressions and records their voice with a microphone. The emotion engine analyzes this data and recognizes the user's emotional state.

[1968] Data reception and analysis

[1969] The server receives preference and emotion data sent from the user's device. This data is stored in a database and analyzed by a generative AI model. The generative AI model learns from past job change and transfer data to improve the accuracy of its analysis.

[1970] suggestion

[1971] The server then suggests appropriate departments and jobs to the user based on the generative AI model. This suggestion information is generated taking into account the user's emotional state. For example, if the user is feeling stressed, the server will suggest departments that will help them reduce stress.

[1972] Providing more information

[1973] If the user is interested in the proposed department or job, they can request more information. The server receives this request, retrieves the relevant details from the database, and provides them to the user.

[1974] Job information registration

[1975] A company's department staff enters job information using a dedicated interface. This job information is analyzed by the emotion engine, along with the staff's intentions and expectations, and saved in a database. The saved information is then used for the next matching process by the user.

[1976] Specific examples

[1977] For example, if a user inputs "Desired job: software development," "Work location: Tokyo," and "Skills: Python, data analysis," and the emotion engine recognizes that the user is feeling stressed, the system will suggest a department that would reduce the user's stress, such as "Software development with remote work capabilities."

[1978] Prompt Sentence Examples

[1979] "Please suggest a department that allows remote work and reduces stress for users with software development and data analysis skills using Python."

[1980] In this way, the present invention is a system that realizes more accurate suggestions that take into account the user's emotional state, enabling employees to work in a more appropriate environment and improving work comfort.

[1981] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1982] Step 1:

[1983] The user uses the device to input the desired job duties, work location, and skills. The input data is temporarily saved on the device. In addition, the device's camera captures the user's facial expressions and records their voice with a microphone. This emotional data is also acquired on the device. After the user has completed input, the data is sent to the server.

[1984] Step 2:

[1985] The terminal transmits the user's preference data and emotion data to the server, including text data on the user's preference, work location, and skills, image data captured by the camera, and voice data recorded by the microphone.

[1986] Step 3:

[1987] The server stores the received user preference data and emotion data in a database, where each user data is associated with the other user data.

[1988] Step 4:

[1989] The server uses an emotion engine to analyze the received facial image data and voice data. It uses OpenCV to extract facial features and the AudioEmotion library to identify the voice emotion. This provides the user's emotional state as numerical data.

[1990] Step 5:

[1991] Based on the analyzed emotion data and preference data, the server uses a generative AI model to create a list of the most suitable departments and jobs. Past job change and transfer data stored in the database is also referenced, and the generative AI model improves the accuracy of the analysis. A prompt sentence is entered to have the generative AI model perform the analysis.

[1992] Step 6:

[1993] The server sends the list of suitable departments and job positions proposed by the generative AI model to the user's device, including a message based on the user's emotions.

[1994] Step 7:

[1995] The user can check the proposal results on the terminal and request detailed information about the department or job they are interested in. The terminal then sends the request to the server.

[1996] Step 8:

[1997] Based on the received request, the server retrieves detailed information about the relevant department and job from the database and sends it to the user's device. The detailed information also includes supplementary explanations based on the analysis results of the emotion engine.

[1998] Step 9:

[1999] A company's department staff uses a dedicated interface to enter new job information, which is then sent to the server.

[2000] Step 10:

[2001] The server stores the received job information in a database. It also uses an emotion engine to analyze the intentions and expectations of the person in charge and stores this information along with the job information. The stored information will be used for the next matching process by the user.

[2002] 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.

[2003] 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.

[2004] 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.

[2005] 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.

[2006] 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.

[2007] 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.

[2008] 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).

[2009] 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.

[2010] 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."

[2011] 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.

[2012] 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).

[2013] 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.

[2014] 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.

[2015] 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.

[2016] 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.

[2017] 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.

[2018] 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.

[2019] 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.

[2020] 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.

[2021] 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.

[2022] 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.

[2023] The following is further disclosed regarding the above embodiment.

[2024] (Claim 1)

[2025] A means to input desired job duties, location, and skills;

[2026] server means for receiving data entered by a user;

[2027] A method to analyze the received data and use generative AI to list appropriate departments,

[2028] means for transmitting the proposed department information to the user;

[2029] A means for obtaining and providing detailed information about departments in which the user is interested;

[2030] A system including:

[2031] (Claim 2)

[2032] The system according to claim 1, further comprising means for acquiring past job change and transfer data and for the generation AI to learn from the data and improve the accuracy of the analysis.

