System

Abstract

A system is provided.SOLUTION: A system, comprising: a terminal for an employee to input his / her skills, experiences, and interests; a database for storing the data input by the employee; an AI engine for analyzing the stored data and recommending an optimal project matched with a project requirement; means for inputting and storing requirement information of the project; means for notifying an analysis result by the AI engine to the employee and a project manager; means for selecting from among the recommended projects and submitting a participation request by the employee; and means for approving or rejecting the submitted participation request by the project manager.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] It is difficult to select appropriate project members within a company, and many employees are unable to participate in projects that allow them to make the most of their skills. As a result, project performance and efficiency decline, making it difficult to improve productivity across the company. Other problems include a decline in employee motivation and underutilization of skills. [Means for solving the problem]

[0005] The present invention is a system that includes a terminal for employees to input their skills, experience, and interests; a database for storing the data entered by employees; an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements; a means for inputting and saving project requirement information; a means for notifying employees and project managers of the results of the AI ​​engine's analysis; a means for employees to select from the recommended projects and submit their participation requests; and a means for the project manager to approve or reject the submitted participation requests. Furthermore, by adding an AI engine that quantifies and ranks the degree of match between employees and project requirements and a means for storing employees' project participation history and reflecting this in the next project recommendation, more accurate matching can be achieved. As a result, improved project performance, increased employee motivation, and more effective skill utilization can be expected.

[0006] A "terminal" is an electronic device that allows a user to enter or view information.

[0007] A "database" is a system that structures and stores input data and allows for quick search and retrieval of that data as needed.

[0008] An "AI engine" is software and algorithms that use artificial intelligence technology to analyze employee skills and project requirements and make optimal matches.

[0009] "Project Requirements" are information that describes the skills, roles, or other requirements needed to carry out a particular project.

[0010] "Notification means" refers to a mechanism by which the system notifies the user or project manager of analysis results and other important information.

[0011] "Want to participate" is a clear expression of intent by the user that they wish to participate in the recommended project.

[0012] A "project manager" is a person responsible for planning and managing the execution of a project, defining project requirements, and selecting project members.

[0013] "Match" is an indicator of how well an employee's skills and experience match the project requirements.

[0014] "Ranking" is the process of ranking the best candidates from among multiple candidates.

[0015] "Project participation history" refers to records of projects in which an employee has participated in the past. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10]1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The AI-based project matching platform of the present invention is a system in which employees use a terminal to input their skills, experience, and interests, and an AI engine recommends optimal projects based on the accumulated data. Specific embodiments of the system are described below.

[0038] System configuration

[0039] 1. Terminals and User Interfaces

[0040] Providing an interface, such as a web-based form or a smartphone application, that allows users to enter their skills, experience, interests, and past project history.

[0041] 2. Database

[0042] A database will be created to store user-entered data and project requirements, including skill sets, years of experience, areas of interest, certifications, and past project history.

[0043] 3. AI Engine

[0044] The AI ​​engine analyzes employee skill sets and project requirements to find the best match, using machine learning models and statistical methods to calculate the degree of match between employee skills and project requirements.

[0045] 4. Project requirement input and storage means

[0046] It provides an interface for a project manager to input information such as the name of the project, the objectives, the required skill set, the start date, the end date, etc., and store this information in a database.

[0047] 5. Means of notification

[0048] Build a system to notify employees and project managers of the results of analysis by the AI ​​engine, possibly via email or a notification function within the platform.

[0049] 6. How to submit your participation request

[0050] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[0051] 7. Approval and Rejection Procedures

[0052] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[0053] System operation example

[0054] Example 1: Skills matching

[0055] User operations

[0056] User A logs in from a device and accesses the profile editing screen. User A enters their skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button.

[0057] The server saves the input data in a database.

[0058] Entering Project Information

[0059] A project manager creates a new project X and inputs the required skill sets (e.g., "data analysis", "modeling skills", "project management").

[0060] The server stores the project information in a database.

[0061] Performing matching

[0062] The server passes User A's data and Project X's data to the AI ​​engine and requests a match.

[0063] The AI ​​engine analyzes User A's skill set and the requirements of Project X and calculates the degree of match.

[0064] The projects are ranked in descending order of degree of match, and a list of recommended projects is notified to user A.

[0065] Example 2: Approval for project participation

[0066] User Selection

[0067] User A selects Project X from the recommended projects list and submits a request to participate.

[0068] The server stores the user's participation request in a database and notifies the project manager.

[0069] Project Manager Operations

[0070] The project manager reviews User A's profile and approves his participation.

[0071] The server updates the database with the approval result and notifies User A.

[0072] This system allows employees to participate in projects that make the most of their skills, improving project efficiency and boosting productivity across the company.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[0076] Step 2:

[0077] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[0078] Step 3:

[0079] The user accesses the profile editing screen from the terminal and enters or updates information such as their skills, experience, interests, and past project history.

[0080] Step 4:

[0081] When the user presses the "Save" button, the data is sent from the device to the server.

[0082] Step 5:

[0083] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[0084] Step 6:

[0085] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[0086] Step 7:

[0087] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[0088] Step 8:

[0089] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[0090] Step 9:

[0091] The server periodically passes user data and project data to the AI ​​engine, requesting analysis and matching.

[0092] Step 10:

[0093] The AI ​​engine analyzes the user's skill map and the required skill set of the project, calculates the degree of match, and generates a matching result.

[0094] Step 11:

[0095] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[0096] Step 12:

[0097] The server stores the match results in a database and sends a notification to the user.

[0098] Step 13:

[0099] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[0100] Step 14:

[0101] The user selects the project they are interested in and presses the "Want to participate" button.

[0102] Step 15:

[0103] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[0104] Step 16:

[0105] The project manager logs in from a terminal, confirms the user's participation, checks the user's profile, and approves or denies the participation.

[0106] Step 17:

[0107] The server receives the approval or rejection result from the project manager, updates the database, and sends a notification of the result to the user.

[0108] Example 1

[0109] 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."

[0110] Conventional project matching systems did not adequately reflect employees' skills and experience, making it difficult to assign them to the most suitable projects. Furthermore, when project managers manually performed matching, it took time and effort, making it difficult to achieve efficient matching. Furthermore, support for employee skill development and career path formation was insufficient, preventing the system from contributing to improved productivity across the company.

[0111] 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.

[0112] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a storage device for saving the data input by the employees, an artificial intelligence device that analyzes the saved data and recommends optimal work that matches the work requirements, a means for inputting and saving information about work requirements, a means for notifying employees and work managers of the analysis results by the artificial intelligence device, a means for employees to select from the recommended work and submit a request to participate, and a means for the work manager to approve or reject the submitted request to participate. This enables optimal matching of employee skills with project requirements.

[0113] A "terminal" is a device that employees use to input information such as their skills, experience, and interests, and includes computers and smartphones.

[0114] "Storage device" refers to a device used to store data entered by employees and project information, such as a database or cloud storage.

[0115] The "artificial intelligence device" analyzes stored data and compares employee skill sets with project requirements to make optimal matches, using machine learning models and statistical methods.

[0116] "Work requirements" refers to information such as the skills, experience, and qualifications needed to complete a specific project or task.

[0117] "Work Manager" refers to the person or position that has the authority to manage projects and tasks and approve or deny employee participation requests.

[0118] "Notification means" refers to a system for communicating the analysis results of the artificial intelligence device to employees and work managers, and includes email and notification functions within the platform.

[0119] "Method for submitting participation requests" refers to the interface through which employees can select from recommended projects and submit their participation requests, including web forms and mobile apps.

[0120] The present invention is a project matching system that utilizes an artificial intelligence device, and aims to enable employees to efficiently input their skills, experience, and interests and be assigned to appropriate projects. The system includes a terminal, a storage device, an artificial intelligence device, and a notification means.

[0121] Hardware and software used

[0122] The system uses the following hardware and software:

[0123] Terminal: A device used by employees to input information. This includes computers and smartphones.

[0124] Storage device: A device for storing employee and project data. Specifically, a database such as MySQL (registered trademark) or PostgreSQL is used.

[0125] Artificial intelligence device: A device that analyzes stored data and performs optimal matching. Specific technologies used include machine learning libraries such as TENSORFLOW (registered trademark) and PyTorch.

[0126] Notification methods: Email and in-system notification functions.

[0127] Entering User Data

[0128] It provides an interface for users to input their skills, experience, and interests. Users input data using a web form or dedicated app on a computer or smartphone, and the data is saved in a storage device by pressing the save button.

[0129] Entering Project Information

[0130] Provides an interface for project managers to enter requirements information for a new project, such as the project name, goal, required skill set, start date, and finish date, and then saves the information.

[0131] Data Matching

[0132] The server retrieves the user's skill data and project requirement data from the storage device and requests an analysis from the AI ​​device. The AI ​​device runs a machine learning model using TensorFlow or PyTorch to calculate the degree of match between the user's skill set and the project requirements. Based on this degree of match, the AI ​​device ranks projects and recommends the most suitable projects to the user.

[0133] Notification of recommended projects

[0134] The server receives the analysis results from the AI ​​device and notifies employees and work managers via email or in-system notifications.

[0135] Project participation application

[0136] The system provides an interface for users to select and apply for participation in recommended projects. When a user submits their "participation request," the server stores the data in a storage device and notifies the project manager.

[0137] Participation Approval Process

[0138] The project manager receives the user's participation request, checks the user's profile and project requirements, and then approves or rejects the participation. The approval result is stored in a storage device by the server and notified to the user.

[0139] Specific examples

[0140] Example 1: Skills matching

[0141] The user inputs their skills (e.g., "Python", "data analysis", "project management") and presses the save button. The server saves this in the storage device. The project manager inputs new project information (e.g., "data analysis", "modeling skills", "project management") and saves it. The server sends this data to an artificial intelligence device, calculates the degree of match, and notifies the user of a list of recommended projects.

[0142] Example 2: Approval for project participation

[0143] The user selects a project from the recommended projects list and submits a "request to participate." The server stores this information in a storage device and notifies the project manager. The project manager checks the user's profile and approves their participation. The server stores the approval result in a storage device and notifies the user.

[0144] Prompt Sentence Examples

[0145] Generate a system description that matches user skill sets with project requirements.

[0146] Skillset: Python, Data Analysis, Project Management

[0147] Project requirements: Data analysis, modeling skills, project management

[0148] Hardware used: PC, smartphone, cloud server

[0149] Software used: Web Forms, MySQL, TensorFlow

[0150] As described above, the present invention makes it possible to make the most of employees' skills and quickly and efficiently match them with the most suitable projects.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] Entering User Data

[0154] User operations

[0155] A user logs in to the system using a terminal (PC or smartphone).

[0156] After logging in, access the profile editing screen and enter your skills (e.g., "Python", "Data Analysis", "Project Management"), experience, and interests.

[0157] Once you have completed the input, press the "Save" button.

[0158] input

[0159] Data such as your skills, experience, and interests.

[0160] Server Processing

[0161] The server receives the data sent by the user.

[0162] The received data is stored in a MySQL database.

[0163] output

[0164] The user data is saved to the database and a confirmation message is sent to the user indicating the save was successful.

[0165] Step 2:

[0166] Entering Project Information

[0167] Project Manager Operations

[0168] The project manager accesses the project creation screen from their own device.

[0169] Enter the name of your new project, its goal, the required skill set (e.g., "Data Analysis", "Modeling Skills", "Project Management"), start date, and end date.

[0170] Once you have completed the input, press the "Save" button.

[0171] input

[0172] Project information such as project name, objectives, required skill sets, start date, and end date.

[0173] Server Processing

[0174] The server receives the data sent from the project manager.

[0175] Store received project information in a MySQL database.

[0176] output

[0177] The project information is saved to the database and a confirmation message is sent to the project manager indicating the save was successful.

[0178] Step 3:

[0179] Data Matching

[0180] Server Processing

[0181] The server retrieves the user's skill data and the project's requirement data from the database.

[0182] The acquired data is sent to an artificial intelligence device and requested for analysis.

[0183] input

[0184] User skill data and project requirement data.

[0185] AI processing

[0186] The artificial intelligence device uses TensorFlow to run machine learning models.

[0187] Calculate the match between a user's skill set and project requirements.

[0188] The calculation result (match score) is sent to the server.

[0189] output

[0190] Match score.

[0191] Step 4:

[0192] Notification of recommended projects

[0193] Server Processing

[0194] The server ranks the projects based on the match scores received from the artificial intelligence device.

[0195] Select the most suitable projects and create a list of recommended projects.

[0196] The recommended project list is notified to the user.

[0197] input

[0198] Match score.

[0199] output

[0200] Notification of recommended project list.

[0201] Step 5:

[0202] Project participation application

[0203] User operations

[0204] The user checks the recommended project list received from the terminal.

[0205] Select the project you are interested in and submit your request to participate.

[0206] input

[0207] Recommended projects list.

[0208] Server Processing

[0209] The server receives the user's participation request data.

[0210] The received data is stored in a database and notified to the project manager.

[0211] output

[0212] Saving your participation request data and informing the project manager.

[0213] Step 6:

[0214] Participation Approval Process

[0215] Project Manager Operations

[0216] The project manager checks the notification received from the device.

[0217] Visit a user's profile page to compare their skills with your project requirements.

[0218] Enter the result of approving or denying participation and press the "Submit" button.

[0219] input

[0220] Participation preference data.

[0221] Server Processing

[0222] The server receives the approval result and stores it in the database.

[0223] The approval result is notified to the user and the project member list is updated.

[0224] output

[0225] Notification of approval results and update of project member list.

[0226] (Application example 1)

[0227] 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."

[0228] While conventional matching systems can recommend projects that take into account employee skills, experience, and interests, they do not support optimal matching of robot skill sets and task information, which is important in factory production. As a result, it becomes difficult to efficiently assign tasks to robots, resulting in reduced productivity and task waste. The present invention aims to improve productivity and reduce waste in factories by efficiently matching the skill sets and task information of both employees and robots.

[0229] 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.

[0230] In this invention, the server includes: a terminal for employees to input their skills, experience, and interests; a database for storing the data input by the employees; an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements; a means for inputting and saving project requirement information; a means for notifying employees and the project manager of the analysis results by the AI ​​engine; a means for employees to select from the recommended projects and submit a request to participate; a means for the project manager to approve or reject the submitted request to participate; a means for inputting and saving robot skill sets and task information; and a means for the AI ​​engine to analyze the robot skill data and task information and recommend the optimal robot. This enables optimal matching not only between employees and projects, but also between robots and tasks, thereby improving productivity and achieving efficient task management throughout the factory.

[0231] An "employee" is an individual who performs certain roles and duties within a company or organization and performs work based on an employment contract.

[0232] "Skills" refer to the techniques and abilities required to carry out a specific job or task.

[0233] "Experience" refers to knowledge and skills based on past work and tasks.

[0234] An "interest" is an individual's interest or preference in a particular field or occupation.

[0235] A "terminal" is a device for inputting, displaying, and processing information, and includes smartphones, tablets, and personal computers.

[0236] A "database" is an information system for systematically storing, managing, searching, and updating data.

[0237] An "AI engine" is software that uses artificial intelligence algorithms to analyze data and make appropriate decisions and predictions.

[0238] A "project" is a set of activities or tasks planned and carried out to achieve a specific purpose or goal.

[0239] A "project manager" is a person who is responsible for planning, executing, managing progress, and completing a project, and who also serves as a leader for the team.

[0240] A "robot" is an automated mechanical device that performs programmed actions to accomplish specific tasks.

[0241] A "task" is a specific unit of work or work, and is an individual job or activity required to accomplish that work.

[0242] "Matching" is the process of selecting the optimal combination based on specific conditions and requirements.

[0243] "Notification" is the action of a system informing relevant parties of specific information, such as email or in-application alerts.

[0244] A "recommendation" is the determination and suggestion of a particular choice or option as appropriate.

[0245] "Approval" is the act of accepting and formally agreeing to a submitted request or proposal.

[0246] "Storage" refers to the act of permanently retaining data and making it available for later access.

[0247] "Input" refers to the act of a user providing data or information to a system.

[0248] The present invention is a project matching system based on the skill sets and task information of employees and robots. This system is configured as follows.

[0249] System configuration

[0250] 1. Terminals and User Interfaces

[0251] Providing an interface for employees to input their skills, experience, interests, and past task history, such as a web-based form or smartphone application. Also includes an interface for inputting the skill sets and task history of robots.

[0252] 2. Database

[0253] A database will be prepared to store data entered by employees and robots, as well as task requirements information, including skill sets, years of experience, areas of interest, qualifications, and history of projects and tasks previously handled. MySQL or PostgreSQL will be used as the database management system.

[0254] 3. AI Engine

[0255] The AI ​​engine analyzes the skill sets of employees and robots with the requirements of a project or task to find the best match. For example, it uses a generative AI model to calculate the degree of match between the skills of employees and robots and the requirements of a project or task. The AI ​​engine used is TensorFlow or PyTorch.

[0256] 4. Project and task information entry and storage method

[0257] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and end date, and save this in a database. It also includes an interface for inputting and saving robot task information.

[0258] 5. Means of notification

[0259] A system will be created to notify employees, project managers, and factory managers of the results of analysis by the AI ​​engine, possibly via email or an in-platform notification function.

[0260] 6. How to submit your participation request

[0261] It provides an interface where employees and robots can choose from recommended projects and tasks and submit their participation requests.

[0262] 7. Approval and Rejection Procedures

[0263] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the membership of projects and tasks.

[0264] System operation example

[0265] Example 1: Skills matching

[0266] 1. Employee and robot skill data entry

[0267] Employee A and Robot A log in from their terminals and access the profile editing screen. Employee A enters his or her skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button. Robot A also automatically updates its skill data (e.g., "Welding", "Assembly").

[0268] 2. Enter task information

[0269] A project manager creates a new project X and enters the required skill set (e.g., "data analysis", "modeling skills", "project management"), and a factory manager enters a new task Y (e.g., "welding").

[0270] 3. Perform matching

[0271] The server passes the data of employee A and robot A, as well as the data of project X and task Y, to the AI ​​engine and requests a match. The AI ​​engine analyzes employee A's skill set and the requirements of project X, and robot A's skill set and the requirements of task Y, and calculates the degree of match. It ranks the projects and tasks in descending order of match and notifies employee A and robot A of the recommended project and task list.

[0272] Example 2: Project and Task Participation Approval

[0273] 1. Employee and robot selection

[0274] Employee A and Robot A select Project X and Task Y from the recommended project and task list and submit their participation requests. The server saves the requests in a database and notifies the project manager and factory manager.

[0275] 2. Project Manager and Factory Manager Operations

[0276] The project manager checks the profile of Employee A and approves his participation. The factory manager checks the profile of Robot A and approves the task. The server updates the database with the approval result and notifies Employee A and Robot A.

[0277] As a concrete example, we use the following prompt sentence: "The administrator enters a new welding task. The system analyzes the skill sets of robots A, B, and C, calculates the degree of match, and notifies the administrator of the recommended ranking. The administrator selects robot A that is best suited to the task and assigns the welding task."

[0278] This will enable efficient matching of employee and robot skill sets with task information, which is expected to improve productivity and reduce waste.

[0279] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0280] Step 1:

[0281] Users input their skills, experience, interests, and past project history through a terminal. The input data is temporarily stored locally and the data format is checked before being sent to the database.

[0282] Input: Skills, experience, interests, and past project history entered by the user into the interface

[0283] Output: Formatted data (e.g., {"Skills": "Python", "Experience": "5 years", "Interests": "Data Analysis", "History": "Project A, Project B"})

[0284] Step 2:

[0285] The server receives the data sent by the user and stores it in a database, categorized by skill set, years of experience, areas of interest, project history, etc.

[0286] Input: Formatted user data

[0287] Output: Categorical data stored in a database

[0288] How it works: It saves data using a database INSERT operation. It uses MySQL or PostgreSQL as the database management system.

[0289] Step 3:

[0290] A project manager uses a terminal to enter requirements for a new project or task, which is validated locally and then sent to the server.

