system

Abstract

We provide a system that efficiently manages human resources within a company and optimally matches employees' career visions with the needs of their departments. [Solution] A system including: a means for receiving and authenticating user authentication information; a means for providing a user interface and allowing the user to interactively input their requests and career vision; a means for analyzing the input data and extracting the user's skill set and desired conditions; a means for retrieving skill requirement data from an internal database and matching it with the user's skill set; a means for generating an optimal placement plan based on the matching results and presenting it to the user; a means for the user to approve the generated placement plan; a means for updating the approved placement plan in the database; a means for scheduling regular AI interviews with the user and department staff; and a means for analyzing new feedback and improving the placement plan.

Description

[Technical Field]

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

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

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

[0004] In modern companies, it is important to optimally match employee skills with departmental needs. However, traditional methods often involve manual allocation of human resources, which is inefficient and makes it difficult to properly reflect employees' career visions and desired conditions. Furthermore, the lack of a system that can match employee skills with departmental needs in real time can lead to a decline in overall organizational performance. Therefore, the objective of this invention is to provide a system that efficiently manages a company's "human capital" resources and optimally matches employees' career visions with departmental demands. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means. The system includes a means for receiving user authentication information and performing authentication, a means for providing a user interface and interactively inputting the user's needs and career vision, and a means for analyzing the input data and extracting the user's skill set and desired conditions. The system also includes a means for retrieving skill requirement data from an internal database and matching it with the user's skill set, and a means for generating an optimal placement plan based on the matching results and presenting it to the user. The system further includes a means for the user to approve the generated placement plan, a means for updating the approved placement plan in the database, a means for scheduling regular AI interviews with the user and department staff, and a means for analyzing new feedback and improving the placement plan. This system proposes optimal placements in real time based on the skills and career vision input by the user, improving the performance of the entire organization and increasing employee satisfaction.

[0006] "User authentication information" means the information required for a user to log in to a system, and typically includes a user ID and password.

[0007] A "user interface" is a software portion that allows a user to interact with a system, and includes a screen for input and output and an interactive input means.

[0008] A "skill set" is a collection of data that compiles the skills, qualifications, knowledge, and experience possessed by a particular user.

[0009] "Career vision" refers to the user's hopes and plans regarding the goals they want to achieve and the direction they want to take in their professional life.

[0010] A "database" is a collection of data that stores information systematically and allows for efficient searching and updating.

[0011] The "matching score" is a numerical representation of the degree of compatibility between the user's skill set and desired conditions and the department's skill requirements.

[0012] "Placement proposals" are plans or scenarios that take into account a user's skill set and career vision and suggest the most suitable department or role.

[0013] "Feedback" refers to opinions and evaluation information provided by users and department staff, and is used to improve the system.

[0014] A "regular AI interview" is a pre-scheduled, regular, interactive session between a user and an AI system.

[0015] "Natural language processing technology" refers to technology that enables computers to understand, analyze, and respond to human language, and includes methods for extracting information from text data. [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 illustrating 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] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with the needs of their departments. This system allows users to interactively input their requests and career visions, and uses AI to propose optimal placement plans. Below, we will explain the program processing of this system in natural language and provide detailed explanations with concrete examples.

[0038] User authentication

[0039] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the terminal allows the user access.

[0040] AI interviews begin

[0041] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[0042] Data extraction and analysis

[0043] The device collects input data sequentially and transfers it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[0044] Skills Matching

[0045] The server retrieves the skill requirements data for each department from the company database, and then uses the generation AI to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[0046] Generation and presentation of layout plans

[0047] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[0048] Implementing the placement plan

[0049] The server updates the database with the approved placement plan, officially reflecting the user's placement information, thereby optimizing the company's overall human resources.

[0050] Regular interviews and feedback

[0051] The server periodically schedules AI interviews with users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[0052] Specific examples

[0053] Example 1: Department Personnel Case

[0054] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[0055] Example 2: Employee case

[0056] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[0057] This system effectively takes into account the user's skills and career vision, proposes optimal personnel allocation, and improves corporate performance.

[0058] The processing flow will be explained below.

[0059] Step 1:

[0060] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[0061] Step 2:

[0062] The terminal sends the received authentication information to the server.

[0063] Step 3:

[0064] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[0065] Step 4:

[0066] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[0067] Step 5:

[0068] Users input their requests and career vision through the AI ​​interview interface.

[0069] Step 6:

[0070] The terminal sequentially collects the user's input data and sends it to the server.

[0071] Step 7:

[0072] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[0073] Step 8:

[0074] The server retrieves the skill requirement data for each department from the company database.

[0075] Step 9:

[0076] The server compares the user's skill set with the skill requirements of each department and calculates a matching score.

[0077] Step 10:

[0078] The server generates an optimal placement plan based on the matching results.

[0079] Step 11:

[0080] The server transmits the generated placement plan to the terminal.

[0081] Step 12:

[0082] The device displays the layout plan to the user, and the user confirms the layout plan.

[0083] Step 13:

[0084] If the user approves of the placement proposal, the device transmits the selection to the server.

[0085] Step 14:

[0086] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[0087] Step 15:

[0088] The server schedules regular AI interviews with users and department personnel.

[0089] Step 16:

[0090] The device will send reminders to the user for regular appointments.

[0091] Step 17:

[0092] The device will conduct a new AI interview with the user.

[0093] Step 18:

[0094] The server analyzes the new interview data and improves placement proposals based on the feedback.

[0095] This process allows the system to best reflect the user's skills and preferences, optimizing the allocation of human resources across the organization.

[0096] Example 1

[0097] 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."

[0098] Optimal management of human resources within a company relies on efficiently matching employees' career visions with the needs of each department. However, traditional methods require manually collecting and analyzing information to find optimal allocations, which takes time and effort, and often requires subjective judgment. Furthermore, regular feedback and allocation improvements are not properly implemented, making it difficult to maximize corporate performance. A solution to these issues is needed.

[0099] 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.

[0100] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an in-house database and matching it with the user's skill set, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department staff, means for analyzing new feedback and improving the placement plan, and means for calculating a matching score using a generative AI model using prompt sentences and generating an optimal placement plan based on the score. This enables optimal management of human resources.

[0101] "User authentication information" means authentication information such as ID and password used by a user when accessing a system.

[0102] A "user interface" is a function that provides a screen and operation methods for a user to interact with a system.

[0103] "Dialogue" refers to a format in which the user and the system exchange information alternately using text, voice, etc.

[0104] "Requests" refer to the hopes, functions, and services that users desire from the system.

[0105] "Career vision" refers to the goals and future plans that users have for their occupation or career.

[0106] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze input data and generate results according to a desired purpose.

[0107] A "prompt" is text that is input to a generative AI model to instruct it on specific analysis or generation.

[0108] An "in-house database" is a database system that structures and stores information managed within a company.

[0109] A "skill set" is the collection of knowledge, skills, and experience that an individual possesses.

[0110] "Skill requirements data" is information about the specific skills and abilities required by each department or project.

[0111] The "matching score" is a numerical representation of the degree of match between the user's skill set and the company's skill requirement data.

[0112] A "placement proposal" is a proposal for placing a user in a specific department or project.

[0113] "Feedback" refers to opinions and evaluations obtained from system users and department staff, and is information used to improve the system and deployment plans.

[0114] An "AI interview" is an interview conducted with a user or departmental staff member using artificial intelligence, and is conducted in an interactive format.

[0115] The present invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. This system has the function of allowing users to interactively input their requests and career visions and then using a generative AI model to propose optimal placement plans. Specific embodiments are described in detail below.

[0116] Overall system configuration

[0117] This system is primarily composed of terminals, servers, and an in-house database. Specific examples of the hardware and software used include PCs (Windows 10, macOS®), tablets (iPad®, Android®), and smartphones (iOS, Android). Servers use AWS® EC2, Microsoft® Azure®, and Google® Cloud Platform. Generative AI models used include OpenAI® GPT-4®, Google BERT, and Microsoft Turing.

[0118] User authentication

[0119] First, the terminal receives a login request from the user and prompts them to enter their user ID and password. The user authentication information is sent to the server using an HTTP POST request, and the server checks this information against the company's database. If there is a match, authentication is successful and the terminal allows the user access.

[0120] AI interviews begin

[0121] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to enter their requests and career vision in a dialogue format. This input is done through a chatbot-style user interface.

[0122] Data collection and analysis

[0123] The device collects the input data sequentially and transmits it to the server using an HTTP POST request. The server then uses a generative AI model to analyze the received data and extract the user's skill set and desired conditions.

[0124] Skills Matching

[0125] The server retrieves the skill requirements data for each department from the company's internal database, and uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[0126] Generation and presentation of layout plans

[0127] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[0128] Implementing the placement plan

[0129] The server updates the approved assignment plan to the company database, officially reflecting the user's new assignment information, thereby optimizing the company's overall human resources.

[0130] Regular interviews and feedback

[0131] The server periodically schedules AI interviews with users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[0132] Examples of concrete examples and prompts

[0133] Example: Department Manager

[0134] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[0135] Example: Employee case

[0136] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[0137] Prompt Sentence Examples

[0138] "We are looking for an employee with the following skill set: Python programming, data analysis experience, and team leadership experience. Please recommend the right candidate."

[0139] "My career vision is to gain experience in project management. Please recommend me to a suitable department."

[0140] Through the above explanation, we can see that this system can optimally manage human resources within a company and efficiently match employees' career visions with the needs of their departments.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Step 1:

[0143] Gathering user credentials

[0144] The terminal receives a login request from the user and prompts them to enter their user ID and password. The user ID and password are entered into the terminal as input. The terminal receives this information and temporarily stores the authentication information within the terminal.

[0145] Step 2:

[0146] Sending user credentials

[0147] The device sends the collected user authentication information to the server. The data is processed by embedding the entered user ID and password in an HTTP POST request and sending it to the server. The request containing the authentication information reaches the server as output.

[0148] Step 3:

[0149] Enforce user authentication

[0150] The server compares the received user authentication information with data in the company's database and performs authentication. For data calculations, the server uses an SQL query to search for user information in the database. The input is the user ID and password, and the output is the result of authentication success or failure. If authentication is successful, the server returns a response indicating successful authentication.

[0151] Step 4:

[0152] Notification of authentication results

[0153] The terminal receives the authentication result from the server and notifies the user. The input is the authentication result from the server, and the output is a screen notifying the user of authentication success or failure. For example, it may display "Login successful" and a dashboard screen for the user to proceed to the next step.

[0154] Step 5:

[0155] Display of AI interview interface

[0156] After successful authentication, the device displays the AI ​​interview interface to the user. The input is the user information after successful authentication, and the output is the AI ​​interview interface screen. The interview begins when the user clicks the start button for the AI ​​interview.

[0157] Step 6:

[0158] Starting a conversation

[0159] The user begins to input their requests and career vision using the AI ​​interview interface. The input is the user's interactive input data, and the output is the user's requests and career vision in text form. The data entered by the user is temporarily stored on the device.

[0160] Step 7:

[0161] Data collection

[0162] The terminal sequentially transfers the input data to the server. The input is the user's interactive input data, and the output is an HTTP POST request containing the collected data. The terminal sends the data to the server at regular intervals.

[0163] Step 8:

[0164] Data analysis

[0165] The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. The input is the user's interactive input data, and the output is the analyzed skill set and desired conditions. For example, a request such as "I want to gain project management experience" can be extracted.

[0166] Step 9:

[0167] Get skill request data

[0168] The server retrieves the skill requirement data of each department from the company's database. There is no input, and the output is the skill requirement data of each department. The necessary information is retrieved from the database using an SQL query.

[0169] Step 10:

[0170] Matching Score Calculation

[0171] The server uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score. The input is the user skill set and the skill requirements data of each department, and the output is the matching score. The comparison calculation quantifies which department is the most suitable.

[0172] Step 11:

[0173] Generate placement proposals

[0174] The server generates an optimal placement plan based on the matching results. The input is the matching score, and the output is the placement plan. For example, it generates a specific proposal such as "place in the data analysis department."

[0175] Step 12:

[0176] Proposal of layout plan

[0177] The server sends the details of the generated placement plan to the terminal, which then displays it to the user. The input is the placement plan data, and the output is the placement plan displayed on the user's screen. An interface is displayed for the user to check the placement plan.

[0178] Step 13:

[0179] Review and approve the layout plan

[0180] The user checks the placement proposal and approves it if it meets their needs. The input is the placement proposal, and the output is the result of approval or rejection. For example, the approval process is completed by clicking the "Approve placement proposal" button.

[0181] Step 14:

[0182] Update placement information

[0183] The server updates the approved deployment plan to the company database. The input is the approval result, and the output is the updated user deployment information. The new deployment information is recorded in the database, officially reflecting the user's deployment.

[0184] Step 15:

[0185] Schedule an interview

[0186] The server periodically schedules AI interviews with users and department staff. The input is user and staff information, and the output is schedule settings. For example, an interview is scheduled for the first Monday of every month.

[0187] Step 16:

[0188] Reminder notifications

[0189] The device notifies the user of reminders and schedules new appointments. The input is schedule information, and the output is the reminder sent to the user. For example, a notification saying "Don't forget about next week's appointment" is sent one week before the appointment.

[0190] Step 17:

[0191] Feedback Analysis

[0192] The server analyzes the new feedback, compares it with the previous placement plan, and makes improvements as necessary. The input is the feedback information, and the output is the improved placement plan. For example, analyzing the feedback "I'm satisfied with my new department" will identify areas for further improvement.

[0193] (Application example 1)

[0194] 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."

[0195] In conventional factories, it was difficult to assign personnel in a way that effectively reflected the skills and career vision of workers. This resulted in problems such as a decline in production efficiency and a decrease in worker motivation. In addition, it was not possible to accurately match the skill requirements of each robot with the skills of workers, and there was a lack of concrete measures to achieve optimal assignment.

[0196] 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.

[0197] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's needs and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an internal database and matching it with the user's skill set, means for generating an optimal placement plan based on the matching results and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department personnel, means for analyzing new feedback and improving the placement plan, means for inputting the skills and career vision of a robot operator and matching it with the skill requirements of each robot in the factory, and means for generating and presenting an optimal placement plan for robot operators using a generative AI model. This enables optimal personnel allocation within the factory, which is expected to improve production efficiency and worker motivation.

[0198] "User authentication information" means information provided by a system user to identify themselves, and typically includes a user ID and password.

[0199] "User interface" refers to the screen or means of interaction that allows a user to interact with a system, including input forms, buttons, etc.

[0200] "Dialogue" refers to an interface method that proceeds in the form of questions and answers, and is used by users to input their wishes and career vision.

[0201] A "skill set" refers to the collection of skills and knowledge a user possesses, including the abilities necessary to carry out specific tasks.

[0202] "Career vision" is a concept that represents the professional goals and desired career path that a user wants to achieve in the future.

[0203] "Skill requirements data" refers to a list of specific skills and knowledge that each organization or robot requires to perform its work.

[0204] The "matching score" is a numerical indicator that represents the degree of match between the user's skill set and the skill requirement data, and serves as a criterion for generating optimal placement plans.

[0205] "Generative AI model" refers to an artificial intelligence model used to analyze data and generate optimal placement proposals based on criteria.

[0206] "Feedback" refers to opinions or evaluations provided to the system by users or department personnel that are useful for improving the system.

[0207] "Robot operator" refers to a worker whose role is to operate and manage robots within a factory.

[0208] "Optimal placement proposal" refers to the most efficient personnel placement proposal generated based on the user's skill set, career vision, and skill requirement data.

[0209] This system maximizes production efficiency by optimally matching factory human resources with the skills of robot operators. The system provides an application that workers can access from their smartphones or smart glasses, and allows them to input their own skills and career vision through the application.

[0210] The system program is configured as follows: First, the server receives user authentication information and performs authentication. Specifically, the worker logs in to their smartphone or smart glasses and enters their ID and password. This authentication information is sent to the server and checked against the database.

[0211] After successful authentication, the terminal provides a user interface where the worker's requests and career vision can be input interactively. Once the worker enters their career vision interactively, the input data is transferred to the server.

[0212] The server analyzes the input data using a generative AI model to extract the user's skill set and desired conditions. The server then retrieves the skill requirements of each robot from an internal database and matches them with the worker's skill set. Based on the results of this matching, the server generates an optimal deployment plan and presents it to the worker via their terminal. Once the worker approves the deployment plan, the information is sent back to the server and updated in the database.

[0213] The server also periodically schedules and reminds users and department personnel for AI interviews. When new feedback is collected, the server analyzes it, compares it with the previous deployment plan, and makes any necessary improvements.

[0214] As a concrete example, consider the case where Worker A enters into the app that his / her career vision is "I want to learn robot programming techniques." This input is accepted by the terminal and sent to the server. The server analyzes this text and finds the corresponding robot operation task. It then presents this task to Worker A, who then approves the proposal and is assigned to the new task.

[0215] Example prompt sentence:

[0216] "A user enters, 'I want to learn robot programming skills.' Find the appropriate department and task, and suggest the best placement for the user."

[0217] The above configuration includes the following hardware and software: smartphones or smart glasses (e.g., Google Glass (registered trademark), Vuzix Blade), factory servers (e.g., Dell PowerEdge series), user authentication systems (e.g., OAuth 2.0), databases (e.g., MySQL (registered trademark), PostgreSQL), generative AI models (e.g., GPT-4, BERT), and front-end UIs (e.g., React Native).

[0218] This will enable optimal allocation of personnel and robot operations within the factory, which is expected to improve production efficiency.

[0219] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0220] Step 1: User authentication

[0221] The user accesses the login screen from the device using a smartphone or smart glasses. The user enters their ID and password, and the device receives the information. The received authentication information is sent to the server, which compares it with a database to determine whether authentication is successful. If authentication is successful, the server returns access permission to the user, and the device closes the login screen and displays the next interface.

[0222] Input: User ID, Password

[0223] Output: Authentication success / failure, access granted

[0224] Step 2: Start the AI ​​interview

[0225] The device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. The user's input is saved on the device in real time and transferred sequentially to the server.

[0226] Input: User's career vision and desires

[0227] Output: Saved input data

[0228] Step 3: Collect and analyze data

[0229] The server receives the input data sequentially and analyzes it using a generative AI model. Specifically, it analyzes the text data entered by the user using natural language processing technology to extract the user's skill set and desired conditions.

[0230] Input: Data on the user's career vision and aspirations

[0231] Output: Extracted skillsets and desired conditions

[0232] Step 4: Skills matching

[0233] The server retrieves the skill requirements data for each robot from the company's database based on the extracted skill set and desired conditions. Using a generative AI model, it compares the user's skill set with the required skills of each robot and generates a matching score.

