system

Abstract

Provide a system. A means for a user to input characteristics and self-reported details; A means for receiving the input information and comparing it with attribute data of multiple departments within the company; A means for generating a list of optimal departments based on the matching results; A means for presenting the generated list of departments to a user; a means for receiving and storing user feedback; A system including:

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] Many employees feel that they don't know which department is best suited to them among the multiple departments within the company. In particular, the optimal department varies depending on how you combine your characteristics, skills, interests, etc., so a system that can match them is needed. Another challenge is to improve the matching accuracy by receiving feedback from employees so that more appropriate suggestions can be made. [Means for solving the problem]

[0005] The present invention provides a system that includes a means for a user to input characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments within the company, a means for generating a list of optimal departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user. The system also includes a means for providing an interface for the user to input characteristics based on the self-reported details, and an algorithm for improving the accuracy of the next department matching based on the feedback. In this way, a system is provided that not only makes it easier for employees to find the department that best suits them, but also allows for constant improvement of accuracy using feedback.

[0006] "User" refers to a person or entity, such as an employee, who uses an information system or application.

[0007] "Characteristics" refers to factors such as a user's unique skills, interests, experiences, and abilities.

[0008] "Self-reported content" refers to all the information that a user enters about their own characteristics.

[0009] "Input means" refers to an interface or device that allows a user to input characteristics and self-reported information.

[0010] "Means for receiving" refers to a device or program that receives and processes input information on the system side.

[0011] "Means of matching" refers to the algorithms and processes used to compare user input with attribute data from multiple departments within the company and identify matching departments.

[0012] "Means for generating" refers to the process or program for creating a list of optimal departments based on the matching results.

[0013] The "presentation means" refers to a display or user interface for displaying the generated list of departments to the user.

[0014] "Means for receiving feedback" refers to interfaces and functions for collecting opinions and evaluations from users and sending them to the system.

[0015] "Means for storing" refers to devices or programs for recording and storing received feedback in a database or the like.

[0016] "Interface" refers to a screen or interactive system through which a user inputs information.

[0017] "Algorithm" refers to the calculation methods or procedures used to improve the accuracy of the next department match based on feedback.

[0018] "System" refers to a collection of a series of devices and programs that have an overall structure and function including the above means. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7]FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] 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.

[0021] First, the terms used in the following description will be explained.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 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.

[0030] 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).

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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."

[0040] The present invention relates to a system that introduces users to the most suitable departments based on their characteristics and self-reported details. This system includes a means for users to input their characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments within the company, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[0041] System program and processing description

[0042] User Registration and Login

[0043] When a user first accesses the system, they enter the necessary information into the registration form, such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0044] Enter information

[0045] After the user logs in, an interface (screen) for entering characteristics and self-declaration details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. The terminal then sends this information to the server.

[0046] matching

[0047] The server receives the user's characteristics and self-reported information and compares it with the attribute data of each department within the company. An algorithm is used to compare the user's information with the department's attribute data and calculate a matching score for each department. For example, if a user is interested in "researching new technologies" and has "programming" skills, the R&D (research and development) department will receive a high score.

[0048] Presentation of results

[0049] The server generates the matching results and creates a list of the best departments. This list is sorted by score and sent to the device. The device displays the results in the format "The departments that are best suited for you are: 1. R&D 2. Technical Support 3. IT Department."

[0050] feedback

[0051] An interface is provided on the terminal for users to input feedback on the results. The user inputs their thoughts and wishes about the departments presented and sends the feedback to the server. For example, they can send a comment such as, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[0052] The server receives this feedback and stores it in a database, which is used to improve the accuracy of the matching algorithm next time.

[0053] Specific examples

[0054] For example, if an employee named Tanaka uses the system, it will look like this:

[0055] 1. Tanaka accesses the system and registers by entering his name, employee number, email address, and password.

[0056] 2. After logging in, a screen will appear where you can enter your characteristics and self-declaration details. Tanaka enters "Programming," "Research into new technologies," and "5 years of sales experience."

[0057] 3. The server receives this and matches it with each department in the company to generate a list of the best fit. R&D, Tech Support, and IT are deemed suitable.

[0058] 4. The generated department list is displayed on Tanaka's terminal, showing, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0059] 5. Tanaka provides feedback on the results presented, commenting, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[0060] 6. The server receives and stores this feedback and uses it to improve the accuracy of next suggestions.

[0061] Thus, the present invention is a system that helps employees easily find the department that best suits them, thereby supporting their career paths.

[0062] The processing flow will be explained below.

[0063] Step 1:

[0064] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[0065] Step 2:

[0066] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[0067] Step 3:

[0068] The user enters a username and password into the login form. The device sends this information to the server.

[0069] Step 4:

[0070] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[0071] Step 5:

[0072] After the user logs in, an interface for entering characteristics and self-declaration details is displayed on the terminal. The user enters characteristics (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[0073] Step 6:

[0074] The device sends the entered characteristics and self-declared information to the server, which receives this information and stores it in a database.

[0075] Step 7:

[0076] The server analyzes the user's characteristics and self-reported information, compares it with the attribute data of multiple departments within the company, and calculates a matching score for each department using a specific algorithm.

[0077] Step 8:

[0078] The server generates a list of the most suitable departments based on the matching scores, sorts the list by score, and sends the list to the terminal.

[0079] Step 9:

[0080] The device receives the list from the server and displays it to the user, for example, "The departments that best suit you are: 1. R&D 2. Tech Support 3. IT Department."

[0081] Step 10:

[0082] An interface for allowing the user to provide feedback on the presented department list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[0083] Step 11:

[0084] The device sends the user's feedback to the server, which receives the feedback and stores it in a database.

[0085] Step 12:

[0086] Based on the feedback information, the server will make adjustments to improve the accuracy of the matching algorithm for the next time, and this information will be used in future department suggestions.

[0087] Example 1

[0088] 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."

[0089] In recent years, placing the right people in the right positions within a company has become increasingly important, but there is a problem in that it is difficult to assign employees to the most appropriate department based on their characteristics and self-reported information. Conventional systems make it difficult to accurately grasp the characteristics and preferences of individual employees and propose appropriate departments. In addition, there is a lack of a mechanism for reflecting employee feedback in future proposals, making it difficult to improve the accuracy of matching.

[0090] 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.

[0091] In this invention, the server includes means for a user to input characteristics and self-reported details, means for receiving the input information and comparing it with attribute data of multiple departments within the organization, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, and means for receiving and saving feedback from the user. This allows employees to easily find the optimal department based on their characteristics and preferences, and also allows the collected feedback to be reflected in improving the accuracy of next suggestions.

[0092] "User" refers to a person who uses the system to input their characteristics and self-declaration information and receive suggestions for the most suitable department.

[0093] "Characteristics" refers to information that describes a person's individual abilities and preferences, including their skills, interests, experience, etc.

[0094] "Self-reporting" refers to the act of a user providing their own characteristics and wishes to the system, or the content of such acts.

[0095] The term "means" refers to functions or devices necessary to realize the present invention.

[0096] "Interface" refers to the screens and utilities that allow a user to input information and provide feedback to a system.

[0097] "Server" refers to a computing device that receives information sent by users and checks it against a database.

[0098] "Database" means a collection of data that stores input information and is used for verification and matching purposes.

[0099] "Algorithm" refers to the calculation procedure for comparing a user's characteristics and self-reported details with department attribute data and calculating a matching score.

[0100] "Feedback" refers to the act of a user providing their thoughts and wishes about the optimal department presented by the system, or the content of such actions.

[0101] The present invention is a system that suggests the most suitable department based on the user's characteristics and self-reported details. This system includes a means for the user to input the characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments existing in the organization, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[0102] To realize this system, a server, terminals, a database, and appropriate algorithms are required. The server acts as a central processing unit, receiving information from users, comparing it with the database to calculate matching results, and finally presenting them to the users.

[0103] First, a user accesses the system using a device (e.g., PC or smartphone) and registers by entering their name, employee number, email address, and password. This information is sent from the device to the server, which then stores it in a database (e.g., MySQL (registered trademark), PostgreSQL). When logging in, the user enters their email address and password again from the same device, and the server compares them with the database for authentication. If authentication is successful, the server generates an authentication token (e.g., JWT token) and sends it to the device.

[0104] Next, after the user logs in, an interface for entering characteristics and self-declared details (e.g., skills, interests, experience) is displayed on the terminal. When the user enters and submits this information (e.g., "programming," "researching new technologies," "five years of sales experience," etc.), the terminal sends the information to the server. Based on the information received, the server compares it with the attribute data of each department within the organization and calculates a matching score using an algorithm (e.g., similarity calculation).

[0105] Based on the matching results, the server generates a list of the most suitable departments, and then compares it with the database to create a list of departments with the highest scores. This list is sent to the device and displayed to the user as, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0106] Additionally, users can provide feedback on the results. For example, they can say, "The R&D department is interesting, but I'd like to know more about the marketing department." This feedback is received by the server and stored in a database. This feedback information is used to improve the accuracy of the matching algorithm next time.

[0107] For example, the following prompt sentence is input to the generative AI model:

[0108] "We are developing a system that introduces the most suitable departments to users based on their characteristics and self-reported information. Please design an algorithm to compare the user's input data with the company's department attribute data, and generate a list of the most suitable departments based on the results. Please also include a mechanism to collect user feedback information and improve the accuracy of the next proposal."

[0109] This allows the invention to help employees find the department that best suits them and support their career path.

[0110] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0111] Step 1: User Registration

[0112] A user accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends the entered information to the server, which receives the information and stores it in a database.

[0113] Input: Name, employee number, email address, password

[0114] Processing: The server receives this information and stores it in the database using an INSERT statement.

[0115] Output: Notification of successful registration

[0116] Specific operation: A user opens a browser, accesses a registration page, fills in each field of the form, and clicks the submit button. The device generates an HTTP request and sends it to the server, which stores it in the database.

[0117] Step 2: User Login

[0118] A user accesses the login page and enters their email address and password. The device sends this information to the server, which then authenticates them by checking the information against a database. If authentication is successful, the server generates an authentication token and sends it to the device.

[0119] Input: Email address, password

[0120] Processing: The server executes a SELECT statement against the database to verify the user information, and if it matches, generates an authentication token.

[0121] Output: Authentication token, successful login notification

[0122] Specific operation: The user enters an email address and password on the login screen and clicks the submit button. The device generates an HTTP request and sends it to the server, which queries the database for authentication and returns the result to the device.

[0123] Step 3: Enter your information

[0124] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, and experience, and the terminal sends this information to the server.

[0125] Input: Skills, Interests, Experience

[0126] Processing: The device collects this information and sends it to the server, which receives it and stores it in a database.

[0127] Output: Notification that input information has been saved

[0128] Specific operation: After logging in, the user enters their own characteristics and skills into the input form and clicks the submit button. The device sends the information to the server, and the server stores the received information in the database.

[0129] Step 4: Matching

[0130] After the server receives the user's input information, it compares it with the department attribute data within the organization. An algorithm is used to match the user information with the department attribute data and calculate a matching score for each department.

[0131] Input: User information, department attribute data

[0132] Processing: The server receives this information and uses an algorithm to calculate a score.

[0133] Output: Matching scores for each department

[0134] Specific operation: The server retrieves department attribute data from the database, runs an algorithm to calculate the match with the user's skills and interests, and stores the calculated score in the database.

[0135] Step 5: Presenting the results

[0136] The server generates matching results and creates a list of the most suitable departments, which is then sent to the terminal and presented to the user.

[0137] Input: Matching score

[0138] Processing: The server generates a department list in order of score and sends it to the terminal.

[0139] Output: List of best departments

[0140] Specific operation: The server creates a list of the most suitable departments based on the matching score and sends it to the terminal. The user's screen displays, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0141] Step 6: Gather feedback

[0142] The user inputs feedback on the results, and the device sends it to the server, which receives the feedback and stores it in a database.

[0143] Input: Feedback (thoughts and wishes)

[0144] Processing: The server receives the feedback information and stores it in a database.

[0145] Output: Feedback saved successfully

[0146] Specific operation: The user enters a comment on the feedback input screen and clicks the send button. The device sends the information to the server, which stores it in the database.

[0147] (Application example 1)

[0148] 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."

[0149] In conventional systems, there was a mechanism for users to find the most suitable department based on their self-reported information, but there was no mechanism for robots in industrial sites to find the most suitable work tasks or work locations based on self-reporting. As a result, the robot's characteristics and skills could not be fully utilized, resulting in a problem of reduced work efficiency.

[0150] 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.

[0151] In this invention, the server includes means for a user to input characteristics and self-declared details, means for receiving the input information and comparing it with attribute data of multiple departments within the company, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, means for receiving and saving feedback from the user, means for a robot to self-declare and input characteristics and skills, means for assigning optimal work locations and work tasks based on the self-declared data, and means for presenting the generated list of work tasks. This makes it easier for the robot to find optimal work tasks and work locations, thereby improving work efficiency.

[0152] A "user" is an entity that uses the system to input characteristics and self-declaration details.

[0153] "Characteristics" refers to the skills, abilities, and experience possessed by the user or robot.

[0154] "Self-reporting" refers to the act of a user or robot inputting information such as characteristics, skills, and experience.

[0155] A "department" is a division within an organization that is responsible for a specific task or job.

[0156] "Attribute data" is information about the requirements and characteristics of each department and work task.

[0157] "Matching" refers to the act of comparing the characteristics of the user or robot with the attribute data of the department or work task, and making the most appropriate proposal.

[0158] "Feedback" is the act of a user or a robot providing their thoughts or opinions on a presented department or work task.

[0159] The "system" is a set of mechanisms that allows users and robots to input their characteristics and self-declared information and suggest the most suitable departments and work tasks.

[0160] A "robot" is a machine that performs tasks in industrial settings and self-reports its characteristics and skills.

[0161] A "work task" refers to a specific job or task that a robot performs in a factory or other setting.

[0162] A "list" is a document that refers to a list of optimal departments and work tasks generated by the system.

[0163] A system embodying this invention includes a means for a user to input characteristics and self-declared details, a means for comparing the received information with attribute data of multiple departments within the company, a means for generating a list of optimal departments, a means for presenting the generated list of departments to the user, a means for receiving and saving feedback from the user, a means for a robot to self-declare and input characteristics and skills, a means for assigning optimal work locations and work tasks based on the self-declared data, and a means for presenting a list of generated work tasks.

[0164] In implementation, the following main requirements are met:

[0165] 1. User Registration and Login:

[0166] A user accesses the system and fills in a registration form with information such as their name, characteristics, skills, etc. The server stores this information in a database and authenticates them when they log in. This process uses a web server and a database (e.g., MySQL, PostgreSQL).

[0167] 2. Enter your information:

[0168] After logging in, the user is provided with a web interface to input their characteristics and self-reported information. The input information is sent to the server and stored in a database. Specifically, a dynamic interface using HTML forms and JavaScript (registered trademark) is used.

[0169] 3. Matching:

[0170] The server compares the received user and robot characteristics and self-reported information with data on departments and work tasks across the company using machine learning algorithms (e.g., k-nearest neighbor (kNN) or random forest). Based on the matching score calculated by the algorithm, the server lists the most suitable departments and work tasks.

[0171] 4. Presentation of results:

[0172] The matching results are sent from the server to the device and displayed in list form to the user or robot. JavaScript frameworks such as React and Vue.js are used for front-end development.

[0173] 5. Feedback:

[0174] The user provides feedback on the presented list, which is then sent to the server to help improve the accuracy of the matching algorithm next time.

[0175] As a concrete example, the following prompt sentence can be used:

[0176] "Robot ID:1's current skill set is welding and cutting. What work task is it suitable for?"

[0177] "Do you want to log in the robot with ID 1?"

[0178] "Robo1's specialty is welding. Would you like to assign it the most suitable work task?"

[0179] Please enter your work feedback.

[0180] This allows users and robots to input their own characteristics, skills, and experience, and based on that, find the most suitable department or work task. Furthermore, feedback improves the system's accuracy, increasing its long-term value.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1: User Registration and Login

[0183] Users access the system using a terminal and enter the required information such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server, and the server compares the information with the database for authentication. If authentication is successful, it generates an authentication token and notifies the user.

[0184] Input: User information (name, employee number, email address, password)

[0185] Output: Authentication token

[0186] Step 2: Enter your information

[0187] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, experience, etc., and the terminal sends this information to the server, which stores it in a database.

[0188] Input: User characteristics and self-reported information (skills, interests, experience)

[0189] Output: User information stored in the database

[0190] Step 3: Robot Registration

[0191] The robot uses a terminal to access the system and input information such as its ID, characteristics, and skills. The terminal then sends this information to the server, which stores it in a database.

[0192] Input: Robot information (ID, characteristics, skills)

[0193] Output: Robot information stored in the database

[0194] Step 4: Matching

[0195] The server receives the user and robot's characteristics and self-reported information, compares it with the attribute data of each department and work task within the company, calculates a matching score using machine learning algorithms (kNN, Random Forest, etc.), and generates a list of the most suitable departments and work tasks.

[0196] Input: User information, robot information, department attribute data

[0197] Output: Matching results (department list, task list)

[0198] Step 5: Presenting the results

[0199] The server sends the generated matching results to the terminal, which displays the optimal departments and work tasks in a list format for the user and the robot. The terminal then presents the results to the user and the robot.

[0200] Input: Matching results

[0201] Output: Displayed optimal department list, task list

[0202] Step 6: Feedback

[0203] The user and the robot input feedback on the presented list, and the terminal sends the feedback information to the server, which stores it in a database and uses it to improve the accuracy of the matching algorithm next time.