[0291] Inputs: Project name, objectives, required skill sets, start date, end date, etc. entered by the project manager into the interface

[0292] Output: Formatted project or task information (e.g., {"Project Name": "Project X", "Goal": "Data Analysis", "Skills": "Python, Data Analysis", "Start Date": "2023-01-01", "End Date": "2023-12-31"})

[0293] Step 4:

[0294] The server receives the project or task information sent by the project manager and stores it in a database.

[0295] Input: Formatted project or task information

[0296] Output: Project or task information stored in a database

[0297] Behavior: Saves data using a database INSERT operation.

[0298] Step 5:

[0299] The server sends the stored user data and project or task information to the AI ​​engine for analysis, and the AI ​​engine analyzes the data using the generative AI model.

[0300] Input: User data and project or task information stored in the database

[0301] Output: Analysis results (e.g., match scores, recommendation list)

[0302] How it works: Using an AI engine (TensorFlow or PyTorch), it calculates the degree of similarity and performs optimal matching.

[0303] Step 6:

[0304] The server receives the analysis results from the AI ​​engine and notifies the user and project manager.

[0305] Input: Analysis results from the AI ​​engine

[0306] Output: Notification message (e.g., recommended project or task list)

[0307] What it does: Sends results using the notification system (email or in-app notification).

[0308] Step 7:

[0309] The user is notified, selects from the recommended projects or tasks, and submits a request to participate from their terminal.

[0310] Input: User's participation request (e.g. "I would like to participate in Project X")

[0311] Output: Formatted participation requests

[0312] How it works: A user submits a request to join through the interface.

[0313] Step 8:

[0314] The server stores the participation request sent by the user in a database and notifies the project manager.

[0315] Input: Formatted participation request data

[0316] Output: Participation requests and notification messages stored in the database

[0317] Behavior: Processes using database INSERT operations and the notification system.

[0318] Step 9:

[0319] The project manager is notified and can approve or deny the request, and the server updates the database with the result and notifies the user.

[0320] Input: Project manager's approval or rejection

[0321] Output: Database update results and notification messages

[0322] What it does: Performs a database UPDATE operation and sends the results using the notification system.

[0323] 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.

[0324] The AI-based project matching platform of the present invention is a system that recommends optimal projects by taking into consideration not only the skills, experience, and interests of employees, but also the emotions of users. Specific embodiments of the present invention are described below.

[0325] System configuration

[0326] 1. Terminals and User Interfaces

[0327] Provide an interface, such as a web form, a smartphone application, or a desktop application, that allows users to input their skills, experience, interests, past project history, and feelings.

[0328] 2. Database

[0329] A database is provided to store user-entered data and project requirements information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[0330] 3. AI Engine

[0331] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements.

[0332] 4. Emotion Engine

[0333] The emotion engine recognizes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's writing or voice and stores them in a database.

[0334] 5. Project requirement input and storage means

[0335] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and finish date, and store it in a database.

[0336] 6. Means of notification

[0337] Create a mechanism to notify employees and project managers of the results of the AI ​​and emotion engine analysis, including email notifications, push notifications, or in-platform notifications.

[0338] 7. How to submit your participation request

[0339] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[0340] 8. Approval and Rejection Procedures

[0341] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[0342] System operation example

[0343] Example 1: Skill matching based on emotions

[0344] User operations

[0345] User B logs in from a terminal and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java (registered trademark)", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotion data through the emotion engine.

[0346] The server saves the input data in a database.

[0347] Entering Project Information

[0348] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[0349] The server stores the project information in a database.

[0350] Performing matching

[0351] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[0352] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[0353] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[0354] Example 2: Approval for project participation

[0355] User Selection

[0356] User B selects Project Y from the recommended projects list and submits a request to participate.

[0357] The server stores the user's participation request in a database and notifies the project manager.

[0358] Project Manager Operations

[0359] The project manager logs in from a terminal and confirms User B's participation. He / she checks User B's profile and emotional data and approves or rejects their participation.

[0360] The server updates the database with the approval result and notifies User B.

[0361] This system allows employees to participate in projects that best utilize their skills and emotional state, improving project efficiency and increasing productivity across the company.

[0362] The processing flow will be explained below.

[0363] Step 1:

[0364] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[0365] Step 2:

[0366] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[0367] Step 3:

[0368] The user accesses a profile editing screen from a terminal and inputs or updates his / her skills, experience, interests, past project history and emotional data.

[0369] Step 4:

[0370] When the user presses the "Save" button, the data is sent from the device to the server.

[0371] Step 5:

[0372] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[0373] Step 6:

[0374] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[0375] Step 7:

[0376] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[0377] Step 8:

[0378] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[0379] Step 9:

[0380] The server periodically passes user data and project data to the AI ​​engine and emotion engine, requesting analysis and matching.

[0381] Step 10:

[0382] The AI ​​engine analyzes the user's skill map and the required skill set of the project and calculates the degree of match.

[0383] Step 11:

[0384] The emotion engine analyzes the user's emotional data and provides the results to the AI ​​engine.

[0385] Step 12:

[0386] The AI ​​engine combines the emotion data and skill data, recalculates the degree of match, and generates a matching result.

[0387] Step 13:

[0388] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[0389] Step 14:

[0390] The server stores the match results in a database and sends a notification to the user.

[0391] Step 15:

[0392] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[0393] Step 16:

[0394] The user selects the project they are interested in and presses the "Want to participate" button.

[0395] Step 17:

[0396] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[0397] Step 18:

[0398] The project manager logs in from a terminal and confirms the user's participation. They then check the user's profile and emotional data and approve or reject the participation.

[0399] Step 19:

[0400] The server receives the result of approval or rejection from the project manager, updates the database, and notifies the user of the result.

[0401] Step 20:

[0402] The server generates a list of members whose participation has been confirmed for the project, and notifies the relevant members of information necessary for starting the project.

[0403] Example 2

[0404] 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."

[0405] It is difficult for employees to find the best projects by considering their emotional data in addition to their skills, experience, and interests. Ignoring the impact of emotional states on project success can lead to reduced efficiency and project satisfaction. Furthermore, the process between employee project participation requests and project manager approval is cumbersome, and there is a lack of ways to do it quickly and efficiently.

[0406] 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.

[0407] In this invention, the server includes a terminal for employees to input their skills, experience, interests, and emotions, a database for saving the data input by the employees, an AI engine that analyzes the saved data and recommends optimal projects that match the project requirements, an emotion engine that analyzes emotions and saves them in the database, means for inputting and saving project requirement information, means for notifying employees and project managers of the results of analysis by the AI ​​engine and the emotion engine, means for employees to select from the recommended projects and submit their participation requests, and means for the project manager to approve or reject the submitted participation requests. This enables appropriate project matching that takes into account the skills and emotional state of employees, enabling efficient and satisfying project execution.

[0408] A "terminal" is an electronic device through which a user enters data, such as a web form, a smartphone application, or a desktop application.

[0409] "Database" refers to a system for storing and managing employee input data and project information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[0410] An "AI engine" is a software system that uses machine learning algorithms and statistical methods to analyze employee skill and emotional data and recommend the most suitable projects that match the project requirements.

[0411] The "emotion engine" is a software system that analyzes users' emotional data and reflects it in project matching, and includes the ability to analyze emotions from text and voice.

[0412] "Project requirement information" refers to information such as the name of the project, the goal, the required skill set, the start date, and the end date, and is the condition or specification required for implementing the project.

[0413] "Notification methods" are mechanisms for informing employees and project managers of the analysis results of the AI ​​engine and emotion engine, and include email notifications, push notifications, and in-platform notification functions.

[0414] The "participation request submission means" is an interface that allows a user to select from recommended projects and submit a request to participate.

[0415] The "approval / rejection method" is an interface that allows the project manager to approve or reject employee participation requests, and is a function for optimizing the project member composition.

[0416] The present invention is a project matching platform that takes into account skills, experience, interests, and emotions, and specific embodiments are described below.

[0417] System configuration

[0418] 1. Terminals and User Interfaces

[0419] Users can enter their skills, experience, interests, past project history, and feelings using a web form, smartphone application, or desktop application. The interface is designed to be user-friendly, allowing users to efficiently enter the required information.

[0420] 2. Database

[0421] The database stores user-entered data and project requirements, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data, to build a detailed user profile.

[0422] 3. AI Engine

[0423] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements. Generative AI models are often used.

[0424] 4. Emotion Engine

[0425] The emotion engine analyzes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's written or spoken words and stores them in a database. This information is used to improve the accuracy of project matching.

[0426] 5. Project requirement input and storage means

[0427] It provides an interface for project managers to enter requirements information about new projects (project name, goals, required skill sets, start date, finish date, etc.) and store it in a database.

[0428] 6. Means of notification

[0429] We will create a system to notify employees and project managers of the results of the analysis of the AI ​​engine and emotion engine via email, push notifications, or in-platform notifications.

[0430] 7. How to submit your participation request

[0431] Users are provided with an interface to submit their participation in recommended projects, clearly expressing their intentions.

[0432] 8. Approval and Rejection Procedures

[0433] Project managers are provided with a mechanism to review submitted requests and approve or reject them, which is useful for optimizing the project's membership structure.

[0434] Examples of concrete examples and prompts

[0435] Example 1: Skill matching based on emotions

[0436] 1. User operations

[0437] User B logs in from a device and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotional data (e.g., "Positive about work" or "Currently feeling stressed") through the emotion engine.

[0438] Example prompt: "Describe a situation where User B enters their skill set and emotion data and then presses the save button."

[0439] 2. Server Processing

[0440] The server saves the input data in a database.

[0441] 3. Enter project information

[0442] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[0443] 4. Server Processing

[0444] The server stores the project information in a database.

[0445] 5. Performing matching

[0446] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[0447] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[0448] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[0449] Example prompt: "Please explain how the AI ​​engine analyzes User B's skill set and emotional data to match him with Project Y."

[0450] Example 2: Approval for project participation

[0451] 1. User Choice

[0452] User B selects Project Y from the recommended projects list and submits a request to participate.

[0453] Example prompt: "Describe a situation in which User B chooses Project Y from the list of recommended projects and submits a request to participate."

[0454] 2. Server Processing

[0455] The server stores the user's participation request in a database and notifies the project manager.

[0456] 3. Project Manager Operations

[0457] The project manager logs in from a terminal and confirms the user's participation. He / she checks User B's profile and emotional data and approves or rejects the participation.

[0458] 4. Server Processing

[0459] The server updates the database with the approval result and notifies the user.

[0460] The system of the present invention allows employees to participate in projects that make the most of their skills and emotional state, improving project efficiency and increasing productivity across the entire company.

[0461] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0462] System program processing flow

[0463] Step 1:

[0464] User data entry and saving

[0465] Input: The user uses the device to input their skills, experience, interests, and emotional data.

[0466] Processing: The user accesses the device's user interface, enters each item, and inputs emotion data through the emotion engine. The user then presses the "Save" button.

[0467] Output: The entered data is sent to the server.

[0468] Specific behavior:

[0469] User B accesses a web form and enters information such as "Java," "front-end development," and "5 years."

[0470] Enter your emotional state by voice or text (e.g., "I feel positive about my job").

[0471] Click the "Save" button to submit the data.

[0472] Step 2:

[0473] Server data storage

[0474] Input: User input data.

[0475] Processing: The server stores the received data in a database. The skill set, years of experience, areas of interest, and sentiment data are stored in the corresponding tables.

[0476] Output: User data stored in the database.

[0477] Specific behavior:

[0478] The server receives the user's skill, experience, and emotion data.

[0479] The process of saving to the database is performed, and the fact that the save was successful is recorded in the log.

[0480] Step 3:

[0481] Entering and saving project information

[0482] Input: The project manager uses a terminal to input project requirement information.

[0483] Process: The project manager accesses the project creation screen, enters the required information (project name, required skills, start date, end date, etc.), and presses the "Save" button.

[0484] Output: Project data is sent to the server.

[0485] Specific behavior:

[0486] The project manager accesses a web form and enters information such as "website renewal," "front-end development," and "start date: January 1, 2024."

[0487] Click the "Save" button to submit the data.

[0488] Step 4:

[0489] Save project information on the server

[0490] Input: Project manager input data.

[0491] Process: The server saves the received project data in the database. It generates a project ID and stores each piece of information in the corresponding table.

[0492] Output: Project data stored in a database.

[0493] Specific behavior:

[0494] The server receives the project name, skill requirements, duration, etc.

[0495] Generate a project ID, execute the process to save it in the database, and log that the save was successful.

[0496] Step 5:

[0497] Performing matching

[0498] Input: Server user data and project data.

[0499] Processing: The server passes the data to the AI ​​engine and emotion engine and requests a match. The AI ​​engine and emotion engine use the user's skill set and emotion data to calculate the degree of match with the project requirements.

[0500] Output: The match calculation results.

[0501] Specific behavior:

[0502] The server executes an API call to pass User B's skill set and emotion data to the AI ​​engine and emotion engine.

[0503] The AI ​​engine runs a matching algorithm and calculates the degree of match.

[0504] The calculation result is returned to the server.

[0505] Step 6:

[0506] Notification of matching results

[0507] Input: The result of the match calculation.

[0508] Processing: The server ranks the projects in order of the degree of matching, generates a recommendation list for the user, and notifies the user of the generated list using a notification means.

[0509] Output: Notification of recommended projects list.

[0510] Specific behavior:

[0511] The server receives the results of the calculation of the degree of match and creates a ranking.

[0512] The recommendation list is displayed on User B's dashboard and an email or app notification is sent.

[0513] Step 7:

[0514] User participation request submission

[0515] Input: Recommended projects list.

[0516] Process: The user selects a project from the recommended list and submits their participation request. The user selects the desired project through the user interface and clicks "Participate."

[0517] Output: Data on willingness to participate.

[0518] Specific behavior:

[0519] User B logs in from their device and checks the list of recommended projects.

[0520] Select a specific project Y and click "Want to participate."

[0521] Step 8:

[0522] Save and notify server join requests

[0523] Input: Your participation data.

[0524] Processing: The server saves the user's participation request in the database and notifies the project manager.

[0525] Output: Database update results and notifications.

[0526] Specific behavior:

[0527] The server receives the participation request data and stores it in a database.

[0528] Send email or app notifications to project managers.

[0529] Step 9:

[0530] Project manager's approval or rejection

[0531] Input: Notification of participation.

[0532] Processing: The project manager logs in from a terminal, checks the participation request and user data, and performs approval or rejection operations.

[0533] Output: Approval or rejection result.

[0534] Specific behavior:

[0535] The project manager checks User B's profile and emotional data.

[0536] Make your decision to approve or reject and click "Decide."

[0537] Step 10:

[0538] Server result notification

[0539] Input: The result of approval or rejection.

[0540] Processing: The server updates the database with the approval result and notifies the user.

[0541] Output: Update results and notifications.

[0542] Specific behavior:

[0543] The server records the project manager's decisions in a database.

[0544] User B will be notified of the results via email or app notification.

[0545] This series of processes enables project matching that makes the most of the user's skills and emotional state, which is expected to improve project efficiency and overall company productivity.

[0546] (Application example 2)

[0547] 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."

[0548] In factories, automatically assigning optimal tasks to robots while taking into account each robot's performance, past work history, current status, and maintenance history is a significant burden for managers and employees. This can lead to problems such as a decrease in robot utilization rate and a deterioration in factory production efficiency. The present invention aims to solve these problems by automating optimal task assignment for factory robots.

[0549] 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.

[0550] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a database for storing the data entered by the employees, an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements, a means for inputting and storing project requirement information, a means for notifying employees and project managers of the analysis results by the AI ​​engine, a means for employees to select from the recommended projects and submit a request to participate, a means for the project manager to approve or reject the submitted request, and a means for automatically matching and assigning optimal work to factory robots based on their performance, past work history, current status, and maintenance history. This makes it possible to maximize the utilization rate of factory robots and improve production efficiency.

[0551] 1. A "terminal" is a device that allows employees to input their skills, experience, and interests.

[0552] 2. "Database" means a system for storing employee-entered data and project requirements information.

[0553] 3. "AI Engine" refers to the artificial intelligence technology used to analyze stored data and recommend the most suitable projects that match project requirements.

[0554] 4. "Means for inputting and storing project requirements information" refers to an interface or device that inputs basic project information and required skill sets and stores them in a database.

[0555] 5. "Means of notification" means the method or device for notifying employees and project managers of analysis results and other important information.

[0556] 6. "Means for submitting participation requests" means an interface or device that allows employees to select from recommended projects and submit participation requests.

[0557] 7. "Means for Approval or Rejection" means the interface or device by which the Project Manager approves or rejects a submitted request to participate.

[0558] 8. "Robot performance" refers to the functions, work speed, accuracy, etc. of factory robots.

[0559] 9. "Past work history" is a record of the work each robot has done so far.

[0560] 10. "Current Status" refers to the real-time operating status, remaining battery level, and usage status of each robot.

[0561] 11. "Maintenance History" is a record of the maintenance and repairs that have been performed on each robot.

[0562] 12. "Means for automatically matching and allocating optimal tasks" refers to a method or system for selecting optimal tasks and issuing instructions to a robot based on the robot's performance, past work history, current status, and maintenance history.

[0563] According to the present invention, a system for efficiently optimizing work in a factory is configured as follows.

[0564] The system first provides employees with a terminal to input their skills, experience, and interests. The terminal can be a user device such as a tablet or PC. The user uses the terminal to input skill information and past work history, and the data is stored in a database on the server.

[0565] The server has an AI engine that analyzes the stored data. The AI ​​engine uses machine learning algorithms and statistical methods to analyze employee skill sets and project requirements and match them with the most suitable work. The server also has integrated into it a means for inputting and saving project requirement information. This allows project managers to input project information and required skill sets and save them in the database.

[0566] The server notifies employees and project managers of the analysis results via email, push notification, or the platform's notification function. Employees receive the notification and can select from the recommended projects and submit their participation requests. The server stores the submitted participation requests in a database and notifies the project manager.

[0567] The project manager checks the received participation request from the terminal and approves or rejects it, and the server updates the database with the result and notifies the employee.

[0568] In particular, one of the features of this invention is the addition of a function that automatically matches and assigns optimal tasks based on the robot's performance, past work history, current status, and maintenance history in the factory, thereby maximizing the robot's operating rate and improving production efficiency.

[0569] Specific examples

[0570] For example, suppose robots A, B, and C are available to perform welding work on a factory production line. Robot A has high welding skills based on its past history and has 80% battery remaining, but robot C needs more maintenance. In this case, the server's AI engine will prioritize robot A when allocating welding work.

[0571] Example prompt for a generative AI model:

[0572] "Optimize work allocation within your factory. Assign the best robot for the welding job in real time, taking into account each robot's skills and battery status."

[0573] This system allows users to reduce unnecessary work and waiting time, increasing overall productivity. It also takes into account the robot's maintenance status, ensuring stable operation over the long term.

[0574] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0575] Step 1:

[0576] Employees use the devices to input their skills, experience, and interests, and the devices send this data to a server, which stores it in a database.

[0577] Input: Employee skills, experience, and interest data

[0578] Output: Employee skills, experience, and interests data stored in a database

[0579] Step 2:

[0580] A project manager uses a terminal to input project requirements information, which is then sent to a server, which stores the received data in a database.

[0581] Input: Project requirement information such as project name, skill requirements, start date, end date, etc.

[0582] Output: Project requirements data stored in a database

[0583] Step 3:

[0584] The server passes the stored data to an AI engine, which analyzes employee skill sets and project requirements, calculates the degree of match, and recommends the most suitable projects.

[0585] Inputs: Employee skill sets, project requirement data

[0586] Output: A list of recommended projects for employees

[0587] Step 4:

[0588] The server notifies the analysis results to employees and project managers using notification means. Employees can then review the list of recommended projects and submit their participation requests.

[0589] Input: Project list of analysis results

[0590] Output: Notification to employees and project managers

[0591] Step 5:

[0592] The server stores the employee's participation request in a database and notifies the project manager.

[0593] Input: Employee participation request

[0594] Output: Participation request data stored in the database, notification to the project manager

[0595] Step 6:

[0596] The project manager uses a terminal to approve or reject the request, and the server updates the database with the result and notifies the employee.