[0234] Input: User's skill set and desired conditions, skill requirements data from internal database

[0235] Output: Matching score

[0236] Step 5: Generate and present placement plans

[0237] The server generates an optimal placement plan based on the generated matching score. The plan is sent to the terminal and displayed to the user. The user checks the plan and clicks the approve button if they are satisfied.

[0238] Input: Matching score

[0239] Output: Optimal layout plan

[0240] Step 6: Approval of the layout plan

[0241] Once the user approves the placement plan, the selection information is sent from the device to the server, which then updates the approved placement plan in the company database to officially reflect the user's placement information.

[0242] Input: User approval

[0243] Output: Updated placement information

[0244] Step 7: Regular meetings and feedback

[0245] The server periodically schedules AI interviews for users and department personnel and issues reminders. The device receives the reminders, notifies the user, and conducts new interviews. The server analyzes feedback from the new interviews and compares them with the previous deployment plan to make any necessary improvements.

[0246] Input: Feedback data

[0247] Output: Improved placement proposal

[0248] By processing each step sequentially in this way, the entire system functions and optimal allocation of human resources and robot operators within the factory is achieved.

[0249] 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.

[0250] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and also combines it with an emotion engine that recognizes user emotions. By taking employee emotions into consideration, this invention achieves more accurate human resource matching and department allocation.

[0251] User authentication

[0252] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[0253] AI interviews begin

[0254] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[0255] Data collection and analysis

[0256] The device sequentially collects the user's input data and sends it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[0257] Emotion recognition

[0258] The server uses an emotion engine to analyze the emotional data expressed by the user during the interaction. The emotion engine uses natural language processing technology and machine learning to identify the user's emotional state (e.g., joy, anxiety, excitement, etc.).

[0259] Skills Matching

[0260] The server retrieves the skill requirements of each department from the company database, compares the user's skill set with the skill requirements of each department, and calculates a matching score, taking into account the recognized emotional data.

[0261] Generation and presentation of layout plans

[0262] The server generates optimal placement proposals based on the matching results and the user's emotional data. For example, if the user is feeling anxious, it may suggest a department with strong support. The server sends the generated placement proposals to the device, which then displays the proposals to the user, who then confirms them.

[0263] Approval and implementation of layout plan

[0264] If the user approves the placement plan, the device sends the selection to the server, which then updates the database with the approved placement plan, officially reflecting the user's placement information. During this process, the user's emotional data is also saved as a log.

[0265] Regular interviews and feedback

[0266] The server periodically schedules AI interviews for users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[0267] Specific examples

[0268] Example 1: Department Personnel Case

[0269] A departmental employee inputs the skill set required for a new project into the AI. If the AI ​​detects signs of anxiety in the user, it takes that emotional data into account and prioritizes departments and work environments with strong support systems as candidates.

[0270] Example 2: Employee case

[0271] An employee can enter "I want to gain project management experience" as their career vision. The server analyzes the text and suggests suitable departments. Furthermore, if the user is highly excited or motivated, it will prioritize positions that allow them to demonstrate leadership skills.

[0272] This system takes into account the user's skills and career vision, and also analyzes emotional data to achieve more optimal and satisfying personnel placement.

[0273] The processing flow will be explained below.

[0274] Step 1:

[0275] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[0276] Step 2:

[0277] The terminal sends the received authentication information to the server.

[0278] Step 3:

[0279] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[0280] Step 4:

[0281] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[0282] Step 5:

[0283] Users input their requests and career vision through the AI ​​interview interface.

[0284] Step 6:

[0285] The terminal sequentially collects the user's input data and sends it to the server.

[0286] Step 7:

[0287] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[0288] Step 8:

[0289] The server uses an emotion engine to analyze emotional data from the user's dialogue and recognize the user's emotional state (happiness, anxiety, excitement, etc.).

[0290] Step 9:

[0291] The server retrieves the skill requirement data for each department from the company database.

[0292] Step 10:

[0293] The server compares the user's skill set with the skill requirements of each department and calculates a matching score, taking into account emotional data to achieve highly accurate matching results.

[0294] Step 11:

[0295] The server generates optimal placement suggestions based on the matching results and emotional data. For example, if the user is feeling anxious, it will suggest a department with strong support.

[0296] Step 12:

[0297] The server transmits the generated placement plan to the terminal.

[0298] Step 13:

[0299] The device displays the layout plan to the user, and the user confirms the layout plan.

[0300] Step 14:

[0301] If the user approves of the placement proposal, the device transmits the selection to the server.

[0302] Step 15:

[0303] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[0304] Step 16:

[0305] The server schedules regular AI interviews with users and department personnel.

[0306] Step 17:

[0307] The device will send reminders to the user for regular appointments.

[0308] Step 18:

[0309] Users participate in regular AI interviews and enter new feedback and requests.

[0310] Step 19:

[0311] The terminal transmits new interview data to the server.

[0312] Step 20:

[0313] The server analyzes new interview data and emotional data and improves placement proposals based on the feedback.

[0314] Specifically, if a user expresses anxiety, the server will recognize this using its emotion engine and suggest departments with a good support system. If a user expresses a desire to gain project management experience and is enthusiastic, the server will prioritize suggesting positions where they can demonstrate leadership.

[0315] This processing step allows the system to best reflect the user's skills and preferences, and also takes emotional data into account, allowing for better optimization of talent allocation across the organization.

[0316] Example 2

[0317] 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."

[0318] With conventional systems, it was difficult to assign employees to appropriate departments by taking into account their emotions as well as their skills and career vision.Assignment that ignores employee emotions can lead to a decrease in motivation and productivity, there is a need for a more accurate human resource matching system that utilizes emotional data.

[0319] 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.

[0320] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data using a generative AI model and extracting the user's skill set and desired conditions, means for analyzing the user's emotional data using an emotion recognition engine, means for acquiring skill requirement data from an internal database and matching the user's skill set and emotional data with consideration, means for generating an optimal placement plan based on the matching results and emotional data and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan to the database, means for scheduling regular AI interviews with the user and department staff, and means for analyzing new feedback and improving the placement plan. This enables optimal department placement that takes into account not only employees' skills and career vision but also their emotions.

[0321] "User Credentials" means the identification information a User provides to access a System, typically including a User ID and password.

[0322] A "user interface" is a software component that provides a screen and operating means for users to input information into a system.

[0323] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate information, specifically using natural language processing and machine learning technologies.

[0324] "Emotion recognition engine" refers to software technology for identifying emotional states from user input data and interaction data.

[0325] "Skill Set" means the collection of technical and professional abilities and knowledge possessed by a User.

[0326] "Desired conditions" refer to the requests and conditions that users have regarding their career vision and work environment.

[0327] "Internal database" means a database system that manages various data stored within an organization.

[0328] "Skill requirements data" refers to data on the skills and abilities required in each department or project.

[0329] "Matching score" refers to the degree of compatibility expressed as a number, obtained by comparing the user's skill set and emotional data with the department's skill requirements data.

[0330] "Placement Proposal" means a proposal for placing a User in a particular department or position.

[0331] "Feedback" refers to opinions and evaluation information obtained from users and department staff.

[0332] "Regular AI interviews" refer to interviews that are conducted based on a pre-set schedule and involve dialogue between the AI ​​and users or department staff.

[0333] The system of the present invention optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and furthermore, it combines an emotion engine that recognizes user emotions. This allows for more accurate human resource matching and department allocation by taking employee emotions into consideration.

[0334] The system mainly uses the following hardware and software:

[0335] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[0336] Software: User authentication systems, user interfaces (UI), generative AI models (e.g., GPT-3 (registered trademark)), emotion recognition engines, database systems

[0337] The server receives the user authentication information and performs authentication. The user sends a login request from the terminal, and the server authenticates it by checking it against a database. For example, the terminal displays a login form and asks the user to enter their user ID and password.

[0338] If authentication is successful, the device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. Specific prompts include the question, "What are your thoughts about your future career?"

[0339] The device sequentially sends the user's input data to the server. The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. For example, if a user inputs "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[0340] The server uses an emotion recognition engine to analyze emotional data from the user's input text. The emotion recognition engine uses natural language processing technology and machine learning to identify the user's emotional state. For example, if a user inputs "I've been feeling anxious lately," the emotion recognition engine will identify the emotion "anxiety."

[0341] The server retrieves the skill requirements of each department from the company database and matches them with the user's skill set and emotional data. For example, if the user's skill set is "programming, data analysis" and the department's requirements are "programming, database management," it calculates a matching score.

[0342] Based on the matching results and emotion data, the server generates an optimal placement plan and sends it to the device. The device displays the placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server. The server then updates the database with the approved placement plan, officially reflecting the user's new placement information. During this process, the user's emotion data is also saved as a log.

[0343] The server periodically schedules AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[0344] The above is a specific description of the embodiment of the present invention, which enables optimal department allocation taking into account the skills, career vision, and emotions of employees.

[0345] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0346] Processing Steps

[0347] Step 1: User authentication

[0348] 1. Input: User ID, Password

[0349] 2. The terminal receives the user ID and password from the user. Specifically, the user enters the ID and password into the login form.

[0350] 3. Output: Authentication information (user ID, password)

[0351] 4. The device sends the received authentication information to the server.

[0352] 5. The server authenticates the user against a database.

[0353] 6. Output: Authentication result (success / failure)

[0354] 7. If the server is successful, it starts a session and returns a successful authentication message to the terminal. Specifically, the server generates a new session ID and sends it to the user.

[0355] 8. The device displays a successful authentication message.

[0356] Step 2: Start the AI ​​interview

[0357] 1. Input: None (automatic process after authentication)

[0358] 2. The device displays the AI ​​interview interface to the user. Specifically, it displays a chatbot-style UI and presents the prompt, "What are your thoughts about your future career?"

[0359] 3. Output: User response (text)

[0360] 4. The user clicks the Start Interview button and begins entering their requests and career vision.

[0361] Step 3: Collect and analyze data

[0362] 1. Input: User response data (text)

[0363] 2. The device collects user response data in real time and sends it to the server via API.

[0364] 3. Output: User data sent to the server

[0365] 4. The server uses a generative AI model (e.g., GPT-3) to analyze the user data and extract skill sets and desired conditions. For example, if a user enters "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[0366] 5. Output: Extracted skill sets and desired conditions

[0367] Step 4: Recognize emotions

[0368] 1. Input: User response data (text)

[0369] 2. The server uses an emotion recognition engine to analyze user data and identify their emotional state. Specifically, it analyzes text using natural language processing technology and machine learning.

[0370] 3. Output: Emotion data (e.g., happiness, anxiety, excitement, etc.)

[0371] Step 5: Skills matching

[0372] 1. Input: User's skill set, desired conditions, and emotional data

[0373] 2. The server retrieves skill requirements data from the internal database and compares the user's skill set with the sentiment data, using SQL queries to retrieve the required data and running a matching algorithm.

[0374] 3. Output: Matching score

[0375] Step 6: Generate and present placement plans

[0376] 1. Input: Matching score, emotion data

[0377] 2. The server generates the optimal placement plan. For example, if the user is feeling anxious, it will suggest a department with strong support.

[0378] 3. Output: Layout plan

[0379] 4. The server sends the generated placement plan to the terminal.

[0380] 5. The device displays the layout plan to the user, who then confirms it.

[0381] Step 7: Approve and implement the layout plan

[0382] 1. Input: Select to approve the layout plan

[0383] 2. If the user approves the placement proposal, the device sends the selection to the server.

[0384] 3. Output: Authorization information

[0385] 4. The server updates the database with the approved placement plan, officially reflecting the user's new placement information.

[0386] 5. Output: Database update results

[0387] Step 8: Regular meetings and feedback

[0388] 1. Input: None (regular schedule)

[0389] 2. The server periodically schedules AI interviews with users and department personnel and sends reminders.

[0390] 3. Output: Reminder notification

[0391] 4. The device will notify the user of the reminder and arrange a new appointment.

[0392] 5. Output: New interview data

[0393] 6. The server analyzes the new interview data and improves placement recommendations based on feedback and emotional data.

[0394] (Application example 2)

[0395] 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."

[0396] While traditional employee management systems can take into account an employee's skill set and career vision, they are unable to take into account their emotional state. This has resulted in insufficient allocation of employees to appropriate departments and improved employee satisfaction. Furthermore, advanced matching is required, such as recognizing employee emotions in real time during interactive AI interviews and reflecting that information in allocation proposals.

[0397] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and allowing the user to input their requests and career vision in an interactive format, means for analyzing the input data and emotional data using natural language processing technology and machine learning, means for acquiring skill requirement data from an internal database and matching it with the user's skill set, and means for calculating a matching score taking the emotional data into consideration, generating an optimal placement plan, and presenting it to the user. This enables optimal department placement that takes into consideration both the skills and emotional state of employees.

[0398] "User Credentials" means the identification and authentication information provided by a User to access a System.

[0399] A "user interface" is a visual and functional interface through which a user interacts with a system.

[0400] "Dialogue" refers to the way in which a user interacts with a system and provides information through a series of questions and responses.

[0401] "Career vision" refers to the job and skill goals that users aim for in the future.

[0402] "Natural language processing technology" is a technology that allows computers to process and understand human language.

[0403] "Machine learning" is a technique that uses algorithms to allow computers to learn from data and make predictions or classifications.

[0404] "Emotional data" is data that evaluates and quantifies a user's emotions.

[0405] A "skill set" is the collection of knowledge, skills, and abilities that a user possesses.

[0406] "Matching score" is a score calculated as a result of comparing a user's skill set with the skill requirement data in the company's database.

[0407] "Placement Suggestions" are the most suitable departments or jobs suggested based on the user's skill set, career vision, and sentiment data.

[0408] An "internal database" is a database system that stores and provides data managed within a company.

[0409] "AI interview" is a process that uses artificial intelligence to interact with users and collect and analyze information.

[0410] "Feedback" refers to evaluations and improvement suggestions generated based on collected information and analysis results.

[0411] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. In particular, by combining it with an emotion engine that recognizes user emotions, it achieves more accurate human resource matching and department allocation.

[0412] User authentication

[0413] The server receives the user credentials and authenticates the user against a database. Once the user is authenticated, a session begins.

[0414] User Interface and AI Interviews

[0415] After authentication, the terminal provides a user interface where the user can interactively input their desires and career vision. The data entered by the user is then sent to the server.

[0416] Data collection and analysis

[0417] The server analyzes the data sent by the user using a generative AI model to extract the user's skill set and desired conditions, and simultaneously analyzes the emotional data using an emotion engine, which uses natural language processing technology and machine learning to identify the user's emotional state.

[0418] Skill matching and placement plan generation

[0419] The server retrieves the skill requirements data of each department from the company database and matches the user's skill set with the skill requirements of each department. In doing so, it calculates a matching score taking into account the emotional data, generates an optimal placement plan, and presents it to the user.

[0420] Approval and implementation of layout plan

[0421] If the user approves the proposed placement, the server updates the database with the user's selection, officially reflecting the user's placement information. The server also logs the user's emotional data.

[0422] Regular interviews and feedback

[0423] The server schedules regular AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[0424] Specific examples

[0425] For example, if an employee types in "I want to improve my customer service skills" and the emotion engine recognizes that they are feeling anxious, it will prioritize suggesting departments with extensive training for that employee.

[0426] Prompt Sentence Examples

[0427] "Create a Python program that allows an AI interview system to suggest an appropriate department if an employee enters, 'My career vision is to improve my customer service skills' at a brick-and-mortar retail store, and the emotion engine recognizes that the employee is feeling anxious."

[0428] This invention combines users' skills, career vision, and emotional data to realize more accurate personnel allocation. For example, by using smartphones and tablets, it can improve the work efficiency and satisfaction of employees in brick-and-mortar stores.

[0429] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0430] Step 1:

[0431] The server receives the user authentication information and authenticates the user by checking it against a database. The input is the user ID and password, and the output is the authentication result (success or failure). If authentication is successful, the server starts a session and generates a session token.

[0432] Step 2:

[0433] After authentication, the terminal provides a user interface and allows the user to interactively input their aspirations and career vision. The input is text data related to the user's aspirations and career vision, and the output is the data entered by the user.

[0434] Step 3:

[0435] The server receives the user's input data sent from the device and analyzes it using a generative AI model. The input is the user's text data, and the output is the user's skill set and desired conditions. Specifically, the generative AI model analyzes the text data and extracts the user's skills and desired conditions.

[0436] Step 4:

[0437] The server passes the analyzed text data to the emotion engine, which analyzes the emotion data. The input is the user's text data, and the output is the user's emotional state (e.g., joy, anxiety, excitement, etc.). The emotion engine uses natural language processing technology and machine learning to identify emotions from the text data.

[0438] Step 5:

[0439] The server retrieves the skill requirement data of each department from the company database and matches the user's skill set with the skill requirements of each department. The input is the user's skill set and desired conditions, and the skill requirement data of each department, and the output is the matching result. Specifically, the server compares the skill set with the skill requirements and calculates a matching score based on the degree of match.

[0440] Step 6:

[0441] The server then modifies the matching score by taking into account the emotional data and generates the optimal placement proposal. The input is the matching result and emotional data, and the output is the optimal placement proposal. For example, if the user is feeling anxious, the server will prioritize departments that offer strong support.

[0442] Step 7:

[0443] The server presents the generated placement plan to the user via the terminal and asks for the user's approval. The input is the optimal placement plan, and the output is the user's approval or rejection.

[0444] Step 8:

[0445] If the user approves the proposed placement, the server updates the database with that selection to officially reflect the user's placement information. The input is the user's approval, and the output is the updated database information.

[0446] Step 9:

[0447] The server schedules regular AI interviews for users and department personnel and sends reminders. The input is the interview schedule and the output is the reminder. The terminal notifies the user of the reminder and schedules a new interview.

[0448] Step 10:

[0449] The server analyzes new interview data and improves placement suggestions based on feedback and emotional data. The input is new interview data and emotional data, and the output is improved placement suggestions and feedback. This process continuously ensures optimal placement based on changes in the user's skills and emotions.

[0450] 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.

[0451] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0452] 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.

[0453] [Second embodiment]

[0454] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0455] 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.

[0456] 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).

[0457] 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.

[0458] 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.

[0459] 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).

[0460] 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.

[0461] 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.

[0462] 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.

[0463] 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.

[0464] 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.

[0465] 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."