[0204] Input: User and robot feedback

[0205] Output: Feedback information stored in a database

[0206] 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.

[0207] This invention combines a system that introduces the most suitable department based on the user's characteristics and self-reported details with an emotion engine that recognizes the user's emotions. By taking into account the user's emotion data in addition to conventional matching algorithms, this system makes it possible to propose a department that is more appropriate and satisfies the user.

[0208] System program and processing description

[0209] User Registration and Login

[0210] A user first accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0211] Information input and emotion recognition

[0212] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information.

[0213] Matching and emotional data utilization

[0214] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This calculation, which also takes into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[0215] Presentation of results

[0216] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0217] Storing feedback and sentiment data

[0218] The user inputs their thoughts and wishes through an interface to provide feedback on the displayed results. The emotion engine recognizes the user's emotions when providing feedback, and sends the data and the feedback content to the server.

[0219] The server receives the feedback and emotion data and stores it in a database, which is then used to improve the accuracy of the matching algorithm next time.

[0220] Specific examples

[0221] For example, if an employee named Suzuki uses the system, it will look like this:

[0222] 1. Suzuki accesses the system and registers by entering his name, employee number, email address, and password.

[0223] 2. After logging in, a screen appears where users can enter their characteristics and self-declaration. Suzuki enters "programming," "research into new technologies," and "five years of sales experience." At the same time, the emotion engine recognizes Suzuki's emotions from his facial expressions and voice, and obtains emotional data such as "satisfaction."

[0224] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[0225] 4. The generated department list is displayed on Suzuki's device, showing, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0226] 5. Suzuki provides feedback on the presented results, commenting, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine recognizes Suzuki's emotions again, and emotional data such as "interest" is sent to the server.

[0227] 6. The server receives and stores this feedback and sentiment data, which it uses to improve the accuracy of its next suggestions.

[0228] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction, and can constantly improve its accuracy using feedback and sentiment data.

[0229] The processing flow will be explained below.

[0230] Step 1:

[0231] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[0232] Step 2:

[0233] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[0234] Step 3:

[0235] The user enters a username and password into the login form. The device sends this information to the server.

[0236] Step 4:

[0237] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[0238] Step 5:

[0239] After logging in, the user is presented with an interface on their device for entering their characteristics and self-declaration information. The user enters their skills (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[0240] Step 6:

[0241] This is a new process in which an emotion engine works to recognize emotions during the user input process. For example, it analyzes emotions from the user's facial expressions and voice through a camera or microphone. The recognized emotion data is sent to the server along with the user's characteristics and self-reported content.

[0242] Step 7:

[0243] The device sends the input characteristics, self-reported details, and emotional data to the server, which receives this information and stores it in a database.

[0244] Step 8:

[0245] The server analyzes the user's characteristics, self-reported details, and emotional data, and compares them with the attribute data of multiple departments within the company. A specific algorithm is used to calculate a matching score for each department. Taking emotional data into account results in more accurate matching results.

[0246] Step 9:

[0247] The server generates a list of the most suitable departments based on the matching scores, and provides the list to the user in order of the scores, taking into account the emotional data.

[0248] Step 10:

[0249] The terminal receives the list from the server and displays it to the user, for example, in the form of "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0250] Step 11:

[0251] An interface for the user to provide feedback on the presented result list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[0252] Step 12:

[0253] This is the process where the emotion engine works again to recognize the emotion when the user inputs feedback. The emotion engine analyzes the emotion from the user's facial expression and voice, and sends the emotion data to the server along with the feedback.

[0254] Step 13:

[0255] The device sends user feedback and emotion data to the server, which receives the feedback and emotion data and stores it in a database. This data is used to improve the accuracy of the matching algorithm next time.

[0256] Example 2

[0257] 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."

[0258] Conventional department matching systems propose the optimal department by considering only the user's characteristics and self-reported information. However, this approach does not take into account the user's emotions or satisfaction, and may not select a department that the user will actually be satisfied with. In addition, since it is not possible to effectively utilize feedback from users, it is difficult to improve the accuracy of the entire system.

[0259] 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.

[0260] In this invention, the server includes: a means for a user to input characteristics and self-reported details; a means for acquiring emotion data using an emotion engine that recognizes the user's emotions; a means for transmitting the acquired emotion data and self-reported information to the server; a means for comparing the information received by the server with attribute data of multiple departments within the company and calculating a matching score; a means for generating a list of optimal departments based on the calculated matching score; a means for presenting the generated list of departments to the user; and a means for receiving and saving feedback from the user regarding the presented list of departments. This enables department matching that takes user emotions into consideration, thereby increasing user satisfaction. Furthermore, by reflecting user feedback in the next matching, the accuracy of the system itself can be continuously improved.

[0261] A "user" is someone who uses the system to input their own characteristics and self-declaration information to find the most suitable department.

[0262] "Characteristics" are elements that users input into the system, such as their skills, interests, and experience.

[0263] "Self-reported content" refers to information reported by users themselves, including characteristics, preferences, and wishes.

[0264] "Terminal" means a device through which a user accesses the system, enters information, and checks results.

[0265] A "server" is a computer system that receives information sent by users, processes and stores data, and generates matching results.

[0266] An "emotion engine" is software or hardware that recognizes emotions from a user's facial expressions, voice, etc.

[0267] "Emotion data" refers to the user's emotional information recognized by the emotion engine.

[0268] A "matching algorithm" is a method or formula for calculating the optimal department based on a user's characteristics and emotional data.

[0269] "Matching score" is a numerical value of compatibility with each department calculated by the matching algorithm.

[0270] "Attribute data" refers to information about the characteristics and requirements of each department within a company.

[0271] "Feedback" refers to opinions or comments provided by a user regarding the results presented.

[0272] This system allows users to find the most suitable department based on their own characteristics and self-reported information. By combining it with an emotion engine, it can achieve more accurate department selection by taking into account the user's emotional data.

[0273] User Registration and Login

[0274] First, a user accesses the system and enters information such as their name, employee number, email address, and password into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the terminal sends the information entered by the user back to the server, and the server compares the information with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0275] Information input and emotion recognition

[0276] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research into new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information. The emotion engine analyzes the user's facial expressions and voice to obtain emotion data such as satisfaction and interest.

[0277] Matching and emotional data utilization

[0278] The server receives the user's characteristic data and emotional data from the device. It then references the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This matching algorithm also includes emotional data, making it possible to identify the most suitable department while taking the user's emotions into consideration. For example, the R&D department may be displayed at the top of the list with a "high" emotional score.

[0279] Presentation of results

[0280] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. The generated department list is displayed on the user's device in the following format, for example, "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0281] Storing feedback and sentiment data

[0282] The user inputs their wishes and thoughts through an interface for providing feedback on the presented matching results. When providing feedback, the emotion engine recognizes the user's emotions again and sends this data and the feedback content to the server. The server receives the feedback and emotion data and stores them in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[0283] Specific examples

[0284] For example, let us assume that a certain user (let's call him Mr. A) uses the system.

[0285] 1. Mr. A accesses the system and registers by entering his name, employee number, email address, and password.

[0286] 2. After logging in, a screen for entering characteristics and self-declaration details appears on the device. Mr. A enters his skill "programming," his interest "researching new technologies," and his experience "five years of sales experience." At the same time, the emotion engine obtains the emotion data "satisfaction" from Mr. A's facial expressions and voice.

[0287] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[0288] 4. The generated department list is displayed on Mr. A's device, and he is told, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0289] 5. Mr. A provides feedback on the presented results, saying, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine then recognizes Mr. A's emotional data, such as his "interest," and sends it to the server.

[0290] 6. The server receives and stores the feedback and sentiment data, which is used to improve the accuracy of the next suggestion.

[0291] Example prompts to input to the generative AI model

[0292] By inputting the following prompt sentences into the generative AI model, system explanations and concrete examples can be generated.

[0293] Prompt statement

[0294] "Generate a description of a system that introduces the most suitable department to a user based on their characteristics and self-reported information. This system uses an emotion engine to recognize the user's emotions, and by incorporating these into the matching algorithm, suggests the most appropriate department. Please provide a detailed explanation, including the overall flow of the system and specific examples."

[0295] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction. It is also possible to use feedback to improve the accuracy of suggestions next time.

[0296] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0297] Step 1: User Registration

[0298] User: A user accesses the system and enters information such as their name, employee number, email address, and password.

[0299] Input: Name, employee number, email address, and password entered by the user

[0300] Terminal: Receives the entered information and sends it to the server.

[0301] Server: Stores the received information in a database.

[0302] Output: User information is registered in the database.

[0303] Step 2: Log in

[0304] User: Attempts to log in by entering email address and password.

[0305] Input: User-entered email address and password

[0306] Terminal: Sends the entered information to the server.

[0307] Server: Compares the received information with a database and performs authentication.

[0308] Data calculation: Matching email addresses and passwords stored in a database.

[0309] Output: An authentication token is generated and sent back to the terminal, informing the user that the login was successful.

[0310] Step 3: Display the information entry screen

[0311] Terminal: After successful login, display an interface for entering characteristics and self-declaration details.

[0312] Output: A screen will appear where you can enter your characteristics and self-declaration information.

[0313] Step 4: Enter your user information

[0314] User: Enter your skills (e.g. programming), interests (e.g. researching new technologies), experience (e.g. 5 years of sales experience), etc.

[0315] Input: User-entered skills, interests, and experience

[0316] Terminal: Receives the input information.

[0317] Output: Get the received information.

[0318] Step 5: Emotion Recognition

[0319] Device: Runs the emotion engine and acquires emotion data from the user's facial expressions and voice.

[0320] Input: User's facial expression, voice

[0321] Data calculation: The emotion engine analyzes facial and voice data to generate emotion data (e.g., satisfaction, interest).

[0322] Output: Generated emotion data

[0323] Step 6: Send data to the server

[0324] Terminal: Sends the acquired characteristic data and emotion data to the server.

[0325] Input: Trait data, emotion data

[0326] Output: Data is sent to the server.

[0327] Step 7: Calculating the Matching Score

[0328] Server: Based on the received user characteristic data and emotion data, it compares it with attribute data from multiple departments within the company.

[0329] Input: User characteristics data, emotion data, and attribute data for each department within the company

[0330] Data calculation: A specific matching algorithm is used to calculate the matching score for each department.

[0331] Output: Matching scores for each department

[0332] Step 8: Generate matching results

[0333] Server: Generates a list of optimal departments based on the calculated matching scores.

[0334] Input: Matching score

[0335] Output: List of best departments

[0336] Step 9: Presenting the results

[0337] Device: The generated list of optimal departments is displayed on the user's device.

[0338] Input: List of best departments

[0339] Output: "The department that best suits you is: 1. R&D (high sentiment score) 2. Tech Support (medium sentiment score) 3. IT (low sentiment score)."

[0340] Step 10: Provide feedback

[0341] User: Provide feedback on the results presented. For example, comment, "The R&D department is interesting, but I'd like to know more about the sales department."

[0342] Input: Feedback

[0343] Device: When providing feedback, the emotion engine is run again to obtain the user's emotion data (e.g., interest).

[0344] Data calculation: The emotion engine retrieves emotion data in relation to the provided feedback content.

[0345] Output: User's emotion data and feedback content

[0346] Step 11: Storing Feedback and Sentiment Data

[0347] Device: Sends the acquired feedback and emotion data to the server.

[0348] Input: Feedback content, emotion data

[0349] Server: Receives feedback and emotion data and stores it in a database.

[0350] Output: The feedback and sentiment data stored in the database will be used to improve the accuracy of the matching algorithm next time.

[0351] (Application example 2)

[0352] 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."

[0353] Conventional department introduction systems matched users based solely on their characteristics and self-reported information, without considering their emotions or reactions, making it difficult to suggest the department that was best suited to them. This resulted in lower user satisfaction and the inability to match users to the appropriate department. Furthermore, because the system did not consider the user's emotional feedback, it was difficult to improve the accuracy of the next match.

[0354] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0355] In this invention, the server includes means for recognizing user emotion data using an emotion engine, means for generating a list of optimal departments based on the matching results including the emotion data, and means further including an emotion engine for recognizing the user's emotion in real time regarding the displayed list of departments. This makes it possible to analyze the user's emotion in real time and suggest optimal departments based on that, improving user satisfaction and also improving the accuracy of the next matching.

[0356] A "user" is an individual user of a particular system or application.

[0357] "Characteristics" refers to information such as a user's skills, experience, and interests.

[0358] "Self-reporting" is information that the user himself / herself applies for through an input interface.

[0359] "Emotion engine" refers to a hardware or software system for recognizing a user's emotion data.

[0360] "Emotion data" is data related to emotions classified from the user's facial expressions and voice recognized by the emotion engine.

[0361] "Matching" is the process of checking the relevance of the input data to the corresponding department, product, etc.

[0362] A "department" refers to a group or division within an organization that performs specific tasks or functions.

[0363] An "algorithm" is a set of procedures or processes that formulate steps or calculation methods to achieve a certain purpose.

[0364] A "list" refers to a list or table organized according to specific criteria.

[0365] "Feedback" refers to information such as ratings and opinions provided by users.

[0366] This invention combines an emotion engine with a system that introduces the most suitable department based on the user's characteristics and self-reported details. By taking into account the user's emotion data in addition to conventional matching algorithms, this system can propose a department that is more appropriate and satisfies the user.

[0367] System program and processing description

[0368] User Registration and Login

[0369] A user first accesses the system and enters the required information (name, identification number, email address, password) into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0370] Information input and emotion recognition

[0371] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. At this time, an emotion engine (e.g., Hugging Face emotion recognition model) recognizes the user's emotions and sends them to the server along with the entered information.

[0372] Matching and emotional data utilization

[0373] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the organization and calculates a matching score for each department using a specific algorithm. This calculation, taking into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[0374] Presentation of results

[0375] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. Research and Development (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0376] Storing feedback and sentiment data

[0377] Users input their thoughts and wishes through an interface to provide feedback on the displayed results. When providing feedback, the emotion engine also recognizes the user's emotions and sends the data and feedback content to the server. The server receives the feedback and emotion data and stores it in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[0378] Specific examples

[0379] For example, if a user is browsing a "smartwatch" page and their facial expressions are captured by a camera, and the emotion engine recognizes expressions such as "excitement" or "satisfaction," the server can recommend similar products (e.g., the latest fitness trackers or high-end smartwatches) based on the emotion data in real time.

[0380] Prompt Sentence Examples

[0381] An example of a prompt sentence to input to the generative AI model is as follows:

[0382] 1. Prompt sentence for emotion recognition model

[0383] "Analyze image data to recognize emotions."

[0384] 2. Prompt for product recommendation API

[0385] "Recommend the best products to users based on this sentiment data."

[0386] This enables real-time emotion-based product recommendations to users.

[0387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0388] Step 1:

[0389] The user enters characteristics and self-declaration details.

[0390] Input: A user enters information into a registration form, such as their name, identification number, email address, and password.

[0391] Processing: The device receives this information and sends it to the server.

[0392] Output: The server stores this information in a database.

[0393] Step 2:

[0394] A user logs in.

[0395] Input: The user enters their email address and password into the login interface.

[0396] Processing: The terminal sends the entered information to the server, which checks it against a database for authentication.

[0397] Output: If authentication is successful, the server generates an authentication token and sends it to the device, which receives it and logs in successfully.

[0398] Step 3:

[0399] The user enters detailed characteristics and self-reported information.

[0400] Input: Users input their skills, interests, experience, etc.

[0401] Processing: The device sends this information to the server, and the emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[0402] Output: The emotion data and input data are sent to the server and stored.

[0403] Step 4:

[0404] The server uses the emotion data to analyze the user's characteristics.

[0405] Input: The server receives the user's characteristic information and emotion data.

[0406] Processing: The server inputs this data into an algorithm, compares it with department attribute data, and calculates a matching score.

[0407] Output: A list of matches is generated.

[0408] Step 5:

[0409] The server presents a list of the most suitable departments.

[0410] Input: The server generates a list of matching results.

[0411] Processing: The list is sorted by score and sent to the user's device, taking into account the emotional data.

[0412] Output: The device displays a list of the following types of departments: 1. Research & Development (High Sentiment Score) 2. Tech Support (Medium Sentiment Score) 3. IT (Low Sentiment Score)

[0413] Step 6:

[0414] The user provides feedback.

[0415] Input: The user enters feedback on the displayed results.

[0416] Processing: The device sends the feedback content to the server. At the same time, the emotion engine recognizes the emotion data when providing the feedback and sends it to the server.

[0417] Output: Feedback data and emotion data are stored on the server.

[0418] Step 7:

[0419] The server stores the feedback and emotion data to improve the accuracy of the next match.

[0420] Input: The server receives feedback data and emotion data.

[0421] Processing: These data are stored in a database and used as training data to improve the accuracy of the matching algorithm.

[0422] Output: The accuracy of the matching algorithm will be improved next time, and it will be possible to suggest more suitable departments to the user.

[0423] In this way, by utilizing emotional data, it is possible to more accurately grasp the characteristics and satisfaction level of users and match them with the appropriate department.

[0424] 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.

[0425] 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.

[0426] 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.

[0427] [Second embodiment]

[0428] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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).

[0434] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0435] 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.

[0436] 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.

[0437] 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.

[0438] In the smart glasses 214, 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.

[0439] 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."