[0597] Input: Project manager's approval or rejection decision

[0598] Output: Approval / rejection results stored in the database, notification to employees

[0599] Step 7:

[0600] The server automatically matches and assigns the optimal work to the factory robots based on their performance, past work history, current status, and maintenance history. It passes the necessary data to the AI ​​engine and assigns the work to the optimal robot.

[0601] Input: Robot performance data, past work history, current status, maintenance history

[0602] Output: Assignment instructions to the robot

[0603] Example of operation

[0604] For example, suppose Robot A has 80% battery remaining and has a history of successfully performing similar welding work in the past. Based on this information, the server uses an AI engine to issue instructions to assign the welding work to Robot A. This optimizes work efficiency within the factory and reduces unnecessary operations and waiting time.

[0605] 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.

[0606] 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.

[0607] 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.

[0608] [Second embodiment]

[0609] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0610] 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.

[0611] 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).

[0612] 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.

[0613] 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.

[0614] 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).

[0615] 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.

[0616] 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.

[0617] 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.

[0618] 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.

[0619] 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.

[0620] 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."

[0621] The AI-based project matching platform of the present invention is a system in which employees use a terminal to input their skills, experience, and interests, and an AI engine recommends optimal projects based on the accumulated data. Specific embodiments of the system are described below.

[0622] System configuration

[0623] 1. Terminals and User Interfaces

[0624] Providing an interface, such as a web-based form or a smartphone application, that allows users to enter their skills, experience, interests, and past project history.

[0625] 2. Database

[0626] A database will be created to store user-entered data and project requirements, including skill sets, years of experience, areas of interest, certifications, and past project history.

[0627] 3. AI Engine

[0628] The AI ​​engine analyzes employee skill sets and project requirements to find the best match, using machine learning models and statistical methods to calculate the degree of match between employee skills and project requirements.

[0629] 4. Project requirement input and storage means

[0630] It provides an interface for a project manager to input information such as the name of the project, the objectives, the required skill set, the start date, the end date, etc., and store this information in a database.

[0631] 5. Means of notification

[0632] Build a system to notify employees and project managers of the results of analysis by the AI ​​engine, possibly via email or a notification function within the platform.

[0633] 6. How to submit your participation request

[0634] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[0635] 7. Approval and Rejection Procedures

[0636] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[0637] System operation example

[0638] Example 1: Skills matching

[0639] User operations

[0640] User A logs in from a device and accesses the profile editing screen. User A enters their skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button.

[0641] The server saves the input data in a database.

[0642] Entering Project Information

[0643] A project manager creates a new project X and inputs the required skill sets (e.g., "data analysis", "modeling skills", "project management").

[0644] The server stores the project information in a database.

[0645] Performing matching

[0646] The server passes User A's data and Project X's data to the AI ​​engine and requests a match.

[0647] The AI ​​engine analyzes User A's skill set and the requirements of Project X and calculates the degree of match.

[0648] The projects are ranked in descending order of degree of match, and a list of recommended projects is notified to user A.

[0649] Example 2: Approval for project participation

[0650] User Selection

[0651] User A selects Project X from the recommended projects list and submits a request to participate.

[0652] The server stores the user's participation request in a database and notifies the project manager.

[0653] Project Manager Operations

[0654] The project manager reviews User A's profile and approves his participation.

[0655] The server updates the database with the approval result and notifies User A.

[0656] This system allows employees to participate in projects that make the most of their skills, improving project efficiency and boosting productivity across the company.

[0657] The processing flow will be explained below.

[0658] Step 1:

[0659] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[0660] Step 2:

[0661] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[0662] Step 3:

[0663] The user accesses the profile editing screen from the terminal and enters or updates information such as their skills, experience, interests, and past project history.

[0664] Step 4:

[0665] When the user presses the "Save" button, the data is sent from the device to the server.

[0666] Step 5:

[0667] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[0668] Step 6:

[0669] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[0670] Step 7:

[0671] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[0672] Step 8:

[0673] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[0674] Step 9:

[0675] The server periodically passes user data and project data to the AI ​​engine, requesting analysis and matching.

[0676] Step 10:

[0677] The AI ​​engine analyzes the user's skill map and the required skill set of the project, calculates the degree of match, and generates a matching result.

[0678] Step 11:

[0679] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[0680] Step 12:

[0681] The server stores the match results in a database and sends a notification to the user.

[0682] Step 13:

[0683] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[0684] Step 14:

[0685] The user selects the project they are interested in and presses the "Want to participate" button.

[0686] Step 15:

[0687] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[0688] Step 16:

[0689] The project manager logs in from a terminal, confirms the user's participation, checks the user's profile, and approves or denies the participation.

[0690] Step 17:

[0691] The server receives the approval or rejection result from the project manager, updates the database, and sends a notification of the result to the user.

[0692] Example 1

[0693] 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."

[0694] Conventional project matching systems did not adequately reflect employees' skills and experience, making it difficult to assign them to the most suitable projects. Furthermore, when project managers manually performed matching, it took time and effort, making it difficult to achieve efficient matching. Furthermore, support for employee skill development and career path formation was insufficient, preventing the system from contributing to improved productivity across the company.

[0695] 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.

[0696] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a storage device for saving the data input by the employees, an artificial intelligence device that analyzes the saved data and recommends optimal work that matches the work requirements, a means for inputting and saving information about work requirements, a means for notifying employees and work managers of the analysis results by the artificial intelligence device, a means for employees to select from the recommended work and submit a request to participate, and a means for the work manager to approve or reject the submitted request to participate. This enables optimal matching of employee skills with project requirements.

[0697] A "terminal" is a device that employees use to input information such as their skills, experience, and interests, and includes computers and smartphones.

[0698] "Storage device" refers to a device used to store data entered by employees and project information, such as a database or cloud storage.

[0699] The "artificial intelligence device" analyzes stored data and compares employee skill sets with project requirements to make optimal matches, using machine learning models and statistical methods.

[0700] "Work requirements" refers to information such as the skills, experience, and qualifications needed to complete a specific project or task.

[0701] "Work Manager" refers to the person or position that has the authority to manage projects and tasks and approve or deny employee participation requests.

[0702] "Notification means" refers to a system for communicating the analysis results of the artificial intelligence device to employees and work managers, and includes email and notification functions within the platform.

[0703] "Method for submitting participation requests" refers to the interface through which employees can select from recommended projects and submit their participation requests, including web forms and mobile apps.

[0704] The present invention is a project matching system that utilizes an artificial intelligence device, and aims to enable employees to efficiently input their skills, experience, and interests and be assigned to appropriate projects. The system includes a terminal, a storage device, an artificial intelligence device, and a notification means.

[0705] Hardware and software used

[0706] The system uses the following hardware and software:

[0707] Terminal: A device used by employees to input information. This includes computers and smartphones.

[0708] Storage: A device for storing employee and project data, specifically a database such as MySQL or PostgreSQL.

[0709] Artificial intelligence device: A device that analyzes stored data and performs optimal matching. Specific technologies include machine learning libraries such as TensorFlow and PyTorch.

[0710] Notification methods: Email and in-system notification functions.

[0711] Entering User Data

[0712] It provides an interface for users to input their skills, experience, and interests. Users input data using a web form or dedicated app on a computer or smartphone, and the data is saved in a storage device by pressing the save button.

[0713] Entering Project Information

[0714] Provides an interface for project managers to enter requirements information for a new project, such as the project name, goal, required skill set, start date, and finish date, and then saves the information.

[0715] Data Matching

[0716] The server retrieves the user's skill data and project requirement data from the storage device and requests an analysis from the AI ​​device. The AI ​​device runs a machine learning model using TensorFlow or PyTorch to calculate the degree of match between the user's skill set and the project requirements. Based on this degree of match, the AI ​​device ranks projects and recommends the most suitable projects to the user.

[0717] Notification of recommended projects

[0718] The server receives the analysis results from the AI ​​device and notifies employees and work managers via email or in-system notifications.

[0719] Project participation application

[0720] The system provides an interface for users to select and apply for participation in recommended projects. When a user submits their "participation request," the server stores the data in a storage device and notifies the project manager.

[0721] Participation Approval Process

[0722] The project manager receives the user's participation request, checks the user's profile and project requirements, and then approves or rejects the participation. The approval result is stored in a storage device by the server and notified to the user.

[0723] Specific examples

[0724] Example 1: Skills matching

[0725] The user inputs their skills (e.g., "Python", "data analysis", "project management") and presses the save button. The server saves this in the storage device. The project manager inputs new project information (e.g., "data analysis", "modeling skills", "project management") and saves it. The server sends this data to an artificial intelligence device, calculates the degree of match, and notifies the user of a list of recommended projects.

[0726] Example 2: Approval for project participation

[0727] The user selects a project from the recommended projects list and submits a "request to participate." The server stores this information in a storage device and notifies the project manager. The project manager checks the user's profile and approves their participation. The server stores the approval result in a storage device and notifies the user.

[0728] Prompt Sentence Examples

[0729] Generate a system description that matches user skill sets with project requirements.

[0730] Skillset: Python, Data Analysis, Project Management

[0731] Project requirements: Data analysis, modeling skills, project management

[0732] Hardware used: PC, smartphone, cloud server

[0733] Software used: Web Forms, MySQL, TensorFlow

[0734] As described above, the present invention makes it possible to make the most of employees' skills and quickly and efficiently match them with the most suitable projects.

[0735] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0736] Step 1:

[0737] Entering User Data

[0738] User operations

[0739] A user logs in to the system using a terminal (PC or smartphone).

[0740] After logging in, access the profile editing screen and enter your skills (e.g., "Python", "Data Analysis", "Project Management"), experience, and interests.

[0741] Once you have completed the input, press the "Save" button.

[0742] input

[0743] Data such as your skills, experience, and interests.

[0744] Server Processing

[0745] The server receives the data sent by the user.

[0746] The received data is stored in a MySQL database.

[0747] output

[0748] The user data is saved to the database and a confirmation message is sent to the user indicating the save was successful.

[0749] Step 2:

[0750] Entering Project Information

[0751] Project Manager Operations

[0752] The project manager accesses the project creation screen from their own device.

[0753] Enter the name of your new project, its goal, the required skill set (e.g., "Data Analysis", "Modeling Skills", "Project Management"), start date, and end date.

[0754] Once you have completed the input, press the "Save" button.

[0755] input

[0756] Project information such as project name, objectives, required skill sets, start date, and end date.

[0757] Server Processing

[0758] The server receives the data sent from the project manager.

[0759] Store received project information in a MySQL database.

[0760] output

[0761] The project information is saved to the database and a confirmation message is sent to the project manager indicating the save was successful.

[0762] Step 3:

[0763] Data Matching

[0764] Server Processing

[0765] The server retrieves the user's skill data and the project's requirement data from the database.

[0766] The acquired data is sent to an artificial intelligence device and requested for analysis.

[0767] input

[0768] User skill data and project requirement data.

[0769] AI processing

[0770] The artificial intelligence device uses TensorFlow to run machine learning models.

[0771] Calculate the match between a user's skill set and project requirements.

[0772] The calculation result (match score) is sent to the server.

[0773] output

[0774] Match score.

[0775] Step 4:

[0776] Notification of recommended projects

[0777] Server Processing

[0778] The server ranks the projects based on the match scores received from the artificial intelligence device.

[0779] Select the most suitable projects and create a list of recommended projects.

[0780] The recommended project list is notified to the user.

[0781] input

[0782] Match score.

[0783] output

[0784] Notification of recommended project list.

[0785] Step 5:

[0786] Project participation application

[0787] User operations

[0788] The user checks the recommended project list received from the terminal.

[0789] Select the project you are interested in and submit your request to participate.

[0790] input

[0791] Recommended projects list.

[0792] Server Processing

[0793] The server receives the user's participation request data.

[0794] The received data is stored in a database and notified to the project manager.

[0795] output

[0796] Saving your participation request data and informing the project manager.

[0797] Step 6:

[0798] Participation Approval Process

[0799] Project Manager Operations

[0800] The project manager checks the notification received from the device.

[0801] Visit a user's profile page to compare their skills with your project requirements.

[0802] Enter the result of approving or denying participation and press the "Submit" button.

[0803] input

[0804] Participation preference data.

[0805] Server Processing

[0806] The server receives the approval result and stores it in the database.

[0807] The approval result is notified to the user and the project member list is updated.

[0808] output

[0809] Notification of approval results and update of project member list.

[0810] (Application example 1)

[0811] 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."

[0812] While conventional matching systems can recommend projects that take into account employee skills, experience, and interests, they do not support optimal matching of robot skill sets and task information, which is important in factory production. As a result, it becomes difficult to efficiently assign tasks to robots, resulting in reduced productivity and task waste. The present invention aims to improve productivity and reduce waste in factories by efficiently matching the skill sets and task information of both employees and robots.

[0813] 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.

[0814] In this invention, the server includes: a terminal for employees to input their skills, experience, and interests; a database for storing the data input by the employees; an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements; a means for inputting and saving project requirement information; a means for notifying employees and the project manager of the analysis results by the AI ​​engine; a means for employees to select from the recommended projects and submit a request to participate; a means for the project manager to approve or reject the submitted request to participate; a means for inputting and saving robot skill sets and task information; and a means for the AI ​​engine to analyze the robot skill data and task information and recommend the optimal robot. This enables optimal matching not only between employees and projects, but also between robots and tasks, thereby improving productivity and achieving efficient task management throughout the factory.

[0815] An "employee" is an individual who performs certain roles and duties within a company or organization and performs work based on an employment contract.

[0816] "Skills" refer to the techniques and abilities required to carry out a specific job or task.

[0817] "Experience" refers to knowledge and skills based on past work and tasks.

[0818] An "interest" is an individual's interest or preference in a particular field or occupation.

[0819] A "terminal" is a device for inputting, displaying, and processing information, and includes smartphones, tablets, and personal computers.

[0820] A "database" is an information system for systematically storing, managing, searching, and updating data.

[0821] An "AI engine" is software that uses artificial intelligence algorithms to analyze data and make appropriate decisions and predictions.

[0822] A "project" is a set of activities or tasks planned and carried out to achieve a specific purpose or goal.

[0823] A "project manager" is a person who is responsible for planning, executing, managing progress, and completing a project, and who also serves as a leader for the team.

[0824] A "robot" is an automated mechanical device that performs programmed actions to accomplish specific tasks.

[0825] A "task" is a specific unit of work or work, and is an individual job or activity required to accomplish that work.

[0826] "Matching" is the process of selecting the optimal combination based on specific conditions and requirements.

[0827] "Notification" is the action of a system informing relevant parties of specific information, such as email or in-application alerts.

[0828] A "recommendation" is the determination and suggestion of a particular choice or option as appropriate.

[0829] "Approval" is the act of accepting and formally agreeing to a submitted request or proposal.

[0830] "Storage" refers to the act of permanently retaining data and making it available for later access.

[0831] "Input" refers to the act of a user providing data or information to a system.

[0832] The present invention is a project matching system based on the skill sets and task information of employees and robots. This system is configured as follows.

[0833] System configuration

[0834] 1. Terminals and User Interfaces

[0835] Providing an interface for employees to input their skills, experience, interests, and past task history, such as a web-based form or smartphone application. Also includes an interface for inputting the skill sets and task history of robots.

[0836] 2. Database

[0837] A database will be prepared to store data entered by employees and robots, as well as task requirements information, including skill sets, years of experience, areas of interest, qualifications, and history of projects and tasks previously handled. MySQL or PostgreSQL will be used as the database management system.

[0838] 3. AI Engine

[0839] The AI ​​engine analyzes the skill sets of employees and robots with the requirements of a project or task to find the best match. For example, it uses a generative AI model to calculate the degree of match between the skills of employees and robots and the requirements of a project or task. The AI ​​engine used is TensorFlow or PyTorch.

[0840] 4. Project and task information entry and storage method

[0841] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and end date, and save this in a database. It also includes an interface for inputting and saving robot task information.

[0842] 5. Means of notification

[0843] A system will be created to notify employees, project managers, and factory managers of the results of analysis by the AI ​​engine, possibly via email or an in-platform notification function.

[0844] 6. How to submit your participation request

[0845] It provides an interface where employees and robots can choose from recommended projects and tasks and submit their participation requests.

[0846] 7. Approval and Rejection Procedures

[0847] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the membership of projects and tasks.

[0848] System operation example

[0849] Example 1: Skills matching

[0850] 1. Employee and robot skill data entry

[0851] Employee A and Robot A log in from their terminals and access the profile editing screen. Employee A enters his or her skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button. Robot A also automatically updates its skill data (e.g., "Welding", "Assembly").

[0852] 2. Enter task information

[0853] A project manager creates a new project X and enters the required skill set (e.g., "data analysis", "modeling skills", "project management"), and a factory manager enters a new task Y (e.g., "welding").

[0854] 3. Perform matching

[0855] The server passes the data of employee A and robot A, as well as the data of project X and task Y, to the AI ​​engine and requests a match. The AI ​​engine analyzes employee A's skill set and the requirements of project X, and robot A's skill set and the requirements of task Y, and calculates the degree of match. It ranks the projects and tasks in descending order of match and notifies employee A and robot A of the recommended project and task list.

[0856] Example 2: Project and Task Participation Approval

[0857] 1. Employee and robot selection

[0858] Employee A and Robot A select Project X and Task Y from the recommended project and task list and submit their participation requests. The server saves the requests in a database and notifies the project manager and factory manager.

[0859] 2. Project Manager and Factory Manager Operations

[0860] The project manager checks the profile of Employee A and approves his participation. The factory manager checks the profile of Robot A and approves the task. The server updates the database with the approval result and notifies Employee A and Robot A.

[0861] As a concrete example, we use the following prompt sentence: "The administrator enters a new welding task. The system analyzes the skill sets of robots A, B, and C, calculates the degree of match, and notifies the administrator of the recommended ranking. The administrator selects robot A that is best suited to the task and assigns the welding task."

[0862] This will enable efficient matching of employee and robot skill sets with task information, which is expected to improve productivity and reduce waste.

[0863] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0864] Step 1:

[0865] Users input their skills, experience, interests, and past project history through a terminal. The input data is temporarily stored locally and the data format is checked before being sent to the database.

[0866] Input: Skills, experience, interests, and past project history entered by the user into the interface

[0867] Output: Formatted data (e.g., {"Skills": "Python", "Experience": "5 years", "Interests": "Data Analysis", "History": "Project A, Project B"})

[0868] Step 2:

[0869] The server receives the data sent by the user and stores it in a database, categorized by skill set, years of experience, areas of interest, project history, etc.

[0870] Input: Formatted user data

[0871] Output: Categorical data stored in a database

[0872] How it works: It saves data using a database INSERT operation. It uses MySQL or PostgreSQL as the database management system.

[0873] Step 3:

[0874] A project manager uses a terminal to enter requirements for a new project or task, which is validated locally and then sent to the server.

[0875] Inputs: Project name, objectives, required skill sets, start date, end date, etc. entered by the project manager into the interface

[0876] Output: Formatted project or task information (e.g., {"Project Name": "Project X", "Goal": "Data Analysis", "Skills": "Python, Data Analysis", "Start Date": "2023-01-01", "End Date": "2023-12-31"})

[0877] Step 4:

[0878] The server receives the project or task information sent by the project manager and stores it in a database.

[0879] Input: Formatted project or task information

[0880] Output: Project or task information stored in a database

[0881] Behavior: Saves data using a database INSERT operation.

[0882] Step 5:

[0883] The server sends the stored user data and project or task information to the AI ​​engine for analysis, and the AI ​​engine analyzes the data using the generative AI model.

[0884] Input: User data and project or task information stored in the database

[0885] Output: Analysis results (e.g., match scores, recommendation list)

[0886] How it works: Using an AI engine (TensorFlow or PyTorch), it calculates the degree of similarity and performs optimal matching.

[0887] Step 6:

[0888] The server receives the analysis results from the AI ​​engine and notifies the user and project manager.

[0889] Input: Analysis results from the AI ​​engine

[0890] Output: Notification message (e.g., recommended project or task list)

[0891] What it does: Sends results using the notification system (email or in-app notification).

[0892] Step 7:

[0893] The user is notified, selects from the recommended projects or tasks, and submits a request to participate from their terminal.