[0466] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with the needs of their departments. This system allows users to interactively input their requests and career visions, and uses AI to propose optimal placement plans. Below, we will explain the program processing of this system in natural language and provide detailed explanations with concrete examples.

[0467] User authentication

[0468] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the terminal allows the user access.

[0469] AI interviews begin

[0470] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[0471] Data extraction and analysis

[0472] The device collects input data sequentially and transfers it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[0473] Skills Matching

[0474] The server retrieves the skill requirements data for each department from the company database, and then uses the generation AI to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[0475] Generation and presentation of layout plans

[0476] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[0477] Implementing the placement plan

[0478] The server updates the database with the approved placement plan, officially reflecting the user's placement information, thereby optimizing the company's overall human resources.

[0479] Regular interviews and feedback

[0480] The server periodically schedules AI interviews with users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[0481] Specific examples

[0482] Example 1: Department Personnel Case

[0483] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[0484] Example 2: Employee case

[0485] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[0486] This system effectively takes into account the user's skills and career vision, proposes optimal personnel allocation, and improves corporate performance.

[0487] The processing flow will be explained below.

[0488] Step 1:

[0489] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[0490] Step 2:

[0491] The terminal sends the received authentication information to the server.

[0492] Step 3:

[0493] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[0494] Step 4:

[0495] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[0496] Step 5:

[0497] Users input their requests and career vision through the AI ​​interview interface.

[0498] Step 6:

[0499] The terminal sequentially collects the user's input data and sends it to the server.

[0500] Step 7:

[0501] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[0502] Step 8:

[0503] The server retrieves the skill requirement data for each department from the company database.

[0504] Step 9:

[0505] The server compares the user's skill set with the skill requirements of each department and calculates a matching score.

[0506] Step 10:

[0507] The server generates an optimal placement plan based on the matching results.

[0508] Step 11:

[0509] The server transmits the generated placement plan to the terminal.

[0510] Step 12:

[0511] The device displays the layout plan to the user, and the user confirms the layout plan.

[0512] Step 13:

[0513] If the user approves of the placement proposal, the device transmits the selection to the server.

[0514] Step 14:

[0515] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[0516] Step 15:

[0517] The server schedules regular AI interviews with users and department personnel.

[0518] Step 16:

[0519] The device will send reminders to the user for regular appointments.

[0520] Step 17:

[0521] The device will conduct a new AI interview with the user.

[0522] Step 18:

[0523] The server analyzes the new interview data and improves placement proposals based on the feedback.

[0524] This process allows the system to best reflect the user's skills and preferences, optimizing the allocation of human resources across the organization.

[0525] Example 1

[0526] 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."

[0527] Optimal management of human resources within a company relies on efficiently matching employees' career visions with the needs of each department. However, traditional methods require manually collecting and analyzing information to find optimal allocations, which takes time and effort, and often requires subjective judgment. Furthermore, regular feedback and allocation improvements are not properly implemented, making it difficult to maximize corporate performance. A solution to these issues is needed.

[0528] 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.

[0529] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an in-house database and matching it with the user's skill set, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department staff, means for analyzing new feedback and improving the placement plan, and means for calculating a matching score using a generative AI model using prompt sentences and generating an optimal placement plan based on the score. This enables optimal management of human resources.

[0530] "User authentication information" means authentication information such as ID and password used by a user when accessing a system.

[0531] A "user interface" is a function that provides a screen and operation methods for a user to interact with a system.

[0532] "Dialogue" refers to a format in which the user and the system exchange information alternately using text, voice, etc.

[0533] "Requests" refer to the hopes, functions, and services that users desire from the system.

[0534] "Career vision" refers to the goals and future plans that users have for their occupation or career.

[0535] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze input data and generate results according to a desired purpose.

[0536] A "prompt" is text that is input to a generative AI model to instruct it on specific analysis or generation.

[0537] An "in-house database" is a database system that structures and stores information managed within a company.

[0538] A "skill set" is the collection of knowledge, skills, and experience that an individual possesses.

[0539] "Skill requirements data" is information about the specific skills and abilities required by each department or project.

[0540] The "matching score" is a numerical representation of the degree of match between the user's skill set and the company's skill requirement data.

[0541] A "placement proposal" is a proposal for placing a user in a specific department or project.

[0542] "Feedback" refers to opinions and evaluations obtained from system users and department staff, and is information used to improve the system and deployment plans.

[0543] An "AI interview" is an interview conducted with a user or departmental staff member using artificial intelligence, and is conducted in an interactive format.

[0544] The present invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. This system has the function of allowing users to interactively input their requests and career visions and then using a generative AI model to propose optimal placement plans. Specific embodiments are described in detail below.

[0545] Overall system configuration

[0546] This system is primarily composed of terminals, servers, and an in-house database. Specific examples of the hardware and software used include PCs (Windows 10, macOS), tablets (iPad, Android), and smartphones (iOS, Android). AWS EC2, Microsoft Azure, and Google Cloud Platform are used for servers. OpenAI GPT-4, Google BERT, and Microsoft Turing are used as generative AI models.

[0547] User authentication

[0548] First, the terminal receives a login request from the user and prompts them to enter their user ID and password. The user authentication information is sent to the server using an HTTP POST request, and the server checks this information against the company's database. If there is a match, authentication is successful and the terminal allows the user access.

[0549] AI interviews begin

[0550] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to enter their requests and career vision in a dialogue format. This input is done through a chatbot-style user interface.

[0551] Data collection and analysis

[0552] The device collects the input data sequentially and transmits it to the server using an HTTP POST request. The server then uses a generative AI model to analyze the received data and extract the user's skill set and desired conditions.

[0553] Skills Matching

[0554] The server retrieves the skill requirements data for each department from the company's internal database, and uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[0555] Generation and presentation of layout plans

[0556] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[0557] Implementing the placement plan

[0558] The server updates the approved assignment plan to the company database, officially reflecting the user's new assignment information, thereby optimizing the company's overall human resources.

[0559] Regular interviews and feedback

[0560] The server periodically schedules AI interviews with users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[0561] Examples of concrete examples and prompts

[0562] Example: Department Manager

[0563] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[0564] Example: Employee case

[0565] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[0566] Prompt Sentence Examples

[0567] "We are looking for an employee with the following skill set: Python programming, data analysis experience, and team leadership experience. Please recommend the right candidate."

[0568] "My career vision is to gain experience in project management. Please recommend me to a suitable department."

[0569] Through the above explanation, we can see that this system can optimally manage human resources within a company and efficiently match employees' career visions with the needs of their departments.

[0570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0571] Step 1:

[0572] Gathering user credentials

[0573] The terminal receives a login request from the user and prompts them to enter their user ID and password. The user ID and password are entered into the terminal as input. The terminal receives this information and temporarily stores the authentication information within the terminal.

[0574] Step 2:

[0575] Sending user credentials

[0576] The device sends the collected user authentication information to the server. The data is processed by embedding the entered user ID and password in an HTTP POST request and sending it to the server. The request containing the authentication information reaches the server as output.

[0577] Step 3:

[0578] Enforce user authentication

[0579] The server compares the received user authentication information with data in the company's database and performs authentication. For data calculations, the server uses an SQL query to search for user information in the database. The input is the user ID and password, and the output is the result of authentication success or failure. If authentication is successful, the server returns a response indicating successful authentication.

[0580] Step 4:

[0581] Notification of authentication results

[0582] The terminal receives the authentication result from the server and notifies the user. The input is the authentication result from the server, and the output is a screen notifying the user of authentication success or failure. For example, it may display "Login successful" and a dashboard screen for the user to proceed to the next step.

[0583] Step 5:

[0584] Display of AI interview interface

[0585] After successful authentication, the device displays the AI ​​interview interface to the user. The input is the user information after successful authentication, and the output is the AI ​​interview interface screen. The interview begins when the user clicks the start button for the AI ​​interview.

[0586] Step 6:

[0587] Starting a conversation

[0588] The user begins to input their requests and career vision using the AI ​​interview interface. The input is the user's interactive input data, and the output is the user's requests and career vision in text form. The data entered by the user is temporarily stored on the device.

[0589] Step 7:

[0590] Data collection

[0591] The terminal sequentially transfers the input data to the server. The input is the user's interactive input data, and the output is an HTTP POST request containing the collected data. The terminal sends the data to the server at regular intervals.

[0592] Step 8:

[0593] Data analysis

[0594] The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. The input is the user's interactive input data, and the output is the analyzed skill set and desired conditions. For example, a request such as "I want to gain project management experience" can be extracted.

[0595] Step 9:

[0596] Get skill request data

[0597] The server retrieves the skill requirement data of each department from the company's database. There is no input, and the output is the skill requirement data of each department. The necessary information is retrieved from the database using an SQL query.

[0598] Step 10:

[0599] Matching Score Calculation

[0600] The server uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score. The input is the user skill set and the skill requirements data of each department, and the output is the matching score. The comparison calculation quantifies which department is the most suitable.

[0601] Step 11:

[0602] Generate placement proposals

[0603] The server generates an optimal placement plan based on the matching results. The input is the matching score, and the output is the placement plan. For example, it generates a specific proposal such as "place in the data analysis department."

[0604] Step 12:

[0605] Proposal of layout plan

[0606] The server sends the details of the generated placement plan to the terminal, which then displays it to the user. The input is the placement plan data, and the output is the placement plan displayed on the user's screen. An interface is displayed for the user to check the placement plan.

[0607] Step 13:

[0608] Review and approve the layout plan

[0609] The user checks the placement proposal and approves it if it meets their needs. The input is the placement proposal, and the output is the result of approval or rejection. For example, the approval process is completed by clicking the "Approve placement proposal" button.

[0610] Step 14:

[0611] Update placement information

[0612] The server updates the approved deployment plan to the company database. The input is the approval result, and the output is the updated user deployment information. The new deployment information is recorded in the database, officially reflecting the user's deployment.

[0613] Step 15:

[0614] Schedule an interview

[0615] The server periodically schedules AI interviews with users and department staff. The input is user and staff information, and the output is schedule settings. For example, an interview is scheduled for the first Monday of every month.

[0616] Step 16:

[0617] Reminder notifications

[0618] The device notifies the user of reminders and schedules new appointments. The input is schedule information, and the output is the reminder sent to the user. For example, a notification saying "Don't forget about next week's appointment" is sent one week before the appointment.

[0619] Step 17:

[0620] Feedback Analysis

[0621] The server analyzes the new feedback, compares it with the previous placement plan, and makes improvements as necessary. The input is the feedback information, and the output is the improved placement plan. For example, analyzing the feedback "I'm satisfied with my new department" will identify areas for further improvement.

[0622] (Application example 1)

[0623] 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."

[0624] In conventional factories, it was difficult to assign personnel in a way that effectively reflected the skills and career vision of workers. This resulted in problems such as a decline in production efficiency and a decrease in worker motivation. In addition, it was not possible to accurately match the skill requirements of each robot with the skills of workers, and there was a lack of concrete measures to achieve optimal assignment.

[0625] 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.

[0626] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's needs and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an internal database and matching it with the user's skill set, means for generating an optimal placement plan based on the matching results and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department personnel, means for analyzing new feedback and improving the placement plan, means for inputting the skills and career vision of a robot operator and matching it with the skill requirements of each robot in the factory, and means for generating and presenting an optimal placement plan for robot operators using a generative AI model. This enables optimal personnel allocation within the factory, which is expected to improve production efficiency and worker motivation.

[0627] "User authentication information" means information provided by a system user to identify themselves, and typically includes a user ID and password.

[0628] "User interface" refers to the screen or means of interaction that allows a user to interact with a system, including input forms, buttons, etc.

[0629] "Dialogue" refers to an interface method that proceeds in the form of questions and answers, and is used by users to input their wishes and career vision.

[0630] A "skill set" refers to the collection of skills and knowledge a user possesses, including the abilities necessary to carry out specific tasks.

[0631] "Career vision" is a concept that represents the professional goals and desired career path that a user wants to achieve in the future.

[0632] "Skill requirements data" refers to a list of specific skills and knowledge that each organization or robot requires to perform its work.

[0633] The "matching score" is a numerical indicator that represents the degree of match between the user's skill set and the skill requirement data, and serves as a criterion for generating optimal placement plans.

[0634] "Generative AI model" refers to an artificial intelligence model used to analyze data and generate optimal placement proposals based on criteria.

[0635] "Feedback" refers to opinions or evaluations provided to the system by users or department personnel that are useful for improving the system.

[0636] "Robot operator" refers to a worker whose role is to operate and manage robots within a factory.

[0637] "Optimal placement proposal" refers to the most efficient personnel placement proposal generated based on the user's skill set, career vision, and skill requirement data.

[0638] This system maximizes production efficiency by optimally matching factory human resources with the skills of robot operators. The system provides an application that workers can access from their smartphones or smart glasses, and allows them to input their own skills and career vision through the application.

[0639] The system program is configured as follows: First, the server receives user authentication information and performs authentication. Specifically, the worker logs in to their smartphone or smart glasses and enters their ID and password. This authentication information is sent to the server and checked against the database.

[0640] After successful authentication, the terminal provides a user interface where the worker's requests and career vision can be input interactively. Once the worker enters their career vision interactively, the input data is transferred to the server.

[0641] The server analyzes the input data using a generative AI model to extract the user's skill set and desired conditions. The server then retrieves the skill requirements of each robot from an internal database and matches them with the worker's skill set. Based on the results of this matching, the server generates an optimal deployment plan and presents it to the worker via their terminal. Once the worker approves the deployment plan, the information is sent back to the server and updated in the database.

[0642] The server also periodically schedules and reminds users and department personnel for AI interviews. When new feedback is collected, the server analyzes it, compares it with the previous deployment plan, and makes any necessary improvements.

[0643] As a concrete example, consider the case where Worker A enters into the app that his / her career vision is "I want to learn robot programming techniques." This input is accepted by the terminal and sent to the server. The server analyzes this text and finds the corresponding robot operation task. It then presents this task to Worker A, who then approves the proposal and is assigned to the new task.

[0644] Example prompt sentence:

[0645] "A user enters, 'I want to learn robot programming skills.' Find the appropriate department and task, and suggest the best placement for the user."

[0646] The above configuration includes the following hardware and software: smartphones or smart glasses (e.g., Google Glass, Vuzix Blade), factory servers (e.g., Dell PowerEdge series), user authentication systems (e.g., OAuth 2.0), databases (e.g., MySQL, PostgreSQL), generative AI models (e.g., GPT-4, BERT), and front-end UI (e.g., React Native).

[0647] This will enable optimal allocation of personnel and robot operations within the factory, which is expected to improve production efficiency.

[0648] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0649] Step 1: User authentication

[0650] The user accesses the login screen from the device using a smartphone or smart glasses. The user enters their ID and password, and the device receives the information. The received authentication information is sent to the server, which compares it with a database to determine whether authentication is successful. If authentication is successful, the server returns access permission to the user, and the device closes the login screen and displays the next interface.

[0651] Input: User ID, Password

[0652] Output: Authentication success / failure, access granted

[0653] Step 2: Start the AI ​​interview

[0654] The device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. The user's input is saved on the device in real time and transferred sequentially to the server.

[0655] Input: User's career vision and desires

[0656] Output: Saved input data

[0657] Step 3: Collect and analyze data

[0658] The server receives the input data sequentially and analyzes it using a generative AI model. Specifically, it analyzes the text data entered by the user using natural language processing technology to extract the user's skill set and desired conditions.

[0659] Input: Data on the user's career vision and aspirations

[0660] Output: Extracted skillsets and desired conditions

[0661] Step 4: Skills matching

[0662] The server retrieves the skill requirements data for each robot from the company's database based on the extracted skill set and desired conditions. Using a generative AI model, it compares the user's skill set with the required skills of each robot and generates a matching score.

[0663] Input: User's skill set and desired conditions, skill requirements data from internal database

[0664] Output: Matching score

[0665] Step 5: Generate and present placement plans

[0666] The server generates an optimal placement plan based on the generated matching score. The plan is sent to the terminal and displayed to the user. The user checks the plan and clicks the approve button if they are satisfied.

[0667] Input: Matching score

[0668] Output: Optimal layout plan

[0669] Step 6: Approval of the layout plan

[0670] Once the user approves the placement plan, the selection information is sent from the device to the server, which then updates the approved placement plan in the company database to officially reflect the user's placement information.

[0671] Input: User approval

[0672] Output: Updated placement information

[0673] Step 7: Regular meetings and feedback

[0674] The server periodically schedules AI interviews for users and department personnel and issues reminders. The device receives the reminders, notifies the user, and conducts new interviews. The server analyzes feedback from the new interviews and compares them with the previous deployment plan to make any necessary improvements.

[0675] Input: Feedback data

[0676] Output: Improved placement proposal

[0677] By processing each step sequentially in this way, the entire system functions and optimal allocation of human resources and robot operators within the factory is achieved.

[0678] 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.

[0679] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and also combines it with an emotion engine that recognizes user emotions. By taking employee emotions into consideration, this invention achieves more accurate human resource matching and department allocation.

[0680] User authentication

[0681] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[0682] AI interviews begin

[0683] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[0684] Data collection and analysis

[0685] The device sequentially collects the user's input data and sends it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[0686] Emotion recognition

[0687] The server uses an emotion engine to analyze the emotional data expressed by the user during the interaction. The emotion engine uses natural language processing technology and machine learning to identify the user's emotional state (e.g., joy, anxiety, excitement, etc.).

[0688] Skills Matching

[0689] The server retrieves the skill requirements of each department from the company database, compares the user's skill set with the skill requirements of each department, and calculates a matching score, taking into account the recognized emotional data.

[0690] Generation and presentation of layout plans

[0691] The server generates optimal placement proposals based on the matching results and the user's emotional data. For example, if the user is feeling anxious, it may suggest a department with strong support. The server sends the generated placement proposals to the device, which then displays the proposals to the user, who then confirms them.

[0692] Approval and implementation of layout plan

[0693] If the user approves the placement plan, the device sends the selection to the server, which then updates the database with the approved placement plan, officially reflecting the user's placement information. During this process, the user's emotional data is also saved as a log.

[0694] Regular interviews and feedback

[0695] The server periodically schedules AI interviews for users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[0696] Specific examples

[0697] Example 1: Department Personnel Case

[0698] A departmental employee inputs the skill set required for a new project into the AI. If the AI ​​detects signs of anxiety in the user, it takes that emotional data into account and prioritizes departments and work environments with strong support systems as candidates.