[0440] The present invention relates to a system that introduces users to the most suitable departments based on their characteristics and self-reported details. This system includes a means for users to input their characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments within the company, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[0441] System program and processing description

[0442] User Registration and Login

[0443] When a user first accesses the system, they enter the necessary information into the registration form, such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0444] Enter information

[0445] After the user logs in, an interface (screen) for entering characteristics and self-declaration details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. The terminal then sends this information to the server.

[0446] matching

[0447] The server receives the user's characteristics and self-reported information and compares it with the attribute data of each department within the company. An algorithm is used to compare the user's information with the department's attribute data and calculate a matching score for each department. For example, if a user is interested in "researching new technologies" and has "programming" skills, the R&D (research and development) department will receive a high score.

[0448] Presentation of results

[0449] The server generates the matching results and creates a list of the best departments. This list is sorted by score and sent to the device. The device displays the results in the format "The departments that are best suited for you are: 1. R&D 2. Technical Support 3. IT Department."

[0450] feedback

[0451] An interface is provided on the terminal for users to input feedback on the results. The user inputs their thoughts and wishes about the departments presented and sends the feedback to the server. For example, they can send a comment such as, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[0452] The server receives this feedback and stores it in a database, which is used to improve the accuracy of the matching algorithm next time.

[0453] Specific examples

[0454] For example, if an employee named Tanaka uses the system, it will look like this:

[0455] 1. Tanaka accesses the system and registers by entering his name, employee number, email address, and password.

[0456] 2. After logging in, a screen will appear where you can enter your characteristics and self-declaration details. Tanaka enters "Programming," "Research into new technologies," and "5 years of sales experience."

[0457] 3. The server receives this and matches it with each department in the company to generate a list of the best fit. R&D, Tech Support, and IT are deemed suitable.

[0458] 4. The generated department list is displayed on Tanaka's terminal, showing, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0459] 5. Tanaka provides feedback on the results presented, commenting, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[0460] 6. The server receives and stores this feedback and uses it to improve the accuracy of next suggestions.

[0461] Thus, the present invention is a system that helps employees easily find the department that best suits them, thereby supporting their career paths.

[0462] The processing flow will be explained below.

[0463] Step 1:

[0464] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[0465] Step 2:

[0466] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[0467] Step 3:

[0468] The user enters a username and password into the login form. The device sends this information to the server.

[0469] Step 4:

[0470] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[0471] Step 5:

[0472] After the user logs in, an interface for entering characteristics and self-declaration details is displayed on the terminal. The user enters characteristics (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[0473] Step 6:

[0474] The device sends the entered characteristics and self-declared information to the server, which receives this information and stores it in a database.

[0475] Step 7:

[0476] The server analyzes the user's characteristics and self-reported information, compares it with the attribute data of multiple departments within the company, and calculates a matching score for each department using a specific algorithm.

[0477] Step 8:

[0478] The server generates a list of the most suitable departments based on the matching scores, sorts the list by score, and sends the list to the terminal.

[0479] Step 9:

[0480] The device receives the list from the server and displays it to the user, for example, "The departments that best suit you are: 1. R&D 2. Tech Support 3. IT Department."

[0481] Step 10:

[0482] An interface for allowing the user to provide feedback on the presented department list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[0483] Step 11:

[0484] The device sends the user's feedback to the server, which receives the feedback and stores it in a database.

[0485] Step 12:

[0486] Based on the feedback information, the server will make adjustments to improve the accuracy of the matching algorithm for the next time, and this information will be used in future department suggestions.

[0487] Example 1

[0488] 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."

[0489] In recent years, placing the right people in the right positions within a company has become increasingly important, but there is a problem in that it is difficult to assign employees to the most appropriate department based on their characteristics and self-reported information. Conventional systems make it difficult to accurately grasp the characteristics and preferences of individual employees and propose appropriate departments. In addition, there is a lack of a mechanism for reflecting employee feedback in future proposals, making it difficult to improve the accuracy of matching.

[0490] 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.

[0491] In this invention, the server includes means for a user to input characteristics and self-reported details, means for receiving the input information and comparing it with attribute data of multiple departments within the organization, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, and means for receiving and saving feedback from the user. This allows employees to easily find the optimal department based on their characteristics and preferences, and also allows the collected feedback to be reflected in improving the accuracy of next suggestions.

[0492] "User" refers to a person who uses the system to input their characteristics and self-declaration information and receive suggestions for the most suitable department.

[0493] "Characteristics" refers to information that describes a person's individual abilities and preferences, including their skills, interests, experience, etc.

[0494] "Self-reporting" refers to the act of a user providing their own characteristics and wishes to the system, or the content of such acts.

[0495] The term "means" refers to functions or devices necessary to realize the present invention.

[0496] "Interface" refers to the screens and utilities that allow a user to input information and provide feedback to a system.

[0497] "Server" refers to a computing device that receives information sent by users and checks it against a database.

[0498] "Database" means a collection of data that stores input information and is used for verification and matching purposes.

[0499] "Algorithm" refers to the calculation procedure for comparing a user's characteristics and self-reported details with department attribute data and calculating a matching score.

[0500] "Feedback" refers to the act of a user providing their thoughts and wishes about the optimal department presented by the system, or the content of such actions.

[0501] The present invention is a system that suggests the most suitable department based on the user's characteristics and self-reported details. This system includes a means for the user to input the characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments existing in the organization, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[0502] To realize this system, a server, terminals, a database, and appropriate algorithms are required. The server acts as a central processing unit, receiving information from users, comparing it with the database to calculate matching results, and finally presenting them to the users.

[0503] First, a user accesses the system using a device (e.g., PC or smartphone) and registers by entering their name, employee number, email address, and password. This information is sent from the device to the server, which stores it in a database (e.g., MySQL or PostgreSQL). When logging in, the user enters their email address and password again from the same device, and the server compares them with the database for authentication. If authentication is successful, the server generates an authentication token (e.g., JWT token) and sends it to the device.

[0504] Next, after the user logs in, an interface for entering characteristics and self-declared details (e.g., skills, interests, experience) is displayed on the terminal. When the user enters and submits this information (e.g., "programming," "researching new technologies," "five years of sales experience," etc.), the terminal sends the information to the server. Based on the information received, the server compares it with the attribute data of each department within the organization and calculates a matching score using an algorithm (e.g., similarity calculation).

[0505] Based on the matching results, the server generates a list of the most suitable departments, and then compares it with the database to create a list of departments with the highest scores. This list is sent to the device and displayed to the user as, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0506] Additionally, users can provide feedback on the results. For example, they can say, "The R&D department is interesting, but I'd like to know more about the marketing department." This feedback is received by the server and stored in a database. This feedback information is used to improve the accuracy of the matching algorithm next time.

[0507] For example, the following prompt sentence is input to the generative AI model:

[0508] "We are developing a system that introduces the most suitable departments to users based on their characteristics and self-reported information. Please design an algorithm to compare the user's input data with the company's department attribute data, and generate a list of the most suitable departments based on the results. Please also include a mechanism to collect user feedback information and improve the accuracy of the next proposal."

[0509] This allows the invention to help employees find the department that best suits them and support their career path.

[0510] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0511] Step 1: User Registration

[0512] A user accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends the entered information to the server, which receives the information and stores it in a database.

[0513] Input: Name, employee number, email address, password

[0514] Processing: The server receives this information and stores it in the database using an INSERT statement.

[0515] Output: Notification of successful registration

[0516] Specific operation: A user opens a browser, accesses a registration page, fills in each field of the form, and clicks the submit button. The device generates an HTTP request and sends it to the server, which stores it in the database.

[0517] Step 2: User Login

[0518] A user accesses the login page and enters their email address and password. The device sends this information to the server, which then authenticates them by checking the information against a database. If authentication is successful, the server generates an authentication token and sends it to the device.

[0519] Input: Email address, password

[0520] Processing: The server executes a SELECT statement against the database to verify the user information, and if it matches, generates an authentication token.

[0521] Output: Authentication token, successful login notification

[0522] Specific operation: The user enters an email address and password on the login screen and clicks the submit button. The device generates an HTTP request and sends it to the server, which queries the database for authentication and returns the result to the device.

[0523] Step 3: Enter your information

[0524] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, and experience, and the terminal sends this information to the server.

[0525] Input: Skills, Interests, Experience

[0526] Processing: The device collects this information and sends it to the server, which receives it and stores it in a database.

[0527] Output: Notification that input information has been saved

[0528] Specific operation: After logging in, the user enters their own characteristics and skills into the input form and clicks the submit button. The device sends the information to the server, and the server stores the received information in the database.

[0529] Step 4: Matching

[0530] After the server receives the user's input information, it compares it with the department attribute data within the organization. An algorithm is used to match the user information with the department attribute data and calculate a matching score for each department.

[0531] Input: User information, department attribute data

[0532] Processing: The server receives this information and uses an algorithm to calculate a score.

[0533] Output: Matching scores for each department

[0534] Specific operation: The server retrieves department attribute data from the database, runs an algorithm to calculate the match with the user's skills and interests, and stores the calculated score in the database.

[0535] Step 5: Presenting the results

[0536] The server generates matching results and creates a list of the most suitable departments, which is then sent to the terminal and presented to the user.

[0537] Input: Matching score

[0538] Processing: The server generates a department list in order of score and sends it to the terminal.

[0539] Output: List of best departments

[0540] Specific operation: The server creates a list of the most suitable departments based on the matching score and sends it to the terminal. The user's screen displays, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0541] Step 6: Gather feedback

[0542] The user inputs feedback on the results, and the device sends it to the server, which receives the feedback and stores it in a database.

[0543] Input: Feedback (thoughts and wishes)

[0544] Processing: The server receives the feedback information and stores it in a database.

[0545] Output: Feedback saved successfully

[0546] Specific operation: The user enters a comment on the feedback input screen and clicks the send button. The device sends the information to the server, which stores it in the database.

[0547] (Application example 1)

[0548] 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."

[0549] In conventional systems, there was a mechanism for users to find the most suitable department based on their self-reported information, but there was no mechanism for robots in industrial sites to find the most suitable work tasks or work locations based on self-reporting. As a result, the robot's characteristics and skills could not be fully utilized, resulting in a problem of reduced work efficiency.

[0550] 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.

[0551] In this invention, the server includes means for a user to input characteristics and self-declared details, means for receiving the input information and comparing it with attribute data of multiple departments within the company, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, means for receiving and saving feedback from the user, means for a robot to self-declare and input characteristics and skills, means for assigning optimal work locations and work tasks based on the self-declared data, and means for presenting the generated list of work tasks. This makes it easier for the robot to find optimal work tasks and work locations, thereby improving work efficiency.

[0552] A "user" is an entity that uses the system to input characteristics and self-declaration details.

[0553] "Characteristics" refers to the skills, abilities, and experience possessed by the user or robot.

[0554] "Self-reporting" refers to the act of a user or robot inputting information such as characteristics, skills, and experience.

[0555] A "department" is a division within an organization that is responsible for a specific task or job.

[0556] "Attribute data" is information about the requirements and characteristics of each department and work task.

[0557] "Matching" refers to the act of comparing the characteristics of the user or robot with the attribute data of the department or work task, and making the most appropriate proposal.

[0558] "Feedback" is the act of a user or a robot providing their thoughts or opinions on a presented department or work task.

[0559] The "system" is a set of mechanisms that allows users and robots to input their characteristics and self-declared information and suggest the most suitable departments and work tasks.

[0560] A "robot" is a machine that performs tasks in industrial settings and self-reports its characteristics and skills.

[0561] A "work task" refers to a specific job or task that a robot performs in a factory or other setting.

[0562] A "list" is a document that refers to a list of optimal departments and work tasks generated by the system.

[0563] A system embodying this invention includes a means for a user to input characteristics and self-declared details, a means for comparing the received information with attribute data of multiple departments within the company, a means for generating a list of optimal departments, a means for presenting the generated list of departments to the user, a means for receiving and saving feedback from the user, a means for a robot to self-declare and input characteristics and skills, a means for assigning optimal work locations and work tasks based on the self-declared data, and a means for presenting a list of generated work tasks.

[0564] In implementation, the following main requirements are met:

[0565] 1. User Registration and Login:

[0566] A user accesses the system and fills in a registration form with information such as their name, characteristics, skills, etc. The server stores this information in a database and authenticates them when they log in. This process uses a web server and a database (e.g., MySQL, PostgreSQL).

[0567] 2. Enter your information:

[0568] After logging in, users are provided with a web interface to input their characteristics and self-reported information. The information is sent to a server and stored in a database. Specifically, a dynamic interface using HTML forms and JavaScript is used.

[0569] 3. Matching:

[0570] The server compares the received user and robot characteristics and self-reported information with data on departments and work tasks across the company using machine learning algorithms (e.g., k-nearest neighbor (kNN) or random forest). Based on the matching score calculated by the algorithm, the server lists the most suitable departments and work tasks.

[0571] 4. Presentation of results:

[0572] The matching results are sent from the server to the device and displayed in list form to the user or robot. JavaScript frameworks such as React and Vue.js are used for front-end development.

[0573] 5. Feedback:

[0574] The user provides feedback on the presented list, which is then sent to the server to help improve the accuracy of the matching algorithm next time.

[0575] As a concrete example, the following prompt sentence can be used:

[0576] "Robot ID:1's current skill set is welding and cutting. What work task is it suitable for?"

[0577] "Do you want to log in the robot with ID 1?"

[0578] "Robo1's specialty is welding. Would you like to assign it the most suitable work task?"

[0579] Please enter your work feedback.

[0580] This allows users and robots to input their own characteristics, skills, and experience, and based on that, find the most suitable department or work task. Furthermore, feedback improves the system's accuracy, increasing its long-term value.

[0581] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0582] Step 1: User Registration and Login

[0583] Users access the system using a terminal and enter the required information such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server, and the server compares the information with the database for authentication. If authentication is successful, it generates an authentication token and notifies the user.

[0584] Input: User information (name, employee number, email address, password)

[0585] Output: Authentication token

[0586] Step 2: Enter your information

[0587] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, experience, etc., and the terminal sends this information to the server, which stores it in a database.

[0588] Input: User characteristics and self-reported information (skills, interests, experience)

[0589] Output: User information stored in the database

[0590] Step 3: Robot Registration

[0591] The robot uses a terminal to access the system and input information such as its ID, characteristics, and skills. The terminal then sends this information to the server, which stores it in a database.

[0592] Input: Robot information (ID, characteristics, skills)

[0593] Output: Robot information stored in the database

[0594] Step 4: Matching

[0595] The server receives the user and robot's characteristics and self-reported information, compares it with the attribute data of each department and work task within the company, calculates a matching score using machine learning algorithms (kNN, Random Forest, etc.), and generates a list of the most suitable departments and work tasks.

[0596] Input: User information, robot information, department attribute data

[0597] Output: Matching results (department list, task list)

[0598] Step 5: Presenting the results

[0599] The server sends the generated matching results to the terminal, which displays the optimal departments and work tasks in a list format for the user and the robot. The terminal then presents the results to the user and the robot.

[0600] Input: Matching results

[0601] Output: Displayed optimal department list, task list

[0602] Step 6: Feedback

[0603] The user and the robot input feedback on the presented list, and the terminal sends the feedback information to the server, which stores it in a database and uses it to improve the accuracy of the matching algorithm next time.

[0604] Input: User and robot feedback

[0605] Output: Feedback information stored in a database

[0606] 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.

[0607] This invention combines a system that introduces the most suitable department based on the user's characteristics and self-reported details with an emotion engine that recognizes the user's emotions. By taking into account the user's emotion data in addition to conventional matching algorithms, this system makes it possible to propose a department that is more appropriate and satisfies the user.

[0608] System program and processing description

[0609] User Registration and Login

[0610] A user first accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0611] Information input and emotion recognition

[0612] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information.

[0613] Matching and emotional data utilization

[0614] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This calculation, which also takes into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[0615] Presentation of results

[0616] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0617] Storing feedback and sentiment data

[0618] The user inputs their thoughts and wishes through an interface to provide feedback on the displayed results. The emotion engine recognizes the user's emotions when providing feedback, and sends the data and the feedback content to the server.

[0619] The server receives the feedback and emotion data and stores it in a database, which is then used to improve the accuracy of the matching algorithm next time.

[0620] Specific examples

[0621] For example, if an employee named Suzuki uses the system, it will look like this:

[0622] 1. Suzuki accesses the system and registers by entering his name, employee number, email address, and password.

[0623] 2. After logging in, a screen appears where users can enter their characteristics and self-declaration. Suzuki enters "programming," "research into new technologies," and "five years of sales experience." At the same time, the emotion engine recognizes Suzuki's emotions from his facial expressions and voice, and obtains emotional data such as "satisfaction."

[0624] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[0625] 4. The generated department list is displayed on Suzuki's device, showing, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0626] 5. Suzuki provides feedback on the presented results, commenting, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine recognizes Suzuki's emotions again, and emotional data such as "interest" is sent to the server.

[0627] 6. The server receives and stores this feedback and sentiment data, which it uses to improve the accuracy of its next suggestions.

[0628] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction, and can constantly improve its accuracy using feedback and sentiment data.

[0629] The processing flow will be explained below.

[0630] Step 1:

[0631] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[0632] Step 2:

[0633] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[0634] Step 3:

[0635] The user enters a username and password into the login form. The device sends this information to the server.

[0636] Step 4:

[0637] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[0638] Step 5:

[0639] After logging in, the user is presented with an interface on their device for entering their characteristics and self-declaration information. The user enters their skills (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[0640] Step 6:

[0641] This is a new process in which an emotion engine works to recognize emotions during the user input process. For example, it analyzes emotions from the user's facial expressions and voice through a camera or microphone. The recognized emotion data is sent to the server along with the user's characteristics and self-reported content.