[0894] Input: User's participation request (e.g. "I would like to participate in Project X")

[0895] Output: Formatted participation requests

[0896] How it works: A user submits a request to join through the interface.

[0897] Step 8:

[0898] The server stores the participation request sent by the user in a database and notifies the project manager.

[0899] Input: Formatted participation request data

[0900] Output: Participation requests and notification messages stored in the database

[0901] Behavior: Processes using database INSERT operations and the notification system.

[0902] Step 9:

[0903] The project manager is notified and can approve or deny the request, and the server updates the database with the result and notifies the user.

[0904] Input: Project manager's approval or rejection

[0905] Output: Database update results and notification messages

[0906] What it does: Performs a database UPDATE operation and sends the results using the notification system.

[0907] 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.

[0908] The AI-based project matching platform of the present invention is a system that recommends optimal projects by taking into consideration not only the skills, experience, and interests of employees, but also the emotions of users. Specific embodiments of the present invention are described below.

[0909] System configuration

[0910] 1. Terminals and User Interfaces

[0911] Provide an interface, such as a web form, a smartphone application, or a desktop application, that allows users to input their skills, experience, interests, past project history, and feelings.

[0912] 2. Database

[0913] A database is provided to store user-entered data and project requirements information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[0914] 3. AI Engine

[0915] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements.

[0916] 4. Emotion Engine

[0917] The emotion engine recognizes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's writing or voice and stores them in a database.

[0918] 5. Project requirement input and storage means

[0919] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and finish date, and store it in a database.

[0920] 6. Means of notification

[0921] Create a mechanism to notify employees and project managers of the results of the AI ​​and emotion engine analysis, including email notifications, push notifications, or in-platform notifications.

[0922] 7. How to submit your participation request

[0923] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[0924] 8. Approval and Rejection Procedures

[0925] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[0926] System operation example

[0927] Example 1: Skill matching based on emotions

[0928] User operations

[0929] User B logs in from a terminal and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotion data through the emotion engine.

[0930] The server saves the input data in a database.

[0931] Entering Project Information

[0932] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[0933] The server stores the project information in a database.

[0934] Performing matching

[0935] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[0936] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[0937] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[0938] Example 2: Approval for project participation

[0939] User Selection

[0940] User B selects Project Y from the recommended projects list and submits a request to participate.

[0941] The server stores the user's participation request in a database and notifies the project manager.

[0942] Project Manager Operations

[0943] The project manager logs in from a terminal and confirms User B's participation. He / she checks User B's profile and emotional data and approves or rejects their participation.

[0944] The server updates the database with the approval result and notifies User B.

[0945] This system allows employees to participate in projects that best utilize their skills and emotional state, improving project efficiency and increasing productivity across the company.

[0946] The processing flow will be explained below.

[0947] Step 1:

[0948] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[0949] Step 2:

[0950] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[0951] Step 3:

[0952] The user accesses a profile editing screen from a terminal and inputs or updates his / her skills, experience, interests, past project history and emotional data.

[0953] Step 4:

[0954] When the user presses the "Save" button, the data is sent from the device to the server.

[0955] Step 5:

[0956] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[0957] Step 6:

[0958] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[0959] Step 7:

[0960] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[0961] Step 8:

[0962] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[0963] Step 9:

[0964] The server periodically passes user data and project data to the AI ​​engine and emotion engine, requesting analysis and matching.

[0965] Step 10:

[0966] The AI ​​engine analyzes the user's skill map and the required skill set of the project and calculates the degree of match.

[0967] Step 11:

[0968] The emotion engine analyzes the user's emotional data and provides the results to the AI ​​engine.

[0969] Step 12:

[0970] The AI ​​engine combines the emotion data and skill data, recalculates the degree of match, and generates a matching result.

[0971] Step 13:

[0972] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[0973] Step 14:

[0974] The server stores the match results in a database and sends a notification to the user.

[0975] Step 15:

[0976] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[0977] Step 16:

[0978] The user selects the project they are interested in and presses the "Want to participate" button.

[0979] Step 17:

[0980] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[0981] Step 18:

[0982] The project manager logs in from a terminal and confirms the user's participation. They then check the user's profile and emotional data and approve or reject the participation.

[0983] Step 19:

[0984] The server receives the result of approval or rejection from the project manager, updates the database, and notifies the user of the result.

[0985] Step 20:

[0986] The server generates a list of members whose participation has been confirmed for the project, and notifies the relevant members of information necessary for starting the project.

[0987] Example 2

[0988] 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."

[0989] It is difficult for employees to find the best projects by considering their emotional data in addition to their skills, experience, and interests. Ignoring the impact of emotional states on project success can lead to reduced efficiency and project satisfaction. Furthermore, the process between employee project participation requests and project manager approval is cumbersome, and there is a lack of ways to do it quickly and efficiently.

[0990] 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.

[0991] In this invention, the server includes a terminal for employees to input their skills, experience, interests, and emotions, a database for saving the data input by the employees, an AI engine that analyzes the saved data and recommends optimal projects that match the project requirements, an emotion engine that analyzes emotions and saves them in the database, means for inputting and saving project requirement information, means for notifying employees and project managers of the results of analysis by the AI ​​engine and the emotion engine, means for employees to select from the recommended projects and submit their participation requests, and means for the project manager to approve or reject the submitted participation requests. This enables appropriate project matching that takes into account the skills and emotional state of employees, enabling efficient and satisfying project execution.

[0992] A "terminal" is an electronic device through which a user enters data, such as a web form, a smartphone application, or a desktop application.

[0993] "Database" refers to a system for storing and managing employee input data and project information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[0994] An "AI engine" is a software system that uses machine learning algorithms and statistical methods to analyze employee skill and emotional data and recommend the most suitable projects that match the project requirements.

[0995] The "emotion engine" is a software system that analyzes users' emotional data and reflects it in project matching, and includes the ability to analyze emotions from text and voice.

[0996] "Project requirement information" refers to information such as the name of the project, the goal, the required skill set, the start date, and the end date, and is the condition or specification required for implementing the project.

[0997] "Notification methods" are mechanisms for informing employees and project managers of the analysis results of the AI ​​engine and emotion engine, and include email notifications, push notifications, and in-platform notification functions.

[0998] The "participation request submission means" is an interface that allows a user to select from recommended projects and submit a request to participate.

[0999] The "approval / rejection method" is an interface that allows the project manager to approve or reject employee participation requests, and is a function for optimizing the project member composition.

[1000] The present invention is a project matching platform that takes into account skills, experience, interests, and emotions, and specific embodiments are described below.

[1001] System configuration

[1002] 1. Terminals and User Interfaces

[1003] Users can enter their skills, experience, interests, past project history, and feelings using a web form, smartphone application, or desktop application. The interface is designed to be user-friendly, allowing users to efficiently enter the required information.

[1004] 2. Database

[1005] The database stores user-entered data and project requirements, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data, to build a detailed user profile.

[1006] 3. AI Engine

[1007] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements. Generative AI models are often used.

[1008] 4. Emotion Engine

[1009] The emotion engine analyzes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's written or spoken words and stores them in a database. This information is used to improve the accuracy of project matching.

[1010] 5. Project requirement input and storage means

[1011] It provides an interface for project managers to enter requirements information about new projects (project name, goals, required skill sets, start date, finish date, etc.) and store it in a database.

[1012] 6. Means of notification

[1013] We will create a system to notify employees and project managers of the results of the analysis of the AI ​​engine and emotion engine via email, push notifications, or in-platform notifications.

[1014] 7. How to submit your participation request

[1015] Users are provided with an interface to submit their participation in recommended projects, clearly expressing their intentions.

[1016] 8. Approval and Rejection Procedures

[1017] Project managers are provided with a mechanism to review submitted requests and approve or reject them, which is useful for optimizing the project's membership structure.

[1018] Examples of concrete examples and prompts

[1019] Example 1: Skill matching based on emotions

[1020] 1. User operations

[1021] User B logs in from a device and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotional data (e.g., "Positive about work" or "Currently feeling stressed") through the emotion engine.

[1022] Example prompt: "Describe a situation where User B enters their skill set and emotion data and then presses the save button."

[1023] 2. Server Processing

[1024] The server saves the input data in a database.

[1025] 3. Enter project information

[1026] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[1027] 4. Server Processing

[1028] The server stores the project information in a database.

[1029] 5. Performing matching

[1030] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[1031] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[1032] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[1033] Example prompt: "Please explain how the AI ​​engine analyzes User B's skill set and emotional data to match him with Project Y."

[1034] Example 2: Approval for project participation

[1035] 1. User Choice

[1036] User B selects Project Y from the recommended projects list and submits a request to participate.

[1037] Example prompt: "Describe a situation in which User B chooses Project Y from the list of recommended projects and submits a request to participate."

[1038] 2. Server Processing

[1039] The server stores the user's participation request in a database and notifies the project manager.

[1040] 3. Project Manager Operations

[1041] The project manager logs in from a terminal and confirms the user's participation. He / she checks User B's profile and emotional data and approves or rejects the participation.

[1042] 4. Server Processing

[1043] The server updates the database with the approval result and notifies the user.

[1044] The system of the present invention allows employees to participate in projects that make the most of their skills and emotional state, improving project efficiency and increasing productivity across the entire company.

[1045] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1046] System program processing flow

[1047] Step 1:

[1048] User data entry and saving

[1049] Input: The user uses the device to input their skills, experience, interests, and emotional data.

[1050] Processing: The user accesses the device's user interface, enters each item, and inputs emotion data through the emotion engine. The user then presses the "Save" button.

[1051] Output: The entered data is sent to the server.

[1052] Specific behavior:

[1053] User B accesses a web form and enters information such as "Java," "front-end development," and "5 years."

[1054] Enter your emotional state by voice or text (e.g., "I feel positive about my job").

[1055] Click the "Save" button to submit the data.

[1056] Step 2:

[1057] Server data storage

[1058] Input: User input data.

[1059] Processing: The server stores the received data in a database. The skill set, years of experience, areas of interest, and sentiment data are stored in the corresponding tables.

[1060] Output: User data stored in the database.

[1061] Specific behavior:

[1062] The server receives the user's skill, experience, and emotion data.

[1063] The process of saving to the database is performed, and the fact that the save was successful is recorded in the log.

[1064] Step 3:

[1065] Entering and saving project information

[1066] Input: The project manager uses a terminal to input project requirement information.

[1067] Process: The project manager accesses the project creation screen, enters the required information (project name, required skills, start date, end date, etc.), and presses the "Save" button.

[1068] Output: Project data is sent to the server.

[1069] Specific behavior:

[1070] The project manager accesses a web form and enters information such as "website renewal," "front-end development," and "start date: January 1, 2024."

[1071] Click the "Save" button to submit the data.

[1072] Step 4:

[1073] Save project information on the server

[1074] Input: Project manager input data.

[1075] Process: The server saves the received project data in the database. It generates a project ID and stores each piece of information in the corresponding table.

[1076] Output: Project data stored in a database.

[1077] Specific behavior:

[1078] The server receives the project name, skill requirements, duration, etc.

[1079] Generate a project ID, execute the process to save it in the database, and log that the save was successful.

[1080] Step 5:

[1081] Performing matching

[1082] Input: Server user data and project data.

[1083] Processing: The server passes the data to the AI ​​engine and emotion engine and requests a match. The AI ​​engine and emotion engine use the user's skill set and emotion data to calculate the degree of match with the project requirements.

[1084] Output: The match calculation results.

[1085] Specific behavior:

[1086] The server executes an API call to pass User B's skill set and emotion data to the AI ​​engine and emotion engine.

[1087] The AI ​​engine runs a matching algorithm and calculates the degree of match.

[1088] The calculation result is returned to the server.

[1089] Step 6:

[1090] Notification of matching results

[1091] Input: The result of the match calculation.

[1092] Processing: The server ranks the projects in order of the degree of matching, generates a recommendation list for the user, and notifies the user of the generated list using a notification means.

[1093] Output: Notification of recommended projects list.

[1094] Specific behavior:

[1095] The server receives the results of the calculation of the degree of match and creates a ranking.

[1096] The recommendation list is displayed on User B's dashboard and an email or app notification is sent.

[1097] Step 7:

[1098] User participation request submission

[1099] Input: Recommended projects list.

[1100] Process: The user selects a project from the recommended list and submits their participation request. The user selects the desired project through the user interface and clicks "Participate."

[1101] Output: Data on willingness to participate.

[1102] Specific behavior:

[1103] User B logs in from their device and checks the list of recommended projects.

[1104] Select a specific project Y and click "Want to participate."

[1105] Step 8:

[1106] Save and notify server join requests

[1107] Input: Your participation data.

[1108] Processing: The server saves the user's participation request in the database and notifies the project manager.

[1109] Output: Database update results and notifications.

[1110] Specific behavior:

[1111] The server receives the participation request data and stores it in a database.

[1112] Send email or app notifications to project managers.

[1113] Step 9:

[1114] Project manager's approval or rejection

[1115] Input: Notification of participation.

[1116] Processing: The project manager logs in from a terminal, checks the participation request and user data, and performs approval or rejection operations.

[1117] Output: Approval or rejection result.

[1118] Specific behavior:

[1119] The project manager checks User B's profile and emotional data.

[1120] Make your decision to approve or reject and click "Decide."

[1121] Step 10:

[1122] Server result notification

[1123] Input: The result of approval or rejection.

[1124] Processing: The server updates the database with the approval result and notifies the user.

[1125] Output: Update results and notifications.

[1126] Specific behavior:

[1127] The server records the project manager's decisions in a database.

[1128] User B will be notified of the results via email or app notification.

[1129] This series of processes enables project matching that makes the most of the user's skills and emotional state, which is expected to improve project efficiency and overall company productivity.

[1130] (Application example 2)

[1131] 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."

[1132] In factories, automatically assigning optimal tasks to robots while taking into account each robot's performance, past work history, current status, and maintenance history is a significant burden for managers and employees. This can lead to problems such as a decrease in robot utilization rate and a deterioration in factory production efficiency. The present invention aims to solve these problems by automating optimal task assignment for factory robots.

[1133] 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.

[1134] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a database for storing the data entered by the employees, an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements, a means for inputting and storing project requirement information, a means for notifying employees and project managers of the analysis results by the AI ​​engine, a means for employees to select from the recommended projects and submit a request to participate, a means for the project manager to approve or reject the submitted request, and a means for automatically matching and assigning optimal work to factory robots based on their performance, past work history, current status, and maintenance history. This makes it possible to maximize the utilization rate of factory robots and improve production efficiency.

[1135] 1. A "terminal" is a device that allows employees to input their skills, experience, and interests.

[1136] 2. "Database" means a system for storing employee-entered data and project requirements information.

[1137] 3. "AI Engine" refers to the artificial intelligence technology used to analyze stored data and recommend the most suitable projects that match project requirements.

[1138] 4. "Means for inputting and storing project requirements information" refers to an interface or device that inputs basic project information and required skill sets and stores them in a database.

[1139] 5. "Means of notification" means the method or device for notifying employees and project managers of analysis results and other important information.

[1140] 6. "Means for submitting participation requests" means an interface or device that allows employees to select from recommended projects and submit participation requests.

[1141] 7. "Means for Approval or Rejection" means the interface or device by which the Project Manager approves or rejects a submitted request to participate.

[1142] 8. "Robot performance" refers to the functions, work speed, accuracy, etc. of factory robots.

[1143] 9. "Past work history" is a record of the work each robot has done so far.

[1144] 10. "Current Status" refers to the real-time operating status, remaining battery level, and usage status of each robot.

[1145] 11. "Maintenance History" is a record of the maintenance and repairs that have been performed on each robot.

[1146] 12. "Means for automatically matching and allocating optimal tasks" refers to a method or system for selecting optimal tasks and issuing instructions to a robot based on the robot's performance, past work history, current status, and maintenance history.

[1147] According to the present invention, a system for efficiently optimizing work in a factory is configured as follows.

[1148] The system first provides employees with a terminal to input their skills, experience, and interests. The terminal can be a user device such as a tablet or PC. The user uses the terminal to input skill information and past work history, and the data is stored in a database on the server.

[1149] The server has an AI engine that analyzes the stored data. The AI ​​engine uses machine learning algorithms and statistical methods to analyze employee skill sets and project requirements and match them with the most suitable work. The server also has integrated into it a means for inputting and saving project requirement information. This allows project managers to input project information and required skill sets and save them in the database.

[1150] The server notifies employees and project managers of the analysis results via email, push notification, or the platform's notification function. Employees receive the notification and can select from the recommended projects and submit their participation requests. The server stores the submitted participation requests in a database and notifies the project manager.

[1151] The project manager checks the received participation request from the terminal and approves or rejects it, and the server updates the database with the result and notifies the employee.

[1152] In particular, one of the features of this invention is the addition of a function that automatically matches and assigns optimal tasks based on the robot's performance, past work history, current status, and maintenance history in the factory, thereby maximizing the robot's operating rate and improving production efficiency.

[1153] Specific examples

[1154] For example, suppose robots A, B, and C are available to perform welding work on a factory production line. Robot A has high welding skills based on its past history and has 80% battery remaining, but robot C needs more maintenance. In this case, the server's AI engine will prioritize robot A when allocating welding work.

[1155] Example prompt for a generative AI model:

[1156] "Optimize work allocation within your factory. Assign the best robot for the welding job in real time, taking into account each robot's skills and battery status."

[1157] This system allows users to reduce unnecessary work and waiting time, increasing overall productivity. It also takes into account the robot's maintenance status, ensuring stable operation over the long term.

[1158] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1159] Step 1:

[1160] Employees use the devices to input their skills, experience, and interests, and the devices send this data to a server, which stores it in a database.

[1161] Input: Employee skills, experience, and interest data

[1162] Output: Employee skills, experience, and interests data stored in a database

[1163] Step 2:

[1164] A project manager uses a terminal to input project requirements information, which is then sent to a server, which stores the received data in a database.

[1165] Input: Project requirement information such as project name, skill requirements, start date, end date, etc.

[1166] Output: Project requirements data stored in a database

[1167] Step 3:

[1168] The server passes the stored data to an AI engine, which analyzes employee skill sets and project requirements, calculates the degree of match, and recommends the most suitable projects.

[1169] Inputs: Employee skill sets, project requirement data

[1170] Output: A list of recommended projects for employees

[1171] Step 4:

[1172] The server notifies the analysis results to employees and project managers using notification means. Employees can then review the list of recommended projects and submit their participation requests.

[1173] Input: Project list of analysis results

[1174] Output: Notification to employees and project managers

[1175] Step 5:

[1176] The server stores the employee's participation request in a database and notifies the project manager.

[1177] Input: Employee participation request

[1178] Output: Participation request data stored in the database, notification to the project manager

[1179] Step 6:

[1180] The project manager uses a terminal to approve or reject the request, and the server updates the database with the result and notifies the employee.

[1181] Input: Project manager's approval or rejection decision

[1182] Output: Approval / rejection results stored in the database, notification to employees

[1183] Step 7:

[1184] The server automatically matches and assigns the optimal work to the factory robots based on their performance, past work history, current status, and maintenance history. It passes the necessary data to the AI ​​engine and assigns the work to the optimal robot.

[1185] Input: Robot performance data, past work history, current status, maintenance history

[1186] Output: Assignment instructions to the robot

[1187] Example of operation

[1188] For example, suppose Robot A has 80% battery remaining and has a history of successfully performing similar welding work in the past. Based on this information, the server uses an AI engine to issue instructions to assign the welding work to Robot A. This optimizes work efficiency within the factory and reduces unnecessary operations and waiting time.

[1189] 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.

[1190] 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.

[1191] 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.

[1192] [Third embodiment]

[1193] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1194] 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.

[1195] 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).

[1196] 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.

[1197] 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.

[1198] 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).

[1199] 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.

[1200] 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.

[1201] 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.

[1202] 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.

[1203] 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.

[1204] 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."

[1205] The AI-based project matching platform of the present invention is a system in which employees use a terminal to input their skills, experience, and interests, and an AI engine recommends optimal projects based on the accumulated data. Specific embodiments of the system are described below.