[0699] Example 2: Employee case

[0700] An employee can enter "I want to gain project management experience" as their career vision. The server analyzes the text and suggests suitable departments. Furthermore, if the user is highly excited or motivated, it will prioritize positions that allow them to demonstrate leadership skills.

[0701] This system takes into account the user's skills and career vision, and also analyzes emotional data to achieve more optimal and satisfying personnel placement.

[0702] The processing flow will be explained below.

[0703] Step 1:

[0704] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[0705] Step 2:

[0706] The terminal sends the received authentication information to the server.

[0707] Step 3:

[0708] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[0709] Step 4:

[0710] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[0711] Step 5:

[0712] Users input their requests and career vision through the AI ​​interview interface.

[0713] Step 6:

[0714] The terminal sequentially collects the user's input data and sends it to the server.

[0715] Step 7:

[0716] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[0717] Step 8:

[0718] The server uses an emotion engine to analyze emotional data from the user's dialogue and recognize the user's emotional state (happiness, anxiety, excitement, etc.).

[0719] Step 9:

[0720] The server retrieves the skill requirement data for each department from the company database.

[0721] Step 10:

[0722] The server compares the user's skill set with the skill requirements of each department and calculates a matching score, taking into account emotional data to achieve highly accurate matching results.

[0723] Step 11:

[0724] The server generates optimal placement suggestions based on the matching results and emotional data. For example, if the user is feeling anxious, it will suggest a department with strong support.

[0725] Step 12:

[0726] The server transmits the generated placement plan to the terminal.

[0727] Step 13:

[0728] The device displays the layout plan to the user, and the user confirms the layout plan.

[0729] Step 14:

[0730] If the user approves of the placement proposal, the device transmits the selection to the server.

[0731] Step 15:

[0732] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[0733] Step 16:

[0734] The server schedules regular AI interviews with users and department personnel.

[0735] Step 17:

[0736] The device will send reminders to the user for regular appointments.

[0737] Step 18:

[0738] Users participate in regular AI interviews and enter new feedback and requests.

[0739] Step 19:

[0740] The terminal transmits new interview data to the server.

[0741] Step 20:

[0742] The server analyzes new interview data and emotional data and improves placement proposals based on the feedback.

[0743] Specifically, if a user expresses anxiety, the server will recognize this using its emotion engine and suggest departments with a good support system. If a user expresses a desire to gain project management experience and is enthusiastic, the server will prioritize suggesting positions where they can demonstrate leadership.

[0744] This processing step allows the system to best reflect the user's skills and preferences, and also takes emotional data into account, allowing for better optimization of talent allocation across the organization.

[0745] Example 2

[0746] 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."

[0747] With conventional systems, it was difficult to assign employees to appropriate departments by taking into account their emotions as well as their skills and career vision.Assignment that ignores employee emotions can lead to a decrease in motivation and productivity, there is a need for a more accurate human resource matching system that utilizes emotional data.

[0748] 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.

[0749] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data using a generative AI model and extracting the user's skill set and desired conditions, means for analyzing the user's emotional data using an emotion recognition engine, means for acquiring skill requirement data from an internal database and matching the user's skill set and emotional data with consideration, means for generating an optimal placement plan based on the matching results and emotional data and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan to the database, means for scheduling regular AI interviews with the user and department staff, and means for analyzing new feedback and improving the placement plan. This enables optimal department placement that takes into account not only employees' skills and career vision but also their emotions.

[0750] "User Credentials" means the identification information a User provides to access a System, typically including a User ID and password.

[0751] A "user interface" is a software component that provides a screen and operating means for users to input information into a system.

[0752] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate information, specifically using natural language processing and machine learning technologies.

[0753] "Emotion recognition engine" refers to software technology for identifying emotional states from user input data and interaction data.

[0754] "Skill Set" means the collection of technical and professional abilities and knowledge possessed by a User.

[0755] "Desired conditions" refer to the requests and conditions that users have regarding their career vision and work environment.

[0756] "Internal database" means a database system that manages various data stored within an organization.

[0757] "Skill requirements data" refers to data on the skills and abilities required in each department or project.

[0758] "Matching score" refers to the degree of compatibility expressed as a number, obtained by comparing the user's skill set and emotional data with the department's skill requirements data.

[0759] "Placement Proposal" means a proposal for placing a User in a particular department or position.

[0760] "Feedback" refers to opinions and evaluation information obtained from users and department staff.

[0761] "Regular AI interviews" refer to interviews that are conducted based on a pre-set schedule and involve dialogue between the AI ​​and users or department staff.

[0762] The system of the present invention optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and furthermore, it combines an emotion engine that recognizes user emotions. This allows for more accurate human resource matching and department allocation by taking employee emotions into consideration.

[0763] The system mainly uses the following hardware and software:

[0764] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[0765] Software: User authentication systems, user interfaces (UI), generative AI models (e.g., GPT-3), emotion recognition engines, database systems

[0766] The server receives the user authentication information and performs authentication. The user sends a login request from the terminal, and the server authenticates it by checking it against a database. For example, the terminal displays a login form and asks the user to enter their user ID and password.

[0767] If authentication is successful, the device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. Specific prompts include the question, "What are your thoughts about your future career?"

[0768] The device sequentially sends the user's input data to the server. The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. For example, if a user inputs "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[0769] The server uses an emotion recognition engine to analyze emotional data from the user's input text. The emotion recognition engine uses natural language processing technology and machine learning to identify the user's emotional state. For example, if a user inputs "I've been feeling anxious lately," the emotion recognition engine will identify the emotion "anxiety."

[0770] The server retrieves the skill requirements of each department from the company database and matches them with the user's skill set and emotional data. For example, if the user's skill set is "programming, data analysis" and the department's requirements are "programming, database management," it calculates a matching score.

[0771] Based on the matching results and emotion data, the server generates an optimal placement plan and sends it to the device. The device displays the placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server. The server then updates the database with the approved placement plan, officially reflecting the user's new placement information. During this process, the user's emotion data is also saved as a log.

[0772] The server periodically schedules AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[0773] The above is a specific description of the embodiment of the present invention, which enables optimal department allocation taking into account the skills, career vision, and emotions of employees.

[0774] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0775] Processing Steps

[0776] Step 1: User authentication

[0777] 1. Input: User ID, Password

[0778] 2. The terminal receives the user ID and password from the user. Specifically, the user enters the ID and password into the login form.

[0779] 3. Output: Authentication information (user ID, password)

[0780] 4. The device sends the received authentication information to the server.

[0781] 5. The server authenticates the user against a database.

[0782] 6. Output: Authentication result (success / failure)

[0783] 7. If the server is successful, it starts a session and returns a successful authentication message to the terminal. Specifically, the server generates a new session ID and sends it to the user.

[0784] 8. The device displays a successful authentication message.

[0785] Step 2: Start the AI ​​interview

[0786] 1. Input: None (automatic process after authentication)

[0787] 2. The device displays the AI ​​interview interface to the user. Specifically, it displays a chatbot-style UI and presents the prompt, "What are your thoughts about your future career?"

[0788] 3. Output: User response (text)

[0789] 4. The user clicks the Start Interview button and begins entering their requests and career vision.

[0790] Step 3: Collect and analyze data

[0791] 1. Input: User response data (text)

[0792] 2. The device collects user response data in real time and sends it to the server via API.

[0793] 3. Output: User data sent to the server

[0794] 4. The server uses a generative AI model (e.g., GPT-3) to analyze the user data and extract skill sets and desired conditions. For example, if a user enters "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[0795] 5. Output: Extracted skill sets and desired conditions

[0796] Step 4: Recognize emotions

[0797] 1. Input: User response data (text)

[0798] 2. The server uses an emotion recognition engine to analyze user data and identify their emotional state. Specifically, it analyzes text using natural language processing technology and machine learning.

[0799] 3. Output: Emotion data (e.g., happiness, anxiety, excitement, etc.)

[0800] Step 5: Skills matching

[0801] 1. Input: User's skill set, desired conditions, and emotional data

[0802] 2. The server retrieves skill requirements data from the internal database and compares the user's skill set with the sentiment data, using SQL queries to retrieve the required data and running a matching algorithm.

[0803] 3. Output: Matching score

[0804] Step 6: Generate and present placement plans

[0805] 1. Input: Matching score, emotion data

[0806] 2. The server generates the optimal placement plan. For example, if the user is feeling anxious, it will suggest a department with strong support.

[0807] 3. Output: Layout plan

[0808] 4. The server sends the generated placement plan to the terminal.

[0809] 5. The device displays the layout plan to the user, who then confirms it.

[0810] Step 7: Approve and implement the layout plan

[0811] 1. Input: Select to approve the layout plan

[0812] 2. If the user approves the placement proposal, the device sends the selection to the server.

[0813] 3. Output: Authorization information

[0814] 4. The server updates the database with the approved placement plan, officially reflecting the user's new placement information.

[0815] 5. Output: Database update results

[0816] Step 8: Regular meetings and feedback

[0817] 1. Input: None (regular schedule)

[0818] 2. The server periodically schedules AI interviews with users and department personnel and sends reminders.

[0819] 3. Output: Reminder notification

[0820] 4. The device will notify the user of the reminder and arrange a new appointment.

[0821] 5. Output: New interview data

[0822] 6. The server analyzes the new interview data and improves placement recommendations based on feedback and emotional data.

[0823] (Application example 2)

[0824] 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."

[0825] While traditional employee management systems can take into account an employee's skill set and career vision, they are unable to take into account their emotional state. This has resulted in insufficient allocation of employees to appropriate departments and improved employee satisfaction. Furthermore, advanced matching is required, such as recognizing employee emotions in real time during interactive AI interviews and reflecting that information in allocation proposals.

[0826] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and allowing the user to input their requests and career vision in an interactive format, means for analyzing the input data and emotional data using natural language processing technology and machine learning, means for acquiring skill requirement data from an internal database and matching it with the user's skill set, and means for calculating a matching score taking the emotional data into consideration, generating an optimal placement plan, and presenting it to the user. This enables optimal department placement that takes into consideration both the skills and emotional state of employees.

[0827] "User Credentials" means the identification and authentication information provided by a User to access a System.

[0828] A "user interface" is a visual and functional interface through which a user interacts with a system.

[0829] "Dialogue" refers to the way in which a user interacts with a system and provides information through a series of questions and responses.

[0830] "Career vision" refers to the job and skill goals that users aim for in the future.

[0831] "Natural language processing technology" is a technology that allows computers to process and understand human language.

[0832] "Machine learning" is a technique that uses algorithms to allow computers to learn from data and make predictions or classifications.

[0833] "Emotional data" is data that evaluates and quantifies a user's emotions.

[0834] A "skill set" is the collection of knowledge, skills, and abilities that a user possesses.

[0835] "Matching score" is a score calculated as a result of comparing a user's skill set with the skill requirement data in the company's database.

[0836] "Placement Suggestions" are the most suitable departments or jobs suggested based on the user's skill set, career vision, and sentiment data.

[0837] An "internal database" is a database system that stores and provides data managed within a company.

[0838] "AI interview" is a process that uses artificial intelligence to interact with users and collect and analyze information.

[0839] "Feedback" refers to evaluations and improvement suggestions generated based on collected information and analysis results.

[0840] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. In particular, by combining it with an emotion engine that recognizes user emotions, it achieves more accurate human resource matching and department allocation.

[0841] User authentication

[0842] The server receives the user credentials and authenticates the user against a database. Once the user is authenticated, a session begins.

[0843] User Interface and AI Interviews

[0844] After authentication, the terminal provides a user interface where the user can interactively input their desires and career vision. The data entered by the user is then sent to the server.

[0845] Data collection and analysis

[0846] The server analyzes the data sent by the user using a generative AI model to extract the user's skill set and desired conditions, and simultaneously analyzes the emotional data using an emotion engine, which uses natural language processing technology and machine learning to identify the user's emotional state.

[0847] Skill matching and placement plan generation

[0848] The server retrieves the skill requirements data of each department from the company database and matches the user's skill set with the skill requirements of each department. In doing so, it calculates a matching score taking into account the emotional data, generates an optimal placement plan, and presents it to the user.

[0849] Approval and implementation of layout plan

[0850] If the user approves the proposed placement, the server updates the database with the user's selection, officially reflecting the user's placement information. The server also logs the user's emotional data.

[0851] Regular interviews and feedback

[0852] The server schedules regular AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[0853] Specific examples

[0854] For example, if an employee types in "I want to improve my customer service skills" and the emotion engine recognizes that they are feeling anxious, it will prioritize suggesting departments with extensive training for that employee.

[0855] Prompt Sentence Examples

[0856] "Create a Python program that allows an AI interview system to suggest an appropriate department if an employee enters, 'My career vision is to improve my customer service skills' at a brick-and-mortar retail store, and the emotion engine recognizes that the employee is feeling anxious."

[0857] This invention combines users' skills, career vision, and emotional data to realize more accurate personnel allocation. For example, by using smartphones and tablets, it can improve the work efficiency and satisfaction of employees in brick-and-mortar stores.

[0858] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0859] Step 1:

[0860] The server receives the user authentication information and authenticates the user by checking it against a database. The input is the user ID and password, and the output is the authentication result (success or failure). If authentication is successful, the server starts a session and generates a session token.

[0861] Step 2:

[0862] After authentication, the terminal provides a user interface and allows the user to interactively input their aspirations and career vision. The input is text data related to the user's aspirations and career vision, and the output is the data entered by the user.

[0863] Step 3:

[0864] The server receives the user's input data sent from the device and analyzes it using a generative AI model. The input is the user's text data, and the output is the user's skill set and desired conditions. Specifically, the generative AI model analyzes the text data and extracts the user's skills and desired conditions.

[0865] Step 4:

[0866] The server passes the analyzed text data to the emotion engine, which analyzes the emotion data. The input is the user's text data, and the output is the user's emotional state (e.g., joy, anxiety, excitement, etc.). The emotion engine uses natural language processing technology and machine learning to identify emotions from the text data.

[0867] Step 5:

[0868] The server retrieves the skill requirement data of each department from the company database and matches the user's skill set with the skill requirements of each department. The input is the user's skill set and desired conditions, and the skill requirement data of each department, and the output is the matching result. Specifically, the server compares the skill set with the skill requirements and calculates a matching score based on the degree of match.

[0869] Step 6:

[0870] The server then modifies the matching score by taking into account the emotional data and generates the optimal placement proposal. The input is the matching result and emotional data, and the output is the optimal placement proposal. For example, if the user is feeling anxious, the server will prioritize departments that offer strong support.

[0871] Step 7:

[0872] The server presents the generated placement plan to the user via the terminal and asks for the user's approval. The input is the optimal placement plan, and the output is the user's approval or rejection.

[0873] Step 8:

[0874] If the user approves the proposed placement, the server updates the database with that selection to officially reflect the user's placement information. The input is the user's approval, and the output is the updated database information.

[0875] Step 9:

[0876] The server schedules regular AI interviews for users and department personnel and sends reminders. The input is the interview schedule and the output is the reminder. The terminal notifies the user of the reminder and schedules a new interview.

[0877] Step 10:

[0878] The server analyzes new interview data and improves placement suggestions based on feedback and emotional data. The input is new interview data and emotional data, and the output is improved placement suggestions and feedback. This process continuously ensures optimal placement based on changes in the user's skills and emotions.

[0879] 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.

[0880] 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.

[0881] 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.

[0882] [Third embodiment]

[0883] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0884] 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.

[0885] 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).

[0886] 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.

[0887] 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.

[0888] 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).

[0889] 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.

[0890] 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.

[0891] 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.

[0892] 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.

[0893] 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.

[0894] 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."

[0895] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with the needs of their departments. This system allows users to interactively input their requests and career visions, and uses AI to propose optimal placement plans. Below, we will explain the program processing of this system in natural language and provide detailed explanations with concrete examples.

[0896] User authentication

[0897] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the terminal allows the user access.

[0898] AI interviews begin

[0899] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[0900] Data extraction and analysis

[0901] The device collects input data sequentially and transfers it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[0902] Skills Matching

[0903] The server retrieves the skill requirements data for each department from the company database, and then uses the generation AI to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[0904] Generation and presentation of layout plans

[0905] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[0906] Implementing the placement plan

[0907] The server updates the database with the approved placement plan, officially reflecting the user's placement information, thereby optimizing the company's overall human resources.

[0908] Regular interviews and feedback

[0909] The server periodically schedules AI interviews with users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[0910] Specific examples

[0911] Example 1: Department Personnel Case

[0912] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[0913] Example 2: Employee case

[0914] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[0915] This system effectively takes into account the user's skills and career vision, proposes optimal personnel allocation, and improves corporate performance.

[0916] The processing flow will be explained below.

[0917] Step 1:

[0918] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[0919] Step 2:

[0920] The terminal sends the received authentication information to the server.

[0921] Step 3:

[0922] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[0923] Step 4:

[0924] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[0925] Step 5:

[0926] Users input their requests and career vision through the AI ​​interview interface.

[0927] Step 6:

[0928] The terminal sequentially collects the user's input data and sends it to the server.

[0929] Step 7:

[0930] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[0931] Step 8:

[0932] The server retrieves the skill requirement data for each department from the company database.

[0933] Step 9:

[0934] The server compares the user's skill set with the skill requirements of each department and calculates a matching score.

[0935] Step 10:

[0936] The server generates an optimal placement plan based on the matching results.

[0937] Step 11:

[0938] The server transmits the generated placement plan to the terminal.

[0939] Step 12:

[0940] The device displays the layout plan to the user, and the user confirms the layout plan.

[0941] Step 13:

[0942] If the user approves of the placement proposal, the device transmits the selection to the server.

[0943] Step 14:

[0944] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[0945] Step 15:

[0946] The server schedules regular AI interviews with users and department personnel.

[0947] Step 16:

[0948] The device will send reminders to the user for regular appointments.

[0949] Step 17:

[0950] The device will conduct a new AI interview with the user.

[0951] Step 18:

[0952] The server analyzes the new interview data and improves placement proposals based on the feedback.

[0953] This process allows the system to best reflect the user's skills and preferences, optimizing the allocation of human resources across the organization.

[0954] Example 1

[0955] 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."

[0956] Optimal management of human resources within a company relies on efficiently matching employees' career visions with the needs of each department. However, traditional methods require manually collecting and analyzing information to find optimal allocations, which takes time and effort, and often requires subjective judgment. Furthermore, regular feedback and allocation improvements are not properly implemented, making it difficult to maximize corporate performance. A solution to these issues is needed.

[0957] 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.