[0642] Step 7:

[0643] The device sends the input characteristics, self-reported details, and emotional data to the server, which receives this information and stores it in a database.

[0644] Step 8:

[0645] The server analyzes the user's characteristics, self-reported details, and emotional data, and compares them with the attribute data of multiple departments within the company. A specific algorithm is used to calculate a matching score for each department. Taking emotional data into account results in more accurate matching results.

[0646] Step 9:

[0647] The server generates a list of the most suitable departments based on the matching scores, and provides the list to the user in order of the scores, taking into account the emotional data.

[0648] Step 10:

[0649] The terminal receives the list from the server and displays it to the user, for example, in the form of "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0650] Step 11:

[0651] An interface for the user to provide feedback on the presented result list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[0652] Step 12:

[0653] This is the process where the emotion engine works again to recognize the emotion when the user inputs feedback. The emotion engine analyzes the emotion from the user's facial expression and voice, and sends the emotion data to the server along with the feedback.

[0654] Step 13:

[0655] The device sends user feedback and emotion data to the server, which receives the feedback and emotion data and stores it in a database. This data is used to improve the accuracy of the matching algorithm next time.

[0656] Example 2

[0657] 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."

[0658] Conventional department matching systems propose the optimal department by considering only the user's characteristics and self-reported information. However, this approach does not take into account the user's emotions or satisfaction, and may not select a department that the user will actually be satisfied with. In addition, since it is not possible to effectively utilize feedback from users, it is difficult to improve the accuracy of the entire system.

[0659] 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.

[0660] In this invention, the server includes: a means for a user to input characteristics and self-reported details; a means for acquiring emotion data using an emotion engine that recognizes the user's emotions; a means for transmitting the acquired emotion data and self-reported information to the server; a means for comparing the information received by the server with attribute data of multiple departments within the company and calculating a matching score; a means for generating a list of optimal departments based on the calculated matching score; a means for presenting the generated list of departments to the user; and a means for receiving and saving feedback from the user regarding the presented list of departments. This enables department matching that takes user emotions into consideration, thereby increasing user satisfaction. Furthermore, by reflecting user feedback in the next matching, the accuracy of the system itself can be continuously improved.

[0661] A "user" is someone who uses the system to input their own characteristics and self-declaration information to find the most suitable department.

[0662] "Characteristics" are elements that users input into the system, such as their skills, interests, and experience.

[0663] "Self-reported content" refers to information reported by users themselves, including characteristics, preferences, and wishes.

[0664] "Terminal" means a device through which a user accesses the system, enters information, and checks results.

[0665] A "server" is a computer system that receives information sent by users, processes and stores data, and generates matching results.

[0666] An "emotion engine" is software or hardware that recognizes emotions from a user's facial expressions, voice, etc.

[0667] "Emotion data" refers to the user's emotional information recognized by the emotion engine.

[0668] A "matching algorithm" is a method or formula for calculating the optimal department based on a user's characteristics and emotional data.

[0669] "Matching score" is a numerical value of compatibility with each department calculated by the matching algorithm.

[0670] "Attribute data" refers to information about the characteristics and requirements of each department within a company.

[0671] "Feedback" refers to opinions or comments provided by a user regarding the results presented.

[0672] This system allows users to find the most suitable department based on their own characteristics and self-reported information. By combining it with an emotion engine, it can achieve more accurate department selection by taking into account the user's emotional data.

[0673] User Registration and Login

[0674] First, a user accesses the system and enters information such as their name, employee number, email address, and password into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the terminal sends the information entered by the user back to the server, and the server compares the information with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0675] Information input and emotion recognition

[0676] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research into new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information. The emotion engine analyzes the user's facial expressions and voice to obtain emotion data such as satisfaction and interest.

[0677] Matching and emotional data utilization

[0678] The server receives the user's characteristic data and emotional data from the device. It then references the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This matching algorithm also includes emotional data, making it possible to identify the most suitable department while taking the user's emotions into consideration. For example, the R&D department may be displayed at the top of the list with a "high" emotional score.

[0679] Presentation of results

[0680] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. The generated department list is displayed on the user's device in the following format, for example, "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0681] Storing feedback and sentiment data

[0682] The user inputs their wishes and thoughts through an interface for providing feedback on the presented matching results. When providing feedback, the emotion engine recognizes the user's emotions again and sends this data and the feedback content to the server. The server receives the feedback and emotion data and stores them in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[0683] Specific examples

[0684] For example, let us assume that a certain user (let's call him Mr. A) uses the system.

[0685] 1. Mr. A accesses the system and registers by entering his name, employee number, email address, and password.

[0686] 2. After logging in, a screen for entering characteristics and self-declaration details appears on the device. Mr. A enters his skill "programming," his interest "researching new technologies," and his experience "five years of sales experience." At the same time, the emotion engine obtains the emotion data "satisfaction" from Mr. A's facial expressions and voice.

[0687] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[0688] 4. The generated department list is displayed on Mr. A's device, and he is told, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0689] 5. Mr. A provides feedback on the presented results, saying, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine then recognizes Mr. A's emotional data, such as his "interest," and sends it to the server.

[0690] 6. The server receives and stores the feedback and sentiment data, which is used to improve the accuracy of the next suggestion.

[0691] Example prompts to input to the generative AI model

[0692] By inputting the following prompt sentences into the generative AI model, system explanations and concrete examples can be generated.

[0693] Prompt statement

[0694] "Generate a description of a system that introduces the most suitable department to a user based on their characteristics and self-reported information. This system uses an emotion engine to recognize the user's emotions, and by incorporating these into the matching algorithm, suggests the most appropriate department. Please provide a detailed explanation, including the overall flow of the system and specific examples."

[0695] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction. It is also possible to use feedback to improve the accuracy of suggestions next time.

[0696] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0697] Step 1: User Registration

[0698] User: A user accesses the system and enters information such as their name, employee number, email address, and password.

[0699] Input: Name, employee number, email address, and password entered by the user

[0700] Terminal: Receives the entered information and sends it to the server.

[0701] Server: Stores the received information in a database.

[0702] Output: User information is registered in the database.

[0703] Step 2: Log in

[0704] User: Attempts to log in by entering email address and password.

[0705] Input: User-entered email address and password

[0706] Terminal: Sends the entered information to the server.

[0707] Server: Compares the received information with a database and performs authentication.

[0708] Data calculation: Matching email addresses and passwords stored in a database.

[0709] Output: An authentication token is generated and sent back to the terminal, informing the user that the login was successful.

[0710] Step 3: Display the information entry screen

[0711] Terminal: After successful login, display an interface for entering characteristics and self-declaration details.

[0712] Output: A screen will appear where you can enter your characteristics and self-declaration information.

[0713] Step 4: Enter your user information

[0714] User: Enter your skills (e.g. programming), interests (e.g. researching new technologies), experience (e.g. 5 years of sales experience), etc.

[0715] Input: User-entered skills, interests, and experience

[0716] Terminal: Receives the input information.

[0717] Output: Get the received information.

[0718] Step 5: Emotion Recognition

[0719] Device: Runs the emotion engine and acquires emotion data from the user's facial expressions and voice.

[0720] Input: User's facial expression, voice

[0721] Data calculation: The emotion engine analyzes facial and voice data to generate emotion data (e.g., satisfaction, interest).

[0722] Output: Generated emotion data

[0723] Step 6: Send data to the server

[0724] Terminal: Sends the acquired characteristic data and emotion data to the server.

[0725] Input: Trait data, emotion data

[0726] Output: Data is sent to the server.

[0727] Step 7: Calculating the Matching Score

[0728] Server: Based on the received user characteristic data and emotion data, it compares it with attribute data from multiple departments within the company.

[0729] Input: User characteristics data, emotion data, and attribute data for each department within the company

[0730] Data calculation: A specific matching algorithm is used to calculate the matching score for each department.

[0731] Output: Matching scores for each department

[0732] Step 8: Generate matching results

[0733] Server: Generates a list of optimal departments based on the calculated matching scores.

[0734] Input: Matching score

[0735] Output: List of best departments

[0736] Step 9: Presenting the results

[0737] Device: The generated list of optimal departments is displayed on the user's device.

[0738] Input: List of best departments

[0739] Output: "The department that best suits you is: 1. R&D (high sentiment score) 2. Tech Support (medium sentiment score) 3. IT (low sentiment score)."

[0740] Step 10: Provide feedback

[0741] User: Provide feedback on the results presented. For example, comment, "The R&D department is interesting, but I'd like to know more about the sales department."

[0742] Input: Feedback

[0743] Device: When providing feedback, the emotion engine is run again to obtain the user's emotion data (e.g., interest).

[0744] Data calculation: The emotion engine retrieves emotion data in relation to the provided feedback content.

[0745] Output: User's emotion data and feedback content

[0746] Step 11: Storing Feedback and Sentiment Data

[0747] Device: Sends the acquired feedback and emotion data to the server.

[0748] Input: Feedback content, emotion data

[0749] Server: Receives feedback and emotion data and stores it in a database.

[0750] Output: The feedback and sentiment data stored in the database will be used to improve the accuracy of the matching algorithm next time.

[0751] (Application example 2)

[0752] 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."

[0753] Conventional department introduction systems matched users based solely on their characteristics and self-reported information, without considering their emotions or reactions, making it difficult to suggest the department that was best suited to them. This resulted in lower user satisfaction and the inability to match users to the appropriate department. Furthermore, because the system did not consider the user's emotional feedback, it was difficult to improve the accuracy of the next match.

[0754] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0755] In this invention, the server includes means for recognizing user emotion data using an emotion engine, means for generating a list of optimal departments based on the matching results including the emotion data, and means further including an emotion engine for recognizing the user's emotion in real time regarding the displayed list of departments. This makes it possible to analyze the user's emotion in real time and suggest optimal departments based on that, improving user satisfaction and also improving the accuracy of the next matching.

[0756] A "user" is an individual user of a particular system or application.

[0757] "Characteristics" refers to information such as a user's skills, experience, and interests.

[0758] "Self-reporting" is information that the user himself / herself applies for through an input interface.

[0759] "Emotion engine" refers to a hardware or software system for recognizing a user's emotion data.

[0760] "Emotion data" is data related to emotions classified from the user's facial expressions and voice recognized by the emotion engine.

[0761] "Matching" is the process of checking the relevance of the input data to the corresponding department, product, etc.

[0762] A "department" refers to a group or division within an organization that performs specific tasks or functions.

[0763] An "algorithm" is a set of procedures or processes that formulate steps or calculation methods to achieve a certain purpose.

[0764] A "list" refers to a list or table organized according to specific criteria.

[0765] "Feedback" refers to information such as ratings and opinions provided by users.

[0766] This invention combines an emotion engine with a system that introduces the most suitable department based on the user's characteristics and self-reported details. By taking into account the user's emotion data in addition to conventional matching algorithms, this system can propose a department that is more appropriate and satisfies the user.

[0767] System program and processing description

[0768] User Registration and Login

[0769] A user first accesses the system and enters the required information (name, identification number, email address, password) into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0770] Information input and emotion recognition

[0771] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. At this time, an emotion engine (e.g., Hugging Face emotion recognition model) recognizes the user's emotions and sends them to the server along with the entered information.

[0772] Matching and emotional data utilization

[0773] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the organization and calculates a matching score for each department using a specific algorithm. This calculation, taking into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[0774] Presentation of results

[0775] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. Research and Development (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[0776] Storing feedback and sentiment data

[0777] Users input their thoughts and wishes through an interface to provide feedback on the displayed results. When providing feedback, the emotion engine also recognizes the user's emotions and sends the data and feedback content to the server. The server receives the feedback and emotion data and stores it in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[0778] Specific examples

[0779] For example, if a user is browsing a "smartwatch" page and their facial expressions are captured by a camera, and the emotion engine recognizes expressions such as "excitement" or "satisfaction," the server can recommend similar products (e.g., the latest fitness trackers or high-end smartwatches) based on the emotion data in real time.

[0780] Prompt Sentence Examples

[0781] An example of a prompt sentence to input to the generative AI model is as follows:

[0782] 1. Prompt sentence for emotion recognition model

[0783] "Analyze image data to recognize emotions."

[0784] 2. Prompt for product recommendation API

[0785] "Recommend the best products to users based on this sentiment data."

[0786] This enables real-time emotion-based product recommendations to users.

[0787] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0788] Step 1:

[0789] The user enters characteristics and self-declaration details.

[0790] Input: A user enters information into a registration form, such as their name, identification number, email address, and password.

[0791] Processing: The device receives this information and sends it to the server.

[0792] Output: The server stores this information in a database.

[0793] Step 2:

[0794] A user logs in.

[0795] Input: The user enters their email address and password into the login interface.

[0796] Processing: The terminal sends the entered information to the server, which checks it against a database for authentication.

[0797] Output: If authentication is successful, the server generates an authentication token and sends it to the device, which receives it and logs in successfully.

[0798] Step 3:

[0799] The user enters detailed characteristics and self-reported information.

[0800] Input: Users input their skills, interests, experience, etc.

[0801] Processing: The device sends this information to the server, and the emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[0802] Output: The emotion data and input data are sent to the server and stored.

[0803] Step 4:

[0804] The server uses the emotion data to analyze the user's characteristics.

[0805] Input: The server receives the user's characteristic information and emotion data.

[0806] Processing: The server inputs this data into an algorithm, compares it with department attribute data, and calculates a matching score.

[0807] Output: A list of matches is generated.

[0808] Step 5:

[0809] The server presents a list of the most suitable departments.

[0810] Input: The server generates a list of matching results.

[0811] Processing: The list is sorted by score and sent to the user's device, taking into account the emotional data.

[0812] Output: The device displays a list of the following types of departments: 1. Research & Development (High Sentiment Score) 2. Tech Support (Medium Sentiment Score) 3. IT (Low Sentiment Score)

[0813] Step 6:

[0814] The user provides feedback.

[0815] Input: The user enters feedback on the displayed results.

[0816] Processing: The device sends the feedback content to the server. At the same time, the emotion engine recognizes the emotion data when providing the feedback and sends it to the server.

[0817] Output: Feedback data and emotion data are stored on the server.

[0818] Step 7:

[0819] The server stores the feedback and emotion data to improve the accuracy of the next match.

[0820] Input: The server receives feedback data and emotion data.

[0821] Processing: These data are stored in a database and used as training data to improve the accuracy of the matching algorithm.

[0822] Output: The accuracy of the matching algorithm will be improved next time, and it will be possible to suggest more suitable departments to the user.

[0823] In this way, by utilizing emotional data, it is possible to more accurately grasp the characteristics and satisfaction level of users and match them with the appropriate department.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] [Third embodiment]

[0828] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0829] 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.

[0830] 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).

[0831] 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.

[0832] 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.

[0833] 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).

[0834] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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."

[0840] The present invention relates to a system that introduces users to the most suitable departments based on their characteristics and self-reported details. This system includes a means for users to input their characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments within the company, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[0841] System program and processing description

[0842] User Registration and Login

[0843] When a user first accesses the system, they enter the necessary information into the registration form, such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[0844] Enter information

[0845] After the user logs in, an interface (screen) for entering characteristics and self-declaration details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. The terminal then sends this information to the server.

[0846] matching

[0847] The server receives the user's characteristics and self-reported information and compares it with the attribute data of each department within the company. An algorithm is used to compare the user's information with the department's attribute data and calculate a matching score for each department. For example, if a user is interested in "researching new technologies" and has "programming" skills, the R&D (research and development) department will receive a high score.

[0848] Presentation of results

[0849] The server generates the matching results and creates a list of the best departments. This list is sorted by score and sent to the device. The device displays the results in the format "The departments that are best suited for you are: 1. R&D 2. Technical Support 3. IT Department."

[0850] feedback

[0851] An interface is provided on the terminal for users to input feedback on the results. The user inputs their thoughts and wishes about the departments presented and sends the feedback to the server. For example, they can send a comment such as, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[0852] The server receives this feedback and stores it in a database, which is used to improve the accuracy of the matching algorithm next time.

[0853] Specific examples

[0854] For example, if an employee named Tanaka uses the system, it will look like this:

[0855] 1. Tanaka accesses the system and registers by entering his name, employee number, email address, and password.

[0856] 2. After logging in, a screen will appear where you can enter your characteristics and self-declaration details. Tanaka enters "Programming," "Research into new technologies," and "5 years of sales experience."

[0857] 3. The server receives this and matches it with each department in the company to generate a list of the best fit. R&D, Tech Support, and IT are deemed suitable.

[0858] 4. The generated department list is displayed on Tanaka's terminal, showing, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0859] 5. Tanaka provides feedback on the results presented, commenting, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[0860] 6. The server receives and stores this feedback and uses it to improve the accuracy of next suggestions.

[0861] Thus, the present invention is a system that helps employees easily find the department that best suits them, thereby supporting their career paths.

[0862] The processing flow will be explained below.

[0863] Step 1:

[0864] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[0865] Step 2:

[0866] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[0867] Step 3:

[0868] The user enters a username and password into the login form. The device sends this information to the server.

[0869] Step 4:

[0870] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[0871] Step 5:

[0872] After the user logs in, an interface for entering characteristics and self-declaration details is displayed on the terminal. The user enters characteristics (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[0873] Step 6:

[0874] The device sends the entered characteristics and self-declared information to the server, which receives this information and stores it in a database.

[0875] Step 7:

[0876] The server analyzes the user's characteristics and self-reported information, compares it with the attribute data of multiple departments within the company, and calculates a matching score for each department using a specific algorithm.