[2033] (Claim 3)

[2034] A means for company department personnel to input job information and send it to the server;

[2035] a means for storing the submitted job information in a database;

[2036] A means for using the saved job information in a next matching process by a user;

[2037] The system of claim 1 further comprising:

[2038] "Example 1"

[2039] (Claim 1)

[2040] A means to input desired job duties, location, and skills;

[2041] server means for receiving data entered by a user;

[2042] A means of analyzing the received data and using a generative AI model to list appropriate departments;

[2043] means for transmitting the proposed department information to the user;

[2044] A means for obtaining and providing detailed information about departments in which the user is interested;

[2045] A means for company department personnel to input job information and send it to the server;

[2046] a means for storing the submitted job information in a database;

[2047] A means for using the saved job information in a next matching process by a user;

[2048] A system including:

[2049] (Claim 2)

[2050] The system of claim 1 further includes means for acquiring past job change and transfer data and for the generative AI model to learn from the data to improve analysis accuracy.

[2051] (Claim 3)

[2052] 2. The system according to claim 1, further comprising means for receiving a request for detailed information about a department in which the user is interested, obtaining the detailed information about the department, and providing the information to the user.

[2053] "Application Example 1"

[2054] (Claim 1)

[2055] A means to input desired job duties, location, and skills;

[2056] server means for receiving data entered by a user;

[2057] A method to analyze the received data and use generative AI to list appropriate departments,

[2058] means for transmitting the proposed department information to the user;

[2059] A means for obtaining and providing detailed information about departments in which the user is interested;

[2060] A means for inputting the specifications, role, placement, and skills of the robot;

[2061] A means of analyzing the robot's specifications, role, placement, and skills, and using generative AI to propose appropriate placement;

[2062] A system including:

[2063] (Claim 2)

[2064] The system according to claim 1, further comprising means for acquiring past job change and transfer data and past robot placement data, and for the generation AI to learn from the data and improve the accuracy of the analysis.

[2065] (Claim 3)

[2066] A means for company department personnel to input job information and send it to the server;

[2067] a means for storing the submitted job information in a database;

[2068] A means for using the saved job information and robot placement data for the next user and robot matching process;

[2069] The system of claim 1 further comprising:

[2070] "Example 2: Combining Emotion Engines"

[2071] (Claim 1)

[2072] A means to input desired job duties, location, and skills;

[2073] a server means for receiving data input by a user and emotion data analyzed by an emotion engine;

[2074] A means to store the received data in a database and use a generative AI model to list the appropriate departments;

[2075] means for transmitting the proposed department information to the user;

[2076] A means for obtaining and providing detailed information about departments in which the user is interested;

[2077] A system including:

[2078] (Claim 2)

[2079] The system of claim 1 further includes means for acquiring past job change and transfer data and for the generative AI model to learn from the data to improve analysis accuracy.

[2080] (Claim 3)

[2081] A means for company department personnel to input job information and send it to the server;

[2082] a means for storing the submitted job information in a database;

[2083] A means for using the saved job information for the next matching process of desired input data by a user;

[2084] The system of claim 1 further comprising:

[2085] "Application example 2 when combining emotion engines"

[2086] (Claim 1)

[2087] A means to input desired job duties, location, and skills;

[2088] server means for receiving data entered by a user;

[2089] A means of analyzing the received data and using a generative AI model to list appropriate departments;

[2090] means for transmitting the proposed department information to the user;

[2091] A means for obtaining and providing detailed information about departments in which the user is interested;

[2092] A means for analyzing the user's facial expressions and voice using an emotion engine and using the emotion data for analysis;

[2093] means for generating a suggestion according to the user's psychological state based on the analyzed emotion data;

[2094] A system including:

[2095] (Claim 2)

[2096] A means to acquire past job change and transfer data, and have the generative AI model lear...

Claims

1. A means to input desired job duties, location, and skills; server means for receiving data entered by a user; A method to analyze the received data and use generative AI to list appropriate departments, means for transmitting the proposed department information to the user; A means for obtaining and providing detailed information about departments in which the user is interested; A system including:

2. The system according to claim 1, further comprising means for acquiring past job change and transfer data and for the generation AI to learn from the data and improve the accuracy of the analysis.

3. A means for company department personnel to input job information and send it to the server; a means for storing the submitted job information in a database; A means for using the saved job information in a next matching process by a user; The system of claim 1 further comprising:

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