[0958] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an in-house database and matching it with the user's skill set, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department staff, means for analyzing new feedback and improving the placement plan, and means for calculating a matching score using a generative AI model using prompt sentences and generating an optimal placement plan based on the score. This enables optimal management of human resources.

[0959] "User authentication information" means authentication information such as ID and password used by a user when accessing a system.

[0960] A "user interface" is a function that provides a screen and operation methods for a user to interact with a system.

[0961] "Dialogue" refers to a format in which the user and the system exchange information alternately using text, voice, etc.

[0962] "Requests" refer to the hopes, functions, and services that users desire from the system.

[0963] "Career vision" refers to the goals and future plans that users have for their occupation or career.

[0964] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze input data and generate results according to a desired purpose.

[0965] A "prompt" is text that is input to a generative AI model to instruct it on specific analysis or generation.

[0966] An "in-house database" is a database system that structures and stores information managed within a company.

[0967] A "skill set" is the collection of knowledge, skills, and experience that an individual possesses.

[0968] "Skill requirements data" is information about the specific skills and abilities required by each department or project.

[0969] The "matching score" is a numerical representation of the degree of match between the user's skill set and the company's skill requirement data.

[0970] A "placement proposal" is a proposal for placing a user in a specific department or project.

[0971] "Feedback" refers to opinions and evaluations obtained from system users and department staff, and is information used to improve the system and deployment plans.

[0972] An "AI interview" is an interview conducted with a user or departmental staff member using artificial intelligence, and is conducted in an interactive format.

[0973] The present invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. This system has the function of allowing users to interactively input their requests and career visions and then using a generative AI model to propose optimal placement plans. Specific embodiments are described in detail below.

[0974] Overall system configuration

[0975] This system is primarily composed of terminals, servers, and an in-house database. Specific examples of the hardware and software used include PCs (Windows 10, macOS), tablets (iPad, Android), and smartphones (iOS, Android). AWS EC2, Microsoft Azure, and Google Cloud Platform are used for servers. OpenAI GPT-4, Google BERT, and Microsoft Turing are used as generative AI models.

[0976] User authentication

[0977] First, the terminal receives a login request from the user and prompts them to enter their user ID and password. The user authentication information is sent to the server using an HTTP POST request, and the server checks this information against the company's database. If there is a match, authentication is successful and the terminal allows the user access.

[0978] AI interviews begin

[0979] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to enter their requests and career vision in a dialogue format. This input is done through a chatbot-style user interface.

[0980] Data collection and analysis

[0981] The device collects the input data sequentially and transmits it to the server using an HTTP POST request. The server then uses a generative AI model to analyze the received data and extract the user's skill set and desired conditions.

[0982] Skills Matching

[0983] The server retrieves the skill requirements data for each department from the company's internal database, and uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[0984] Generation and presentation of layout plans

[0985] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[0986] Implementing the placement plan

[0987] The server updates the approved assignment plan to the company database, officially reflecting the user's new assignment information, thereby optimizing the company's overall human resources.

[0988] Regular interviews and feedback

[0989] The server periodically schedules AI interviews with users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[0990] Examples of concrete examples and prompts

[0991] Example: Department Manager

[0992] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[0993] Example: Employee case

[0994] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[0995] Prompt Sentence Examples

[0996] "We are looking for an employee with the following skill set: Python programming, data analysis experience, and team leadership experience. Please recommend the right candidate."

[0997] "My career vision is to gain experience in project management. Please recommend me to a suitable department."

[0998] Through the above explanation, we can see that this system can optimally manage human resources within a company and efficiently match employees' career visions with the needs of their departments.

[0999] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1000] Step 1:

[1001] Gathering user credentials

[1002] The terminal receives a login request from the user and prompts them to enter their user ID and password. The user ID and password are entered into the terminal as input. The terminal receives this information and temporarily stores the authentication information within the terminal.

[1003] Step 2:

[1004] Sending user credentials

[1005] The device sends the collected user authentication information to the server. The data is processed by embedding the entered user ID and password in an HTTP POST request and sending it to the server. The request containing the authentication information reaches the server as output.

[1006] Step 3:

[1007] Enforce user authentication

[1008] The server compares the received user authentication information with data in the company's database and performs authentication. For data calculations, the server uses an SQL query to search for user information in the database. The input is the user ID and password, and the output is the result of authentication success or failure. If authentication is successful, the server returns a response indicating successful authentication.

[1009] Step 4:

[1010] Notification of authentication results

[1011] The terminal receives the authentication result from the server and notifies the user. The input is the authentication result from the server, and the output is a screen notifying the user of authentication success or failure. For example, it may display "Login successful" and a dashboard screen for the user to proceed to the next step.

[1012] Step 5:

[1013] Display of AI interview interface

[1014] After successful authentication, the device displays the AI ​​interview interface to the user. The input is the user information after successful authentication, and the output is the AI ​​interview interface screen. The interview begins when the user clicks the start button for the AI ​​interview.

[1015] Step 6:

[1016] Starting a conversation

[1017] The user begins to input their requests and career vision using the AI ​​interview interface. The input is the user's interactive input data, and the output is the user's requests and career vision in text form. The data entered by the user is temporarily stored on the device.

[1018] Step 7:

[1019] Data collection

[1020] The terminal sequentially transfers the input data to the server. The input is the user's interactive input data, and the output is an HTTP POST request containing the collected data. The terminal sends the data to the server at regular intervals.

[1021] Step 8:

[1022] Data analysis

[1023] The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. The input is the user's interactive input data, and the output is the analyzed skill set and desired conditions. For example, a request such as "I want to gain project management experience" can be extracted.

[1024] Step 9:

[1025] Get skill request data

[1026] The server retrieves the skill requirement data of each department from the company's database. There is no input, and the output is the skill requirement data of each department. The necessary information is retrieved from the database using an SQL query.

[1027] Step 10:

[1028] Matching Score Calculation

[1029] The server uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score. The input is the user skill set and the skill requirements data of each department, and the output is the matching score. The comparison calculation quantifies which department is the most suitable.

[1030] Step 11:

[1031] Generate placement proposals

[1032] The server generates an optimal placement plan based on the matching results. The input is the matching score, and the output is the placement plan. For example, it generates a specific proposal such as "place in the data analysis department."

[1033] Step 12:

[1034] Proposal of layout plan

[1035] The server sends the details of the generated placement plan to the terminal, which then displays it to the user. The input is the placement plan data, and the output is the placement plan displayed on the user's screen. An interface is displayed for the user to check the placement plan.

[1036] Step 13:

[1037] Review and approve the layout plan

[1038] The user checks the placement proposal and approves it if it meets their needs. The input is the placement proposal, and the output is the result of approval or rejection. For example, the approval process is completed by clicking the "Approve placement proposal" button.

[1039] Step 14:

[1040] Update placement information

[1041] The server updates the approved deployment plan to the company database. The input is the approval result, and the output is the updated user deployment information. The new deployment information is recorded in the database, officially reflecting the user's deployment.

[1042] Step 15:

[1043] Schedule an interview

[1044] The server periodically schedules AI interviews with users and department staff. The input is user and staff information, and the output is schedule settings. For example, an interview is scheduled for the first Monday of every month.

[1045] Step 16:

[1046] Reminder notifications

[1047] The device notifies the user of reminders and schedules new appointments. The input is schedule information, and the output is the reminder sent to the user. For example, a notification saying "Don't forget about next week's appointment" is sent one week before the appointment.

[1048] Step 17:

[1049] Feedback Analysis

[1050] The server analyzes the new feedback, compares it with the previous placement plan, and makes improvements as necessary. The input is the feedback information, and the output is the improved placement plan. For example, analyzing the feedback "I'm satisfied with my new department" will identify areas for further improvement.

[1051] (Application example 1)

[1052] 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."

[1053] In conventional factories, it was difficult to assign personnel in a way that effectively reflected the skills and career vision of workers. This resulted in problems such as a decline in production efficiency and a decrease in worker motivation. In addition, it was not possible to accurately match the skill requirements of each robot with the skills of workers, and there was a lack of concrete measures to achieve optimal assignment.

[1054] 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.

[1055] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's needs and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an internal database and matching it with the user's skill set, means for generating an optimal placement plan based on the matching results and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department personnel, means for analyzing new feedback and improving the placement plan, means for inputting the skills and career vision of a robot operator and matching it with the skill requirements of each robot in the factory, and means for generating and presenting an optimal placement plan for robot operators using a generative AI model. This enables optimal personnel allocation within the factory, which is expected to improve production efficiency and worker motivation.

[1056] "User authentication information" means information provided by a system user to identify themselves, and typically includes a user ID and password.

[1057] "User interface" refers to the screen or means of interaction that allows a user to interact with a system, including input forms, buttons, etc.

[1058] "Dialogue" refers to an interface method that proceeds in the form of questions and answers, and is used by users to input their wishes and career vision.

[1059] A "skill set" refers to the collection of skills and knowledge a user possesses, including the abilities necessary to carry out specific tasks.

[1060] "Career vision" is a concept that represents the professional goals and desired career path that a user wants to achieve in the future.

[1061] "Skill requirements data" refers to a list of specific skills and knowledge that each organization or robot requires to perform its work.

[1062] The "matching score" is a numerical indicator that represents the degree of match between the user's skill set and the skill requirement data, and serves as a criterion for generating optimal placement plans.

[1063] "Generative AI model" refers to an artificial intelligence model used to analyze data and generate optimal placement proposals based on criteria.

[1064] "Feedback" refers to opinions or evaluations provided to the system by users or department personnel that are useful for improving the system.

[1065] "Robot operator" refers to a worker whose role is to operate and manage robots within a factory.

[1066] "Optimal placement proposal" refers to the most efficient personnel placement proposal generated based on the user's skill set, career vision, and skill requirement data.

[1067] This system maximizes production efficiency by optimally matching factory human resources with the skills of robot operators. The system provides an application that workers can access from their smartphones or smart glasses, and allows them to input their own skills and career vision through the application.

[1068] The system program is configured as follows: First, the server receives user authentication information and performs authentication. Specifically, the worker logs in to their smartphone or smart glasses and enters their ID and password. This authentication information is sent to the server and checked against the database.

[1069] After successful authentication, the terminal provides a user interface where the worker's requests and career vision can be input interactively. Once the worker enters their career vision interactively, the input data is transferred to the server.

[1070] The server analyzes the input data using a generative AI model to extract the user's skill set and desired conditions. The server then retrieves the skill requirements of each robot from an internal database and matches them with the worker's skill set. Based on the results of this matching, the server generates an optimal deployment plan and presents it to the worker via their terminal. Once the worker approves the deployment plan, the information is sent back to the server and updated in the database.

[1071] The server also periodically schedules and reminds users and department personnel for AI interviews. When new feedback is collected, the server analyzes it, compares it with the previous deployment plan, and makes any necessary improvements.

[1072] As a concrete example, consider the case where Worker A enters into the app that his / her career vision is "I want to learn robot programming techniques." This input is accepted by the terminal and sent to the server. The server analyzes this text and finds the corresponding robot operation task. It then presents this task to Worker A, who then approves the proposal and is assigned to the new task.

[1073] Example prompt sentence:

[1074] "A user enters, 'I want to learn robot programming skills.' Find the appropriate department and task, and suggest the best placement for the user."

[1075] The above configuration includes the following hardware and software: smartphones or smart glasses (e.g., Google Glass, Vuzix Blade), factory servers (e.g., Dell PowerEdge series), user authentication systems (e.g., OAuth 2.0), databases (e.g., MySQL, PostgreSQL), generative AI models (e.g., GPT-4, BERT), and front-end UI (e.g., React Native).

[1076] This will enable optimal allocation of personnel and robot operations within the factory, which is expected to improve production efficiency.

[1077] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1078] Step 1: User authentication

[1079] The user accesses the login screen from the device using a smartphone or smart glasses. The user enters their ID and password, and the device receives the information. The received authentication information is sent to the server, which compares it with a database to determine whether authentication is successful. If authentication is successful, the server returns access permission to the user, and the device closes the login screen and displays the next interface.

[1080] Input: User ID, Password

[1081] Output: Authentication success / failure, access granted

[1082] Step 2: Start the AI ​​interview

[1083] The device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. The user's input is saved on the device in real time and transferred sequentially to the server.

[1084] Input: User's career vision and desires

[1085] Output: Saved input data

[1086] Step 3: Collect and analyze data

[1087] The server receives the input data sequentially and analyzes it using a generative AI model. Specifically, it analyzes the text data entered by the user using natural language processing technology to extract the user's skill set and desired conditions.

[1088] Input: Data on the user's career vision and aspirations

[1089] Output: Extracted skillsets and desired conditions

[1090] Step 4: Skills matching

[1091] The server retrieves the skill requirements data for each robot from the company's database based on the extracted skill set and desired conditions. Using a generative AI model, it compares the user's skill set with the required skills of each robot and generates a matching score.

[1092] Input: User's skill set and desired conditions, skill requirements data from internal database

[1093] Output: Matching score

[1094] Step 5: Generate and present placement plans

[1095] The server generates an optimal placement plan based on the generated matching score. The plan is sent to the terminal and displayed to the user. The user checks the plan and clicks the approve button if they are satisfied.

[1096] Input: Matching score

[1097] Output: Optimal layout plan

[1098] Step 6: Approval of the layout plan

[1099] Once the user approves the placement plan, the selection information is sent from the device to the server, which then updates the approved placement plan in the company database to officially reflect the user's placement information.

[1100] Input: User approval

[1101] Output: Updated placement information

[1102] Step 7: Regular meetings and feedback

[1103] The server periodically schedules AI interviews for users and department personnel and issues reminders. The device receives the reminders, notifies the user, and conducts new interviews. The server analyzes feedback from the new interviews and compares them with the previous deployment plan to make any necessary improvements.

[1104] Input: Feedback data

[1105] Output: Improved placement proposal

[1106] By processing each step sequentially in this way, the entire system functions and optimal allocation of human resources and robot operators within the factory is achieved.

[1107] 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.

[1108] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and also combines it with an emotion engine that recognizes user emotions. By taking employee emotions into consideration, this invention achieves more accurate human resource matching and department allocation.

[1109] User authentication

[1110] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[1111] AI interviews begin

[1112] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[1113] Data collection and analysis

[1114] The device sequentially collects the user's input data and sends it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[1115] Emotion recognition

[1116] The server uses an emotion engine to analyze the emotional data expressed by the user during the interaction. The emotion engine uses natural language processing technology and machine learning to identify the user's emotional state (e.g., joy, anxiety, excitement, etc.).

[1117] Skills Matching

[1118] The server retrieves the skill requirements of each department from the company database, compares the user's skill set with the skill requirements of each department, and calculates a matching score, taking into account the recognized emotional data.

[1119] Generation and presentation of layout plans

[1120] The server generates optimal placement proposals based on the matching results and the user's emotional data. For example, if the user is feeling anxious, it may suggest a department with strong support. The server sends the generated placement proposals to the device, which then displays the proposals to the user, who then confirms them.

[1121] Approval and implementation of layout plan

[1122] If the user approves the placement plan, the device sends the selection to the server, which then updates the database with the approved placement plan, officially reflecting the user's placement information. During this process, the user's emotional data is also saved as a log.

[1123] Regular interviews and feedback

[1124] The server periodically schedules AI interviews for users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[1125] Specific examples

[1126] Example 1: Department Personnel Case

[1127] A departmental employee inputs the skill set required for a new project into the AI. If the AI ​​detects signs of anxiety in the user, it takes that emotional data into account and prioritizes departments and work environments with strong support systems as candidates.

[1128] Example 2: Employee case

[1129] An employee can enter "I want to gain project management experience" as their career vision. The server analyzes the text and suggests suitable departments. Furthermore, if the user is highly excited or motivated, it will prioritize positions that allow them to demonstrate leadership skills.

[1130] This system takes into account the user's skills and career vision, and also analyzes emotional data to achieve more optimal and satisfying personnel placement.

[1131] The processing flow will be explained below.

[1132] Step 1:

[1133] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[1134] Step 2:

[1135] The terminal sends the received authentication information to the server.

[1136] Step 3:

[1137] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[1138] Step 4:

[1139] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[1140] Step 5:

[1141] Users input their requests and career vision through the AI ​​interview interface.

[1142] Step 6:

[1143] The terminal sequentially collects the user's input data and sends it to the server.

[1144] Step 7:

[1145] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[1146] Step 8:

[1147] The server uses an emotion engine to analyze emotional data from the user's dialogue and recognize the user's emotional state (happiness, anxiety, excitement, etc.).

[1148] Step 9:

[1149] The server retrieves the skill requirement data for each department from the company database.

[1150] Step 10:

[1151] The server compares the user's skill set with the skill requirements of each department and calculates a matching score, taking into account emotional data to achieve highly accurate matching results.

[1152] Step 11:

[1153] The server generates optimal placement suggestions based on the matching results and emotional data. For example, if the user is feeling anxious, it will suggest a department with strong support.

[1154] Step 12:

[1155] The server transmits the generated placement plan to the terminal.

[1156] Step 13:

[1157] The device displays the layout plan to the user, and the user confirms the layout plan.

[1158] Step 14:

[1159] If the user approves of the placement proposal, the device transmits the selection to the server.

[1160] Step 15:

[1161] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[1162] Step 16:

[1163] The server schedules regular AI interviews with users and department personnel.

[1164] Step 17:

[1165] The device will send reminders to the user for regular appointments.

[1166] Step 18:

[1167] Users participate in regular AI interviews and enter new feedback and requests.

[1168] Step 19:

[1169] The terminal transmits new interview data to the server.

[1170] Step 20:

[1171] The server analyzes new interview data and emotional data and improves placement proposals based on the feedback.

[1172] Specifically, if a user expresses anxiety, the server will recognize this using its emotion engine and suggest departments with a good support system. If a user expresses a desire to gain project management experience and is enthusiastic, the server will prioritize suggesting positions where they can demonstrate leadership.

[1173] This processing step allows the system to best reflect the user's skills and preferences, and also takes emotional data into account, allowing for better optimization of talent allocation across the organization.

[1174] Example 2

[1175] 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."

[1176] With conventional systems, it was difficult to assign employees to appropriate departments by taking into account their emotions as well as their skills and career vision.Assignment that ignores employee emotions can lead to a decrease in motivation and productivity, there is a need for a more accurate human resource matching system that utilizes emotional data.

[1177] 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.