[0877] Step 8:

[0878] The server generates a list of the most suitable departments based on the matching scores, sorts the list by score, and sends the list to the terminal.

[0879] Step 9:

[0880] The device receives the list from the server and displays it to the user, for example, "The departments that best suit you are: 1. R&D 2. Tech Support 3. IT Department."

[0881] Step 10:

[0882] An interface for allowing the user to provide feedback on the presented department list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[0883] Step 11:

[0884] The device sends the user's feedback to the server, which receives the feedback and stores it in a database.

[0885] Step 12:

[0886] Based on the feedback information, the server will make adjustments to improve the accuracy of the matching algorithm for the next time, and this information will be used in future department suggestions.

[0887] Example 1

[0888] 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."

[0889] In recent years, placing the right people in the right positions within a company has become increasingly important, but there is a problem in that it is difficult to assign employees to the most appropriate department based on their characteristics and self-reported information. Conventional systems make it difficult to accurately grasp the characteristics and preferences of individual employees and propose appropriate departments. In addition, there is a lack of a mechanism for reflecting employee feedback in future proposals, making it difficult to improve the accuracy of matching.

[0890] 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.

[0891] In this invention, the server includes means for a user to input characteristics and self-reported details, means for receiving the input information and comparing it with attribute data of multiple departments within the organization, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, and means for receiving and saving feedback from the user. This allows employees to easily find the optimal department based on their characteristics and preferences, and also allows the collected feedback to be reflected in improving the accuracy of next suggestions.

[0892] "User" refers to a person who uses the system to input their characteristics and self-declaration information and receive suggestions for the most suitable department.

[0893] "Characteristics" refers to information that describes a person's individual abilities and preferences, including their skills, interests, experience, etc.

[0894] "Self-reporting" refers to the act of a user providing their own characteristics and wishes to the system, or the content of such acts.

[0895] The term "means" refers to functions or devices necessary to realize the present invention.

[0896] "Interface" refers to the screens and utilities that allow a user to input information and provide feedback to a system.

[0897] "Server" refers to a computing device that receives information sent by users and checks it against a database.

[0898] "Database" means a collection of data that stores input information and is used for verification and matching purposes.

[0899] "Algorithm" refers to the calculation procedure for comparing a user's characteristics and self-reported details with department attribute data and calculating a matching score.

[0900] "Feedback" refers to the act of a user providing their thoughts and wishes about the optimal department presented by the system, or the content of such actions.

[0901] The present invention is a system that suggests the most suitable department based on the user's characteristics and self-reported details. This system includes a means for the user to input the characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments existing in the organization, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[0902] To realize this system, a server, terminals, a database, and appropriate algorithms are required. The server acts as a central processing unit, receiving information from users, comparing it with the database to calculate matching results, and finally presenting them to the users.

[0903] First, a user accesses the system using a device (e.g., PC or smartphone) and registers by entering their name, employee number, email address, and password. This information is sent from the device to the server, which stores it in a database (e.g., MySQL or PostgreSQL). When logging in, the user enters their email address and password again from the same device, and the server compares them with the database for authentication. If authentication is successful, the server generates an authentication token (e.g., JWT token) and sends it to the device.

[0904] Next, after the user logs in, an interface for entering characteristics and self-declared details (e.g., skills, interests, experience) is displayed on the terminal. When the user enters and submits this information (e.g., "programming," "researching new technologies," "five years of sales experience," etc.), the terminal sends the information to the server. Based on the information received, the server compares it with the attribute data of each department within the organization and calculates a matching score using an algorithm (e.g., similarity calculation).

[0905] Based on the matching results, the server generates a list of the most suitable departments, and then compares it with the database to create a list of departments with the highest scores. This list is sent to the device and displayed to the user as, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0906] Additionally, users can provide feedback on the results. For example, they can say, "The R&D department is interesting, but I'd like to know more about the marketing department." This feedback is received by the server and stored in a database. This feedback information is used to improve the accuracy of the matching algorithm next time.

[0907] For example, the following prompt sentence is input to the generative AI model:

[0908] "We are developing a system that introduces the most suitable departments to users based on their characteristics and self-reported information. Please design an algorithm to compare the user's input data with the company's department attribute data, and generate a list of the most suitable departments based on the results. Please also include a mechanism to collect user feedback information and improve the accuracy of the next proposal."

[0909] This allows the invention to help employees find the department that best suits them and support their career path.

[0910] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0911] Step 1: User Registration

[0912] A user accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends the entered information to the server, which receives the information and stores it in a database.

[0913] Input: Name, employee number, email address, password

[0914] Processing: The server receives this information and stores it in the database using an INSERT statement.

[0915] Output: Notification of successful registration

[0916] Specific operation: A user opens a browser, accesses a registration page, fills in each field of the form, and clicks the submit button. The device generates an HTTP request and sends it to the server, which stores it in the database.

[0917] Step 2: User Login

[0918] A user accesses the login page and enters their email address and password. The device sends this information to the server, which then authenticates them by checking the information against a database. If authentication is successful, the server generates an authentication token and sends it to the device.

[0919] Input: Email address, password

[0920] Processing: The server executes a SELECT statement against the database to verify the user information, and if it matches, generates an authentication token.

[0921] Output: Authentication token, successful login notification

[0922] Specific operation: The user enters an email address and password on the login screen and clicks the submit button. The device generates an HTTP request and sends it to the server, which queries the database for authentication and returns the result to the device.

[0923] Step 3: Enter your information

[0924] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, and experience, and the terminal sends this information to the server.

[0925] Input: Skills, Interests, Experience

[0926] Processing: The device collects this information and sends it to the server, which receives it and stores it in a database.

[0927] Output: Notification that input information has been saved

[0928] Specific operation: After logging in, the user enters their own characteristics and skills into the input form and clicks the submit button. The device sends the information to the server, and the server stores the received information in the database.

[0929] Step 4: Matching

[0930] After the server receives the user's input information, it compares it with the department attribute data within the organization. An algorithm is used to match the user information with the department attribute data and calculate a matching score for each department.

[0931] Input: User information, department attribute data

[0932] Processing: The server receives this information and uses an algorithm to calculate a score.

[0933] Output: Matching scores for each department

[0934] Specific operation: The server retrieves department attribute data from the database, runs an algorithm to calculate the match with the user's skills and interests, and stores the calculated score in the database.

[0935] Step 5: Presenting the results

[0936] The server generates matching results and creates a list of the most suitable departments, which is then sent to the terminal and presented to the user.

[0937] Input: Matching score

[0938] Processing: The server generates a department list in order of score and sends it to the terminal.

[0939] Output: List of best departments

[0940] Specific operation: The server creates a list of the most suitable departments based on the matching score and sends it to the terminal. The user's screen displays, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[0941] Step 6: Gather feedback

[0942] The user inputs feedback on the results, and the device sends it to the server, which receives the feedback and stores it in a database.

[0943] Input: Feedback (thoughts and wishes)

[0944] Processing: The server receives the feedback information and stores it in a database.

[0945] Output: Feedback saved successfully

[0946] Specific operation: The user enters a comment on the feedback input screen and clicks the send button. The device sends the information to the server, which stores it in the database.

[0947] (Application example 1)

[0948] 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."

[0949] In conventional systems, there was a mechanism for users to find the most suitable department based on their self-reported information, but there was no mechanism for robots in industrial sites to find the most suitable work tasks or work locations based on self-reporting. As a result, the robot's characteristics and skills could not be fully utilized, resulting in a problem of reduced work efficiency.

[0950] 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.

[0951] In this invention, the server includes means for a user to input characteristics and self-declared details, means for receiving the input information and comparing it with attribute data of multiple departments within the company, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, means for receiving and saving feedback from the user, means for a robot to self-declare and input characteristics and skills, means for assigning optimal work locations and work tasks based on the self-declared data, and means for presenting the generated list of work tasks. This makes it easier for the robot to find optimal work tasks and work locations, thereby improving work efficiency.

[0952] A "user" is an entity that uses the system to input characteristics and self-declaration details.

[0953] "Characteristics" refers to the skills, abilities, and experience possessed by the user or robot.

[0954] "Self-reporting" refers to the act of a user or robot inputting information such as characteristics, skills, and experience.

[0955] A "department" is a division within an organization that is responsible for a specific task or job.

[0956] "Attribute data" is information about the requirements and characteristics of each department and work task.

[0957] "Matching" refers to the act of comparing the characteristics of the user or robot with the attribute data of the department or work task, and making the most appropriate proposal.

[0958] "Feedback" is the act of a user or a robot providing their thoughts or opinions on a presented department or work task.

[0959] The "system" is a set of mechanisms that allows users and robots to input their characteristics and self-declared information and suggest the most suitable departments and work tasks.

[0960] A "robot" is a machine that performs tasks in industrial settings and self-reports its characteristics and skills.

[0961] A "work task" refers to a specific job or task that a robot performs in a factory or other setting.

[0962] A "list" is a document that refers to a list of optimal departments and work tasks generated by the system.

[0963] A system embodying this invention includes a means for a user to input characteristics and self-declared details, a means for comparing the received information with attribute data of multiple departments within the company, a means for generating a list of optimal departments, a means for presenting the generated list of departments to the user, a means for receiving and saving feedback from the user, a means for a robot to self-declare and input characteristics and skills, a means for assigning optimal work locations and work tasks based on the self-declared data, and a means for presenting a list of generated work tasks.

[0964] In implementation, the following main requirements are met:

[0965] 1. User Registration and Login:

[0966] A user accesses the system and fills in a registration form with information such as their name, characteristics, skills, etc. The server stores this information in a database and authenticates them when they log in. This process uses a web server and a database (e.g., MySQL, PostgreSQL).

[0967] 2. Enter your information:

[0968] After logging in, users are provided with a web interface to input their characteristics and self-reported information. The information is sent to a server and stored in a database. Specifically, a dynamic interface using HTML forms and JavaScript is used.

[0969] 3. Matching:

[0970] The server compares the received user and robot characteristics and self-reported information with data on departments and work tasks across the company using machine learning algorithms (e.g., k-nearest neighbor (kNN) or random forest). Based on the matching score calculated by the algorithm, the server lists the most suitable departments and work tasks.

[0971] 4. Presentation of results:

[0972] The matching results are sent from the server to the device and displayed in list form to the user or robot. JavaScript frameworks such as React and Vue.js are used for front-end development.

[0973] 5. Feedback:

[0974] The user provides feedback on the presented list, which is then sent to the server to help improve the accuracy of the matching algorithm next time.

[0975] As a concrete example, the following prompt sentence can be used:

[0976] "Robot ID:1's current skill set is welding and cutting. What work task is it suitable for?"

[0977] "Do you want to log in the robot with ID 1?"

[0978] "Robo1's specialty is welding. Would you like to assign it the most suitable work task?"

[0979] Please enter your work feedback.

[0980] This allows users and robots to input their own characteristics, skills, and experience, and based on that, find the most suitable department or work task. Furthermore, feedback improves the system's accuracy, increasing its long-term value.

[0981] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0982] Step 1: User Registration and Login

[0983] Users access the system using a terminal and enter the required information such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server, and the server compares the information with the database for authentication. If authentication is successful, it generates an authentication token and notifies the user.

[0984] Input: User information (name, employee number, email address, password)

[0985] Output: Authentication token

[0986] Step 2: Enter your information

[0987] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, experience, etc., and the terminal sends this information to the server, which stores it in a database.

[0988] Input: User characteristics and self-reported information (skills, interests, experience)

[0989] Output: User information stored in the database

[0990] Step 3: Robot Registration

[0991] The robot uses a terminal to access the system and input information such as its ID, characteristics, and skills. The terminal then sends this information to the server, which stores it in a database.

[0992] Input: Robot information (ID, characteristics, skills)

[0993] Output: Robot information stored in the database

[0994] Step 4: Matching

[0995] The server receives the user and robot's characteristics and self-reported information, compares it with the attribute data of each department and work task within the company, calculates a matching score using machine learning algorithms (kNN, Random Forest, etc.), and generates a list of the most suitable departments and work tasks.

[0996] Input: User information, robot information, department attribute data

[0997] Output: Matching results (department list, task list)

[0998] Step 5: Presenting the results

[0999] The server sends the generated matching results to the terminal, which displays the optimal departments and work tasks in a list format for the user and the robot. The terminal then presents the results to the user and the robot.

[1000] Input: Matching results

[1001] Output: Displayed optimal department list, task list

[1002] Step 6: Feedback

[1003] The user and the robot input feedback on the presented list, and the terminal sends the feedback information to the server, which stores it in a database and uses it to improve the accuracy of the matching algorithm next time.

[1004] Input: User and robot feedback

[1005] Output: Feedback information stored in a database

[1006] 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.

[1007] This invention combines a system that introduces the most suitable department based on the user's characteristics and self-reported details with an emotion engine that recognizes the user's emotions. By taking into account the user's emotion data in addition to conventional matching algorithms, this system makes it possible to propose a department that is more appropriate and satisfies the user.

[1008] System program and processing description

[1009] User Registration and Login

[1010] A user first accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[1011] Information input and emotion recognition

[1012] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information.

[1013] Matching and emotional data utilization

[1014] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This calculation, which also takes into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[1015] Presentation of results

[1016] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1017] Storing feedback and sentiment data

[1018] The user inputs their thoughts and wishes through an interface to provide feedback on the displayed results. The emotion engine recognizes the user's emotions when providing feedback, and sends the data and the feedback content to the server.

[1019] The server receives the feedback and emotion data and stores it in a database, which is then used to improve the accuracy of the matching algorithm next time.

[1020] Specific examples

[1021] For example, if an employee named Suzuki uses the system, it will look like this:

[1022] 1. Suzuki accesses the system and registers by entering his name, employee number, email address, and password.

[1023] 2. After logging in, a screen appears where users can enter their characteristics and self-declaration. Suzuki enters "programming," "research into new technologies," and "five years of sales experience." At the same time, the emotion engine recognizes Suzuki's emotions from his facial expressions and voice, and obtains emotional data such as "satisfaction."

[1024] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[1025] 4. The generated department list is displayed on Suzuki's device, showing, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1026] 5. Suzuki provides feedback on the presented results, commenting, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine recognizes Suzuki's emotions again, and emotional data such as "interest" is sent to the server.

[1027] 6. The server receives and stores this feedback and sentiment data, which it uses to improve the accuracy of its next suggestions.

[1028] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction, and can constantly improve its accuracy using feedback and sentiment data.

[1029] The processing flow will be explained below.

[1030] Step 1:

[1031] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[1032] Step 2:

[1033] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[1034] Step 3:

[1035] The user enters a username and password into the login form. The device sends this information to the server.

[1036] Step 4:

[1037] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[1038] Step 5:

[1039] After logging in, the user is presented with an interface on their device for entering their characteristics and self-declaration information. The user enters their skills (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[1040] Step 6:

[1041] This is a new process in which an emotion engine works to recognize emotions during the user input process. For example, it analyzes emotions from the user's facial expressions and voice through a camera or microphone. The recognized emotion data is sent to the server along with the user's characteristics and self-reported content.

[1042] Step 7:

[1043] The device sends the input characteristics, self-reported details, and emotional data to the server, which receives this information and stores it in a database.

[1044] Step 8:

[1045] The server analyzes the user's characteristics, self-reported details, and emotional data, and compares them with the attribute data of multiple departments within the company. A specific algorithm is used to calculate a matching score for each department. Taking emotional data into account results in more accurate matching results.

[1046] Step 9:

[1047] The server generates a list of the most suitable departments based on the matching scores, and provides the list to the user in order of the scores, taking into account the emotional data.

[1048] Step 10:

[1049] The terminal receives the list from the server and displays it to the user, for example, in the form of "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1050] Step 11:

[1051] An interface for the user to provide feedback on the presented result list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[1052] Step 12:

[1053] This is the process where the emotion engine works again to recognize the emotion when the user inputs feedback. The emotion engine analyzes the emotion from the user's facial expression and voice, and sends the emotion data to the server along with the feedback.

[1054] Step 13:

[1055] The device sends user feedback and emotion data to the server, which receives the feedback and emotion data and stores it in a database. This data is used to improve the accuracy of the matching algorithm next time.

[1056] Example 2

[1057] 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."

[1058] Conventional department matching systems propose the optimal department by considering only the user's characteristics and self-reported information. However, this approach does not take into account the user's emotions or satisfaction, and may not select a department that the user will actually be satisfied with. In addition, since it is not possible to effectively utilize feedback from users, it is difficult to improve the accuracy of the entire system.

[1059] 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.

[1060] In this invention, the server includes: a means for a user to input characteristics and self-reported details; a means for acquiring emotion data using an emotion engine that recognizes the user's emotions; a means for transmitting the acquired emotion data and self-reported information to the server; a means for comparing the information received by the server with attribute data of multiple departments within the company and calculating a matching score; a means for generating a list of optimal departments based on the calculated matching score; a means for presenting the generated list of departments to the user; and a means for receiving and saving feedback from the user regarding the presented list of departments. This enables department matching that takes user emotions into consideration, thereby increasing user satisfaction. Furthermore, by reflecting user feedback in the next matching, the accuracy of the system itself can be continuously improved.

[1061] A "user" is someone who uses the system to input their own characteristics and self-declaration information to find the most suitable department.

[1062] "Characteristics" are elements that users input into the system, such as their skills, interests, and experience.

[1063] "Self-reported content" refers to information reported by users themselves, including characteristics, preferences, and wishes.

[1064] "Terminal" means a device through which a user accesses the system, enters information, and checks results.