[1206] System configuration

[1207] 1. Terminals and User Interfaces

[1208] Providing an interface, such as a web-based form or a smartphone application, that allows users to enter their skills, experience, interests, and past project history.

[1209] 2. Database

[1210] A database will be created to store user-entered data and project requirements, including skill sets, years of experience, areas of interest, certifications, and past project history.

[1211] 3. AI Engine

[1212] The AI ​​engine analyzes employee skill sets and project requirements to find the best match, using machine learning models and statistical methods to calculate the degree of match between employee skills and project requirements.

[1213] 4. Project requirement input and storage means

[1214] It provides an interface for a project manager to input information such as the name of the project, the objectives, the required skill set, the start date, the end date, etc., and store this information in a database.

[1215] 5. Means of notification

[1216] Build a system to notify employees and project managers of the results of analysis by the AI ​​engine, possibly via email or a notification function within the platform.

[1217] 6. How to submit your participation request

[1218] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[1219] 7. Approval and Rejection Procedures

[1220] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[1221] System operation example

[1222] Example 1: Skills matching

[1223] User operations

[1224] User A logs in from a device and accesses the profile editing screen. User A enters their skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button.

[1225] The server saves the input data in a database.

[1226] Entering Project Information

[1227] A project manager creates a new project X and inputs the required skill sets (e.g., "data analysis", "modeling skills", "project management").

[1228] The server stores the project information in a database.

[1229] Performing matching

[1230] The server passes User A's data and Project X's data to the AI ​​engine and requests a match.

[1231] The AI ​​engine analyzes User A's skill set and the requirements of Project X and calculates the degree of match.

[1232] The projects are ranked in descending order of degree of match, and a list of recommended projects is notified to user A.

[1233] Example 2: Approval for project participation

[1234] User Selection

[1235] User A selects Project X from the recommended projects list and submits a request to participate.

[1236] The server stores the user's participation request in a database and notifies the project manager.

[1237] Project Manager Operations

[1238] The project manager reviews User A's profile and approves his participation.

[1239] The server updates the database with the approval result and notifies User A.

[1240] This system allows employees to participate in projects that make the most of their skills, improving project efficiency and boosting productivity across the company.

[1241] The processing flow will be explained below.

[1242] Step 1:

[1243] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[1244] Step 2:

[1245] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[1246] Step 3:

[1247] The user accesses the profile editing screen from the terminal and enters or updates information such as their skills, experience, interests, and past project history.

[1248] Step 4:

[1249] When the user presses the "Save" button, the data is sent from the device to the server.

[1250] Step 5:

[1251] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[1252] Step 6:

[1253] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[1254] Step 7:

[1255] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[1256] Step 8:

[1257] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[1258] Step 9:

[1259] The server periodically passes user data and project data to the AI ​​engine, requesting analysis and matching.

[1260] Step 10:

[1261] The AI ​​engine analyzes the user's skill map and the required skill set of the project, calculates the degree of match, and generates a matching result.

[1262] Step 11:

[1263] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[1264] Step 12:

[1265] The server stores the match results in a database and sends a notification to the user.

[1266] Step 13:

[1267] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[1268] Step 14:

[1269] The user selects the project they are interested in and presses the "Want to participate" button.

[1270] Step 15:

[1271] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[1272] Step 16:

[1273] The project manager logs in from a terminal, confirms the user's participation, checks the user's profile, and approves or denies the participation.

[1274] Step 17:

[1275] The server receives the approval or rejection result from the project manager, updates the database, and sends a notification of the result to the user.

[1276] Example 1

[1277] 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."

[1278] Conventional project matching systems did not adequately reflect employees' skills and experience, making it difficult to assign them to the most suitable projects. Furthermore, when project managers manually performed matching, it took time and effort, making it difficult to achieve efficient matching. Furthermore, support for employee skill development and career path formation was insufficient, preventing the system from contributing to improved productivity across the company.

[1279] 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.

[1280] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a storage device for saving the data input by the employees, an artificial intelligence device that analyzes the saved data and recommends optimal work that matches the work requirements, a means for inputting and saving information about work requirements, a means for notifying employees and work managers of the analysis results by the artificial intelligence device, a means for employees to select from the recommended work and submit a request to participate, and a means for the work manager to approve or reject the submitted request to participate. This enables optimal matching of employee skills with project requirements.

[1281] A "terminal" is a device that employees use to input information such as their skills, experience, and interests, and includes computers and smartphones.

[1282] "Storage device" refers to a device used to store data entered by employees and project information, such as a database or cloud storage.

[1283] The "artificial intelligence device" analyzes stored data and compares employee skill sets with project requirements to make optimal matches, using machine learning models and statistical methods.

[1284] "Work requirements" refers to information such as the skills, experience, and qualifications needed to complete a specific project or task.

[1285] "Work Manager" refers to the person or position that has the authority to manage projects and tasks and approve or deny employee participation requests.

[1286] "Notification means" refers to a system for communicating the analysis results of the artificial intelligence device to employees and work managers, and includes email and notification functions within the platform.

[1287] "Method for submitting participation requests" refers to the interface through which employees can select from recommended projects and submit their participation requests, including web forms and mobile apps.

[1288] The present invention is a project matching system that utilizes an artificial intelligence device, and aims to enable employees to efficiently input their skills, experience, and interests and be assigned to appropriate projects. The system includes a terminal, a storage device, an artificial intelligence device, and a notification means.

[1289] Hardware and software used

[1290] The system uses the following hardware and software:

[1291] Terminal: A device used by employees to input information. This includes computers and smartphones.

[1292] Storage: A device for storing employee and project data, specifically a database such as MySQL or PostgreSQL.

[1293] Artificial intelligence device: A device that analyzes stored data and performs optimal matching. Specific technologies include machine learning libraries such as TensorFlow and PyTorch.

[1294] Notification methods: Email and in-system notification functions.

[1295] Entering User Data

[1296] It provides an interface for users to input their skills, experience, and interests. Users input data using a web form or dedicated app on a computer or smartphone, and the data is saved in a storage device by pressing the save button.

[1297] Entering Project Information

[1298] Provides an interface for project managers to enter requirements information for a new project, such as the project name, goal, required skill set, start date, and finish date, and then saves the information.

[1299] Data Matching

[1300] The server retrieves the user's skill data and project requirement data from the storage device and requests an analysis from the AI ​​device. The AI ​​device runs a machine learning model using TensorFlow or PyTorch to calculate the degree of match between the user's skill set and the project requirements. Based on this degree of match, the AI ​​device ranks projects and recommends the most suitable projects to the user.

[1301] Notification of recommended projects

[1302] The server receives the analysis results from the AI ​​device and notifies employees and work managers via email or in-system notifications.

[1303] Project participation application

[1304] The system provides an interface for users to select and apply for participation in recommended projects. When a user submits their "participation request," the server stores the data in a storage device and notifies the project manager.

[1305] Participation Approval Process

[1306] The project manager receives the user's participation request, checks the user's profile and project requirements, and then approves or rejects the participation. The approval result is stored in a storage device by the server and notified to the user.

[1307] Specific examples

[1308] Example 1: Skills matching

[1309] The user inputs their skills (e.g., "Python", "data analysis", "project management") and presses the save button. The server saves this in the storage device. The project manager inputs new project information (e.g., "data analysis", "modeling skills", "project management") and saves it. The server sends this data to an artificial intelligence device, calculates the degree of match, and notifies the user of a list of recommended projects.

[1310] Example 2: Approval for project participation

[1311] The user selects a project from the recommended projects list and submits a "request to participate." The server stores this information in a storage device and notifies the project manager. The project manager checks the user's profile and approves their participation. The server stores the approval result in a storage device and notifies the user.

[1312] Prompt Sentence Examples

[1313] Generate a system description that matches user skill sets with project requirements.

[1314] Skillset: Python, Data Analysis, Project Management

[1315] Project requirements: Data analysis, modeling skills, project management

[1316] Hardware used: PC, smartphone, cloud server

[1317] Software used: Web Forms, MySQL, TensorFlow

[1318] As described above, the present invention makes it possible to make the most of employees' skills and quickly and efficiently match them with the most suitable projects.

[1319] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1320] Step 1:

[1321] Entering User Data

[1322] User operations

[1323] A user logs in to the system using a terminal (PC or smartphone).

[1324] After logging in, access the profile editing screen and enter your skills (e.g., "Python", "Data Analysis", "Project Management"), experience, and interests.

[1325] Once you have completed the input, press the "Save" button.

[1326] input

[1327] Data such as your skills, experience, and interests.

[1328] Server Processing

[1329] The server receives the data sent by the user.

[1330] The received data is stored in a MySQL database.

[1331] output

[1332] The user data is saved to the database and a confirmation message is sent to the user indicating the save was successful.

[1333] Step 2:

[1334] Entering Project Information

[1335] Project Manager Operations

[1336] The project manager accesses the project creation screen from their own device.

[1337] Enter the name of your new project, its goal, the required skill set (e.g., "Data Analysis", "Modeling Skills", "Project Management"), start date, and end date.

[1338] Once you have completed the input, press the "Save" button.

[1339] input

[1340] Project information such as project name, objectives, required skill sets, start date, and end date.

[1341] Server Processing

[1342] The server receives the data sent from the project manager.

[1343] Store received project information in a MySQL database.

[1344] output

[1345] The project information is saved to the database and a confirmation message is sent to the project manager indicating the save was successful.

[1346] Step 3:

[1347] Data Matching

[1348] Server Processing

[1349] The server retrieves the user's skill data and the project's requirement data from the database.

[1350] The acquired data is sent to an artificial intelligence device and requested for analysis.

[1351] input

[1352] User skill data and project requirement data.

[1353] AI processing

[1354] The artificial intelligence device uses TensorFlow to run machine learning models.

[1355] Calculate the match between a user's skill set and project requirements.

[1356] The calculation result (match score) is sent to the server.

[1357] output

[1358] Match score.

[1359] Step 4:

[1360] Notification of recommended projects

[1361] Server Processing

[1362] The server ranks the projects based on the match scores received from the artificial intelligence device.

[1363] Select the most suitable projects and create a list of recommended projects.

[1364] The recommended project list is notified to the user.

[1365] input

[1366] Match score.

[1367] output

[1368] Notification of recommended project list.

[1369] Step 5:

[1370] Project participation application

[1371] User operations

[1372] The user checks the recommended project list received from the terminal.

[1373] Select the project you are interested in and submit your request to participate.

[1374] input

[1375] Recommended projects list.

[1376] Server Processing

[1377] The server receives the user's participation request data.

[1378] The received data is stored in a database and notified to the project manager.

[1379] output

[1380] Saving your participation request data and informing the project manager.

[1381] Step 6:

[1382] Participation Approval Process

[1383] Project Manager Operations

[1384] The project manager checks the notification received from the device.

[1385] Visit a user's profile page to compare their skills with your project requirements.

[1386] Enter the result of approving or denying participation and press the "Submit" button.

[1387] input

[1388] Participation preference data.

[1389] Server Processing

[1390] The server receives the approval result and stores it in the database.

[1391] The approval result is notified to the user and the project member list is updated.

[1392] output

[1393] Notification of approval results and update of project member list.

[1394] (Application example 1)

[1395] 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."

[1396] While conventional matching systems can recommend projects that take into account employee skills, experience, and interests, they do not support optimal matching of robot skill sets and task information, which is important in factory production. As a result, it becomes difficult to efficiently assign tasks to robots, resulting in reduced productivity and task waste. The present invention aims to improve productivity and reduce waste in factories by efficiently matching the skill sets and task information of both employees and robots.

[1397] 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.

[1398] In this invention, the server includes: a terminal for employees to input their skills, experience, and interests; a database for storing the data input by the employees; an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements; a means for inputting and saving project requirement information; a means for notifying employees and the project manager of the analysis results by the AI ​​engine; a means for employees to select from the recommended projects and submit a request to participate; a means for the project manager to approve or reject the submitted request to participate; a means for inputting and saving robot skill sets and task information; and a means for the AI ​​engine to analyze the robot skill data and task information and recommend the optimal robot. This enables optimal matching not only between employees and projects, but also between robots and tasks, thereby improving productivity and achieving efficient task management throughout the factory.

[1399] An "employee" is an individual who performs certain roles and duties within a company or organization and performs work based on an employment contract.

[1400] "Skills" refer to the techniques and abilities required to carry out a specific job or task.

[1401] "Experience" refers to knowledge and skills based on past work and tasks.

[1402] An "interest" is an individual's interest or preference in a particular field or occupation.

[1403] A "terminal" is a device for inputting, displaying, and processing information, and includes smartphones, tablets, and personal computers.

[1404] A "database" is an information system for systematically storing, managing, searching, and updating data.

[1405] An "AI engine" is software that uses artificial intelligence algorithms to analyze data and make appropriate decisions and predictions.

[1406] A "project" is a set of activities or tasks planned and carried out to achieve a specific purpose or goal.

[1407] A "project manager" is a person who is responsible for planning, executing, managing progress, and completing a project, and who also serves as a leader for the team.

[1408] A "robot" is an automated mechanical device that performs programmed actions to accomplish specific tasks.

[1409] A "task" is a specific unit of work or work, and is an individual job or activity required to accomplish that work.

[1410] "Matching" is the process of selecting the optimal combination based on specific conditions and requirements.

[1411] "Notification" is the action of a system informing relevant parties of specific information, such as email or in-application alerts.

[1412] A "recommendation" is the determination and suggestion of a particular choice or option as appropriate.

[1413] "Approval" is the act of accepting and formally agreeing to a submitted request or proposal.

[1414] "Storage" refers to the act of permanently retaining data and making it available for later access.

[1415] "Input" refers to the act of a user providing data or information to a system.

[1416] The present invention is a project matching system based on the skill sets and task information of employees and robots. This system is configured as follows.

[1417] System configuration

[1418] 1. Terminals and User Interfaces

[1419] Providing an interface for employees to input their skills, experience, interests, and past task history, such as a web-based form or smartphone application. Also includes an interface for inputting the skill sets and task history of robots.

[1420] 2. Database

[1421] A database will be prepared to store data entered by employees and robots, as well as task requirements information, including skill sets, years of experience, areas of interest, qualifications, and history of projects and tasks previously handled. MySQL or PostgreSQL will be used as the database management system.

[1422] 3. AI Engine

[1423] The AI ​​engine analyzes the skill sets of employees and robots with the requirements of a project or task to find the best match. For example, it uses a generative AI model to calculate the degree of match between the skills of employees and robots and the requirements of a project or task. The AI ​​engine used is TensorFlow or PyTorch.

[1424] 4. Project and task information entry and storage method

[1425] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and end date, and save this in a database. It also includes an interface for inputting and saving robot task information.

[1426] 5. Means of notification

[1427] A system will be created to notify employees, project managers, and factory managers of the results of analysis by the AI ​​engine, possibly via email or an in-platform notification function.

[1428] 6. How to submit your participation request

[1429] It provides an interface where employees and robots can choose from recommended projects and tasks and submit their participation requests.

[1430] 7. Approval and Rejection Procedures

[1431] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the membership of projects and tasks.

[1432] System operation example

[1433] Example 1: Skills matching

[1434] 1. Employee and robot skill data entry

[1435] Employee A and Robot A log in from their terminals and access the profile editing screen. Employee A enters his or her skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button. Robot A also automatically updates its skill data (e.g., "Welding", "Assembly").

[1436] 2. Enter task information

[1437] A project manager creates a new project X and enters the required skill set (e.g., "data analysis", "modeling skills", "project management"), and a factory manager enters a new task Y (e.g., "welding").

[1438] 3. Perform matching

[1439] The server passes the data of employee A and robot A, as well as the data of project X and task Y, to the AI ​​engine and requests a match. The AI ​​engine analyzes employee A's skill set and the requirements of project X, and robot A's skill set and the requirements of task Y, and calculates the degree of match. It ranks the projects and tasks in descending order of match and notifies employee A and robot A of the recommended project and task list.

[1440] Example 2: Project and Task Participation Approval

[1441] 1. Employee and robot selection

[1442] Employee A and Robot A select Project X and Task Y from the recommended project and task list and submit their participation requests. The server saves the requests in a database and notifies the project manager and factory manager.

[1443] 2. Project Manager and Factory Manager Operations

[1444] The project manager checks the profile of Employee A and approves his participation. The factory manager checks the profile of Robot A and approves the task. The server updates the database with the approval result and notifies Employee A and Robot A.

[1445] As a concrete example, we use the following prompt sentence: "The administrator enters a new welding task. The system analyzes the skill sets of robots A, B, and C, calculates the degree of match, and notifies the administrator of the recommended ranking. The administrator selects robot A that is best suited to the task and assigns the welding task."

[1446] This will enable efficient matching of employee and robot skill sets with task information, which is expected to improve productivity and reduce waste.

[1447] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1448] Step 1:

[1449] Users input their skills, experience, interests, and past project history through a terminal. The input data is temporarily stored locally and the data format is checked before being sent to the database.

[1450] Input: Skills, experience, interests, and past project history entered by the user into the interface

[1451] Output: Formatted data (e.g., {"Skills": "Python", "Experience": "5 years", "Interests": "Data Analysis", "History": "Project A, Project B"})

[1452] Step 2:

[1453] The server receives the data sent by the user and stores it in a database, categorized by skill set, years of experience, areas of interest, project history, etc.

[1454] Input: Formatted user data

[1455] Output: Categorical data stored in a database

[1456] How it works: It saves data using a database INSERT operation. It uses MySQL or PostgreSQL as the database management system.

[1457] Step 3:

[1458] A project manager uses a terminal to enter requirements for a new project or task, which is validated locally and then sent to the server.

[1459] Inputs: Project name, objectives, required skill sets, start date, end date, etc. entered by the project manager into the interface

[1460] Output: Formatted project or task information (e.g., {"Project Name": "Project X", "Goal": "Data Analysis", "Skills": "Python, Data Analysis", "Start Date": "2023-01-01", "End Date": "2023-12-31"})

[1461] Step 4:

[1462] The server receives the project or task information sent by the project manager and stores it in a database.

[1463] Input: Formatted project or task information

[1464] Output: Project or task information stored in a database

[1465] Behavior: Saves data using a database INSERT operation.

[1466] Step 5:

[1467] The server sends the stored user data and project or task information to the AI ​​engine for analysis, and the AI ​​engine analyzes the data using the generative AI model.

[1468] Input: User data and project or task information stored in the database

[1469] Output: Analysis results (e.g., match scores, recommendation list)

[1470] How it works: Using an AI engine (TensorFlow or PyTorch), it calculates the degree of similarity and performs optimal matching.

[1471] Step 6:

[1472] The server receives the analysis results from the AI ​​engine and notifies the user and project manager.

[1473] Input: Analysis results from the AI ​​engine

[1474] Output: Notification message (e.g., recommended project or task list)

[1475] What it does: Sends results using the notification system (email or in-app notification).

[1476] Step 7:

[1477] The user is notified, selects from the recommended projects or tasks, and submits a request to participate from their terminal.

[1478] Input: User's participation request (e.g. "I would like to participate in Project X")

[1479] Output: Formatted participation requests

[1480] How it works: A user submits a request to join through the interface.

[1481] Step 8:

[1482] The server stores the participation request sent by the user in a database and notifies the project manager.

[1483] Input: Formatted participation request data

[1484] Output: Participation requests and notification messages stored in the database

[1485] Behavior: Processes using database INSERT operations and the notification system.

[1486] Step 9:

[1487] The project manager is notified and can approve or deny the request, and the server updates the database with the result and notifies the user.

[1488] Input: Project manager's approval or rejection

[1489] Output: Database update results and notification messages

[1490] What it does: Performs a database UPDATE operation and sends the results using the notification system.

[1491] 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.

[1492] The AI-based project matching platform of the present invention is a system that recommends optimal projects by taking into consideration not only the skills, experience, and interests of employees, but also the emotions of users. Specific embodiments of the present invention are described below.

[1493] System configuration

[1494] 1. Terminals and User Interfaces

[1495] Provide an interface, such as a web form, a smartphone application, or a desktop application, that allows users to input their skills, experience, interests, past project history, and feelings.

[1496] 2. Database

[1497] A database is provided to store user-entered data and project requirements information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[1498] 3. AI Engine

[1499] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements.