[1178] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data using a generative AI model and extracting the user's skill set and desired conditions, means for analyzing the user's emotional data using an emotion recognition engine, means for acquiring skill requirement data from an internal database and matching the user's skill set and emotional data with consideration, means for generating an optimal placement plan based on the matching results and emotional data and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan to the database, means for scheduling regular AI interviews with the user and department staff, and means for analyzing new feedback and improving the placement plan. This enables optimal department placement that takes into account not only employees' skills and career vision but also their emotions.

[1179] "User Credentials" means the identification information a User provides to access a System, typically including a User ID and password.

[1180] A "user interface" is a software component that provides a screen and operating means for users to input information into a system.

[1181] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate information, specifically using natural language processing and machine learning technologies.

[1182] "Emotion recognition engine" refers to software technology for identifying emotional states from user input data and interaction data.

[1183] "Skill Set" means the collection of technical and professional abilities and knowledge possessed by a User.

[1184] "Desired conditions" refer to the requests and conditions that users have regarding their career vision and work environment.

[1185] "Internal database" means a database system that manages various data stored within an organization.

[1186] "Skill requirements data" refers to data on the skills and abilities required in each department or project.

[1187] "Matching score" refers to the degree of compatibility expressed as a number, obtained by comparing the user's skill set and emotional data with the department's skill requirements data.

[1188] "Placement Proposal" means a proposal for placing a User in a particular department or position.

[1189] "Feedback" refers to opinions and evaluation information obtained from users and department staff.

[1190] "Regular AI interviews" refer to interviews that are conducted based on a pre-set schedule and involve dialogue between the AI ​​and users or department staff.

[1191] The system of the present invention optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and furthermore, it combines an emotion engine that recognizes user emotions. This allows for more accurate human resource matching and department allocation by taking employee emotions into consideration.

[1192] The system mainly uses the following hardware and software:

[1193] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[1194] Software: User authentication systems, user interfaces (UI), generative AI models (e.g., GPT-3), emotion recognition engines, database systems

[1195] The server receives the user authentication information and performs authentication. The user sends a login request from the terminal, and the server authenticates it by checking it against a database. For example, the terminal displays a login form and asks the user to enter their user ID and password.

[1196] If authentication is successful, the device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. Specific prompts include the question, "What are your thoughts about your future career?"

[1197] The device sequentially sends the user's input data to the server. The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. For example, if a user inputs "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[1198] The server uses an emotion recognition engine to analyze emotional data from the user's input text. The emotion recognition engine uses natural language processing technology and machine learning to identify the user's emotional state. For example, if a user inputs "I've been feeling anxious lately," the emotion recognition engine will identify the emotion "anxiety."

[1199] The server retrieves the skill requirements of each department from the company database and matches them with the user's skill set and emotional data. For example, if the user's skill set is "programming, data analysis" and the department's requirements are "programming, database management," it calculates a matching score.

[1200] Based on the matching results and emotion data, the server generates an optimal placement plan and sends it to the device. The device displays the placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server. The server then updates the database with the approved placement plan, officially reflecting the user's new placement information. During this process, the user's emotion data is also saved as a log.

[1201] The server periodically schedules AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[1202] The above is a specific description of the embodiment of the present invention, which enables optimal department allocation taking into account the skills, career vision, and emotions of employees.

[1203] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1204] Processing Steps

[1205] Step 1: User authentication

[1206] 1. Input: User ID, Password

[1207] 2. The terminal receives the user ID and password from the user. Specifically, the user enters the ID and password into the login form.

[1208] 3. Output: Authentication information (user ID, password)

[1209] 4. The device sends the received authentication information to the server.

[1210] 5. The server authenticates the user against a database.

[1211] 6. Output: Authentication result (success / failure)

[1212] 7. If the server is successful, it starts a session and returns a successful authentication message to the terminal. Specifically, the server generates a new session ID and sends it to the user.

[1213] 8. The device displays a successful authentication message.

[1214] Step 2: Start the AI ​​interview

[1215] 1. Input: None (automatic process after authentication)

[1216] 2. The device displays the AI ​​interview interface to the user. Specifically, it displays a chatbot-style UI and presents the prompt, "What are your thoughts about your future career?"

[1217] 3. Output: User response (text)

[1218] 4. The user clicks the Start Interview button and begins entering their requests and career vision.

[1219] Step 3: Collect and analyze data

[1220] 1. Input: User response data (text)

[1221] 2. The device collects user response data in real time and sends it to the server via API.

[1222] 3. Output: User data sent to the server

[1223] 4. The server uses a generative AI model (e.g., GPT-3) to analyze the user data and extract skill sets and desired conditions. For example, if a user enters "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[1224] 5. Output: Extracted skill sets and desired conditions

[1225] Step 4: Recognize emotions

[1226] 1. Input: User response data (text)

[1227] 2. The server uses an emotion recognition engine to analyze user data and identify their emotional state. Specifically, it analyzes text using natural language processing technology and machine learning.

[1228] 3. Output: Emotion data (e.g., happiness, anxiety, excitement, etc.)

[1229] Step 5: Skills matching

[1230] 1. Input: User's skill set, desired conditions, and emotional data

[1231] 2. The server retrieves skill requirements data from the internal database and compares the user's skill set with the sentiment data, using SQL queries to retrieve the required data and running a matching algorithm.

[1232] 3. Output: Matching score

[1233] Step 6: Generate and present placement plans

[1234] 1. Input: Matching score, emotion data

[1235] 2. The server generates the optimal placement plan. For example, if the user is feeling anxious, it will suggest a department with strong support.

[1236] 3. Output: Layout plan

[1237] 4. The server sends the generated placement plan to the terminal.

[1238] 5. The device displays the layout plan to the user, who then confirms it.

[1239] Step 7: Approve and implement the layout plan

[1240] 1. Input: Select to approve the layout plan

[1241] 2. If the user approves the placement proposal, the device sends the selection to the server.

[1242] 3. Output: Authorization information

[1243] 4. The server updates the database with the approved placement plan, officially reflecting the user's new placement information.

[1244] 5. Output: Database update results

[1245] Step 8: Regular meetings and feedback

[1246] 1. Input: None (regular schedule)

[1247] 2. The server periodically schedules AI interviews with users and department personnel and sends reminders.

[1248] 3. Output: Reminder notification

[1249] 4. The device will notify the user of the reminder and arrange a new appointment.

[1250] 5. Output: New interview data

[1251] 6. The server analyzes the new interview data and improves placement recommendations based on feedback and emotional data.

[1252] (Application example 2)

[1253] 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."

[1254] While traditional employee management systems can take into account an employee's skill set and career vision, they are unable to take into account their emotional state. This has resulted in insufficient allocation of employees to appropriate departments and improved employee satisfaction. Furthermore, advanced matching is required, such as recognizing employee emotions in real time during interactive AI interviews and reflecting that information in allocation proposals.

[1255] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and allowing the user to input their requests and career vision in an interactive format, means for analyzing the input data and emotional data using natural language processing technology and machine learning, means for acquiring skill requirement data from an internal database and matching it with the user's skill set, and means for calculating a matching score taking the emotional data into consideration, generating an optimal placement plan, and presenting it to the user. This enables optimal department placement that takes into consideration both the skills and emotional state of employees.

[1256] "User Credentials" means the identification and authentication information provided by a User to access a System.

[1257] A "user interface" is a visual and functional interface through which a user interacts with a system.

[1258] "Dialogue" refers to the way in which a user interacts with a system and provides information through a series of questions and responses.

[1259] "Career vision" refers to the job and skill goals that users aim for in the future.

[1260] "Natural language processing technology" is a technology that allows computers to process and understand human language.

[1261] "Machine learning" is a technique that uses algorithms to allow computers to learn from data and make predictions or classifications.

[1262] "Emotional data" is data that evaluates and quantifies a user's emotions.

[1263] A "skill set" is the collection of knowledge, skills, and abilities that a user possesses.

[1264] "Matching score" is a score calculated as a result of comparing a user's skill set with the skill requirement data in the company's database.

[1265] "Placement Suggestions" are the most suitable departments or jobs suggested based on the user's skill set, career vision, and sentiment data.

[1266] An "internal database" is a database system that stores and provides data managed within a company.

[1267] "AI interview" is a process that uses artificial intelligence to interact with users and collect and analyze information.

[1268] "Feedback" refers to evaluations and improvement suggestions generated based on collected information and analysis results.

[1269] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. In particular, by combining it with an emotion engine that recognizes user emotions, it achieves more accurate human resource matching and department allocation.

[1270] User authentication

[1271] The server receives the user credentials and authenticates the user against a database. Once the user is authenticated, a session begins.

[1272] User Interface and AI Interviews

[1273] After authentication, the terminal provides a user interface where the user can interactively input their desires and career vision. The data entered by the user is then sent to the server.

[1274] Data collection and analysis

[1275] The server analyzes the data sent by the user using a generative AI model to extract the user's skill set and desired conditions, and simultaneously analyzes the emotional data using an emotion engine, which uses natural language processing technology and machine learning to identify the user's emotional state.

[1276] Skill matching and placement plan generation

[1277] The server retrieves the skill requirements data of each department from the company database and matches the user's skill set with the skill requirements of each department. In doing so, it calculates a matching score taking into account the emotional data, generates an optimal placement plan, and presents it to the user.

[1278] Approval and implementation of layout plan

[1279] If the user approves the proposed placement, the server updates the database with the user's selection, officially reflecting the user's placement information. The server also logs the user's emotional data.

[1280] Regular interviews and feedback

[1281] The server schedules regular AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[1282] Specific examples

[1283] For example, if an employee types in "I want to improve my customer service skills" and the emotion engine recognizes that they are feeling anxious, it will prioritize suggesting departments with extensive training for that employee.

[1284] Prompt Sentence Examples

[1285] "Create a Python program that allows an AI interview system to suggest an appropriate department if an employee enters, 'My career vision is to improve my customer service skills' at a brick-and-mortar retail store, and the emotion engine recognizes that the employee is feeling anxious."

[1286] This invention combines users' skills, career vision, and emotional data to realize more accurate personnel allocation. For example, by using smartphones and tablets, it can improve the work efficiency and satisfaction of employees in brick-and-mortar stores.

[1287] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1288] Step 1:

[1289] The server receives the user authentication information and authenticates the user by checking it against a database. The input is the user ID and password, and the output is the authentication result (success or failure). If authentication is successful, the server starts a session and generates a session token.

[1290] Step 2:

[1291] After authentication, the terminal provides a user interface and allows the user to interactively input their aspirations and career vision. The input is text data related to the user's aspirations and career vision, and the output is the data entered by the user.

[1292] Step 3:

[1293] The server receives the user's input data sent from the device and analyzes it using a generative AI model. The input is the user's text data, and the output is the user's skill set and desired conditions. Specifically, the generative AI model analyzes the text data and extracts the user's skills and desired conditions.

[1294] Step 4:

[1295] The server passes the analyzed text data to the emotion engine, which analyzes the emotion data. The input is the user's text data, and the output is the user's emotional state (e.g., joy, anxiety, excitement, etc.). The emotion engine uses natural language processing technology and machine learning to identify emotions from the text data.

[1296] Step 5:

[1297] The server retrieves the skill requirement data of each department from the company database and matches the user's skill set with the skill requirements of each department. The input is the user's skill set and desired conditions, and the skill requirement data of each department, and the output is the matching result. Specifically, the server compares the skill set with the skill requirements and calculates a matching score based on the degree of match.

[1298] Step 6:

[1299] The server then modifies the matching score by taking into account the emotional data and generates the optimal placement proposal. The input is the matching result and emotional data, and the output is the optimal placement proposal. For example, if the user is feeling anxious, the server will prioritize departments that offer strong support.

[1300] Step 7:

[1301] The server presents the generated placement plan to the user via the terminal and asks for the user's approval. The input is the optimal placement plan, and the output is the user's approval or rejection.

[1302] Step 8:

[1303] If the user approves the proposed placement, the server updates the database with that selection to officially reflect the user's placement information. The input is the user's approval, and the output is the updated database information.

[1304] Step 9:

[1305] The server schedules regular AI interviews for users and department personnel and sends reminders. The input is the interview schedule and the output is the reminder. The terminal notifies the user of the reminder and schedules a new interview.

[1306] Step 10:

[1307] The server analyzes new interview data and improves placement suggestions based on feedback and emotional data. The input is new interview data and emotional data, and the output is improved placement suggestions and feedback. This process continuously ensures optimal placement based on changes in the user's skills and emotions.

[1308] 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.

[1309] 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.

[1310] 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.

[1311] [Fourth embodiment]

[1312] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1313] 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.

[1314] 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).

[1315] 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.

[1316] 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.

[1317] 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).

[1318] 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.

[1319] 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.

[1320] 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.

[1321] 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.

[1322] 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.

[1323] 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.

[1324] 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."

[1325] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with the needs of their departments. This system allows users to interactively input their requests and career visions, and uses AI to propose optimal placement plans. Below, we will explain the program processing of this system in natural language and provide detailed explanations with concrete examples.

[1326] User authentication

[1327] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the terminal allows the user access.

[1328] AI interviews begin

[1329] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[1330] Data extraction and analysis

[1331] The device collects input data sequentially and transfers it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[1332] Skills Matching

[1333] The server retrieves the skill requirements data for each department from the company database, and then uses the generation AI to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[1334] Generation and presentation of layout plans

[1335] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[1336] Implementing the placement plan

[1337] The server updates the database with the approved placement plan, officially reflecting the user's placement information, thereby optimizing the company's overall human resources.

[1338] Regular interviews and feedback

[1339] The server periodically schedules AI interviews with users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[1340] Specific examples

[1341] Example 1: Department Personnel Case

[1342] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[1343] Example 2: Employee case

[1344] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[1345] This system effectively takes into account the user's skills and career vision, proposes optimal personnel allocation, and improves corporate performance.

[1346] The processing flow will be explained below.

[1347] Step 1:

[1348] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[1349] Step 2:

[1350] The terminal sends the received authentication information to the server.

[1351] Step 3:

[1352] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[1353] Step 4:

[1354] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[1355] Step 5:

[1356] Users input their requests and career vision through the AI ​​interview interface.

[1357] Step 6:

[1358] The terminal sequentially collects the user's input data and sends it to the server.

[1359] Step 7:

[1360] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[1361] Step 8:

[1362] The server retrieves the skill requirement data for each department from the company database.

[1363] Step 9:

[1364] The server compares the user's skill set with the skill requirements of each department and calculates a matching score.

[1365] Step 10:

[1366] The server generates an optimal placement plan based on the matching results.

[1367] Step 11:

[1368] The server transmits the generated placement plan to the terminal.

[1369] Step 12:

[1370] The device displays the layout plan to the user, and the user confirms the layout plan.

[1371] Step 13:

[1372] If the user approves of the placement proposal, the device transmits the selection to the server.

[1373] Step 14:

[1374] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[1375] Step 15:

[1376] The server schedules regular AI interviews with users and department personnel.

[1377] Step 16:

[1378] The device will send reminders to the user for regular appointments.

[1379] Step 17:

[1380] The device will conduct a new AI interview with the user.

[1381] Step 18:

[1382] The server analyzes the new interview data and improves placement proposals based on the feedback.

[1383] This process allows the system to best reflect the user's skills and preferences, optimizing the allocation of human resources across the organization.

[1384] Example 1

[1385] 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."

[1386] Optimal management of human resources within a company relies on efficiently matching employees' career visions with the needs of each department. However, traditional methods require manually collecting and analyzing information to find optimal allocations, which takes time and effort, and often requires subjective judgment. Furthermore, regular feedback and allocation improvements are not properly implemented, making it difficult to maximize corporate performance. A solution to these issues is needed.

[1387] 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.

[1388] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an in-house database and matching it with the user's skill set, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department staff, means for analyzing new feedback and improving the placement plan, and means for calculating a matching score using a generative AI model using prompt sentences and generating an optimal placement plan based on the score. This enables optimal management of human resources.

[1389] "User authentication information" means authentication information such as ID and password used by a user when accessing a system.

[1390] A "user interface" is a function that provides a screen and operation methods for a user to interact with a system.

[1391] "Dialogue" refers to a format in which the user and the system exchange information alternately using text, voice, etc.

[1392] "Requests" refer to the hopes, functions, and services that users desire from the system.

[1393] "Career vision" refers to the goals and future plans that users have for their occupation or career.

[1394] A "generative AI model" is an algorithm or system that uses artificial intelligence to analyze input data and generate results according to a desired purpose.

[1395] A "prompt" is text that is input to a generative AI model to instruct it on specific analysis or generation.

[1396] An "in-house database" is a database system that structures and stores information managed within a company.

[1397] A "skill set" is the collection of knowledge, skills, and experience that an individual possesses.

[1398] "Skill requirements data" is information about the specific skills and abilities required by each department or project.

[1399] The "matching score" is a numerical representation of the degree of match between the user's skill set and the company's skill requirement data.

[1400] A "placement proposal" is a proposal for placing a user in a specific department or project.

[1401] "Feedback" refers to opinions and evaluations obtained from system users and department staff, and is information used to improve the system and deployment plans.

[1402] An "AI interview" is an interview conducted with a user or departmental staff member using artificial intelligence, and is conducted in an interactive format.

[1403] The present invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. This system has the function of allowing users to interactively input their requests and career visions and then using a generative AI model to propose optimal placement plans. Specific embodiments are described in detail below.

[1404] Overall system configuration

[1405] This system is primarily composed of terminals, servers, and an in-house database. Specific examples of the hardware and software used include PCs (Windows 10, macOS), tablets (iPad, Android), and smartphones (iOS, Android). AWS EC2, Microsoft Azure, and Google Cloud Platform are used for servers. OpenAI GPT-4, Google BERT, and Microsoft Turing are used as generative AI models.

[1406] User authentication

[1407] First, the terminal receives a login request from the user and prompts them to enter their user ID and password. The user authentication information is sent to the server using an HTTP POST request, and the server checks this information against the company's database. If there is a match, authentication is successful and the terminal allows the user access.

[1408] AI interviews begin

[1409] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to enter their requests and career vision in a dialogue format. This input is done through a chatbot-style user interface.

[1410] Data collection and analysis

[1411] The device collects the input data sequentially and transmits it to the server using an HTTP POST request. The server then uses a generative AI model to analyze the received data and extract the user's skill set and desired conditions.

[1412] Skills Matching

[1413] The server retrieves the skill requirements data for each department from the company's internal database, and uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score.

[1414] Generation and presentation of layout plans

[1415] The server generates an optimal placement plan based on the matching results and sends the details to the device. The device displays the created placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server.

[1416] Implementing the placement plan

[1417] The server updates the approved assignment plan to the company database, officially reflecting the user's new assignment information, thereby optimizing the company's overall human resources.