[1065] A "server" is a computer system that receives information sent by users, processes and stores data, and generates matching results.

[1066] An "emotion engine" is software or hardware that recognizes emotions from a user's facial expressions, voice, etc.

[1067] "Emotion data" refers to the user's emotional information recognized by the emotion engine.

[1068] A "matching algorithm" is a method or formula for calculating the optimal department based on a user's characteristics and emotional data.

[1069] "Matching score" is a numerical value of compatibility with each department calculated by the matching algorithm.

[1070] "Attribute data" refers to information about the characteristics and requirements of each department within a company.

[1071] "Feedback" refers to opinions or comments provided by a user regarding the results presented.

[1072] This system allows users to find the most suitable department based on their own characteristics and self-reported information. By combining it with an emotion engine, it can achieve more accurate department selection by taking into account the user's emotional data.

[1073] User Registration and Login

[1074] First, a user accesses the system and enters information such as their name, employee number, email address, and password into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the terminal sends the information entered by the user back to the server, and the server compares the information with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[1075] Information input and emotion recognition

[1076] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research into new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information. The emotion engine analyzes the user's facial expressions and voice to obtain emotion data such as satisfaction and interest.

[1077] Matching and emotional data utilization

[1078] The server receives the user's characteristic data and emotional data from the device. It then references the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This matching algorithm also includes emotional data, making it possible to identify the most suitable department while taking the user's emotions into consideration. For example, the R&D department may be displayed at the top of the list with a "high" emotional score.

[1079] Presentation of results

[1080] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. The generated department list is displayed on the user's device in the following format, for example, "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1081] Storing feedback and sentiment data

[1082] The user inputs their wishes and thoughts through an interface for providing feedback on the presented matching results. When providing feedback, the emotion engine recognizes the user's emotions again and sends this data and the feedback content to the server. The server receives the feedback and emotion data and stores them in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[1083] Specific examples

[1084] For example, let us assume that a certain user (let's call him Mr. A) uses the system.

[1085] 1. Mr. A accesses the system and registers by entering his name, employee number, email address, and password.

[1086] 2. After logging in, a screen for entering characteristics and self-declaration details appears on the device. Mr. A enters his skill "programming," his interest "researching new technologies," and his experience "five years of sales experience." At the same time, the emotion engine obtains the emotion data "satisfaction" from Mr. A's facial expressions and voice.

[1087] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[1088] 4. The generated department list is displayed on Mr. A's device, and he is told, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1089] 5. Mr. A provides feedback on the presented results, saying, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine then recognizes Mr. A's emotional data, such as his "interest," and sends it to the server.

[1090] 6. The server receives and stores the feedback and sentiment data, which is used to improve the accuracy of the next suggestion.

[1091] Example prompts to input to the generative AI model

[1092] By inputting the following prompt sentences into the generative AI model, system explanations and concrete examples can be generated.

[1093] Prompt statement

[1094] "Generate a description of a system that introduces the most suitable department to a user based on their characteristics and self-reported information. This system uses an emotion engine to recognize the user's emotions, and by incorporating these into the matching algorithm, suggests the most appropriate department. Please provide a detailed explanation, including the overall flow of the system and specific examples."

[1095] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction. It is also possible to use feedback to improve the accuracy of suggestions next time.

[1096] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1097] Step 1: User Registration

[1098] User: A user accesses the system and enters information such as their name, employee number, email address, and password.

[1099] Input: Name, employee number, email address, and password entered by the user

[1100] Terminal: Receives the entered information and sends it to the server.

[1101] Server: Stores the received information in a database.

[1102] Output: User information is registered in the database.

[1103] Step 2: Log in

[1104] User: Attempts to log in by entering email address and password.

[1105] Input: User-entered email address and password

[1106] Terminal: Sends the entered information to the server.

[1107] Server: Compares the received information with a database and performs authentication.

[1108] Data calculation: Matching email addresses and passwords stored in a database.

[1109] Output: An authentication token is generated and sent back to the terminal, informing the user that the login was successful.

[1110] Step 3: Display the information entry screen

[1111] Terminal: After successful login, display an interface for entering characteristics and self-declaration details.

[1112] Output: A screen will appear where you can enter your characteristics and self-declaration information.

[1113] Step 4: Enter your user information

[1114] User: Enter your skills (e.g. programming), interests (e.g. researching new technologies), experience (e.g. 5 years of sales experience), etc.

[1115] Input: User-entered skills, interests, and experience

[1116] Terminal: Receives the input information.

[1117] Output: Get the received information.

[1118] Step 5: Emotion Recognition

[1119] Device: Runs the emotion engine and acquires emotion data from the user's facial expressions and voice.

[1120] Input: User's facial expression, voice

[1121] Data calculation: The emotion engine analyzes facial and voice data to generate emotion data (e.g., satisfaction, interest).

[1122] Output: Generated emotion data

[1123] Step 6: Send data to the server

[1124] Terminal: Sends the acquired characteristic data and emotion data to the server.

[1125] Input: Trait data, emotion data

[1126] Output: Data is sent to the server.

[1127] Step 7: Calculating the Matching Score

[1128] Server: Based on the received user characteristic data and emotion data, it compares it with attribute data from multiple departments within the company.

[1129] Input: User characteristics data, emotion data, and attribute data for each department within the company

[1130] Data calculation: A specific matching algorithm is used to calculate the matching score for each department.

[1131] Output: Matching scores for each department

[1132] Step 8: Generate matching results

[1133] Server: Generates a list of optimal departments based on the calculated matching scores.

[1134] Input: Matching score

[1135] Output: List of best departments

[1136] Step 9: Presenting the results

[1137] Device: The generated list of optimal departments is displayed on the user's device.

[1138] Input: List of best departments

[1139] Output: "The department that best suits you is: 1. R&D (high sentiment score) 2. Tech Support (medium sentiment score) 3. IT (low sentiment score)."

[1140] Step 10: Provide feedback

[1141] User: Provide feedback on the results presented. For example, comment, "The R&D department is interesting, but I'd like to know more about the sales department."

[1142] Input: Feedback

[1143] Device: When providing feedback, the emotion engine is run again to obtain the user's emotion data (e.g., interest).

[1144] Data calculation: The emotion engine retrieves emotion data in relation to the provided feedback content.

[1145] Output: User's emotion data and feedback content

[1146] Step 11: Storing Feedback and Sentiment Data

[1147] Device: Sends the acquired feedback and emotion data to the server.

[1148] Input: Feedback content, emotion data

[1149] Server: Receives feedback and emotion data and stores it in a database.

[1150] Output: The feedback and sentiment data stored in the database will be used to improve the accuracy of the matching algorithm next time.

[1151] (Application example 2)

[1152] 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."

[1153] Conventional department introduction systems matched users based solely on their characteristics and self-reported information, without considering their emotions or reactions, making it difficult to suggest the department that was best suited to them. This resulted in lower user satisfaction and the inability to match users to the appropriate department. Furthermore, because the system did not consider the user's emotional feedback, it was difficult to improve the accuracy of the next match.

[1154] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1155] In this invention, the server includes means for recognizing user emotion data using an emotion engine, means for generating a list of optimal departments based on the matching results including the emotion data, and means further including an emotion engine for recognizing the user's emotion in real time regarding the displayed list of departments. This makes it possible to analyze the user's emotion in real time and suggest optimal departments based on that, improving user satisfaction and also improving the accuracy of the next matching.

[1156] A "user" is an individual user of a particular system or application.

[1157] "Characteristics" refers to information such as a user's skills, experience, and interests.

[1158] "Self-reporting" is information that the user himself / herself applies for through an input interface.

[1159] "Emotion engine" refers to a hardware or software system for recognizing a user's emotion data.

[1160] "Emotion data" is data related to emotions classified from the user's facial expressions and voice recognized by the emotion engine.

[1161] "Matching" is the process of checking the relevance of the input data to the corresponding department, product, etc.

[1162] A "department" refers to a group or division within an organization that performs specific tasks or functions.

[1163] An "algorithm" is a set of procedures or processes that formulate steps or calculation methods to achieve a certain purpose.

[1164] A "list" refers to a list or table organized according to specific criteria.

[1165] "Feedback" refers to information such as ratings and opinions provided by users.

[1166] This invention combines an emotion engine with a system that introduces the most suitable department based on the user's characteristics and self-reported details. By taking into account the user's emotion data in addition to conventional matching algorithms, this system can propose a department that is more appropriate and satisfies the user.

[1167] System program and processing description

[1168] User Registration and Login

[1169] A user first accesses the system and enters the required information (name, identification number, email address, password) into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[1170] Information input and emotion recognition

[1171] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. At this time, an emotion engine (e.g., Hugging Face emotion recognition model) recognizes the user's emotions and sends them to the server along with the entered information.

[1172] Matching and emotional data utilization

[1173] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the organization and calculates a matching score for each department using a specific algorithm. This calculation, taking into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[1174] Presentation of results

[1175] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. Research and Development (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1176] Storing feedback and sentiment data

[1177] Users input their thoughts and wishes through an interface to provide feedback on the displayed results. When providing feedback, the emotion engine also recognizes the user's emotions and sends the data and feedback content to the server. The server receives the feedback and emotion data and stores it in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[1178] Specific examples

[1179] For example, if a user is browsing a "smartwatch" page and their facial expressions are captured by a camera, and the emotion engine recognizes expressions such as "excitement" or "satisfaction," the server can recommend similar products (e.g., the latest fitness trackers or high-end smartwatches) based on the emotion data in real time.

[1180] Prompt Sentence Examples

[1181] An example of a prompt sentence to input to the generative AI model is as follows:

[1182] 1. Prompt sentence for emotion recognition model

[1183] "Analyze image data to recognize emotions."

[1184] 2. Prompt for product recommendation API

[1185] "Recommend the best products to users based on this sentiment data."

[1186] This enables real-time emotion-based product recommendations to users.

[1187] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1188] Step 1:

[1189] The user enters characteristics and self-declaration details.

[1190] Input: A user enters information into a registration form, such as their name, identification number, email address, and password.

[1191] Processing: The device receives this information and sends it to the server.

[1192] Output: The server stores this information in a database.

[1193] Step 2:

[1194] A user logs in.

[1195] Input: The user enters their email address and password into the login interface.

[1196] Processing: The terminal sends the entered information to the server, which checks it against a database for authentication.

[1197] Output: If authentication is successful, the server generates an authentication token and sends it to the device, which receives it and logs in successfully.

[1198] Step 3:

[1199] The user enters detailed characteristics and self-reported information.

[1200] Input: Users input their skills, interests, experience, etc.

[1201] Processing: The device sends this information to the server, and the emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[1202] Output: The emotion data and input data are sent to the server and stored.

[1203] Step 4:

[1204] The server uses the emotion data to analyze the user's characteristics.

[1205] Input: The server receives the user's characteristic information and emotion data.

[1206] Processing: The server inputs this data into an algorithm, compares it with department attribute data, and calculates a matching score.

[1207] Output: A list of matches is generated.

[1208] Step 5:

[1209] The server presents a list of the most suitable departments.

[1210] Input: The server generates a list of matching results.

[1211] Processing: The list is sorted by score and sent to the user's device, taking into account the emotional data.

[1212] Output: The device displays a list of the following types of departments: 1. Research & Development (High Sentiment Score) 2. Tech Support (Medium Sentiment Score) 3. IT (Low Sentiment Score)

[1213] Step 6:

[1214] The user provides feedback.

[1215] Input: The user enters feedback on the displayed results.

[1216] Processing: The device sends the feedback content to the server. At the same time, the emotion engine recognizes the emotion data when providing the feedback and sends it to the server.

[1217] Output: Feedback data and emotion data are stored on the server.

[1218] Step 7:

[1219] The server stores the feedback and emotion data to improve the accuracy of the next match.

[1220] Input: The server receives feedback data and emotion data.

[1221] Processing: These data are stored in a database and used as training data to improve the accuracy of the matching algorithm.

[1222] Output: The accuracy of the matching algorithm will be improved next time, and it will be possible to suggest more suitable departments to the user.

[1223] In this way, by utilizing emotional data, it is possible to more accurately grasp the characteristics and satisfaction level of users and match them with the appropriate department.

[1224] 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.

[1225] 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.

[1226] 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.

[1227] [Fourth embodiment]

[1228] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1229] 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.

[1230] 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).

[1231] 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.

[1232] 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.

[1233] 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).

[1234] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1235] 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.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] 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.

[1240] 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."

[1241] The present invention relates to a system that introduces users to the most suitable departments based on their characteristics and self-reported details. This system includes a means for users to input their characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments within the company, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[1242] System program and processing description

[1243] User Registration and Login

[1244] When a user first accesses the system, they enter the necessary information into the registration form, such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[1245] Enter information

[1246] After the user logs in, an interface (screen) for entering characteristics and self-declaration details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. The terminal then sends this information to the server.

[1247] matching

[1248] The server receives the user's characteristics and self-reported information and compares it with the attribute data of each department within the company. An algorithm is used to compare the user's information with the department's attribute data and calculate a matching score for each department. For example, if a user is interested in "researching new technologies" and has "programming" skills, the R&D (research and development) department will receive a high score.

[1249] Presentation of results

[1250] The server generates the matching results and creates a list of the best departments. This list is sorted by score and sent to the device. The device displays the results in the format "The departments that are best suited for you are: 1. R&D 2. Technical Support 3. IT Department."

[1251] feedback

[1252] An interface is provided on the terminal for users to input feedback on the results. The user inputs their thoughts and wishes about the departments presented and sends the feedback to the server. For example, they can send a comment such as, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[1253] The server receives this feedback and stores it in a database, which is used to improve the accuracy of the matching algorithm next time.

[1254] Specific examples

[1255] For example, if an employee named Tanaka uses the system, it will look like this:

[1256] 1. Tanaka accesses the system and registers by entering his name, employee number, email address, and password.

[1257] 2. After logging in, a screen will appear where you can enter your characteristics and self-declaration details. Tanaka enters "Programming," "Research into new technologies," and "5 years of sales experience."

[1258] 3. The server receives this and matches it with each department in the company to generate a list of the best fit. R&D, Tech Support, and IT are deemed suitable.

[1259] 4. The generated department list is displayed on Tanaka's terminal, showing, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[1260] 5. Tanaka provides feedback on the results presented, commenting, "The R&D department is interesting, but I'd like to know more about the marketing-related departments."

[1261] 6. The server receives and stores this feedback and uses it to improve the accuracy of next suggestions.

[1262] Thus, the present invention is a system that helps employees easily find the department that best suits them, thereby supporting their career paths.

[1263] The processing flow will be explained below.

[1264] Step 1:

[1265] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[1266] Step 2:

[1267] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[1268] Step 3:

[1269] The user enters a username and password into the login form. The device sends this information to the server.

[1270] Step 4:

[1271] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[1272] Step 5:

[1273] After the user logs in, an interface for entering characteristics and self-declaration details is displayed on the terminal. The user enters characteristics (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[1274] Step 6:

[1275] The device sends the entered characteristics and self-declared information to the server, which receives this information and stores it in a database.

[1276] Step 7:

[1277] The server analyzes the user's characteristics and self-reported information, compares it with the attribute data of multiple departments within the company, and calculates a matching score for each department using a specific algorithm.

[1278] Step 8:

[1279] The server generates a list of the most suitable departments based on the matching scores, sorts the list by score, and sends the list to the terminal.

[1280] Step 9:

[1281] The device receives the list from the server and displays it to the user, for example, "The departments that best suit you are: 1. R&D 2. Tech Support 3. IT Department."

[1282] Step 10:

[1283] An interface for allowing the user to provide feedback on the presented department list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[1284] Step 11:

[1285] The device sends the user's feedback to the server, which receives the feedback and stores it in a database.

[1286] Step 12:

[1287] Based on the feedback information, the server will make adjustments to improve the accuracy of the matching algorithm for the next time, and this information will be used in future department suggestions.

[1288] Example 1

[1289] 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."

[1290] In recent years, placing the right people in the right positions within a company has become increasingly important, but there is a problem in that it is difficult to assign employees to the most appropriate department based on their characteristics and self-reported information. Conventional systems make it difficult to accurately grasp the characteristics and preferences of individual employees and propose appropriate departments. In addition, there is a lack of a mechanism for reflecting employee feedback in future proposals, making it difficult to improve the accuracy of matching.

[1291] 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.

[1292] In this invention, the server includes means for a user to input characteristics and self-reported details, means for receiving the input information and comparing it with attribute data of multiple departments within the organization, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, and means for receiving and saving feedback from the user. This allows employees to easily find the optimal department based on their characteristics and preferences, and also allows the collected feedback to be reflected in improving the accuracy of next suggestions.

[1293] "User" refers to a person who uses the system to input their characteristics and self-declaration information and receive suggestions for the most suitable department.

[1294] "Characteristics" refers to information that describes a person's individual abilities and preferences, including their skills, interests, experience, etc.

[1295] "Self-reporting" refers to the act of a user providing their own characteristics and wishes to the system, or the content of such acts.

[1296] The term "means" refers to functions or devices necessary to realize the present invention.

[1297] "Interface" refers to the screens and utilities that allow a user to input information and provide feedback to a system.

[1298] "Server" refers to a computing device that receives information sent by users and checks it against a database.

[1299] "Database" means a collection of data that stores input information and is used for verification and matching purposes.

[1300] "Algorithm" refers to the calculation procedure for comparing a user's characteristics and self-reported details with department attribute data and calculating a matching score.

[1301] "Feedback" refers to the act of a user providing their thoughts and wishes about the optimal department presented by the system, or the content of such actions.