[1500] 4. Emotion Engine

[1501] The emotion engine recognizes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's writing or voice and stores them in a database.

[1502] 5. Project requirement input and storage means

[1503] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and finish date, and store it in a database.

[1504] 6. Means of notification

[1505] Create a mechanism to notify employees and project managers of the results of the AI ​​and emotion engine analysis, including email notifications, push notifications, or in-platform notifications.

[1506] 7. How to submit your participation request

[1507] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[1508] 8. Approval and Rejection Procedures

[1509] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[1510] System operation example

[1511] Example 1: Skill matching based on emotions

[1512] User operations

[1513] User B logs in from a terminal and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotion data through the emotion engine.

[1514] The server saves the input data in a database.

[1515] Entering Project Information

[1516] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[1517] The server stores the project information in a database.

[1518] Performing matching

[1519] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[1520] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[1521] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[1522] Example 2: Approval for project participation

[1523] User Selection

[1524] User B selects Project Y from the recommended projects list and submits a request to participate.

[1525] The server stores the user's participation request in a database and notifies the project manager.

[1526] Project Manager Operations

[1527] The project manager logs in from a terminal and confirms User B's participation. He / she checks User B's profile and emotional data and approves or rejects their participation.

[1528] The server updates the database with the approval result and notifies User B.

[1529] This system allows employees to participate in projects that best utilize their skills and emotional state, improving project efficiency and increasing productivity across the company.

[1530] The processing flow will be explained below.

[1531] Step 1:

[1532] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[1533] Step 2:

[1534] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[1535] Step 3:

[1536] The user accesses a profile editing screen from a terminal and inputs or updates his / her skills, experience, interests, past project history and emotional data.

[1537] Step 4:

[1538] When the user presses the "Save" button, the data is sent from the device to the server.

[1539] Step 5:

[1540] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[1541] Step 6:

[1542] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[1543] Step 7:

[1544] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[1545] Step 8:

[1546] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[1547] Step 9:

[1548] The server periodically passes user data and project data to the AI ​​engine and emotion engine, requesting analysis and matching.

[1549] Step 10:

[1550] The AI ​​engine analyzes the user's skill map and the required skill set of the project and calculates the degree of match.

[1551] Step 11:

[1552] The emotion engine analyzes the user's emotional data and provides the results to the AI ​​engine.

[1553] Step 12:

[1554] The AI ​​engine combines the emotion data and skill data, recalculates the degree of match, and generates a matching result.

[1555] Step 13:

[1556] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[1557] Step 14:

[1558] The server stores the match results in a database and sends a notification to the user.

[1559] Step 15:

[1560] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[1561] Step 16:

[1562] The user selects the project they are interested in and presses the "Want to participate" button.

[1563] Step 17:

[1564] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[1565] Step 18:

[1566] The project manager logs in from a terminal and confirms the user's participation. They then check the user's profile and emotional data and approve or reject the participation.

[1567] Step 19:

[1568] The server receives the result of approval or rejection from the project manager, updates the database, and notifies the user of the result.

[1569] Step 20:

[1570] The server generates a list of members whose participation has been confirmed for the project, and notifies the relevant members of information necessary for starting the project.

[1571] Example 2

[1572] 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."

[1573] It is difficult for employees to find the best projects by considering their emotional data in addition to their skills, experience, and interests. Ignoring the impact of emotional states on project success can lead to reduced efficiency and project satisfaction. Furthermore, the process between employee project participation requests and project manager approval is cumbersome, and there is a lack of ways to do it quickly and efficiently.

[1574] 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.

[1575] In this invention, the server includes a terminal for employees to input their skills, experience, interests, and emotions, a database for saving the data input by the employees, an AI engine that analyzes the saved data and recommends optimal projects that match the project requirements, an emotion engine that analyzes emotions and saves them in the database, means for inputting and saving project requirement information, means for notifying employees and project managers of the results of analysis by the AI ​​engine and the emotion engine, means for employees to select from the recommended projects and submit their participation requests, and means for the project manager to approve or reject the submitted participation requests. This enables appropriate project matching that takes into account the skills and emotional state of employees, enabling efficient and satisfying project execution.

[1576] A "terminal" is an electronic device through which a user enters data, such as a web form, a smartphone application, or a desktop application.

[1577] "Database" refers to a system for storing and managing employee input data and project information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[1578] An "AI engine" is a software system that uses machine learning algorithms and statistical methods to analyze employee skill and emotional data and recommend the most suitable projects that match the project requirements.

[1579] The "emotion engine" is a software system that analyzes users' emotional data and reflects it in project matching, and includes the ability to analyze emotions from text and voice.

[1580] "Project requirement information" refers to information such as the name of the project, the goal, the required skill set, the start date, and the end date, and is the condition or specification required for implementing the project.

[1581] "Notification methods" are mechanisms for informing employees and project managers of the analysis results of the AI ​​engine and emotion engine, and include email notifications, push notifications, and in-platform notification functions.

[1582] The "participation request submission means" is an interface that allows a user to select from recommended projects and submit a request to participate.

[1583] The "approval / rejection method" is an interface that allows the project manager to approve or reject employee participation requests, and is a function for optimizing the project member composition.

[1584] The present invention is a project matching platform that takes into account skills, experience, interests, and emotions, and specific embodiments are described below.

[1585] System configuration

[1586] 1. Terminals and User Interfaces

[1587] Users can enter their skills, experience, interests, past project history, and feelings using a web form, smartphone application, or desktop application. The interface is designed to be user-friendly, allowing users to efficiently enter the required information.

[1588] 2. Database

[1589] The database stores user-entered data and project requirements, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data, to build a detailed user profile.

[1590] 3. AI Engine

[1591] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements. Generative AI models are often used.

[1592] 4. Emotion Engine

[1593] The emotion engine analyzes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's written or spoken words and stores them in a database. This information is used to improve the accuracy of project matching.

[1594] 5. Project requirement input and storage means

[1595] It provides an interface for project managers to enter requirements information about new projects (project name, goals, required skill sets, start date, finish date, etc.) and store it in a database.

[1596] 6. Means of notification

[1597] We will create a system to notify employees and project managers of the results of the analysis of the AI ​​engine and emotion engine via email, push notifications, or in-platform notifications.

[1598] 7. How to submit your participation request

[1599] Users are provided with an interface to submit their participation in recommended projects, clearly expressing their intentions.

[1600] 8. Approval and Rejection Procedures

[1601] Project managers are provided with a mechanism to review submitted requests and approve or reject them, which is useful for optimizing the project's membership structure.

[1602] Examples of concrete examples and prompts

[1603] Example 1: Skill matching based on emotions

[1604] 1. User operations

[1605] User B logs in from a device and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotional data (e.g., "Positive about work" or "Currently feeling stressed") through the emotion engine.

[1606] Example prompt: "Describe a situation where User B enters their skill set and emotion data and then presses the save button."

[1607] 2. Server Processing

[1608] The server saves the input data in a database.

[1609] 3. Enter project information

[1610] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[1611] 4. Server Processing

[1612] The server stores the project information in a database.

[1613] 5. Performing matching

[1614] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[1615] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[1616] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[1617] Example prompt: "Please explain how the AI ​​engine analyzes User B's skill set and emotional data to match him with Project Y."

[1618] Example 2: Approval for project participation

[1619] 1. User Choice

[1620] User B selects Project Y from the recommended projects list and submits a request to participate.

[1621] Example prompt: "Describe a situation in which User B chooses Project Y from the list of recommended projects and submits a request to participate."

[1622] 2. Server Processing

[1623] The server stores the user's participation request in a database and notifies the project manager.

[1624] 3. Project Manager Operations

[1625] The project manager logs in from a terminal and confirms the user's participation. He / she checks User B's profile and emotional data and approves or rejects the participation.

[1626] 4. Server Processing

[1627] The server updates the database with the approval result and notifies the user.

[1628] The system of the present invention allows employees to participate in projects that make the most of their skills and emotional state, improving project efficiency and increasing productivity across the entire company.

[1629] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1630] System program processing flow

[1631] Step 1:

[1632] User data entry and saving

[1633] Input: The user uses the device to input their skills, experience, interests, and emotional data.

[1634] Processing: The user accesses the device's user interface, enters each item, and inputs emotion data through the emotion engine. The user then presses the "Save" button.

[1635] Output: The entered data is sent to the server.

[1636] Specific behavior:

[1637] User B accesses a web form and enters information such as "Java," "front-end development," and "5 years."

[1638] Enter your emotional state by voice or text (e.g., "I feel positive about my job").

[1639] Click the "Save" button to submit the data.

[1640] Step 2:

[1641] Server data storage

[1642] Input: User input data.

[1643] Processing: The server stores the received data in a database. The skill set, years of experience, areas of interest, and sentiment data are stored in the corresponding tables.

[1644] Output: User data stored in the database.

[1645] Specific behavior:

[1646] The server receives the user's skill, experience, and emotion data.

[1647] The process of saving to the database is performed, and the fact that the save was successful is recorded in the log.

[1648] Step 3:

[1649] Entering and saving project information

[1650] Input: The project manager uses a terminal to input project requirement information.

[1651] Process: The project manager accesses the project creation screen, enters the required information (project name, required skills, start date, end date, etc.), and presses the "Save" button.

[1652] Output: Project data is sent to the server.

[1653] Specific behavior:

[1654] The project manager accesses a web form and enters information such as "website renewal," "front-end development," and "start date: January 1, 2024."

[1655] Click the "Save" button to submit the data.

[1656] Step 4:

[1657] Save project information on the server

[1658] Input: Project manager input data.

[1659] Process: The server saves the received project data in the database. It generates a project ID and stores each piece of information in the corresponding table.

[1660] Output: Project data stored in a database.

[1661] Specific behavior:

[1662] The server receives the project name, skill requirements, duration, etc.

[1663] Generate a project ID, execute the process to save it in the database, and log that the save was successful.

[1664] Step 5:

[1665] Performing matching

[1666] Input: Server user data and project data.

[1667] Processing: The server passes the data to the AI ​​engine and emotion engine and requests a match. The AI ​​engine and emotion engine use the user's skill set and emotion data to calculate the degree of match with the project requirements.

[1668] Output: The match calculation results.

[1669] Specific behavior:

[1670] The server executes an API call to pass User B's skill set and emotion data to the AI ​​engine and emotion engine.

[1671] The AI ​​engine runs a matching algorithm and calculates the degree of match.

[1672] The calculation result is returned to the server.

[1673] Step 6:

[1674] Notification of matching results

[1675] Input: The result of the match calculation.

[1676] Processing: The server ranks the projects in order of the degree of matching, generates a recommendation list for the user, and notifies the user of the generated list using a notification means.

[1677] Output: Notification of recommended projects list.

[1678] Specific behavior:

[1679] The server receives the results of the calculation of the degree of match and creates a ranking.

[1680] The recommendation list is displayed on User B's dashboard and an email or app notification is sent.

[1681] Step 7:

[1682] User participation request submission

[1683] Input: Recommended projects list.

[1684] Process: The user selects a project from the recommended list and submits their participation request. The user selects the desired project through the user interface and clicks "Participate."

[1685] Output: Data on willingness to participate.

[1686] Specific behavior:

[1687] User B logs in from their device and checks the list of recommended projects.

[1688] Select a specific project Y and click "Want to participate."

[1689] Step 8:

[1690] Save and notify server join requests

[1691] Input: Your participation data.

[1692] Processing: The server saves the user's participation request in the database and notifies the project manager.

[1693] Output: Database update results and notifications.

[1694] Specific behavior:

[1695] The server receives the participation request data and stores it in a database.

[1696] Send email or app notifications to project managers.

[1697] Step 9:

[1698] Project manager's approval or rejection

[1699] Input: Notification of participation.

[1700] Processing: The project manager logs in from a terminal, checks the participation request and user data, and performs approval or rejection operations.

[1701] Output: Approval or rejection result.

[1702] Specific behavior:

[1703] The project manager checks User B's profile and emotional data.

[1704] Make your decision to approve or reject and click "Decide."

[1705] Step 10:

[1706] Server result notification

[1707] Input: The result of approval or rejection.

[1708] Processing: The server updates the database with the approval result and notifies the user.

[1709] Output: Update results and notifications.

[1710] Specific behavior:

[1711] The server records the project manager's decisions in a database.

[1712] User B will be notified of the results via email or app notification.

[1713] This series of processes enables project matching that makes the most of the user's skills and emotional state, which is expected to improve project efficiency and overall company productivity.

[1714] (Application example 2)

[1715] 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."

[1716] In factories, automatically assigning optimal tasks to robots while taking into account each robot's performance, past work history, current status, and maintenance history is a significant burden for managers and employees. This can lead to problems such as a decrease in robot utilization rate and a deterioration in factory production efficiency. The present invention aims to solve these problems by automating optimal task assignment for factory robots.

[1717] 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.

[1718] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a database for storing the data entered by the employees, an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements, a means for inputting and storing project requirement information, a means for notifying employees and project managers of the analysis results by the AI ​​engine, a means for employees to select from the recommended projects and submit a request to participate, a means for the project manager to approve or reject the submitted request, and a means for automatically matching and assigning optimal work to factory robots based on their performance, past work history, current status, and maintenance history. This makes it possible to maximize the utilization rate of factory robots and improve production efficiency.

[1719] 1. A "terminal" is a device that allows employees to input their skills, experience, and interests.

[1720] 2. "Database" means a system for storing employee-entered data and project requirements information.

[1721] 3. "AI Engine" refers to the artificial intelligence technology used to analyze stored data and recommend the most suitable projects that match project requirements.

[1722] 4. "Means for inputting and storing project requirements information" refers to an interface or device that inputs basic project information and required skill sets and stores them in a database.

[1723] 5. "Means of notification" means the method or device for notifying employees and project managers of analysis results and other important information.

[1724] 6. "Means for submitting participation requests" means an interface or device that allows employees to select from recommended projects and submit participation requests.

[1725] 7. "Means for Approval or Rejection" means the interface or device by which the Project Manager approves or rejects a submitted request to participate.

[1726] 8. "Robot performance" refers to the functions, work speed, accuracy, etc. of factory robots.

[1727] 9. "Past work history" is a record of the work each robot has done so far.

[1728] 10. "Current Status" refers to the real-time operating status, remaining battery level, and usage status of each robot.

[1729] 11. "Maintenance History" is a record of the maintenance and repairs that have been performed on each robot.

[1730] 12. "Means for automatically matching and allocating optimal tasks" refers to a method or system for selecting optimal tasks and issuing instructions to a robot based on the robot's performance, past work history, current status, and maintenance history.

[1731] According to the present invention, a system for efficiently optimizing work in a factory is configured as follows.

[1732] The system first provides employees with a terminal to input their skills, experience, and interests. The terminal can be a user device such as a tablet or PC. The user uses the terminal to input skill information and past work history, and the data is stored in a database on the server.

[1733] The server has an AI engine that analyzes the stored data. The AI ​​engine uses machine learning algorithms and statistical methods to analyze employee skill sets and project requirements and match them with the most suitable work. The server also has integrated into it a means for inputting and saving project requirement information. This allows project managers to input project information and required skill sets and save them in the database.

[1734] The server notifies employees and project managers of the analysis results via email, push notification, or the platform's notification function. Employees receive the notification and can select from the recommended projects and submit their participation requests. The server stores the submitted participation requests in a database and notifies the project manager.

[1735] The project manager checks the received participation request from the terminal and approves or rejects it, and the server updates the database with the result and notifies the employee.

[1736] In particular, one of the features of this invention is the addition of a function that automatically matches and assigns optimal tasks based on the robot's performance, past work history, current status, and maintenance history in the factory, thereby maximizing the robot's operating rate and improving production efficiency.

[1737] Specific examples

[1738] For example, suppose robots A, B, and C are available to perform welding work on a factory production line. Robot A has high welding skills based on its past history and has 80% battery remaining, but robot C needs more maintenance. In this case, the server's AI engine will prioritize robot A when allocating welding work.

[1739] Example prompt for a generative AI model:

[1740] "Optimize work allocation within your factory. Assign the best robot for the welding job in real time, taking into account each robot's skills and battery status."

[1741] This system allows users to reduce unnecessary work and waiting time, increasing overall productivity. It also takes into account the robot's maintenance status, ensuring stable operation over the long term.

[1742] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1743] Step 1:

[1744] Employees use the devices to input their skills, experience, and interests, and the devices send this data to a server, which stores it in a database.

[1745] Input: Employee skills, experience, and interest data

[1746] Output: Employee skills, experience, and interests data stored in a database

[1747] Step 2:

[1748] A project manager uses a terminal to input project requirements information, which is then sent to a server, which stores the received data in a database.

[1749] Input: Project requirement information such as project name, skill requirements, start date, end date, etc.

[1750] Output: Project requirements data stored in a database

[1751] Step 3:

[1752] The server passes the stored data to an AI engine, which analyzes employee skill sets and project requirements, calculates the degree of match, and recommends the most suitable projects.

[1753] Inputs: Employee skill sets, project requirement data

[1754] Output: A list of recommended projects for employees

[1755] Step 4:

[1756] The server notifies the analysis results to employees and project managers using notification means. Employees can then review the list of recommended projects and submit their participation requests.

[1757] Input: Project list of analysis results

[1758] Output: Notification to employees and project managers

[1759] Step 5:

[1760] The server stores the employee's participation request in a database and notifies the project manager.

[1761] Input: Employee participation request

[1762] Output: Participation request data stored in the database, notification to the project manager

[1763] Step 6:

[1764] The project manager uses a terminal to approve or reject the request, and the server updates the database with the result and notifies the employee.

[1765] Input: Project manager's approval or rejection decision

[1766] Output: Approval / rejection results stored in the database, notification to employees

[1767] Step 7:

[1768] The server automatically matches and assigns the optimal work to the factory robots based on their performance, past work history, current status, and maintenance history. It passes the necessary data to the AI ​​engine and assigns the work to the optimal robot.

[1769] Input: Robot performance data, past work history, current status, maintenance history

[1770] Output: Assignment instructions to the robot

[1771] Example of operation

[1772] For example, suppose Robot A has 80% battery remaining and has a history of successfully performing similar welding work in the past. Based on this information, the server uses an AI engine to issue instructions to assign the welding work to Robot A. This optimizes work efficiency within the factory and reduces unnecessary operations and waiting time.

[1773] 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.

[1774] 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.

[1775] 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.

[1776] [Fourth embodiment]

[1777] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1778] 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.

[1779] 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).

[1780] 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.

[1781] 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.

[1782] 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).

[1783] 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.

[1784] 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.

[1785] 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.

[1786] 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.

[1787] 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.

[1788] 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.

[1789] 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."

[1790] The AI-based project matching platform of the present invention is a system in which employees use a terminal to input their skills, experience, and interests, and an AI engine recommends optimal projects based on the accumulated data. Specific embodiments of the system are described below.

[1791] System configuration

[1792] 1. Terminals and User Interfaces

[1793] Providing an interface, such as a web-based form or a smartphone application, that allows users to enter their skills, experience, interests, and past project history.

[1794] 2. Database

[1795] A database will be created to store user-entered data and project requirements, including skill sets, years of experience, areas of interest, certifications, and past project history.

[1796] 3. AI Engine

[1797] The AI ​​engine analyzes employee skill sets and project requirements to find the best match, using machine learning models and statistical methods to calculate the degree of match between employee skills and project requirements.

[1798] 4. Project requirement input and storage means

[1799] It provides an interface for a project manager to input information such as the name of the project, the objectives, the required skill set, the start date, the end date, etc., and store this information in a database.

[1800] 5. Means of notification

[1801] Build a system to notify employees and project managers of the results of analysis by the AI ​​engine, possibly via email or a notification function within the platform.

[1802] 6. How to submit your participation request

[1803] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[1804] 7. Approval and Rejection Procedures

[1805] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[1806] System operation example

[1807] Example 1: Skills matching

[1808] User operations

[1809] User A logs in from a device and accesses the profile editing screen. User A enters their skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button.

[1810] The server saves the input data in a database.

[1811] Entering Project Information

[1812] A project manager creates a new project X and inputs the required skill sets (e.g., "data analysis", "modeling skills", "project management").