[1418] Regular interviews and feedback

[1419] The server periodically schedules AI interviews with users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new feedback, compares it with the previous placement proposal, and makes improvements as needed.

[1420] Examples of concrete examples and prompts

[1421] Example: Department Manager

[1422] A departmental employee inputs the skill sets required for a new project into the AI. The device accepts the input and sends it to the server. The server analyzes the required skill sets and compares them with the company's internal database to generate a list of optimal candidates. The server then sends the list generated by the server to the device, where the departmental employee reviews it and decides on the placement of the most suitable candidates.

[1423] Example: Employee case

[1424] An employee enters "I want to gain experience in project management" as their career vision. The terminal receives the input and sends it to the server. The server analyzes the text and finds an appropriate department. The server generates a placement plan and sends it to the terminal. The employee reviews the placement plan and approves it if it meets their needs.

[1425] Prompt Sentence Examples

[1426] "We are looking for an employee with the following skill set: Python programming, data analysis experience, and team leadership experience. Please recommend the right candidate."

[1427] "My career vision is to gain experience in project management. Please recommend me to a suitable department."

[1428] Through the above explanation, we can see that this system can optimally manage human resources within a company and efficiently match employees' career visions with the needs of their departments.

[1429] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1430] Step 1:

[1431] Gathering user credentials

[1432] The terminal receives a login request from the user and prompts them to enter their user ID and password. The user ID and password are entered into the terminal as input. The terminal receives this information and temporarily stores the authentication information within the terminal.

[1433] Step 2:

[1434] Sending user credentials

[1435] The device sends the collected user authentication information to the server. The data is processed by embedding the entered user ID and password in an HTTP POST request and sending it to the server. The request containing the authentication information reaches the server as output.

[1436] Step 3:

[1437] Enforce user authentication

[1438] The server compares the received user authentication information with data in the company's database and performs authentication. For data calculations, the server uses an SQL query to search for user information in the database. The input is the user ID and password, and the output is the result of authentication success or failure. If authentication is successful, the server returns a response indicating successful authentication.

[1439] Step 4:

[1440] Notification of authentication results

[1441] The terminal receives the authentication result from the server and notifies the user. The input is the authentication result from the server, and the output is a screen notifying the user of authentication success or failure. For example, it may display "Login successful" and a dashboard screen for the user to proceed to the next step.

[1442] Step 5:

[1443] Display of AI interview interface

[1444] After successful authentication, the device displays the AI ​​interview interface to the user. The input is the user information after successful authentication, and the output is the AI ​​interview interface screen. The interview begins when the user clicks the start button for the AI ​​interview.

[1445] Step 6:

[1446] Starting a conversation

[1447] The user begins to input their requests and career vision using the AI ​​interview interface. The input is the user's interactive input data, and the output is the user's requests and career vision in text form. The data entered by the user is temporarily stored on the device.

[1448] Step 7:

[1449] Data collection

[1450] The terminal sequentially transfers the input data to the server. The input is the user's interactive input data, and the output is an HTTP POST request containing the collected data. The terminal sends the data to the server at regular intervals.

[1451] Step 8:

[1452] Data analysis

[1453] The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. The input is the user's interactive input data, and the output is the analyzed skill set and desired conditions. For example, a request such as "I want to gain project management experience" can be extracted.

[1454] Step 9:

[1455] Get skill request data

[1456] The server retrieves the skill requirement data of each department from the company's database. There is no input, and the output is the skill requirement data of each department. The necessary information is retrieved from the database using an SQL query.

[1457] Step 10:

[1458] Matching Score Calculation

[1459] The server uses a generative AI model to compare the extracted user skill set with the skill requirements of each department and calculate a matching score. The input is the user skill set and the skill requirements data of each department, and the output is the matching score. The comparison calculation quantifies which department is the most suitable.

[1460] Step 11:

[1461] Generate placement proposals

[1462] The server generates an optimal placement plan based on the matching results. The input is the matching score, and the output is the placement plan. For example, it generates a specific proposal such as "place in the data analysis department."

[1463] Step 12:

[1464] Proposal of layout plan

[1465] The server sends the details of the generated placement plan to the terminal, which then displays it to the user. The input is the placement plan data, and the output is the placement plan displayed on the user's screen. An interface is displayed for the user to check the placement plan.

[1466] Step 13:

[1467] Review and approve the layout plan

[1468] The user checks the placement proposal and approves it if it meets their needs. The input is the placement proposal, and the output is the result of approval or rejection. For example, the approval process is completed by clicking the "Approve placement proposal" button.

[1469] Step 14:

[1470] Update placement information

[1471] The server updates the approved deployment plan to the company database. The input is the approval result, and the output is the updated user deployment information. The new deployment information is recorded in the database, officially reflecting the user's deployment.

[1472] Step 15:

[1473] Schedule an interview

[1474] The server periodically schedules AI interviews with users and department staff. The input is user and staff information, and the output is schedule settings. For example, an interview is scheduled for the first Monday of every month.

[1475] Step 16:

[1476] Reminder notifications

[1477] The device notifies the user of reminders and schedules new appointments. The input is schedule information, and the output is the reminder sent to the user. For example, a notification saying "Don't forget about next week's appointment" is sent one week before the appointment.

[1478] Step 17:

[1479] Feedback Analysis

[1480] The server analyzes the new feedback, compares it with the previous placement plan, and makes improvements as necessary. The input is the feedback information, and the output is the improved placement plan. For example, analyzing the feedback "I'm satisfied with my new department" will identify areas for further improvement.

[1481] (Application example 1)

[1482] 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."

[1483] In conventional factories, it was difficult to assign personnel in a way that effectively reflected the skills and career vision of workers. This resulted in problems such as a decline in production efficiency and a decrease in worker motivation. In addition, it was not possible to accurately match the skill requirements of each robot with the skills of workers, and there was a lack of concrete measures to achieve optimal assignment.

[1484] 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.

[1485] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's needs and career vision, means for analyzing the input data and extracting the user's skill set and desired conditions, means for retrieving skill requirement data from an internal database and matching it with the user's skill set, means for generating an optimal placement plan based on the matching results and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan in the database, means for scheduling regular AI interviews with the user and department personnel, means for analyzing new feedback and improving the placement plan, means for inputting the skills and career vision of a robot operator and matching it with the skill requirements of each robot in the factory, and means for generating and presenting an optimal placement plan for robot operators using a generative AI model. This enables optimal personnel allocation within the factory, which is expected to improve production efficiency and worker motivation.

[1486] "User authentication information" means information provided by a system user to identify themselves, and typically includes a user ID and password.

[1487] "User interface" refers to the screen or means of interaction that allows a user to interact with a system, including input forms, buttons, etc.

[1488] "Dialogue" refers to an interface method that proceeds in the form of questions and answers, and is used by users to input their wishes and career vision.

[1489] A "skill set" refers to the collection of skills and knowledge a user possesses, including the abilities necessary to carry out specific tasks.

[1490] "Career vision" is a concept that represents the professional goals and desired career path that a user wants to achieve in the future.

[1491] "Skill requirements data" refers to a list of specific skills and knowledge that each organization or robot requires to perform its work.

[1492] The "matching score" is a numerical indicator that represents the degree of match between the user's skill set and the skill requirement data, and serves as a criterion for generating optimal placement plans.

[1493] "Generative AI model" refers to an artificial intelligence model used to analyze data and generate optimal placement proposals based on criteria.

[1494] "Feedback" refers to opinions or evaluations provided to the system by users or department personnel that are useful for improving the system.

[1495] "Robot operator" refers to a worker whose role is to operate and manage robots within a factory.

[1496] "Optimal placement proposal" refers to the most efficient personnel placement proposal generated based on the user's skill set, career vision, and skill requirement data.

[1497] This system maximizes production efficiency by optimally matching factory human resources with the skills of robot operators. The system provides an application that workers can access from their smartphones or smart glasses, and allows them to input their own skills and career vision through the application.

[1498] The system program is configured as follows: First, the server receives user authentication information and performs authentication. Specifically, the worker logs in to their smartphone or smart glasses and enters their ID and password. This authentication information is sent to the server and checked against the database.

[1499] After successful authentication, the terminal provides a user interface where the worker's requests and career vision can be input interactively. Once the worker enters their career vision interactively, the input data is transferred to the server.

[1500] The server analyzes the input data using a generative AI model to extract the user's skill set and desired conditions. The server then retrieves the skill requirements of each robot from an internal database and matches them with the worker's skill set. Based on the results of this matching, the server generates an optimal deployment plan and presents it to the worker via their terminal. Once the worker approves the deployment plan, the information is sent back to the server and updated in the database.

[1501] The server also periodically schedules and reminds users and department personnel for AI interviews. When new feedback is collected, the server analyzes it, compares it with the previous deployment plan, and makes any necessary improvements.

[1502] As a concrete example, consider the case where Worker A enters into the app that his / her career vision is "I want to learn robot programming techniques." This input is accepted by the terminal and sent to the server. The server analyzes this text and finds the corresponding robot operation task. It then presents this task to Worker A, who then approves the proposal and is assigned to the new task.

[1503] Example prompt sentence:

[1504] "A user enters, 'I want to learn robot programming skills.' Find the appropriate department and task, and suggest the best placement for the user."

[1505] The above configuration includes the following hardware and software: smartphones or smart glasses (e.g., Google Glass, Vuzix Blade), factory servers (e.g., Dell PowerEdge series), user authentication systems (e.g., OAuth 2.0), databases (e.g., MySQL, PostgreSQL), generative AI models (e.g., GPT-4, BERT), and front-end UI (e.g., React Native).

[1506] This will enable optimal allocation of personnel and robot operations within the factory, which is expected to improve production efficiency.

[1507] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1508] Step 1: User authentication

[1509] The user accesses the login screen from the device using a smartphone or smart glasses. The user enters their ID and password, and the device receives the information. The received authentication information is sent to the server, which compares it with a database to determine whether authentication is successful. If authentication is successful, the server returns access permission to the user, and the device closes the login screen and displays the next interface.

[1510] Input: User ID, Password

[1511] Output: Authentication success / failure, access granted

[1512] Step 2: Start the AI ​​interview

[1513] The device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. The user's input is saved on the device in real time and transferred sequentially to the server.

[1514] Input: User's career vision and desires

[1515] Output: Saved input data

[1516] Step 3: Collect and analyze data

[1517] The server receives the input data sequentially and analyzes it using a generative AI model. Specifically, it analyzes the text data entered by the user using natural language processing technology to extract the user's skill set and desired conditions.

[1518] Input: Data on the user's career vision and aspirations

[1519] Output: Extracted skillsets and desired conditions

[1520] Step 4: Skills matching

[1521] The server retrieves the skill requirements data for each robot from the company's database based on the extracted skill set and desired conditions. Using a generative AI model, it compares the user's skill set with the required skills of each robot and generates a matching score.

[1522] Input: User's skill set and desired conditions, skill requirements data from internal database

[1523] Output: Matching score

[1524] Step 5: Generate and present placement plans

[1525] The server generates an optimal placement plan based on the generated matching score. The plan is sent to the terminal and displayed to the user. The user checks the plan and clicks the approve button if they are satisfied.

[1526] Input: Matching score

[1527] Output: Optimal layout plan

[1528] Step 6: Approval of the layout plan

[1529] Once the user approves the placement plan, the selection information is sent from the device to the server, which then updates the approved placement plan in the company database to officially reflect the user's placement information.

[1530] Input: User approval

[1531] Output: Updated placement information

[1532] Step 7: Regular meetings and feedback

[1533] The server periodically schedules AI interviews for users and department personnel and issues reminders. The device receives the reminders, notifies the user, and conducts new interviews. The server analyzes feedback from the new interviews and compares them with the previous deployment plan to make any necessary improvements.

[1534] Input: Feedback data

[1535] Output: Improved placement proposal

[1536] By processing each step sequentially in this way, the entire system functions and optimal allocation of human resources and robot operators within the factory is achieved.

[1537] 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.

[1538] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and also combines it with an emotion engine that recognizes user emotions. By taking employee emotions into consideration, this invention achieves more accurate human resource matching and department allocation.

[1539] User authentication

[1540] The terminal receives a login request from the user and prompts them to enter their user ID and password. The terminal sends the received authentication information to the server. The server compares it with a database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[1541] AI interviews begin

[1542] After authentication, the device displays the AI ​​interview interface to the user. When the user clicks the start interview button, they begin to interactively input their needs and career vision.

[1543] Data collection and analysis

[1544] The device sequentially collects the user's input data and sends it to the server, which then analyzes the received data using generative AI to extract the user's skill set and desired conditions.

[1545] Emotion recognition

[1546] The server uses an emotion engine to analyze the emotional data expressed by the user during the interaction. The emotion engine uses natural language processing technology and machine learning to identify the user's emotional state (e.g., joy, anxiety, excitement, etc.).

[1547] Skills Matching

[1548] The server retrieves the skill requirements of each department from the company database, compares the user's skill set with the skill requirements of each department, and calculates a matching score, taking into account the recognized emotional data.

[1549] Generation and presentation of layout plans

[1550] The server generates optimal placement proposals based on the matching results and the user's emotional data. For example, if the user is feeling anxious, it may suggest a department with strong support. The server sends the generated placement proposals to the device, which then displays the proposals to the user, who then confirms them.

[1551] Approval and implementation of layout plan

[1552] If the user approves the placement plan, the device sends the selection to the server, which then updates the database with the approved placement plan, officially reflecting the user's placement information. During this process, the user's emotional data is also saved as a log.

[1553] Regular interviews and feedback

[1554] The server periodically schedules AI interviews for users and department staff and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[1555] Specific examples

[1556] Example 1: Department Personnel Case

[1557] A departmental employee inputs the skill set required for a new project into the AI. If the AI ​​detects signs of anxiety in the user, it takes that emotional data into account and prioritizes departments and work environments with strong support systems as candidates.

[1558] Example 2: Employee case

[1559] An employee can enter "I want to gain project management experience" as their career vision. The server analyzes the text and suggests suitable departments. Furthermore, if the user is highly excited or motivated, it will prioritize positions that allow them to demonstrate leadership skills.

[1560] This system takes into account the user's skills and career vision, and also analyzes emotional data to achieve more optimal and satisfying personnel placement.

[1561] The processing flow will be explained below.

[1562] Step 1:

[1563] The terminal receives a login request from the user and prompts them to enter their user ID and password.

[1564] Step 2:

[1565] The terminal sends the received authentication information to the server.

[1566] Step 3:

[1567] The server checks the database and authenticates the user. If authentication is successful, the server starts a session and returns the result to the terminal.

[1568] Step 4:

[1569] The device displays the AI ​​interview interface to the user, who then clicks the start interview button.

[1570] Step 5:

[1571] Users input their requests and career vision through the AI ​​interview interface.

[1572] Step 6:

[1573] The terminal sequentially collects the user's input data and sends it to the server.

[1574] Step 7:

[1575] The server uses generative AI to analyze the user's input data and extract skill sets and desired conditions.

[1576] Step 8:

[1577] The server uses an emotion engine to analyze emotional data from the user's dialogue and recognize the user's emotional state (happiness, anxiety, excitement, etc.).

[1578] Step 9:

[1579] The server retrieves the skill requirement data for each department from the company database.

[1580] Step 10:

[1581] The server compares the user's skill set with the skill requirements of each department and calculates a matching score, taking into account emotional data to achieve highly accurate matching results.

[1582] Step 11:

[1583] The server generates optimal placement suggestions based on the matching results and emotional data. For example, if the user is feeling anxious, it will suggest a department with strong support.

[1584] Step 12:

[1585] The server transmits the generated placement plan to the terminal.

[1586] Step 13:

[1587] The device displays the layout plan to the user, and the user confirms the layout plan.

[1588] Step 14:

[1589] If the user approves of the placement proposal, the device transmits the selection to the server.

[1590] Step 15:

[1591] The server updates the database with the approved placement plan to officially reflect the user's placement information.

[1592] Step 16:

[1593] The server schedules regular AI interviews with users and department personnel.

[1594] Step 17:

[1595] The device will send reminders to the user for regular appointments.

[1596] Step 18:

[1597] Users participate in regular AI interviews and enter new feedback and requests.

[1598] Step 19:

[1599] The terminal transmits new interview data to the server.

[1600] Step 20:

[1601] The server analyzes new interview data and emotional data and improves placement proposals based on the feedback.

[1602] Specifically, if a user expresses anxiety, the server will recognize this using its emotion engine and suggest departments with a good support system. If a user expresses a desire to gain project management experience and is enthusiastic, the server will prioritize suggesting positions where they can demonstrate leadership.

[1603] This processing step allows the system to best reflect the user's skills and preferences, and also takes emotional data into account, allowing for better optimization of talent allocation across the organization.

[1604] Example 2

[1605] 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."

[1606] With conventional systems, it was difficult to assign employees to appropriate departments by taking into account their emotions as well as their skills and career vision.Assignment that ignores employee emotions can lead to a decrease in motivation and productivity, there is a need for a more accurate human resource matching system that utilizes emotional data.

[1607] 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.

[1608] In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and interactively inputting the user's requests and career vision, means for analyzing the input data using a generative AI model and extracting the user's skill set and desired conditions, means for analyzing the user's emotional data using an emotion recognition engine, means for acquiring skill requirement data from an internal database and matching the user's skill set and emotional data with consideration, means for generating an optimal placement plan based on the matching results and emotional data and presenting it to the user, means for the user to approve the generated placement plan, means for updating the approved placement plan to the database, means for scheduling regular AI interviews with the user and department staff, and means for analyzing new feedback and improving the placement plan. This enables optimal department placement that takes into account not only employees' skills and career vision but also their emotions.

[1609] "User Credentials" means the identification information a User provides to access a System, typically including a User ID and password.

[1610] A "user interface" is a software component that provides a screen and operating means for users to input information into a system.

[1611] A "generative AI model" is a model that uses artificial intelligence technology to analyze data and generate information, specifically using natural language processing and machine learning technologies.

[1612] "Emotion recognition engine" refers to software technology for identifying emotional states from user input data and interaction data.

[1613] "Skill Set" means the collection of technical and professional abilities and knowledge possessed by a User.

[1614] "Desired conditions" refer to the requests and conditions that users have regarding their career vision and work environment.

[1615] "Internal database" means a database system that manages various data stored within an organization.

[1616] "Skill requirements data" refers to data on the skills and abilities required in each department or project.