[1302] The present invention is a system that suggests the most suitable department based on the user's characteristics and self-reported details. This system includes a means for the user to input the characteristics and self-reported details, a means for receiving the input information and comparing it with attribute data of multiple departments existing in the organization, a means for generating a list of the most suitable departments based on the comparison results, a means for presenting the generated list of departments to the user, and a means for receiving and saving feedback from the user.

[1303] To realize this system, a server, terminals, a database, and appropriate algorithms are required. The server acts as a central processing unit, receiving information from users, comparing it with the database to calculate matching results, and finally presenting them to the users.

[1304] First, a user accesses the system using a device (e.g., PC or smartphone) and registers by entering their name, employee number, email address, and password. This information is sent from the device to the server, which stores it in a database (e.g., MySQL or PostgreSQL). When logging in, the user enters their email address and password again from the same device, and the server compares them with the database for authentication. If authentication is successful, the server generates an authentication token (e.g., JWT token) and sends it to the device.

[1305] Next, after the user logs in, an interface for entering characteristics and self-declared details (e.g., skills, interests, experience) is displayed on the terminal. When the user enters and submits this information (e.g., "programming," "researching new technologies," "five years of sales experience," etc.), the terminal sends the information to the server. Based on the information received, the server compares it with the attribute data of each department within the organization and calculates a matching score using an algorithm (e.g., similarity calculation).

[1306] Based on the matching results, the server generates a list of the most suitable departments, and then compares it with the database to create a list of departments with the highest scores. This list is sent to the device and displayed to the user as, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[1307] Additionally, users can provide feedback on the results. For example, they can say, "The R&D department is interesting, but I'd like to know more about the marketing department." This feedback is received by the server and stored in a database. This feedback information is used to improve the accuracy of the matching algorithm next time.

[1308] For example, the following prompt sentence is input to the generative AI model:

[1309] "We are developing a system that introduces the most suitable departments to users based on their characteristics and self-reported information. Please design an algorithm to compare the user's input data with the company's department attribute data, and generate a list of the most suitable departments based on the results. Please also include a mechanism to collect user feedback information and improve the accuracy of the next proposal."

[1310] This allows the invention to help employees find the department that best suits them and support their career path.

[1311] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1312] Step 1: User Registration

[1313] A user accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends the entered information to the server, which receives the information and stores it in a database.

[1314] Input: Name, employee number, email address, password

[1315] Processing: The server receives this information and stores it in the database using an INSERT statement.

[1316] Output: Notification of successful registration

[1317] Specific operation: A user opens a browser, accesses a registration page, fills in each field of the form, and clicks the submit button. The device generates an HTTP request and sends it to the server, which stores it in the database.

[1318] Step 2: User Login

[1319] A user accesses the login page and enters their email address and password. The device sends this information to the server, which then authenticates them by checking the information against a database. If authentication is successful, the server generates an authentication token and sends it to the device.

[1320] Input: Email address, password

[1321] Processing: The server executes a SELECT statement against the database to verify the user information, and if it matches, generates an authentication token.

[1322] Output: Authentication token, successful login notification

[1323] Specific operation: The user enters an email address and password on the login screen and clicks the submit button. The device generates an HTTP request and sends it to the server, which queries the database for authentication and returns the result to the device.

[1324] Step 3: Enter your information

[1325] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, and experience, and the terminal sends this information to the server.

[1326] Input: Skills, Interests, Experience

[1327] Processing: The device collects this information and sends it to the server, which receives it and stores it in a database.

[1328] Output: Notification that input information has been saved

[1329] Specific operation: After logging in, the user enters their own characteristics and skills into the input form and clicks the submit button. The device sends the information to the server, and the server stores the received information in the database.

[1330] Step 4: Matching

[1331] After the server receives the user's input information, it compares it with the department attribute data within the organization. An algorithm is used to match the user information with the department attribute data and calculate a matching score for each department.

[1332] Input: User information, department attribute data

[1333] Processing: The server receives this information and uses an algorithm to calculate a score.

[1334] Output: Matching scores for each department

[1335] Specific operation: The server retrieves department attribute data from the database, runs an algorithm to calculate the match with the user's skills and interests, and stores the calculated score in the database.

[1336] Step 5: Presenting the results

[1337] The server generates matching results and creates a list of the most suitable departments, which is then sent to the terminal and presented to the user.

[1338] Input: Matching score

[1339] Processing: The server generates a department list in order of score and sends it to the terminal.

[1340] Output: List of best departments

[1341] Specific operation: The server creates a list of the most suitable departments based on the matching score and sends it to the terminal. The user's screen displays, "The departments that are suitable for you are: 1. R&D 2. Technical Support 3. IT Department."

[1342] Step 6: Gather feedback

[1343] The user inputs feedback on the results, and the device sends it to the server, which receives the feedback and stores it in a database.

[1344] Input: Feedback (thoughts and wishes)

[1345] Processing: The server receives the feedback information and stores it in a database.

[1346] Output: Feedback saved successfully

[1347] Specific operation: The user enters a comment on the feedback input screen and clicks the send button. The device sends the information to the server, which stores it in the database.

[1348] (Application example 1)

[1349] 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."

[1350] In conventional systems, there was a mechanism for users to find the most suitable department based on their self-reported information, but there was no mechanism for robots in industrial sites to find the most suitable work tasks or work locations based on self-reporting. As a result, the robot's characteristics and skills could not be fully utilized, resulting in a problem of reduced work efficiency.

[1351] 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.

[1352] In this invention, the server includes means for a user to input characteristics and self-declared details, means for receiving the input information and comparing it with attribute data of multiple departments within the company, means for generating a list of optimal departments based on the comparison results, means for presenting the generated list of departments to the user, means for receiving and saving feedback from the user, means for a robot to self-declare and input characteristics and skills, means for assigning optimal work locations and work tasks based on the self-declared data, and means for presenting the generated list of work tasks. This makes it easier for the robot to find optimal work tasks and work locations, thereby improving work efficiency.

[1353] A "user" is an entity that uses the system to input characteristics and self-declaration details.

[1354] "Characteristics" refers to the skills, abilities, and experience possessed by the user or robot.

[1355] "Self-reporting" refers to the act of a user or robot inputting information such as characteristics, skills, and experience.

[1356] A "department" is a division within an organization that is responsible for a specific task or job.

[1357] "Attribute data" is information about the requirements and characteristics of each department and work task.

[1358] "Matching" refers to the act of comparing the characteristics of the user or robot with the attribute data of the department or work task, and making the most appropriate proposal.

[1359] "Feedback" is the act of a user or a robot providing their thoughts or opinions on a presented department or work task.

[1360] The "system" is a set of mechanisms that allows users and robots to input their characteristics and self-declared information and suggest the most suitable departments and work tasks.

[1361] A "robot" is a machine that performs tasks in industrial settings and self-reports its characteristics and skills.

[1362] A "work task" refers to a specific job or task that a robot performs in a factory or other setting.

[1363] A "list" is a document that refers to a list of optimal departments and work tasks generated by the system.

[1364] A system embodying this invention includes a means for a user to input characteristics and self-declared details, a means for comparing the received information with attribute data of multiple departments within the company, a means for generating a list of optimal departments, a means for presenting the generated list of departments to the user, a means for receiving and saving feedback from the user, a means for a robot to self-declare and input characteristics and skills, a means for assigning optimal work locations and work tasks based on the self-declared data, and a means for presenting a list of generated work tasks.

[1365] In implementation, the following main requirements are met:

[1366] 1. User Registration and Login:

[1367] A user accesses the system and fills in a registration form with information such as their name, characteristics, skills, etc. The server stores this information in a database and authenticates them when they log in. This process uses a web server and a database (e.g., MySQL, PostgreSQL).

[1368] 2. Enter your information:

[1369] After logging in, users are provided with a web interface to input their characteristics and self-reported information. The information is sent to a server and stored in a database. Specifically, a dynamic interface using HTML forms and JavaScript is used.

[1370] 3. Matching:

[1371] The server compares the received user and robot characteristics and self-reported information with data on departments and work tasks across the company using machine learning algorithms (e.g., k-nearest neighbor (kNN) or random forest). Based on the matching score calculated by the algorithm, the server lists the most suitable departments and work tasks.

[1372] 4. Presentation of results:

[1373] The matching results are sent from the server to the device and displayed in list form to the user or robot. JavaScript frameworks such as React and Vue.js are used for front-end development.

[1374] 5. Feedback:

[1375] The user provides feedback on the presented list, which is then sent to the server to help improve the accuracy of the matching algorithm next time.

[1376] As a concrete example, the following prompt sentence can be used:

[1377] "Robot ID:1's current skill set is welding and cutting. What work task is it suitable for?"

[1378] "Do you want to log in the robot with ID 1?"

[1379] "Robo1's specialty is welding. Would you like to assign it the most suitable work task?"

[1380] Please enter your work feedback.

[1381] This allows users and robots to input their own characteristics, skills, and experience, and based on that, find the most suitable department or work task. Furthermore, feedback improves the system's accuracy, increasing its long-term value.

[1382] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1383] Step 1: User Registration and Login

[1384] Users access the system using a terminal and enter the required information such as their name, employee number, email address, and password. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server, and the server compares the information with the database for authentication. If authentication is successful, it generates an authentication token and notifies the user.

[1385] Input: User information (name, employee number, email address, password)

[1386] Output: Authentication token

[1387] Step 2: Enter your information

[1388] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills, interests, experience, etc., and the terminal sends this information to the server, which stores it in a database.

[1389] Input: User characteristics and self-reported information (skills, interests, experience)

[1390] Output: User information stored in the database

[1391] Step 3: Robot Registration

[1392] The robot uses a terminal to access the system and input information such as its ID, characteristics, and skills. The terminal then sends this information to the server, which stores it in a database.

[1393] Input: Robot information (ID, characteristics, skills)

[1394] Output: Robot information stored in the database

[1395] Step 4: Matching

[1396] The server receives the user and robot's characteristics and self-reported information, compares it with the attribute data of each department and work task within the company, calculates a matching score using machine learning algorithms (kNN, Random Forest, etc.), and generates a list of the most suitable departments and work tasks.

[1397] Input: User information, robot information, department attribute data

[1398] Output: Matching results (department list, task list)

[1399] Step 5: Presenting the results

[1400] The server sends the generated matching results to the terminal, which displays the optimal departments and work tasks in a list format for the user and the robot. The terminal then presents the results to the user and the robot.

[1401] Input: Matching results

[1402] Output: Displayed optimal department list, task list

[1403] Step 6: Feedback

[1404] The user and the robot input feedback on the presented list, and the terminal sends the feedback information to the server, which stores it in a database and uses it to improve the accuracy of the matching algorithm next time.

[1405] Input: User and robot feedback

[1406] Output: Feedback information stored in a database

[1407] 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.

[1408] This invention combines a system that introduces the most suitable department based on the user's characteristics and self-reported details with an emotion engine that recognizes the user's emotions. By taking into account the user's emotion data in addition to conventional matching algorithms, this system makes it possible to propose a department that is more appropriate and satisfies the user.

[1409] System program and processing description

[1410] User Registration and Login

[1411] A user first accesses the system and enters the required information (name, employee number, email address, password) into the registration form. The terminal sends this information to the server, which then stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[1412] Information input and emotion recognition

[1413] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information.

[1414] Matching and emotional data utilization

[1415] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This calculation, which also takes into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[1416] Presentation of results

[1417] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1418] Storing feedback and sentiment data

[1419] The user inputs their thoughts and wishes through an interface to provide feedback on the displayed results. The emotion engine recognizes the user's emotions when providing feedback, and sends the data and the feedback content to the server.

[1420] The server receives the feedback and emotion data and stores it in a database, which is then used to improve the accuracy of the matching algorithm next time.

[1421] Specific examples

[1422] For example, if an employee named Suzuki uses the system, it will look like this:

[1423] 1. Suzuki accesses the system and registers by entering his name, employee number, email address, and password.

[1424] 2. After logging in, a screen appears where users can enter their characteristics and self-declaration. Suzuki enters "programming," "research into new technologies," and "five years of sales experience." At the same time, the emotion engine recognizes Suzuki's emotions from his facial expressions and voice, and obtains emotional data such as "satisfaction."

[1425] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[1426] 4. The generated department list is displayed on Suzuki's device, showing, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1427] 5. Suzuki provides feedback on the presented results, commenting, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine recognizes Suzuki's emotions again, and emotional data such as "interest" is sent to the server.

[1428] 6. The server receives and stores this feedback and sentiment data, which it uses to improve the accuracy of its next suggestions.

[1429] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction, and can constantly improve its accuracy using feedback and sentiment data.

[1430] The processing flow will be explained below.

[1431] Step 1:

[1432] When a user accesses the system for the first time, they enter the required information in the registration form (name, employee number, email address, password). The terminal temporarily stores this information and sends a request to the server when the "Submit" button is pressed.

[1433] Step 2:

[1434] The server receives the information sent by the user and verifies the entered data. If the verification is successful, it saves the new user information in the database and returns a "registration complete" response to the terminal.

[1435] Step 3:

[1436] The user enters a username and password into the login form. The device sends this information to the server.

[1437] Step 4:

[1438] The server compares the received login information with the database, and if authentication is successful, it generates an authentication token and notifies the user that they have successfully logged in. If authentication fails, it sends an error message to the terminal.

[1439] Step 5:

[1440] After logging in, the user is presented with an interface on their device for entering their characteristics and self-declaration information. The user enters their skills (e.g., programming, project management), interests (e.g., research on new technologies), and experience (e.g., 5 years of sales experience), and then presses the submit button.

[1441] Step 6:

[1442] This is a new process in which an emotion engine works to recognize emotions during the user input process. For example, it analyzes emotions from the user's facial expressions and voice through a camera or microphone. The recognized emotion data is sent to the server along with the user's characteristics and self-reported content.

[1443] Step 7:

[1444] The device sends the input characteristics, self-reported details, and emotional data to the server, which receives this information and stores it in a database.

[1445] Step 8:

[1446] The server analyzes the user's characteristics, self-reported details, and emotional data, and compares them with the attribute data of multiple departments within the company. A specific algorithm is used to calculate a matching score for each department. Taking emotional data into account results in more accurate matching results.

[1447] Step 9:

[1448] The server generates a list of the most suitable departments based on the matching scores, and provides the list to the user in order of the scores, taking into account the emotional data.

[1449] Step 10:

[1450] The terminal receives the list from the server and displays it to the user, for example, in the form of "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1451] Step 11:

[1452] An interface for the user to provide feedback on the presented result list is displayed on the terminal, and the user inputs their thoughts and wishes about the presented departments and submits the feedback.

[1453] Step 12:

[1454] This is the process where the emotion engine works again to recognize the emotion when the user inputs feedback. The emotion engine analyzes the emotion from the user's facial expression and voice, and sends the emotion data to the server along with the feedback.

[1455] Step 13:

[1456] The device sends user feedback and emotion data to the server, which receives the feedback and emotion data and stores it in a database. This data is used to improve the accuracy of the matching algorithm next time.

[1457] Example 2

[1458] 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."

[1459] Conventional department matching systems propose the optimal department by considering only the user's characteristics and self-reported information. However, this approach does not take into account the user's emotions or satisfaction, and may not select a department that the user will actually be satisfied with. In addition, since it is not possible to effectively utilize feedback from users, it is difficult to improve the accuracy of the entire system.

[1460] 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.

[1461] In this invention, the server includes: a means for a user to input characteristics and self-reported details; a means for acquiring emotion data using an emotion engine that recognizes the user's emotions; a means for transmitting the acquired emotion data and self-reported information to the server; a means for comparing the information received by the server with attribute data of multiple departments within the company and calculating a matching score; a means for generating a list of optimal departments based on the calculated matching score; a means for presenting the generated list of departments to the user; and a means for receiving and saving feedback from the user regarding the presented list of departments. This enables department matching that takes user emotions into consideration, thereby increasing user satisfaction. Furthermore, by reflecting user feedback in the next matching, the accuracy of the system itself can be continuously improved.

[1462] A "user" is someone who uses the system to input their own characteristics and self-declaration information to find the most suitable department.

[1463] "Characteristics" are elements that users input into the system, such as their skills, interests, and experience.

[1464] "Self-reported content" refers to information reported by users themselves, including characteristics, preferences, and wishes.

[1465] "Terminal" means a device through which a user accesses the system, enters information, and checks results.

[1466] A "server" is a computer system that receives information sent by users, processes and stores data, and generates matching results.

[1467] An "emotion engine" is software or hardware that recognizes emotions from a user's facial expressions, voice, etc.

[1468] "Emotion data" refers to the user's emotional information recognized by the emotion engine.

[1469] A "matching algorithm" is a method or formula for calculating the optimal department based on a user's characteristics and emotional data.

[1470] "Matching score" is a numerical value of compatibility with each department calculated by the matching algorithm.

[1471] "Attribute data" refers to information about the characteristics and requirements of each department within a company.

[1472] "Feedback" refers to opinions or comments provided by a user regarding the results presented.

[1473] This system allows users to find the most suitable department based on their own characteristics and self-reported information. By combining it with an emotion engine, it can achieve more accurate department selection by taking into account the user's emotional data.

[1474] User Registration and Login

[1475] First, a user accesses the system and enters information such as their name, employee number, email address, and password into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the terminal sends the information entered by the user back to the server, and the server compares the information with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[1476] Information input and emotion recognition

[1477] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research into new technologies), experience (e.g., five years of sales experience), etc. At this time, the emotion engine recognizes the user's emotions and sends them to the server along with the entered information. The emotion engine analyzes the user's facial expressions and voice to obtain emotion data such as satisfaction and interest.