[1813] The server stores the project information in a database.

[1814] Performing matching

[1815] The server passes User A's data and Project X's data to the AI ​​engine and requests a match.

[1816] The AI ​​engine analyzes User A's skill set and the requirements of Project X and calculates the degree of match.

[1817] The projects are ranked in descending order of degree of match, and a list of recommended projects is notified to user A.

[1818] Example 2: Approval for project participation

[1819] User Selection

[1820] User A selects Project X from the recommended projects list and submits a request to participate.

[1821] The server stores the user's participation request in a database and notifies the project manager.

[1822] Project Manager Operations

[1823] The project manager reviews User A's profile and approves his participation.

[1824] The server updates the database with the approval result and notifies User A.

[1825] This system allows employees to participate in projects that make the most of their skills, improving project efficiency and boosting productivity across the company.

[1826] The processing flow will be explained below.

[1827] Step 1:

[1828] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[1829] Step 2:

[1830] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[1831] Step 3:

[1832] The user accesses the profile editing screen from the terminal and enters or updates information such as their skills, experience, interests, and past project history.

[1833] Step 4:

[1834] When the user presses the "Save" button, the data is sent from the device to the server.

[1835] Step 5:

[1836] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[1837] Step 6:

[1838] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[1839] Step 7:

[1840] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[1841] Step 8:

[1842] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[1843] Step 9:

[1844] The server periodically passes user data and project data to the AI ​​engine, requesting analysis and matching.

[1845] Step 10:

[1846] The AI ​​engine analyzes the user's skill map and the required skill set of the project, calculates the degree of match, and generates a matching result.

[1847] Step 11:

[1848] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[1849] Step 12:

[1850] The server stores the match results in a database and sends a notification to the user.

[1851] Step 13:

[1852] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[1853] Step 14:

[1854] The user selects the project they are interested in and presses the "Want to participate" button.

[1855] Step 15:

[1856] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[1857] Step 16:

[1858] The project manager logs in from a terminal, confirms the user's participation, checks the user's profile, and approves or denies the participation.

[1859] Step 17:

[1860] The server receives the approval or rejection result from the project manager, updates the database, and sends a notification of the result to the user.

[1861] Example 1

[1862] 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."

[1863] Conventional project matching systems did not adequately reflect employees' skills and experience, making it difficult to assign them to the most suitable projects. Furthermore, when project managers manually performed matching, it took time and effort, making it difficult to achieve efficient matching. Furthermore, support for employee skill development and career path formation was insufficient, preventing the system from contributing to improved productivity across the company.

[1864] 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.

[1865] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a storage device for saving the data input by the employees, an artificial intelligence device that analyzes the saved data and recommends optimal work that matches the work requirements, a means for inputting and saving information about work requirements, a means for notifying employees and work managers of the analysis results by the artificial intelligence device, a means for employees to select from the recommended work and submit a request to participate, and a means for the work manager to approve or reject the submitted request to participate. This enables optimal matching of employee skills with project requirements.

[1866] A "terminal" is a device that employees use to input information such as their skills, experience, and interests, and includes computers and smartphones.

[1867] "Storage device" refers to a device used to store data entered by employees and project information, such as a database or cloud storage.

[1868] The "artificial intelligence device" analyzes stored data and compares employee skill sets with project requirements to make optimal matches, using machine learning models and statistical methods.

[1869] "Work requirements" refers to information such as the skills, experience, and qualifications needed to complete a specific project or task.

[1870] "Work Manager" refers to the person or position that has the authority to manage projects and tasks and approve or deny employee participation requests.

[1871] "Notification means" refers to a system for communicating the analysis results of the artificial intelligence device to employees and work managers, and includes email and notification functions within the platform.

[1872] "Method for submitting participation requests" refers to the interface through which employees can select from recommended projects and submit their participation requests, including web forms and mobile apps.

[1873] The present invention is a project matching system that utilizes an artificial intelligence device, and aims to enable employees to efficiently input their skills, experience, and interests and be assigned to appropriate projects. The system includes a terminal, a storage device, an artificial intelligence device, and a notification means.

[1874] Hardware and software used

[1875] The system uses the following hardware and software:

[1876] Terminal: A device used by employees to input information. This includes computers and smartphones.

[1877] Storage: A device for storing employee and project data, specifically a database such as MySQL or PostgreSQL.

[1878] Artificial intelligence device: A device that analyzes stored data and performs optimal matching. Specific technologies include machine learning libraries such as TensorFlow and PyTorch.

[1879] Notification methods: Email and in-system notification functions.

[1880] Entering User Data

[1881] It provides an interface for users to input their skills, experience, and interests. Users input data using a web form or dedicated app on a computer or smartphone, and the data is saved in a storage device by pressing the save button.

[1882] Entering Project Information

[1883] Provides an interface for project managers to enter requirements information for a new project, such as the project name, goal, required skill set, start date, and finish date, and then saves the information.

[1884] Data Matching

[1885] The server retrieves the user's skill data and project requirement data from the storage device and requests an analysis from the AI ​​device. The AI ​​device runs a machine learning model using TensorFlow or PyTorch to calculate the degree of match between the user's skill set and the project requirements. Based on this degree of match, the AI ​​device ranks projects and recommends the most suitable projects to the user.

[1886] Notification of recommended projects

[1887] The server receives the analysis results from the AI ​​device and notifies employees and work managers via email or in-system notifications.

[1888] Project participation application

[1889] The system provides an interface for users to select and apply for participation in recommended projects. When a user submits their "participation request," the server stores the data in a storage device and notifies the project manager.

[1890] Participation Approval Process

[1891] The project manager receives the user's participation request, checks the user's profile and project requirements, and then approves or rejects the participation. The approval result is stored in a storage device by the server and notified to the user.

[1892] Specific examples

[1893] Example 1: Skills matching

[1894] The user inputs their skills (e.g., "Python", "data analysis", "project management") and presses the save button. The server saves this in the storage device. The project manager inputs new project information (e.g., "data analysis", "modeling skills", "project management") and saves it. The server sends this data to an artificial intelligence device, calculates the degree of match, and notifies the user of a list of recommended projects.

[1895] Example 2: Approval for project participation

[1896] The user selects a project from the recommended projects list and submits a "request to participate." The server stores this information in a storage device and notifies the project manager. The project manager checks the user's profile and approves their participation. The server stores the approval result in a storage device and notifies the user.

[1897] Prompt Sentence Examples

[1898] Generate a system description that matches user skill sets with project requirements.

[1899] Skillset: Python, Data Analysis, Project Management

[1900] Project requirements: Data analysis, modeling skills, project management

[1901] Hardware used: PC, smartphone, cloud server

[1902] Software used: Web Forms, MySQL, TensorFlow

[1903] As described above, the present invention makes it possible to make the most of employees' skills and quickly and efficiently match them with the most suitable projects.

[1904] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1905] Step 1:

[1906] Entering User Data

[1907] User operations

[1908] A user logs in to the system using a terminal (PC or smartphone).

[1909] After logging in, access the profile editing screen and enter your skills (e.g., "Python", "Data Analysis", "Project Management"), experience, and interests.

[1910] Once you have completed the input, press the "Save" button.

[1911] input

[1912] Data such as your skills, experience, and interests.

[1913] Server Processing

[1914] The server receives the data sent by the user.

[1915] The received data is stored in a MySQL database.

[1916] output

[1917] The user data is saved to the database and a confirmation message is sent to the user indicating the save was successful.

[1918] Step 2:

[1919] Entering Project Information

[1920] Project Manager Operations

[1921] The project manager accesses the project creation screen from their own device.

[1922] Enter the name of your new project, its goal, the required skill set (e.g., "Data Analysis", "Modeling Skills", "Project Management"), start date, and end date.

[1923] Once you have completed the input, press the "Save" button.

[1924] input

[1925] Project information such as project name, objectives, required skill sets, start date, and end date.

[1926] Server Processing

[1927] The server receives the data sent from the project manager.

[1928] Store received project information in a MySQL database.

[1929] output

[1930] The project information is saved to the database and a confirmation message is sent to the project manager indicating the save was successful.

[1931] Step 3:

[1932] Data Matching

[1933] Server Processing

[1934] The server retrieves the user's skill data and the project's requirement data from the database.

[1935] The acquired data is sent to an artificial intelligence device and requested for analysis.

[1936] input

[1937] User skill data and project requirement data.

[1938] AI processing

[1939] The artificial intelligence device uses TensorFlow to run machine learning models.

[1940] Calculate the match between a user's skill set and project requirements.

[1941] The calculation result (match score) is sent to the server.

[1942] output

[1943] Match score.

[1944] Step 4:

[1945] Notification of recommended projects

[1946] Server Processing

[1947] The server ranks the projects based on the match scores received from the artificial intelligence device.

[1948] Select the most suitable projects and create a list of recommended projects.

[1949] The recommended project list is notified to the user.

[1950] input

[1951] Match score.

[1952] output

[1953] Notification of recommended project list.

[1954] Step 5:

[1955] Project participation application

[1956] User operations

[1957] The user checks the recommended project list received from the terminal.

[1958] Select the project you are interested in and submit your request to participate.

[1959] input

[1960] Recommended projects list.

[1961] Server Processing

[1962] The server receives the user's participation request data.

[1963] The received data is stored in a database and notified to the project manager.

[1964] output

[1965] Saving your participation request data and informing the project manager.

[1966] Step 6:

[1967] Participation Approval Process

[1968] Project Manager Operations

[1969] The project manager checks the notification received from the device.

[1970] Visit a user's profile page to compare their skills with your project requirements.

[1971] Enter the result of approving or denying participation and press the "Submit" button.

[1972] input

[1973] Participation preference data.

[1974] Server Processing

[1975] The server receives the approval result and stores it in the database.

[1976] The approval result is notified to the user and the project member list is updated.

[1977] output

[1978] Notification of approval results and update of project member list.

[1979] (Application example 1)

[1980] 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."

[1981] While conventional matching systems can recommend projects that take into account employee skills, experience, and interests, they do not support optimal matching of robot skill sets and task information, which is important in factory production. As a result, it becomes difficult to efficiently assign tasks to robots, resulting in reduced productivity and task waste. The present invention aims to improve productivity and reduce waste in factories by efficiently matching the skill sets and task information of both employees and robots.

[1982] 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.

[1983] In this invention, the server includes: a terminal for employees to input their skills, experience, and interests; a database for storing the data input by the employees; an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements; a means for inputting and saving project requirement information; a means for notifying employees and the project manager of the analysis results by the AI ​​engine; a means for employees to select from the recommended projects and submit a request to participate; a means for the project manager to approve or reject the submitted request to participate; a means for inputting and saving robot skill sets and task information; and a means for the AI ​​engine to analyze the robot skill data and task information and recommend the optimal robot. This enables optimal matching not only between employees and projects, but also between robots and tasks, thereby improving productivity and achieving efficient task management throughout the factory.

[1984] An "employee" is an individual who performs certain roles and duties within a company or organization and performs work based on an employment contract.

[1985] "Skills" refer to the techniques and abilities required to carry out a specific job or task.

[1986] "Experience" refers to knowledge and skills based on past work and tasks.

[1987] An "interest" is an individual's interest or preference in a particular field or occupation.

[1988] A "terminal" is a device for inputting, displaying, and processing information, and includes smartphones, tablets, and personal computers.

[1989] A "database" is an information system for systematically storing, managing, searching, and updating data.

[1990] An "AI engine" is software that uses artificial intelligence algorithms to analyze data and make appropriate decisions and predictions.

[1991] A "project" is a set of activities or tasks planned and carried out to achieve a specific purpose or goal.

[1992] A "project manager" is a person who is responsible for planning, executing, managing progress, and completing a project, and who also serves as a leader for the team.

[1993] A "robot" is an automated mechanical device that performs programmed actions to accomplish specific tasks.

[1994] A "task" is a specific unit of work or work, and is an individual job or activity required to accomplish that work.

[1995] "Matching" is the process of selecting the optimal combination based on specific conditions and requirements.

[1996] "Notification" is the action of a system informing relevant parties of specific information, such as email or in-application alerts.

[1997] A "recommendation" is the determination and suggestion of a particular choice or option as appropriate.

[1998] "Approval" is the act of accepting and formally agreeing to a submitted request or proposal.

[1999] "Storage" refers to the act of permanently retaining data and making it available for later access.

[2000] "Input" refers to the act of a user providing data or information to a system.

[2001] The present invention is a project matching system based on the skill sets and task information of employees and robots. This system is configured as follows.

[2002] System configuration

[2003] 1. Terminals and User Interfaces

[2004] Providing an interface for employees to input their skills, experience, interests, and past task history, such as a web-based form or smartphone application. Also includes an interface for inputting the skill sets and task history of robots.

[2005] 2. Database

[2006] A database will be prepared to store data entered by employees and robots, as well as task requirements information, including skill sets, years of experience, areas of interest, qualifications, and history of projects and tasks previously handled. MySQL or PostgreSQL will be used as the database management system.

[2007] 3. AI Engine

[2008] The AI ​​engine analyzes the skill sets of employees and robots with the requirements of a project or task to find the best match. For example, it uses a generative AI model to calculate the degree of match between the skills of employees and robots and the requirements of a project or task. The AI ​​engine used is TensorFlow or PyTorch.

[2009] 4. Project and task information entry and storage method

[2010] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and end date, and save this in a database. It also includes an interface for inputting and saving robot task information.

[2011] 5. Means of notification

[2012] A system will be created to notify employees, project managers, and factory managers of the results of analysis by the AI ​​engine, possibly via email or an in-platform notification function.

[2013] 6. How to submit your participation request

[2014] It provides an interface where employees and robots can choose from recommended projects and tasks and submit their participation requests.

[2015] 7. Approval and Rejection Procedures

[2016] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the membership of projects and tasks.

[2017] System operation example

[2018] Example 1: Skills matching

[2019] 1. Employee and robot skill data entry

[2020] Employee A and Robot A log in from their terminals and access the profile editing screen. Employee A enters his or her skill set (e.g., "Python", "Data Analysis", "Project Management") and presses the "Save" button. Robot A also automatically updates its skill data (e.g., "Welding", "Assembly").

[2021] 2. Enter task information

[2022] A project manager creates a new project X and enters the required skill set (e.g., "data analysis", "modeling skills", "project management"), and a factory manager enters a new task Y (e.g., "welding").

[2023] 3. Perform matching

[2024] The server passes the data of employee A and robot A, as well as the data of project X and task Y, to the AI ​​engine and requests a match. The AI ​​engine analyzes employee A's skill set and the requirements of project X, and robot A's skill set and the requirements of task Y, and calculates the degree of match. It ranks the projects and tasks in descending order of match and notifies employee A and robot A of the recommended project and task list.

[2025] Example 2: Project and Task Participation Approval

[2026] 1. Employee and robot selection

[2027] Employee A and Robot A select Project X and Task Y from the recommended project and task list and submit their participation requests. The server saves the requests in a database and notifies the project manager and factory manager.

[2028] 2. Project Manager and Factory Manager Operations

[2029] The project manager checks the profile of Employee A and approves his participation. The factory manager checks the profile of Robot A and approves the task. The server updates the database with the approval result and notifies Employee A and Robot A.

[2030] As a concrete example, we use the following prompt sentence: "The administrator enters a new welding task. The system analyzes the skill sets of robots A, B, and C, calculates the degree of match, and notifies the administrator of the recommended ranking. The administrator selects robot A that is best suited to the task and assigns the welding task."

[2031] This will enable efficient matching of employee and robot skill sets with task information, which is expected to improve productivity and reduce waste.

[2032] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2033] Step 1:

[2034] Users input their skills, experience, interests, and past project history through a terminal. The input data is temporarily stored locally and the data format is checked before being sent to the database.

[2035] Input: Skills, experience, interests, and past project history entered by the user into the interface

[2036] Output: Formatted data (e.g., {"Skills": "Python", "Experience": "5 years", "Interests": "Data Analysis", "History": "Project A, Project B"})

[2037] Step 2:

[2038] The server receives the data sent by the user and stores it in a database, categorized by skill set, years of experience, areas of interest, project history, etc.

[2039] Input: Formatted user data

[2040] Output: Categorical data stored in a database

[2041] How it works: It saves data using a database INSERT operation. It uses MySQL or PostgreSQL as the database management system.

[2042] Step 3:

[2043] A project manager uses a terminal to enter requirements for a new project or task, which is validated locally and then sent to the server.

[2044] Inputs: Project name, objectives, required skill sets, start date, end date, etc. entered by the project manager into the interface

[2045] Output: Formatted project or task information (e.g., {"Project Name": "Project X", "Goal": "Data Analysis", "Skills": "Python, Data Analysis", "Start Date": "2023-01-01", "End Date": "2023-12-31"})

[2046] Step 4:

[2047] The server receives the project or task information sent by the project manager and stores it in a database.

[2048] Input: Formatted project or task information

[2049] Output: Project or task information stored in a database

[2050] Behavior: Saves data using a database INSERT operation.

[2051] Step 5:

[2052] The server sends the stored user data and project or task information to the AI ​​engine for analysis, and the AI ​​engine analyzes the data using the generative AI model.

[2053] Input: User data and project or task information stored in the database

[2054] Output: Analysis results (e.g., match scores, recommendation list)

[2055] How it works: Using an AI engine (TensorFlow or PyTorch), it calculates the degree of similarity and performs optimal matching.

[2056] Step 6:

[2057] The server receives the analysis results from the AI ​​engine and notifies the user and project manager.

[2058] Input: Analysis results from the AI ​​engine

[2059] Output: Notification message (e.g., recommended project or task list)

[2060] What it does: Sends results using the notification system (email or in-app notification).

[2061] Step 7:

[2062] The user is notified, selects from the recommended projects or tasks, and submits a request to participate from their terminal.

[2063] Input: User's participation request (e.g. "I would like to participate in Project X")

[2064] Output: Formatted participation requests

[2065] How it works: A user submits a request to join through the interface.

[2066] Step 8:

[2067] The server stores the participation request sent by the user in a database and notifies the project manager.

[2068] Input: Formatted participation request data

[2069] Output: Participation requests and notification messages stored in the database

[2070] Behavior: Processes using database INSERT operations and the notification system.

[2071] Step 9:

[2072] The project manager is notified and can approve or deny the request, and the server updates the database with the result and notifies the user.

[2073] Input: Project manager's approval or rejection

[2074] Output: Database update results and notification messages

[2075] What it does: Performs a database UPDATE operation and sends the results using the notification system.

[2076] 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.

[2077] The AI-based project matching platform of the present invention is a system that recommends optimal projects by taking into consideration not only the skills, experience, and interests of employees, but also the emotions of users. Specific embodiments of the present invention are described below.

[2078] System configuration

[2079] 1. Terminals and User Interfaces

[2080] Provide an interface, such as a web form, a smartphone application, or a desktop application, that allows users to input their skills, experience, interests, past project history, and feelings.

[2081] 2. Database

[2082] A database is provided to store user-entered data and project requirements information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[2083] 3. AI Engine

[2084] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements.

[2085] 4. Emotion Engine

[2086] The emotion engine recognizes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's writing or voice and stores them in a database.

[2087] 5. Project requirement input and storage means

[2088] It provides an interface for project managers to input information such as the project name, goal, required skill set, start date, and finish date, and store it in a database.

[2089] 6. Means of notification

[2090] Create a mechanism to notify employees and project managers of the results of the AI ​​and emotion engine analysis, including email notifications, push notifications, or in-platform notifications.

[2091] 7. How to submit your participation request

[2092] An interface is provided that allows users to select from recommended projects and submit their participation requests.

[2093] 8. Approval and Rejection Procedures

[2094] Provide a mechanism for project managers to approve or reject requests for participation, thereby optimizing the composition of project members.

[2095] System operation example

[2096] Example 1: Skill matching based on emotions

[2097] User operations

[2098] User B logs in from a terminal and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotion data through the emotion engine.

[2099] The server saves the input data in a database.

[2100] Entering Project Information

[2101] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[2102] The server stores the project information in a database.

[2103] Performing matching

[2104] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[2105] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[2106] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[2107] Example 2: Approval for project participation

[2108] User Selection

[2109] User B selects Project Y from the recommended projects list and submits a request to participate.