[1617] "Matching score" refers to the degree of compatibility expressed as a number, obtained by comparing the user's skill set and emotional data with the department's skill requirements data.

[1618] "Placement Proposal" means a proposal for placing a User in a particular department or position.

[1619] "Feedback" refers to opinions and evaluation information obtained from users and department staff.

[1620] "Regular AI interviews" refer to interviews that are conducted based on a pre-set schedule and involve dialogue between the AI ​​and users or department staff.

[1621] The system of the present invention optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements, and furthermore, it combines an emotion engine that recognizes user emotions. This allows for more accurate human resource matching and department allocation by taking employee emotions into consideration.

[1622] The system mainly uses the following hardware and software:

[1623] Hardware: Servers, devices (PCs, tablets, smartphones, etc.)

[1624] Software: User authentication systems, user interfaces (UI), generative AI models (e.g., GPT-3), emotion recognition engines, database systems

[1625] The server receives the user authentication information and performs authentication. The user sends a login request from the terminal, and the server authenticates it by checking it against a database. For example, the terminal displays a login form and asks the user to enter their user ID and password.

[1626] If authentication is successful, the device displays the AI ​​interview interface to the user. The user clicks the start interview button and begins to interactively input their career vision and requests. Specific prompts include the question, "What are your thoughts about your future career?"

[1627] The device sequentially sends the user's input data to the server. The server analyzes the received data using a generative AI model to extract the user's skill set and desired conditions. For example, if a user inputs "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[1628] The server uses an emotion recognition engine to analyze emotional data from the user's input text. The emotion recognition engine uses natural language processing technology and machine learning to identify the user's emotional state. For example, if a user inputs "I've been feeling anxious lately," the emotion recognition engine will identify the emotion "anxiety."

[1629] The server retrieves the skill requirements of each department from the company database and matches them with the user's skill set and emotional data. For example, if the user's skill set is "programming, data analysis" and the department's requirements are "programming, database management," it calculates a matching score.

[1630] Based on the matching results and emotion data, the server generates an optimal placement plan and sends it to the device. The device displays the placement plan to the user, who then confirms it. If the user approves the placement plan, the device sends the selection to the server. The server then updates the database with the approved placement plan, officially reflecting the user's new placement information. During this process, the user's emotion data is also saved as a log.

[1631] The server periodically schedules AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[1632] The above is a specific description of the embodiment of the present invention, which enables optimal department allocation taking into account the skills, career vision, and emotions of employees.

[1633] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1634] Processing Steps

[1635] Step 1: User authentication

[1636] 1. Input: User ID, Password

[1637] 2. The terminal receives the user ID and password from the user. Specifically, the user enters the ID and password into the login form.

[1638] 3. Output: Authentication information (user ID, password)

[1639] 4. The device sends the received authentication information to the server.

[1640] 5. The server authenticates the user against a database.

[1641] 6. Output: Authentication result (success / failure)

[1642] 7. If the server is successful, it starts a session and returns a successful authentication message to the terminal. Specifically, the server generates a new session ID and sends it to the user.

[1643] 8. The device displays a successful authentication message.

[1644] Step 2: Start the AI ​​interview

[1645] 1. Input: None (automatic process after authentication)

[1646] 2. The device displays the AI ​​interview interface to the user. Specifically, it displays a chatbot-style UI and presents the prompt, "What are your thoughts about your future career?"

[1647] 3. Output: User response (text)

[1648] 4. The user clicks the Start Interview button and begins entering their requests and career vision.

[1649] Step 3: Collect and analyze data

[1650] 1. Input: User response data (text)

[1651] 2. The device collects user response data in real time and sends it to the server via API.

[1652] 3. Output: User data sent to the server

[1653] 4. The server uses a generative AI model (e.g., GPT-3) to analyze the user data and extract skill sets and desired conditions. For example, if a user enters "I want to gain experience as a data scientist," the generative AI will extract the keyword "data scientist."

[1654] 5. Output: Extracted skill sets and desired conditions

[1655] Step 4: Recognize emotions

[1656] 1. Input: User response data (text)

[1657] 2. The server uses an emotion recognition engine to analyze user data and identify their emotional state. Specifically, it analyzes text using natural language processing technology and machine learning.

[1658] 3. Output: Emotion data (e.g., happiness, anxiety, excitement, etc.)

[1659] Step 5: Skills matching

[1660] 1. Input: User's skill set, desired conditions, and emotional data

[1661] 2. The server retrieves skill requirements data from the internal database and compares the user's skill set with the sentiment data, using SQL queries to retrieve the required data and running a matching algorithm.

[1662] 3. Output: Matching score

[1663] Step 6: Generate and present placement plans

[1664] 1. Input: Matching score, emotion data

[1665] 2. The server generates the optimal placement plan. For example, if the user is feeling anxious, it will suggest a department with strong support.

[1666] 3. Output: Layout plan

[1667] 4. The server sends the generated placement plan to the terminal.

[1668] 5. The device displays the layout plan to the user, who then confirms it.

[1669] Step 7: Approve and implement the layout plan

[1670] 1. Input: Select to approve the layout plan

[1671] 2. If the user approves the placement proposal, the device sends the selection to the server.

[1672] 3. Output: Authorization information

[1673] 4. The server updates the database with the approved placement plan, officially reflecting the user's new placement information.

[1674] 5. Output: Database update results

[1675] Step 8: Regular meetings and feedback

[1676] 1. Input: None (regular schedule)

[1677] 2. The server periodically schedules AI interviews with users and department personnel and sends reminders.

[1678] 3. Output: Reminder notification

[1679] 4. The device will notify the user of the reminder and arrange a new appointment.

[1680] 5. Output: New interview data

[1681] 6. The server analyzes the new interview data and improves placement recommendations based on feedback and emotional data.

[1682] (Application example 2)

[1683] 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."

[1684] While traditional employee management systems can take into account an employee's skill set and career vision, they are unable to take into account their emotional state. This has resulted in insufficient allocation of employees to appropriate departments and improved employee satisfaction. Furthermore, advanced matching is required, such as recognizing employee emotions in real time during interactive AI interviews and reflecting that information in allocation proposals.

[1685] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving user authentication information and performing authentication, means for providing a user interface and allowing the user to input their requests and career vision in an interactive format, means for analyzing the input data and emotional data using natural language processing technology and machine learning, means for acquiring skill requirement data from an internal database and matching it with the user's skill set, and means for calculating a matching score taking the emotional data into consideration, generating an optimal placement plan, and presenting it to the user. This enables optimal department placement that takes into consideration both the skills and emotional state of employees.

[1686] "User Credentials" means the identification and authentication information provided by a User to access a System.

[1687] A "user interface" is a visual and functional interface through which a user interacts with a system.

[1688] "Dialogue" refers to the way in which a user interacts with a system and provides information through a series of questions and responses.

[1689] "Career vision" refers to the job and skill goals that users aim for in the future.

[1690] "Natural language processing technology" is a technology that allows computers to process and understand human language.

[1691] "Machine learning" is a technique that uses algorithms to allow computers to learn from data and make predictions or classifications.

[1692] "Emotional data" is data that evaluates and quantifies a user's emotions.

[1693] A "skill set" is the collection of knowledge, skills, and abilities that a user possesses.

[1694] "Matching score" is a score calculated as a result of comparing a user's skill set with the skill requirement data in the company's database.

[1695] "Placement Suggestions" are the most suitable departments or jobs suggested based on the user's skill set, career vision, and sentiment data.

[1696] An "internal database" is a database system that stores and provides data managed within a company.

[1697] "AI interview" is a process that uses artificial intelligence to interact with users and collect and analyze information.

[1698] "Feedback" refers to evaluations and improvement suggestions generated based on collected information and analysis results.

[1699] This invention is a system that optimally manages human resources within a company and efficiently matches employees' career visions with departmental requirements. In particular, by combining it with an emotion engine that recognizes user emotions, it achieves more accurate human resource matching and department allocation.

[1700] User authentication

[1701] The server receives the user credentials and authenticates the user against a database. Once the user is authenticated, a session begins.

[1702] User Interface and AI Interviews

[1703] After authentication, the terminal provides a user interface where the user can interactively input their desires and career vision. The data entered by the user is then sent to the server.

[1704] Data collection and analysis

[1705] The server analyzes the data sent by the user using a generative AI model to extract the user's skill set and desired conditions, and simultaneously analyzes the emotional data using an emotion engine, which uses natural language processing technology and machine learning to identify the user's emotional state.

[1706] Skill matching and placement plan generation

[1707] The server retrieves the skill requirements data of each department from the company database and matches the user's skill set with the skill requirements of each department. In doing so, it calculates a matching score taking into account the emotional data, generates an optimal placement plan, and presents it to the user.

[1708] Approval and implementation of layout plan

[1709] If the user approves the proposed placement, the server updates the database with the user's selection, officially reflecting the user's placement information. The server also logs the user's emotional data.

[1710] Regular interviews and feedback

[1711] The server schedules regular AI interviews for users and department personnel and sends reminders. The device notifies the user of the reminder and conducts new interviews. The server analyzes the new interview data and improves the placement plan based on feedback and emotion data.

[1712] Specific examples

[1713] For example, if an employee types in "I want to improve my customer service skills" and the emotion engine recognizes that they are feeling anxious, it will prioritize suggesting departments with extensive training for that employee.

[1714] Prompt Sentence Examples

[1715] "Create a Python program that allows an AI interview system to suggest an appropriate department if an employee enters, 'My career vision is to improve my customer service skills' at a brick-and-mortar retail store, and the emotion engine recognizes that the employee is feeling anxious."

[1716] This invention combines users' skills, career vision, and emotional data to realize more accurate personnel allocation. For example, by using smartphones and tablets, it can improve the work efficiency and satisfaction of employees in brick-and-mortar stores.

[1717] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1718] Step 1:

[1719] The server receives the user authentication information and authenticates the user by checking it against a database. The input is the user ID and password, and the output is the authentication result (success or failure). If authentication is successful, the server starts a session and generates a session token.

[1720] Step 2:

[1721] After authentication, the terminal provides a user interface and allows the user to interactively input their aspirations and career vision. The input is text data related to the user's aspirations and career vision, and the output is the data entered by the user.

[1722] Step 3:

[1723] The server receives the user's input data sent from the device and analyzes it using a generative AI model. The input is the user's text data, and the output is the user's skill set and desired conditions. Specifically, the generative AI model analyzes the text data and extracts the user's skills and desired conditions.

[1724] Step 4:

[1725] The server passes the analyzed text data to the emotion engine, which analyzes the emotion data. The input is the user's text data, and the output is the user's emotional state (e.g., joy, anxiety, excitement, etc.). The emotion engine uses natural language processing technology and machine learning to identify emotions from the text data.

[1726] Step 5:

[1727] The server retrieves the skill requirement data of each department from the company database and matches the user's skill set with the skill requirements of each department. The input is the user's skill set and desired conditions, and the skill requirement data of each department, and the output is the matching result. Specifically, the server compares the skill set with the skill requirements and calculates a matching score based on the degree of match.

[1728] Step 6:

[1729] The server then modifies the matching score by taking into account the emotional data and generates the optimal placement proposal. The input is the matching result and emotional data, and the output is the optimal placement proposal. For example, if the user is feeling anxious, the server will prioritize departments that offer strong support.

[1730] Step 7:

[1731] The server presents the generated placement plan to the user via the terminal and asks for the user's approval. The input is the optimal placement plan, and the output is the user's approval or rejection.

[1732] Step 8:

[1733] If the user approves the proposed placement, the server updates the database with that selection to officially reflect the user's placement information. The input is the user's approval, and the output is the updated database information.

[1734] Step 9:

[1735] The server schedules regular AI interviews for users and department personnel and sends reminders. The input is the interview schedule and the output is the reminder. The terminal notifies the user of the reminder and schedules a new interview.

[1736] Step 10:

[1737] The server analyzes new interview data and improves placement suggestions based on feedback and emotional data. The input is new interview data and emotional data, and the output is improved placement suggestions and feedback. This process continuously ensures optimal placement based on changes in the user's skills and emotions.

[1738] 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.

[1739] 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.

[1740] 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.

[1741] 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.

[1742] 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.

[1743] 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.

[1744] 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).

[1745] 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.

[1746] 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."

[1747] 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.

[1748] 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).

[1749] 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.

[1750] 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.

[1751] 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.

[1752] 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.

[1753] 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.

[1754] 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.

[1755] 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.

[1756] 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.

[1757] 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.

[1758] 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.

[1759] The following is further disclosed regarding the above embodiment.

[1760] (Claim 1)

[1761] means for receiving and authenticating user authentication information;

[1762] A means for providing a user interface and allowing the user to input their desires and career vision in an interactive format;

[1763] A means for analyzing the input data and extracting the user's skill set and desired conditions;

[1764] A means of retrieving skill requirements data from an internal database and matching it with the user's skill set;

[1765] A means for generating an optimal placement plan based on the matching result and presenting it to the user;

[1766] a means for the user to approve the generated placement proposal;

[1767] a means for updating the database with approved placement proposals; and

[1768] A means to schedule regular AI interviews with users and department personnel;

[1769] A means of analyzing new feedback and improving placement proposals; and

[1770] A system including:

[1771] (Claim 2)

[1772] 2. The system according to claim 1, wherein the user's interactive input data is analyzed using natural language processing techniques.

[1773] (Claim 3)

[1774] The system of claim 1, wherein the system calculates a matching score and generates an optimal placement plan based thereon.

[1775] "Example 1"

[1776] (Claim 1)

[1777] means for receiving and authenticating user authentication information;

[1778] A means for providing a user interface and allowing the user to input their desires and career vision in an interactive format;

[1779] A means for analyzing the input data and extracting the user's skill set and desired conditions;

[1780] A means for retrieving skill requirement data from an enterprise database and matching it with a user's skill set;

[1781] a means for the user to approve the generated placement proposal;

[1782] a means for updating the database with approved placement proposals; and

[1783] A means to schedule regular AI interviews with users and department personnel;

[1784] A means of analyzing new feedback and improving placement proposals; and

[1785] A means for calculating a matching score using a generative AI model using a prompt sentence and generating an optimal placement plan based on the matching score;

[1786] A system including:

[1787] (Claim 2)

[1788] 2. The system according to claim 1, wherein the user's interactive input data is analyzed using natural language processing techniques.

[1789] (Claim 3)

[1790] The system of claim 1, further comprising: generating an optimal placement plan based on the matching score.

[1791] "Application Example 1"

[1792] (Claim 1)

[1793] means for receiving and authenticating user authentication information;

[1794] A means for providing a user interface and allowing the user to input their desires and career vision in an interactive format;

[1795] A means for analyzing the input data and extracting the user's skill set and desired conditions;

[1796] A means of retrieving skill requirements data from an internal database and matching it with the user's skill set;

[1797] A means for generating an optimal placement plan based on the matching result and presenting it to the user;

[1798] a means for the user to approve the generated placement proposal;

[1799] a means for updating the database with approved placement proposals; and

[1800] A means to schedule regular AI interviews with users and department personnel;

[1801] A means of analyzing new feedback and improving placement proposals; and

[1802] A means for inputting the skills and career vision of robot operators and matching them with the skill requirements of each robot in the factory;

[1803] A means for generating and presenting an optimal placement plan for a robot operator using a generative AI model;

[1804] A system including:

[1805] (Claim 2)

[1806] 2. The system according to claim 1, wherein the user's interactive input data is analyzed using natural language processing techniques.

[1807] (Claim 3)

[1808] The system of claim 1, wherein the system calculates a matching score and generates an optimal placement plan based thereon.

[1809] "Example 2: Combining Emotion Engines"

[1810] (Claim 1)

[1811] means for receiving and authenticating user authentication information;

[1812] A means for providing a user interface and allowing the user to input their desires and career vision in an interactive format;

[1813] A means of analyzing input data using a generative AI model to extract the user's skill set and desired conditions;

[1814] means for analyzing the user's emotion data using an emotion recognition engine;

[1815] A means of obtaining skill requirement data from an internal database and matching it with the user's skill set and emotional data.

[1816] A means for generating an optimal placement plan based on the matching result and emotion data and presenting the plan to the user;

[1817] a means for the user to approve the generated placement proposal;

[1818] a means for updating the database with approved placement proposals; and

[1819] A means to schedule regular AI interviews with users and department personnel;

[1820] A means of analyzing new feedback and improving placement proposals; and

[1821] A system including:

[1822] (Claim 2)

[1823] 2. The system according to claim 1, wherein the user's interactive input data is analyzed using natural language processing techniques.

[1824] (Claim 3)

[1825] 2. The system according to claim 1, wherein the system analyzes user emotion data and generates optimal placement plans based on the data.

[1826] "Application example 2 when combining emotion engines"

[1827] (Claim 1)

[1828] means for receiving and authenticating user authentication information;

[1829] A means for providing a user interface and allowing the user to input their desires and career vision in an interactive format;

[1830] A means for analyzing the input data and extracting the user's skill set and desired conditions;

[1831] A means for analyzing the emotion data of the input data using natural language processing technology and machine learning;

[1832] A means of retrieving skill requirements data from an internal database and matching it with the user's skill set;

[1833] A means for calculating a matching score taking into account the emotion data, generating an optimal placement plan, and presenting it to the user;

[1834] a means for the user to approve the generated placement proposal;

[1835] a means for updating the database with approved placement proposals; and

[1836] A means to schedule regular AI interviews with users and department personnel;

[1837] A means of analyzing new feedback and improving placement proposals; and

[1838] A system including:

[1839] (Claim 2)

[1840] 2. The system of claim 1, wherein the user's interactive input data and emotion data are analyzed using natural language processing techniques.

[1841] (Claim 3)

[1842] The system of claim 1, wherein the system calculates a matching score taking into account emotion data, and generates an optimal placement plan based thereon. [Explanation of symbols]

[1843] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving and authenticating user authentication information; A means for providing a user interface and allowing the user to input their desires and career vision in an interactive format; A means for analyzing the input data and extracting the user's skill set and desired conditions; A means of retrieving skill requirements data from an internal database and matching it with the user's skill set; A means for generating an optimal placement plan based on the matching result and presenting it to the user; a means for the user to approve the generated placement proposal; a means for updating the database with approved placement proposals; A means to schedule regular AI interviews with users and department personnel; A means of analyzing new feedback and improving placement proposals; and A system including:

2. 10. The system of claim 1, wherein the user's interactive input data is analyzed using natural language processing techniques.

3. The system of claim 1 , further comprising: calculating a matching score; and generating an optimal placement plan based thereon.

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