[1478] Matching and emotional data utilization

[1479] The server receives the user's characteristic data and emotional data from the device. It then references the attribute data of multiple departments within the company and calculates a matching score for each department using a specific algorithm. This matching algorithm also includes emotional data, making it possible to identify the most suitable department while taking the user's emotions into consideration. For example, the R&D department may be displayed at the top of the list with a "high" emotional score.

[1480] Presentation of results

[1481] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. The generated department list is displayed on the user's device in the following format, for example, "The departments that are most suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1482] Storing feedback and sentiment data

[1483] The user inputs their wishes and thoughts through an interface for providing feedback on the presented matching results. When providing feedback, the emotion engine recognizes the user's emotions again and sends this data and the feedback content to the server. The server receives the feedback and emotion data and stores them in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[1484] Specific examples

[1485] For example, let us assume that a certain user (let's call him Mr. A) uses the system.

[1486] 1. Mr. A accesses the system and registers by entering his name, employee number, email address, and password.

[1487] 2. After logging in, a screen for entering characteristics and self-declaration details appears on the device. Mr. A enters his skill "programming," his interest "researching new technologies," and his experience "five years of sales experience." At the same time, the emotion engine obtains the emotion data "satisfaction" from Mr. A's facial expressions and voice.

[1488] 3. The server receives this, matches it with each department in the company, and generates a list of the most suitable departments, taking into account the emotional data. For example, the R&D department would be displayed at the top of the list with a high emotional score.

[1489] 4. The generated department list is displayed on Mr. A's device, and he is told, "The departments that are suitable for you are: 1. R&D (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1490] 5. Mr. A provides feedback on the presented results, saying, "The R&D department is interesting, but I'd like to know more about the sales departments." The emotion engine then recognizes Mr. A's emotional data, such as his "interest," and sends it to the server.

[1491] 6. The server receives and stores the feedback and sentiment data, which is used to improve the accuracy of the next suggestion.

[1492] Example prompts to input to the generative AI model

[1493] By inputting the following prompt sentences into the generative AI model, system explanations and concrete examples can be generated.

[1494] Prompt statement

[1495] "Generate a description of a system that introduces the most suitable department to a user based on their characteristics and self-reported information. This system uses an emotion engine to recognize the user's emotions, and by incorporating these into the matching algorithm, suggests the most appropriate department. Please provide a detailed explanation, including the overall flow of the system and specific examples."

[1496] In this way, the present invention is a system that helps users easily find the department that best suits them, thereby increasing user satisfaction. It is also possible to use feedback to improve the accuracy of suggestions next time.

[1497] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1498] Step 1: User Registration

[1499] User: A user accesses the system and enters information such as their name, employee number, email address, and password.

[1500] Input: Name, employee number, email address, and password entered by the user

[1501] Terminal: Receives the entered information and sends it to the server.

[1502] Server: Stores the received information in a database.

[1503] Output: User information is registered in the database.

[1504] Step 2: Log in

[1505] User: Attempts to log in by entering email address and password.

[1506] Input: User-entered email address and password

[1507] Terminal: Sends the entered information to the server.

[1508] Server: Compares the received information with a database and performs authentication.

[1509] Data calculation: Matching email addresses and passwords stored in a database.

[1510] Output: An authentication token is generated and sent back to the terminal, informing the user that the login was successful.

[1511] Step 3: Display the information entry screen

[1512] Terminal: After successful login, display an interface for entering characteristics and self-declaration details.

[1513] Output: A screen will appear where you can enter your characteristics and self-declaration information.

[1514] Step 4: Enter your user information

[1515] User: Enter your skills (e.g. programming), interests (e.g. researching new technologies), experience (e.g. 5 years of sales experience), etc.

[1516] Input: User-entered skills, interests, and experience

[1517] Terminal: Receives the input information.

[1518] Output: Get the received information.

[1519] Step 5: Emotion Recognition

[1520] Device: Runs the emotion engine and acquires emotion data from the user's facial expressions and voice.

[1521] Input: User's facial expression, voice

[1522] Data calculation: The emotion engine analyzes facial and voice data to generate emotion data (e.g., satisfaction, interest).

[1523] Output: Generated emotion data

[1524] Step 6: Send data to the server

[1525] Terminal: Sends the acquired characteristic data and emotion data to the server.

[1526] Input: Trait data, emotion data

[1527] Output: Data is sent to the server.

[1528] Step 7: Calculating the Matching Score

[1529] Server: Based on the received user characteristic data and emotion data, it compares it with attribute data from multiple departments within the company.

[1530] Input: User characteristics data, emotion data, and attribute data for each department within the company

[1531] Data calculation: A specific matching algorithm is used to calculate the matching score for each department.

[1532] Output: Matching scores for each department

[1533] Step 8: Generate matching results

[1534] Server: Generates a list of optimal departments based on the calculated matching scores.

[1535] Input: Matching score

[1536] Output: List of best departments

[1537] Step 9: Presenting the results

[1538] Device: The generated list of optimal departments is displayed on the user's device.

[1539] Input: List of best departments

[1540] Output: "The department that best suits you is: 1. R&D (high sentiment score) 2. Tech Support (medium sentiment score) 3. IT (low sentiment score)."

[1541] Step 10: Provide feedback

[1542] User: Provide feedback on the results presented. For example, comment, "The R&D department is interesting, but I'd like to know more about the sales department."

[1543] Input: Feedback

[1544] Device: When providing feedback, the emotion engine is run again to obtain the user's emotion data (e.g., interest).

[1545] Data calculation: The emotion engine retrieves emotion data in relation to the provided feedback content.

[1546] Output: User's emotion data and feedback content

[1547] Step 11: Storing Feedback and Sentiment Data

[1548] Device: Sends the acquired feedback and emotion data to the server.

[1549] Input: Feedback content, emotion data

[1550] Server: Receives feedback and emotion data and stores it in a database.

[1551] Output: The feedback and sentiment data stored in the database will be used to improve the accuracy of the matching algorithm next time.

[1552] (Application example 2)

[1553] 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."

[1554] Conventional department introduction systems matched users based solely on their characteristics and self-reported information, without considering their emotions or reactions, making it difficult to suggest the department that was best suited to them. This resulted in lower user satisfaction and the inability to match users to the appropriate department. Furthermore, because the system did not consider the user's emotional feedback, it was difficult to improve the accuracy of the next match.

[1555] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1556] In this invention, the server includes means for recognizing user emotion data using an emotion engine, means for generating a list of optimal departments based on the matching results including the emotion data, and means further including an emotion engine for recognizing the user's emotion in real time regarding the displayed list of departments. This makes it possible to analyze the user's emotion in real time and suggest optimal departments based on that, improving user satisfaction and also improving the accuracy of the next matching.

[1557] A "user" is an individual user of a particular system or application.

[1558] "Characteristics" refers to information such as a user's skills, experience, and interests.

[1559] "Self-reporting" is information that the user himself / herself applies for through an input interface.

[1560] "Emotion engine" refers to a hardware or software system for recognizing a user's emotion data.

[1561] "Emotion data" is data related to emotions classified from the user's facial expressions and voice recognized by the emotion engine.

[1562] "Matching" is the process of checking the relevance of the input data to the corresponding department, product, etc.

[1563] A "department" refers to a group or division within an organization that performs specific tasks or functions.

[1564] An "algorithm" is a set of procedures or processes that formulate steps or calculation methods to achieve a certain purpose.

[1565] A "list" refers to a list or table organized according to specific criteria.

[1566] "Feedback" refers to information such as ratings and opinions provided by users.

[1567] This invention combines an emotion engine with a system that introduces the most suitable department based on the user's characteristics and self-reported details. By taking into account the user's emotion data in addition to conventional matching algorithms, this system can propose a department that is more appropriate and satisfies the user.

[1568] System program and processing description

[1569] User Registration and Login

[1570] A user first accesses the system and enters the required information (name, identification number, email address, password) into the registration form. The terminal sends this information to the server, which stores it in a database. When logging in, the information entered by the user is sent from the terminal to the server again, and the server compares it with the database for authentication. If authentication is successful, an authentication token is generated and the user is notified that login was successful.

[1571] Information input and emotion recognition

[1572] After the user logs in, an interface for entering characteristics and self-declared details is displayed on the terminal. The user enters their skills (e.g., programming), interests (e.g., research on new technologies), experience (e.g., 5 years of sales experience), etc. At this time, an emotion engine (e.g., Hugging Face emotion recognition model) recognizes the user's emotions and sends them to the server along with the entered information.

[1573] Matching and emotional data utilization

[1574] The server receives the user's characteristics and self-reported information, as well as the emotion data recognized by the emotion engine. It then compares the attribute data of multiple departments within the organization and calculates a matching score for each department using a specific algorithm. This calculation, taking into account the user's emotion data, aims to identify the most suitable department with the highest satisfaction.

[1575] Presentation of results

[1576] The server generates matching results and creates a list of the most suitable departments. This list is sorted by score and provided to the user with emotional data taken into account. For example, it might be displayed as follows: "The departments that are most suitable for you are: 1. Research and Development (emotion score: high) 2. Technical Support (emotion score: medium) 3. IT Department (emotion score: low)."

[1577] Storing feedback and sentiment data

[1578] Users input their thoughts and wishes through an interface to provide feedback on the displayed results. When providing feedback, the emotion engine also recognizes the user's emotions and sends the data and feedback content to the server. The server receives the feedback and emotion data and stores it in a database. This stored data is used to improve the accuracy of the matching algorithm next time.

[1579] Specific examples

[1580] For example, if a user is browsing a "smartwatch" page and their facial expressions are captured by a camera, and the emotion engine recognizes expressions such as "excitement" or "satisfaction," the server can recommend similar products (e.g., the latest fitness trackers or high-end smartwatches) based on the emotion data in real time.

[1581] Prompt Sentence Examples

[1582] An example of a prompt sentence to input to the generative AI model is as follows:

[1583] 1. Prompt sentence for emotion recognition model

[1584] "Analyze image data to recognize emotions."

[1585] 2. Prompt for product recommendation API

[1586] "Recommend the best products to users based on this sentiment data."

[1587] This enables real-time emotion-based product recommendations to users.

[1588] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1589] Step 1:

[1590] The user enters characteristics and self-declaration details.

[1591] Input: A user enters information into a registration form, such as their name, identification number, email address, and password.

[1592] Processing: The device receives this information and sends it to the server.

[1593] Output: The server stores this information in a database.

[1594] Step 2:

[1595] A user logs in.

[1596] Input: The user enters their email address and password into the login interface.

[1597] Processing: The terminal sends the entered information to the server, which checks it against a database for authentication.

[1598] Output: If authentication is successful, the server generates an authentication token and sends it to the device, which receives it and logs in successfully.

[1599] Step 3:

[1600] The user enters detailed characteristics and self-reported information.

[1601] Input: Users input their skills, interests, experience, etc.

[1602] Processing: The device sends this information to the server, and the emotion engine analyzes the user's facial expressions and voice to generate emotion data.

[1603] Output: The emotion data and input data are sent to the server and stored.

[1604] Step 4:

[1605] The server uses the emotion data to analyze the user's characteristics.

[1606] Input: The server receives the user's characteristic information and emotion data.

[1607] Processing: The server inputs this data into an algorithm, compares it with department attribute data, and calculates a matching score.

[1608] Output: A list of matches is generated.

[1609] Step 5:

[1610] The server presents a list of the most suitable departments.

[1611] Input: The server generates a list of matching results.

[1612] Processing: The list is sorted by score and sent to the user's device, taking into account the emotional data.

[1613] Output: The device displays a list of the following types of departments: 1. Research & Development (High Sentiment Score) 2. Tech Support (Medium Sentiment Score) 3. IT (Low Sentiment Score)

[1614] Step 6:

[1615] The user provides feedback.

[1616] Input: The user enters feedback on the displayed results.

[1617] Processing: The device sends the feedback content to the server. At the same time, the emotion engine recognizes the emotion data when providing the feedback and sends it to the server.

[1618] Output: Feedback data and emotion data are stored on the server.

[1619] Step 7:

[1620] The server stores the feedback and emotion data to improve the accuracy of the next match.

[1621] Input: The server receives feedback data and emotion data.

[1622] Processing: These data are stored in a database and used as training data to improve the accuracy of the matching algorithm.

[1623] Output: The accuracy of the matching algorithm will be improved next time, and it will be possible to suggest more suitable departments to the user.

[1624] In this way, by utilizing emotional data, it is possible to more accurately grasp the characteristics and satisfaction level of users and match them with the appropriate department.

[1625] 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.

[1626] 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.

[1627] 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.

[1628] 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.

[1629] 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.

[1630] 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.

[1631] 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).

[1632] 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.

[1633] 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."

[1634] 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.

[1635] 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).

[1636] 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.

[1637] 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.

[1638] 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.

[1639] 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.

[1640] 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.

[1641] 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.

[1642] 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.

[1643] 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.

[1644] 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.

[1645] 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.

[1646] The following is further disclosed regarding the above embodiment.

[1647] (Claim 1)

[1648] a means for the user to input characteristics and self-reported information;

[1649] A means for receiving the input information and comparing it with attribute data of multiple departments within the company;

[1650] A means for generating a list of optimal departments based on the matching results;

[1651] A means for presenting the generated list of departments to a user;

[1652] a means for receiving and storing user feedback;

[1653] A system including:

[1654] (Claim 2)

[1655] 10. The system of claim 1, further comprising means for providing an interface for a user to input characteristics based on self-reported content.

[1656] (Claim 3)

[1657] 10. The system of claim 1, comprising an algorithm for improving the accuracy of subsequent department matches based on feedback provided by the user.

[1658] "Example 1"

[1659] (Claim 1)

[1660] a means for the user to input characteristics and self-reported information;

[1661] A means for receiving input information and comparing it with attribute data of multiple departments within an organization;

[1662] A means for generating a list of optimal departments based on the matching results;

[1663] A means for presenting the generated list of departments to a user;

[1664] a means for receiving and storing user feedback;

[1665] A system including:

[1666] (Claim 2)

[1667] 10. The system of claim 1, further comprising means for providing an interface for a user to input characteristics and self-reported content.

[1668] (Claim 3)

[1669] 10. The system of claim 1, comprising an algorithm for improving the accuracy of subsequent department matches based on feedback provided by the user.

[1670] "Application Example 1"

[1671] (Claim 1)

[1672] a means for the user to input characteristics and self-reported information;

[1673] A means for receiving the input information and comparing it with attribute data of multiple departments within the company;

[1674] A means for generating a list of optimal departments based on the matching results;

[1675] A means for presenting the generated list of departments to a user;

[1676] a means for receiving and storing user feedback;

[1677] A means for the robot to self-report and input its characteristics and skills;

[1678] A means of assigning optimal work locations and work tasks based on self-reported data;

[1679] means for presenting a list of generated work tasks;

[1680] A system including:

[1681] (Claim 2)

[1682] 10. The system of claim 1, further comprising means for providing an interface for a user to input characteristics based on self-reported content.

[1683] (Claim 3)

[1684] 10. The system of claim 1, comprising an algorithm for improving the accuracy of subsequent department matches based on feedback provided by the user.

[1685] "Example 2: Combining Emotion Engines"

[1686] (Claim 1)

[1687] a means for the user to input characteristics and self-reported information;

[1688] means for receiving input information and acquiring emotion data using an emotion engine that recognizes the emotion of a user;

[1689] means for transmitting the acquired emotion data and self-reported information to a server;

[1690] A means for comparing the information received by the server with attribute data of multiple departments within the company and calculating a matching score;

[1691] A means for generating a list of optimal departments based on the calculated matching scores;

[1692] A means for presenting the generated list of departments to a user;

[1693] a means for receiving and storing feedback from the user regarding the presented department list;

[1694] A system including:

[1695] (Claim 2)

[1696] 10. The system of claim 1, further comprising means for providing an interface for a user to input characteristics based on self-reported content.

[1697] (Claim 3)

[1698] 10. The system of claim 1, comprising an algorithm for improving the accuracy of subsequent department matches based on feedback provided by the user.

[1699] "Application example 2 when combining emotion engines"

[1700] (Claim 1)

[1701] a means for the user to input characteristics and self-reported information;

[1702] A means for receiving the input information and comparing it with attribute data of multiple departments within the company;

[1703] means for recognizing user emotion data using an emotion engine;

[1704] A means for generating a list of optimal departments based on the matching results including the emotion data;

[1705] A means for presenting the generated list of departments to a user;

[1706] a means for receiving and storing user feedback;

[1707] A system including:

[1708] (Claim 2)

[1709] means for providing an interface for a user to input characteristics based on self-reported content;

[1710] The system further includes an emotion engine that recognizes the user's emotion regarding the displayed department list in real time.

[1711] 10. The system of claim 1.

[1712] (Claim 3)

[1713] Equipped with algorithms to improve the accuracy of the next department match based on user-provided feedback and recognized emotional data;

[1714] 10. The system of claim 1. [Explanation of symbols]

[1715] 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. a means for the user to input characteristics and self-reported information; A means for receiving the input information and comparing it with attribute data of multiple departments within the company; A means for generating a list of optimal departments based on the matching results; A means for presenting the generated list of departments to a user; a means for receiving and storing user feedback; A system including:

2. The system of claim 1 , further comprising means for providing an interface for a user to input characteristics based on self-reported content.

3. The system of claim 1 , further comprising an algorithm for improving the accuracy of subsequent department matches based on feedback provided by the user.

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