[2110] The server stores the user's participation request in a database and notifies the project manager.

[2111] Project Manager Operations

[2112] The project manager logs in from a terminal and confirms User B's participation. He / she checks User B's profile and emotional data and approves or rejects their participation.

[2113] The server updates the database with the approval result and notifies User B.

[2114] This system allows employees to participate in projects that best utilize their skills and emotional state, improving project efficiency and increasing productivity across the company.

[2115] The processing flow will be explained below.

[2116] Step 1:

[2117] The user accesses the platform from a terminal and enters authentication information (user ID and password).

[2118] Step 2:

[2119] The server receives the credentials, checks the database to verify the user's credentials, and if successful, redirects the user to the dashboard.

[2120] Step 3:

[2121] The user accesses a profile editing screen from a terminal and inputs or updates his / her skills, experience, interests, past project history and emotional data.

[2122] Step 4:

[2123] When the user presses the "Save" button, the data is sent from the device to the server.

[2124] Step 5:

[2125] The server receives the user's input data, saves it in the database, and displays a confirmation message to the user once the save is complete.

[2126] Step 6:

[2127] The project manager accesses the project creation / editing screen from a terminal and enters information such as the project name, goal, required skill set, start date, and end date.

[2128] Step 7:

[2129] When the project manager presses the "Save" button, the project data is sent from the terminal to the server.

[2130] Step 8:

[2131] The server receives the project information and stores it in the database. Once the storage is complete, a confirmation message is displayed to the project manager.

[2132] Step 9:

[2133] The server periodically passes user data and project data to the AI ​​engine and emotion engine, requesting analysis and matching.

[2134] Step 10:

[2135] The AI ​​engine analyzes the user's skill map and the required skill set of the project and calculates the degree of match.

[2136] Step 11:

[2137] The emotion engine analyzes the user's emotional data and provides the results to the AI ​​engine.

[2138] Step 12:

[2139] The AI ​​engine combines the emotion data and skill data, recalculates the degree of match, and generates a matching result.

[2140] Step 13:

[2141] The AI ​​engine lists projects with high matching scores and sends the results back to the server.

[2142] Step 14:

[2143] The server stores the match results in a database and sends a notification to the user.

[2144] Step 15:

[2145] The user logs in from their device and checks the notification. The user views the recommended projects list and checks the details.

[2146] Step 16:

[2147] The user selects the project they are interested in and presses the "Want to participate" button.

[2148] Step 17:

[2149] The server receives the user's participation request, stores it in a database, and sends a notification of the participation request to the project manager.

[2150] Step 18:

[2151] The project manager logs in from a terminal and confirms the user's participation. They then check the user's profile and emotional data and approve or reject the participation.

[2152] Step 19:

[2153] The server receives the result of approval or rejection from the project manager, updates the database, and notifies the user of the result.

[2154] Step 20:

[2155] The server generates a list of members whose participation has been confirmed for the project, and notifies the relevant members of information necessary for starting the project.

[2156] Example 2

[2157] 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."

[2158] It is difficult for employees to find the best projects by considering their emotional data in addition to their skills, experience, and interests. Ignoring the impact of emotional states on project success can lead to reduced efficiency and project satisfaction. Furthermore, the process between employee project participation requests and project manager approval is cumbersome, and there is a lack of ways to do it quickly and efficiently.

[2159] 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.

[2160] In this invention, the server includes a terminal for employees to input their skills, experience, interests, and emotions, a database for saving the data input by the employees, an AI engine that analyzes the saved data and recommends optimal projects that match the project requirements, an emotion engine that analyzes emotions and saves them in the database, means for inputting and saving project requirement information, means for notifying employees and project managers of the results of analysis by the AI ​​engine and the emotion engine, means for employees to select from the recommended projects and submit their participation requests, and means for the project manager to approve or reject the submitted participation requests. This enables appropriate project matching that takes into account the skills and emotional state of employees, enabling efficient and satisfying project execution.

[2161] A "terminal" is an electronic device through which a user enters data, such as a web form, a smartphone application, or a desktop application.

[2162] "Database" refers to a system for storing and managing employee input data and project information, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data.

[2163] An "AI engine" is a software system that uses machine learning algorithms and statistical methods to analyze employee skill and emotional data and recommend the most suitable projects that match the project requirements.

[2164] The "emotion engine" is a software system that analyzes users' emotional data and reflects it in project matching, and includes the ability to analyze emotions from text and voice.

[2165] "Project requirement information" refers to information such as the name of the project, the goal, the required skill set, the start date, and the end date, and is the condition or specification required for implementing the project.

[2166] "Notification methods" are mechanisms for informing employees and project managers of the analysis results of the AI ​​engine and emotion engine, and include email notifications, push notifications, and in-platform notification functions.

[2167] The "participation request submission means" is an interface that allows a user to select from recommended projects and submit a request to participate.

[2168] The "approval / rejection method" is an interface that allows the project manager to approve or reject employee participation requests, and is a function for optimizing the project member composition.

[2169] The present invention is a project matching platform that takes into account skills, experience, interests, and emotions, and specific embodiments are described below.

[2170] System configuration

[2171] 1. Terminals and User Interfaces

[2172] Users can enter their skills, experience, interests, past project history, and feelings using a web form, smartphone application, or desktop application. The interface is designed to be user-friendly, allowing users to efficiently enter the required information.

[2173] 2. Database

[2174] The database stores user-entered data and project requirements, including skill sets, years of experience, areas of interest, qualifications, past project history, and sentiment data, to build a detailed user profile.

[2175] 3. AI Engine

[2176] The AI ​​engine analyzes employee skill sets and sentiment data with project requirements to find the best match. It uses machine learning algorithms and statistical methods to calculate the degree of match between employee skills and project requirements. Generative AI models are often used.

[2177] 4. Emotion Engine

[2178] The emotion engine analyzes the user's emotions and incorporates that data as part of its analysis. For example, it analyzes emotions from the user's written or spoken words and stores them in a database. This information is used to improve the accuracy of project matching.

[2179] 5. Project requirement input and storage means

[2180] It provides an interface for project managers to enter requirements information about new projects (project name, goals, required skill sets, start date, finish date, etc.) and store it in a database.

[2181] 6. Means of notification

[2182] We will create a system to notify employees and project managers of the results of the analysis of the AI ​​engine and emotion engine via email, push notifications, or in-platform notifications.

[2183] 7. How to submit your participation request

[2184] Users are provided with an interface to submit their participation in recommended projects, clearly expressing their intentions.

[2185] 8. Approval and Rejection Procedures

[2186] Project managers are provided with a mechanism to review submitted requests and approve or reject them, which is useful for optimizing the project's membership structure.

[2187] Examples of concrete examples and prompts

[2188] Example 1: Skill matching based on emotions

[2189] 1. User operations

[2190] User B logs in from a device and accesses the profile editing screen. User B enters his / her skill set (e.g., "Java", "Front-end development", "Team leadership") and presses the "Save" button. The user also enters emotional data (e.g., "Positive about work" or "Currently feeling stressed") through the emotion engine.

[2191] Example prompt: "Describe a situation where User B enters their skill set and emotion data and then presses the save button."

[2192] 2. Server Processing

[2193] The server saves the input data in a database.

[2194] 3. Enter project information

[2195] A project manager creates a new project Y and enters the required skill sets (e.g., "Front-end development", "Leadership").

[2196] 4. Server Processing

[2197] The server stores the project information in a database.

[2198] 5. Performing matching

[2199] The server passes user B's data and project Y's data to the AI ​​engine and emotion engine and requests a match.

[2200] The AI ​​engine and emotion engine analyze User B's skill set and emotion data with the requirements of Project Y and calculate the degree of match.

[2201] The projects are ranked in descending order of degree of match, and the recommended project list is notified to user B.

[2202] Example prompt: "Please explain how the AI ​​engine analyzes User B's skill set and emotional data to match him with Project Y."

[2203] Example 2: Approval for project participation

[2204] 1. User Choice

[2205] User B selects Project Y from the recommended projects list and submits a request to participate.

[2206] Example prompt: "Describe a situation in which User B chooses Project Y from the list of recommended projects and submits a request to participate."

[2207] 2. Server Processing

[2208] The server stores the user's participation request in a database and notifies the project manager.

[2209] 3. Project Manager Operations

[2210] The project manager logs in from a terminal and confirms the user's participation. He / she checks User B's profile and emotional data and approves or rejects the participation.

[2211] 4. Server Processing

[2212] The server updates the database with the approval result and notifies the user.

[2213] The system of the present invention allows employees to participate in projects that make the most of their skills and emotional state, improving project efficiency and increasing productivity across the entire company.

[2214] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2215] System program processing flow

[2216] Step 1:

[2217] User data entry and saving

[2218] Input: The user uses the device to input their skills, experience, interests, and emotional data.

[2219] Processing: The user accesses the device's user interface, enters each item, and inputs emotion data through the emotion engine. The user then presses the "Save" button.

[2220] Output: The entered data is sent to the server.

[2221] Specific behavior:

[2222] User B accesses a web form and enters information such as "Java," "front-end development," and "5 years."

[2223] Enter your emotional state by voice or text (e.g., "I feel positive about my job").

[2224] Click the "Save" button to submit the data.

[2225] Step 2:

[2226] Server data storage

[2227] Input: User input data.

[2228] Processing: The server stores the received data in a database. The skill set, years of experience, areas of interest, and sentiment data are stored in the corresponding tables.

[2229] Output: User data stored in the database.

[2230] Specific behavior:

[2231] The server receives the user's skill, experience, and emotion data.

[2232] The process of saving to the database is performed, and the fact that the save was successful is recorded in the log.

[2233] Step 3:

[2234] Entering and saving project information

[2235] Input: The project manager uses a terminal to input project requirement information.

[2236] Process: The project manager accesses the project creation screen, enters the required information (project name, required skills, start date, end date, etc.), and presses the "Save" button.

[2237] Output: Project data is sent to the server.

[2238] Specific behavior:

[2239] The project manager accesses a web form and enters information such as "website renewal," "front-end development," and "start date: January 1, 2024."

[2240] Click the "Save" button to submit the data.

[2241] Step 4:

[2242] Save project information on the server

[2243] Input: Project manager input data.

[2244] Process: The server saves the received project data in the database. It generates a project ID and stores each piece of information in the corresponding table.

[2245] Output: Project data stored in a database.

[2246] Specific behavior:

[2247] The server receives the project name, skill requirements, duration, etc.

[2248] Generate a project ID, execute the process to save it in the database, and log that the save was successful.

[2249] Step 5:

[2250] Performing matching

[2251] Input: Server user data and project data.

[2252] Processing: The server passes the data to the AI ​​engine and emotion engine and requests a match. The AI ​​engine and emotion engine use the user's skill set and emotion data to calculate the degree of match with the project requirements.

[2253] Output: The match calculation results.

[2254] Specific behavior:

[2255] The server executes an API call to pass User B's skill set and emotion data to the AI ​​engine and emotion engine.

[2256] The AI ​​engine runs a matching algorithm and calculates the degree of match.

[2257] The calculation result is returned to the server.

[2258] Step 6:

[2259] Notification of matching results

[2260] Input: The result of the match calculation.

[2261] Processing: The server ranks the projects in order of the degree of matching, generates a recommendation list for the user, and notifies the user of the generated list using a notification means.

[2262] Output: Notification of recommended projects list.

[2263] Specific behavior:

[2264] The server receives the results of the calculation of the degree of match and creates a ranking.

[2265] The recommendation list is displayed on User B's dashboard and an email or app notification is sent.

[2266] Step 7:

[2267] User participation request submission

[2268] Input: Recommended projects list.

[2269] Process: The user selects a project from the recommended list and submits their participation request. The user selects the desired project through the user interface and clicks "Participate."

[2270] Output: Data on willingness to participate.

[2271] Specific behavior:

[2272] User B logs in from their device and checks the list of recommended projects.

[2273] Select a specific project Y and click "Want to participate."

[2274] Step 8:

[2275] Save and notify server join requests

[2276] Input: Your participation data.

[2277] Processing: The server saves the user's participation request in the database and notifies the project manager.

[2278] Output: Database update results and notifications.

[2279] Specific behavior:

[2280] The server receives the participation request data and stores it in a database.

[2281] Send email or app notifications to project managers.

[2282] Step 9:

[2283] Project manager's approval or rejection

[2284] Input: Notification of participation.

[2285] Processing: The project manager logs in from a terminal, checks the participation request and user data, and performs approval or rejection operations.

[2286] Output: Approval or rejection result.

[2287] Specific behavior:

[2288] The project manager checks User B's profile and emotional data.

[2289] Make your decision to approve or reject and click "Decide."

[2290] Step 10:

[2291] Server result notification

[2292] Input: The result of approval or rejection.

[2293] Processing: The server updates the database with the approval result and notifies the user.

[2294] Output: Update results and notifications.

[2295] Specific behavior:

[2296] The server records the project manager's decisions in a database.

[2297] User B will be notified of the results via email or app notification.

[2298] This series of processes enables project matching that makes the most of the user's skills and emotional state, which is expected to improve project efficiency and overall company productivity.

[2299] (Application example 2)

[2300] 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."

[2301] In factories, automatically assigning optimal tasks to robots while taking into account each robot's performance, past work history, current status, and maintenance history is a significant burden for managers and employees. This can lead to problems such as a decrease in robot utilization rate and a deterioration in factory production efficiency. The present invention aims to solve these problems by automating optimal task assignment for factory robots.

[2302] 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.

[2303] In this invention, the server includes a terminal for employees to input their skills, experience, and interests, a database for storing the data entered by the employees, an AI engine that analyzes the stored data and recommends optimal projects that match the project requirements, a means for inputting and storing project requirement information, a means for notifying employees and project managers of the analysis results by the AI ​​engine, a means for employees to select from the recommended projects and submit a request to participate, a means for the project manager to approve or reject the submitted request, and a means for automatically matching and assigning optimal work to factory robots based on their performance, past work history, current status, and maintenance history. This makes it possible to maximize the utilization rate of factory robots and improve production efficiency.

[2304] 1. A "terminal" is a device that allows employees to input their skills, experience, and interests.

[2305] 2. "Database" means a system for storing employee-entered data and project requirements information.

[2306] 3. "AI Engine" refers to the artificial intelligence technology used to analyze stored data and recommend the most suitable projects that match project requirements.

[2307] 4. "Means for inputting and storing project requirements information" refers to an interface or device that inputs basic project information and required skill sets and stores them in a database.

[2308] 5. "Means of notification" means the method or device for notifying employees and project managers of analysis results and other important information.

[2309] 6. "Means for submitting participation requests" means an interface or device that allows employees to select from recommended projects and submit participation requests.

[2310] 7. "Means for Approval or Rejection" means the interface or device by which the Project Manager approves or rejects a submitted request to participate.

[2311] 8. "Robot performance" refers to the functions, work speed, accuracy, etc. of factory robots.

[2312] 9. "Past work history" is a record of the work each robot has done so far.

[2313] 10. "Current Status" refers to the real-time operating status, remaining battery level, and usage status of each robot.

[2314] 11. "Maintenance History" is a record of the maintenance and repairs that have been performed on each robot.

[2315] 12. "Means for automatically matching and allocating optimal tasks" refers to a method or system for selecting optimal tasks and issuing instructions to a robot based on the robot's performance, past work history, current status, and maintenance history.

[2316] According to the present invention, a system for efficiently optimizing work in a factory is configured as follows.

[2317] The system first provides employees with a terminal to input their skills, experience, and interests. The terminal can be a user device such as a tablet or PC. The user uses the terminal to input skill information and past work history, and the data is stored in a database on the server.

[2318] The server has an AI engine that analyzes the stored data. The AI ​​engine uses machine learning algorithms and statistical methods to analyze employee skill sets and project requirements and match them with the most suitable work. The server also has integrated into it a means for inputting and saving project requirement information. This allows project managers to input project information and required skill sets and save them in the database.

[2319] The server notifies employees and project managers of the analysis results via email, push notification, or the platform's notification function. Employees receive the notification and can select from the recommended projects and submit their participation requests. The server stores the submitted participation requests in a database and notifies the project manager.

[2320] The project manager checks the received participation request from the terminal and approves or rejects it, and the server updates the database with the result and notifies the employee.

[2321] In particular, one of the features of this invention is the addition of a function that automatically matches and assigns optimal tasks based on the robot's performance, past work history, current status, and maintenance history in the factory, thereby maximizing the robot's operating rate and improving production efficiency.

[2322] Specific examples

[2323] For example, suppose robots A, B, and C are available to perform welding work on a factory production line. Robot A has high welding skills based on its past history and has 80% battery remaining, but robot C needs more maintenance. In this case, the server's AI engine will prioritize robot A when allocating welding work.

[2324] Example prompt for a generative AI model:

[2325] "Optimize work allocation within your factory. Assign the best robot for the welding job in real time, taking into account each robot's skills and battery status."

[2326] This system allows users to reduce unnecessary work and waiting time, increasing overall productivity. It also takes into account the robot's maintenance status, ensuring stable operation over the long term.

[2327] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2328] Step 1:

[2329] Employees use the devices to input their skills, experience, and interests, and the devices send this data to a server, which stores it in a database.

[2330] Input: Employee skills, experience, and interest data

[2331] Output: Employee skills, experience, and interests data stored in a database

[2332] Step 2:

[2333] A project manager uses a terminal to input project requirements information, which is then sent to a server, which stores the received data in a database.

[2334] Input: Project requirement information such as project name, skill requirements, start date, end date, etc.

[2335] Output: Project requirements data stored in a database

[2336] Step 3:

[2337] The server passes the stored data to an AI engine, which analyzes employee skill sets and project requirements, calculates the degree of match, and recommends the most suitable projects.

[2338] Inputs: Employee skill sets, project requirement data

[2339] Output: A list of recommended projects for employees

[2340] Step 4:

[2341] The server notifies the analysis results to employees and project managers using notification means. Employees can then review the list of recommended projects and submit their participation requests.

[2342] Input: Project list of analysis results

[2343] Output: Notification to employees and project managers

[2344] Step 5:

[2345] The server stores the employee's participation request in a database and notifies the project manager.

[2346] Input: Employee participation request

[2347] Output: Participation request data stored in the database, notification to the project manager

[2348] Step 6:

[2349] The project manager uses a terminal to approve or reject the request, and the server updates the database with the result and notifies the employee.

[2350] Input: Project manager's approval or rejection decision

[2351] Output: Approval / rejection results stored in the database, notification to employees

[2352] Step 7:

[2353] The server automatically matches and assigns the optimal work to the factory robots based on their performance, past work history, current status, and maintenance history. It passes the necessary data to the AI ​​engine and assigns the work to the optimal robot.

[2354] Input: Robot performance data, past work history, current status, maintenance history

[2355] Output: Assignment instructions to the robot

[2356] Example of operation

[2357] For example, suppose Robot A has 80% battery remaining and has a history of successfully performing similar welding work in the past. Based on this information, the server uses an AI engine to issue instructions to assign the welding work to Robot A. This optimizes work efficiency within the factory and reduces unnecessary operations and waiting time.

[2358] 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.

[2359] 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.

[2360] 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.

[2361] 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.

[2362] 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.

[2363] 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.

[2364] 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).

[2365] 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.

[2366] 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."

[2367] 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.

[2368] 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).

[2369] 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.

[2370] 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.

[2371] 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.

[2372] 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.

[2373] 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.

[2374] 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.

[2375] 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.

[2376] 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.

[2377] 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.

[2378] 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 speci...

Claims

1. Terminals for employees to input their skills, experience, and interests; A database for storing employee-entered data; An AI engine that analyzes stored data and recommends the best projects that match project requirements. A means of entering and saving project requirements information; A means of notifying employees and project managers of the results of the analysis by the AI ​​engine; A means for employees to select from recommended projects and submit their participation requests; A means for the project manager to approve or reject submitted participation requests; and A system including:

2. The system of claim 1 , further comprising an AI engine that quantifies and ranks the degree of match between employees and project requirements.

3. 2. The system according to claim 1, further comprising means for storing an employee's project participation history and reflecting this history in the next project recommendation.

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